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Repository: sileod/tasksource
Branch: main
Commit: ef6535aebaed
Files: 26
Total size: 491.5 KB
Directory structure:
gitextract__ri1waap/
├── .github/
│ ├── scripts/
│ │ └── release.py
│ └── workflows/
│ ├── python-publish.yml
│ └── release.yml
├── .gitignore
├── CITATION.cff
├── LICENSE
├── README.md
├── mtasks.md
├── pyproject.toml
├── setup.cfg
├── src/
│ └── tasksource/
│ ├── .ipynb_checkpoints/
│ │ ├── access-checkpoint.py
│ │ ├── preprocess-checkpoint.py
│ │ ├── recast-checkpoint.py
│ │ └── tasks-checkpoint.py
│ ├── __init__.py
│ ├── access.py
│ ├── metadata/
│ │ ├── __init__.py
│ │ ├── bigbench_groups.py
│ │ ├── blimp_groups.py
│ │ ├── original.txt
│ │ └── popularity.py
│ ├── mtasks.py
│ ├── preprocess.py
│ ├── recast.py
│ └── tasks.py
└── tasks.md
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/scripts/release.py
================================================
#!/usr/bin/env python3
import json
import subprocess
def get_last_version() -> str:
"""Return the version number of the last release."""
json_string = (
subprocess.run(
["gh", "release", "view", "--json", "tagName"],
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
.stdout.decode("utf8")
.strip()
)
return json.loads(json_string)["tagName"]
def bump_patch_number(version_number: str) -> str:
"""Return a copy of `version_number` with the patch number incremented."""
major, minor, patch = version_number.split(".")
return f"{major}.{minor}.{int(patch) + 1}"
def create_new_patch_release():
"""Create a new patch release on GitHub."""
try:
last_version_number = get_last_version()
except subprocess.CalledProcessError as err:
if err.stderr.decode("utf8").startswith("HTTP 404:"):
# The project doesn't have any releases yet.
new_version_number = "0.0.1"
else:
raise
else:
new_version_number = bump_patch_number(last_version_number)
subprocess.run(
["gh", "release", "create", "--generate-notes", new_version_number],
check=True,
)
if __name__ == "__main__":
create_new_patch_release()
================================================
FILE: .github/workflows/python-publish.yml
================================================
name: Publish to PyPI.org
on:
release:
types: [published]
jobs:
pypi:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
- run: python3 -m pip install --upgrade build && python3 -m build
- name: Publish package
uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_API_TOKEN }}
================================================
FILE: .github/workflows/release.yml
================================================
name: Create a new patch release
on: workflow_dispatch
jobs:
github:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Create new patch release
run: .github/scripts/release.py
env:
GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*,cover
# Translations
*.mo
*.pot
# Django stuff:
*.log
# Sphinx documentation
docs/_build/
# PyBuilder
target/
================================================
FILE: CITATION.cff
================================================
cff-version: 1.1.0
message: "If you use this work, please cite it as below."
authors:
- family-names: "Sileo"
given-names: "Damien"
title: "tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation"
version: "1.0.0"
date-released: 2023-01-01
url: "https://arxiv.org/abs/2301.05948"
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
## tasksource  600+ curated datasets and preprocessings for instant and interchangeable use
Huggingface Datasets is an excellent library, but it lacks standardization, and datasets often require preprocessing work to be used interchangeably.
`tasksource` streamlines interchangeable datasets usage to scale evaluation or multi-task learning.
Each dataset is standardized to a `MultipleChoice`, `Classification`, or `TokenClassification` template with canonical fields. We focus on discriminative tasks (= with negative examples or classes) for our annotations but also provide a `SequenceToSequence` template. All implemented preprocessings are in [tasks.py](https://github.com/sileod/tasksource/blob/main/src/tasksource/tasks.py) or [tasks.md](https://github.com/sileod/tasksource/blob/main/tasks.md). A preprocessing is a function that accepts a dataset and returns the standardized dataset. Preprocessing code is concise and human-readable.
### Installation and usage:
`pip install tasksource`
```python
from tasksource import list_tasks, load_task
df = list_tasks(multilingual=False) # takes some time
for id in df[df.task_type=="MultipleChoice"].id:
dataset = load_task(id) # all yielded datasets can be used interchangeably
```
Browse the 500+ curated tasks in tasks.md (200+ MultipleChoice tasks, 200+ Classification tasks), and feel free to request a new task. Datasets are downloaded to `$HF_DATASETS_CACHE` (like any Hugging Face dataset), so ensure you have more than 100GB of space available.
You can now also use:
```python
load_dataset("tasksource/data", "glue/rte",max_rows=30_000)
```
### Pretrained models:
Text encoder pretrained on tasksource reached state-of-the-art results: [🤗/deberta-v3-base-tasksource-nli](https://hf.co/sileod/deberta-v3-base-tasksource-nli)
Tasksource pretraining is notably helpful for RLHF reward modeling or any kind of classification, including zero-shot. You can also find a large and a multilingual version.
### tasksource-instruct
The repo also contains some recasting code to convert tasksource datasets to instructions, providing one of the richest instruction-tuning datasets:
[🤗/tasksource-instruct-v0](https://hf.co/datasets/tasksource/tasksource-instruct-v0)
### tasksource-label-nli
We also recast all classification tasks as natural language inference, to improve entailment-based zero-shot classification detection:
[🤗/zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli)
### Write and use custom preprocessings
```python
from tasksource import MultipleChoice
codah = MultipleChoice('question_propmt',choices_list='candidate_answers',
labels='correct_answer_idx',
dataset_name='codah', config_name='codah')
winogrande = MultipleChoice('sentence',['option1','option2'],'answer',
dataset_name='winogrande',config_name='winogrande_xl',
splits=['train','validation',None]) # test labels are not usable
tasks = [winogrande.load(), codah.load()]) # Aligned datasets (same columns) can be used interchangably
```
### Citation and contact
For more details, refer to this [article:](https://arxiv.org/abs/2301.05948)
```bib
@inproceedings{sileo-2024-tasksource,
title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
author = "Sileo, Damien",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1361",
pages = "15655--15684",
}
```
For help integrating tasksource into your experiments, please contact [damien.sileo@inria.fr](mailto:damien.sileo@inria.fr).
================================================
FILE: mtasks.md
================================================
| | id | dataset_name | config_name | task_name | preprocessing_name | task_type |
|----:|:-------------------------------------------------------------|:--------------------------------------------|:-------------------------------|:------------|:------------------------|:--------------------|
| 0 | xnli/ru | metaeval/xnli | ru | | xnli | Classification |
| 1 | xnli/tr | metaeval/xnli | tr | | xnli | Classification |
| 2 | xnli/ur | metaeval/xnli | ur | | xnli | Classification |
| 3 | xnli/vi | metaeval/xnli | vi | | xnli | Classification |
| 4 | xnli/zh | metaeval/xnli | zh | | xnli | Classification |
| 5 | xnli/hi | metaeval/xnli | hi | | xnli | Classification |
| 6 | xnli/fr | metaeval/xnli | fr | | xnli | Classification |
| 7 | xnli/es | metaeval/xnli | es | | xnli | Classification |
| 8 | xnli/en | metaeval/xnli | en | | xnli | Classification |
| 9 | xnli/el | metaeval/xnli | el | | xnli | Classification |
| 10 | xnli/de | metaeval/xnli | de | | xnli | Classification |
| 11 | xnli/bg | metaeval/xnli | bg | | xnli | Classification |
| 12 | xnli/ar | metaeval/xnli | ar | | xnli | Classification |
| 13 | xnli/th | metaeval/xnli | th | | xnli | Classification |
| 14 | xnli/sw | metaeval/xnli | sw | | xnli | Classification |
| 15 | americas_nli/all_languages | americas_nli | all_languages | | americas_nli | Classification |
| 16 | multilingual-NLI-26lang-2mil7/MoritzLaurer--multilingual_nli | MoritzLaurer/multilingual-NLI-26lang-2mil7 | MoritzLaurer--multilingual_nli | | moritz_xnli | Classification |
| 17 | stsb_multi_mt/en | stsb_multi_mt | en | | stsb_multi_mt | Classification |
| 18 | stsb_multi_mt/fr | stsb_multi_mt | fr | | stsb_multi_mt | Classification |
| 19 | stsb_multi_mt/de | stsb_multi_mt | de | | stsb_multi_mt | Classification |
| 20 | stsb_multi_mt/es | stsb_multi_mt | es | | stsb_multi_mt | Classification |
| 21 | stsb_multi_mt/it | stsb_multi_mt | it | | stsb_multi_mt | Classification |
| 22 | stsb_multi_mt/nl | stsb_multi_mt | nl | | stsb_multi_mt | Classification |
| 23 | stsb_multi_mt/pl | stsb_multi_mt | pl | | stsb_multi_mt | Classification |
| 24 | stsb_multi_mt/pt | stsb_multi_mt | pt | | stsb_multi_mt | Classification |
| 25 | stsb_multi_mt/ru | stsb_multi_mt | ru | | stsb_multi_mt | Classification |
| 26 | stsb_multi_mt/zh | stsb_multi_mt | zh | | stsb_multi_mt | Classification |
| 27 | paws-x/zh | paws-x | zh | | pawsx | Classification |
| 28 | paws-x/ja | paws-x | ja | | pawsx | Classification |
| 29 | paws-x/ko | paws-x | ko | | pawsx | Classification |
| 30 | paws-x/en | paws-x | en | | pawsx | Classification |
| 31 | paws-x/de | paws-x | de | | pawsx | Classification |
| 32 | paws-x/es | paws-x | es | | pawsx | Classification |
| 33 | paws-x/fr | paws-x | fr | | pawsx | Classification |
| 34 | miam/vm2 | miam | vm2 | | miam | Classification |
| 35 | miam/maptask | miam | maptask | | miam | Classification |
| 36 | miam/loria | miam | loria | | miam | Classification |
| 37 | miam/dihana | miam | dihana | | miam | Classification |
| 38 | miam/ilisten | miam | ilisten | | miam | Classification |
| 39 | x-stance/fr | strombergnlp/x-stance | fr | | xstance | Classification |
| 40 | x-stance/de | strombergnlp/x-stance | de | | xstance | Classification |
| 41 | offenseval_2020/da | strombergnlp/offenseval_2020 | da | | offenseval | Classification |
| 42 | offenseval_2020/tr | strombergnlp/offenseval_2020 | tr | | offenseval | Classification |
| 43 | offenseval_2020/gr | strombergnlp/offenseval_2020 | gr | | offenseval | Classification |
| 44 | offenseval_2020/ar | strombergnlp/offenseval_2020 | ar | | offenseval | Classification |
| 45 | offenseval_dravidian/tamil | offenseval_dravidian | tamil | | offenseval_dravidian | Classification |
| 46 | offenseval_dravidian/malayalam | offenseval_dravidian | malayalam | | offenseval_dravidian | Classification |
| 47 | offenseval_dravidian/kannada | offenseval_dravidian | kannada | | offenseval_dravidian | Classification |
| 48 | MLMA_hate_speech | nedjmaou/MLMA_hate_speech | | | mlma_hate | Classification |
| 49 | xglue/qam | xglue | qam | | qam | Classification |
| 50 | x-fact | metaeval/x-fact | | | x_fact | Classification |
| 51 | xglue/nc | xglue | nc | | xglue___nc | Classification |
| 52 | xglue/qadsm | xglue | qadsm | | xglue___qadsm | Classification |
| 53 | xglue/qam | xglue | qam | | xglue___qam | Classification |
| 54 | xglue/wpr | xglue | wpr | | xglue___wpr | Classification |
| 55 | xlwic/xlwic_fr_fr | pasinit/xlwic | xlwic_fr_fr | | xlwic | Classification |
| 56 | xlwic/xlwic_en_ko | pasinit/xlwic | xlwic_en_ko | | xlwic | Classification |
| 57 | xlwic/xlwic_it_it | pasinit/xlwic | xlwic_it_it | | xlwic | Classification |
| 58 | xlwic/xlwic_de_de | pasinit/xlwic | xlwic_de_de | | xlwic | Classification |
| 59 | oasst1_dense_flat/quality | tasksource/oasst1_dense_flat | | quality | oasst1__quality | Classification |
| 60 | oasst1_dense_flat/toxicity | tasksource/oasst1_dense_flat | | toxicity | oasst1__toxicity | Classification |
| 61 | oasst1_dense_flat/helpfulness | tasksource/oasst1_dense_flat | | helpfulness | oasst1__helpfulness | Classification |
| 62 | language-identification | papluca/language-identification | | | language_identification | Classification |
| 63 | wili_2018 | wili_2018 | | | wili_2018_langid | Classification |
| 64 | exams/multilingual | exams | multilingual | | exams | MultipleChoice |
| 65 | xcsr/X-CSQA-ar | xcsr | X-CSQA-ar | | xcsr | MultipleChoice |
| 66 | xcsr/X-CODAH-zh | xcsr | X-CODAH-zh | | xcsr | MultipleChoice |
| 67 | xcsr/X-CODAH-de | xcsr | X-CODAH-de | | xcsr | MultipleChoice |
| 68 | xcsr/X-CSQA-ru | xcsr | X-CSQA-ru | | xcsr | MultipleChoice |
| 69 | xcsr/X-CODAH-fr | xcsr | X-CODAH-fr | | xcsr | MultipleChoice |
| 70 | xcsr/X-CODAH-it | xcsr | X-CODAH-it | | xcsr | MultipleChoice |
| 71 | xcsr/X-CODAH-jap | xcsr | X-CODAH-jap | | xcsr | MultipleChoice |
| 72 | xcsr/X-CODAH-nl | xcsr | X-CODAH-nl | | xcsr | MultipleChoice |
| 73 | xcsr/X-CODAH-pt | xcsr | X-CODAH-pt | | xcsr | MultipleChoice |
| 74 | xcsr/X-CODAH-en | xcsr | X-CODAH-en | | xcsr | MultipleChoice |
| 75 | xcsr/X-CODAH-ru | xcsr | X-CODAH-ru | | xcsr | MultipleChoice |
| 76 | xcsr/X-CODAH-ar | xcsr | X-CODAH-ar | | xcsr | MultipleChoice |
| 77 | xcsr/X-CODAH-vi | xcsr | X-CODAH-vi | | xcsr | MultipleChoice |
| 78 | xcsr/X-CODAH-hi | xcsr | X-CODAH-hi | | xcsr | MultipleChoice |
| 79 | xcsr/X-CODAH-sw | xcsr | X-CODAH-sw | | xcsr | MultipleChoice |
| 80 | xcsr/X-CODAH-ur | xcsr | X-CODAH-ur | | xcsr | MultipleChoice |
| 81 | xcsr/X-CODAH-pl | xcsr | X-CODAH-pl | | xcsr | MultipleChoice |
| 82 | xcsr/X-CSQA-ur | xcsr | X-CSQA-ur | | xcsr | MultipleChoice |
| 83 | xcsr/X-CODAH-es | xcsr | X-CODAH-es | | xcsr | MultipleChoice |
| 84 | xcsr/X-CSQA-pt | xcsr | X-CSQA-pt | | xcsr | MultipleChoice |
| 85 | xcsr/X-CSQA-vi | xcsr | X-CSQA-vi | | xcsr | MultipleChoice |
| 86 | xcsr/X-CSQA-hi | xcsr | X-CSQA-hi | | xcsr | MultipleChoice |
| 87 | xcsr/X-CSQA-pl | xcsr | X-CSQA-pl | | xcsr | MultipleChoice |
| 88 | xcsr/X-CSQA-sw | xcsr | X-CSQA-sw | | xcsr | MultipleChoice |
| 89 | xcsr/X-CSQA-nl | xcsr | X-CSQA-nl | | xcsr | MultipleChoice |
| 90 | xcsr/X-CSQA-jap | xcsr | X-CSQA-jap | | xcsr | MultipleChoice |
| 91 | xcsr/X-CSQA-it | xcsr | X-CSQA-it | | xcsr | MultipleChoice |
| 92 | xcsr/X-CSQA-es | xcsr | X-CSQA-es | | xcsr | MultipleChoice |
| 93 | xcsr/X-CSQA-fr | xcsr | X-CSQA-fr | | xcsr | MultipleChoice |
| 94 | xcsr/X-CSQA-zh | xcsr | X-CSQA-zh | | xcsr | MultipleChoice |
| 95 | xcsr/X-CSQA-en | xcsr | X-CSQA-en | | xcsr | MultipleChoice |
| 96 | xcsr/X-CSQA-de | xcsr | X-CSQA-de | | xcsr | MultipleChoice |
| 97 | xcopa/qu | xcopa | qu | | xcopa | MultipleChoice |
| 98 | xcopa/it | xcopa | it | | xcopa | MultipleChoice |
| 99 | xcopa/ht | xcopa | ht | | xcopa | MultipleChoice |
| 100 | xcopa/et | xcopa | et | | xcopa | MultipleChoice |
| 101 | xcopa/vi | xcopa | vi | | xcopa | MultipleChoice |
| 102 | xcopa/id | xcopa | id | | xcopa | MultipleChoice |
| 103 | xcopa/translation-et | xcopa | translation-et | | xcopa | MultipleChoice |
| 104 | xcopa/th | xcopa | th | | xcopa | MultipleChoice |
| 105 | xcopa/sw | xcopa | sw | | xcopa | MultipleChoice |
| 106 | xcopa/translation-sw | xcopa | translation-sw | | xcopa | MultipleChoice |
| 107 | xcopa/translation-ht | xcopa | translation-ht | | xcopa | MultipleChoice |
| 108 | xcopa/translation-it | xcopa | translation-it | | xcopa | MultipleChoice |
| 109 | xcopa/ta | xcopa | ta | | xcopa | MultipleChoice |
| 110 | xcopa/translation-zh | xcopa | translation-zh | | xcopa | MultipleChoice |
| 111 | xcopa/translation-vi | xcopa | translation-vi | | xcopa | MultipleChoice |
| 112 | xcopa/translation-id | xcopa | translation-id | | xcopa | MultipleChoice |
| 113 | xcopa/translation-tr | xcopa | translation-tr | | xcopa | MultipleChoice |
| 114 | xcopa/translation-th | xcopa | translation-th | | xcopa | MultipleChoice |
| 115 | xcopa/translation-ta | xcopa | translation-ta | | xcopa | MultipleChoice |
| 116 | xcopa/zh | xcopa | zh | | xcopa | MultipleChoice |
| 117 | xcopa/tr | xcopa | tr | | xcopa | MultipleChoice |
| 118 | xstory_cloze/eu | juletxara/xstory_cloze | eu | | xstory | MultipleChoice |
| 119 | xstory_cloze/my | juletxara/xstory_cloze | my | | xstory | MultipleChoice |
| 120 | xstory_cloze/te | juletxara/xstory_cloze | te | | xstory | MultipleChoice |
| 121 | xstory_cloze/sw | juletxara/xstory_cloze | sw | | xstory | MultipleChoice |
| 122 | xstory_cloze/en | juletxara/xstory_cloze | en | | xstory | MultipleChoice |
| 123 | xstory_cloze/ru | juletxara/xstory_cloze | ru | | xstory | MultipleChoice |
| 124 | xstory_cloze/zh | juletxara/xstory_cloze | zh | | xstory | MultipleChoice |
| 125 | xstory_cloze/es | juletxara/xstory_cloze | es | | xstory | MultipleChoice |
| 126 | xstory_cloze/ar | juletxara/xstory_cloze | ar | | xstory | MultipleChoice |
| 127 | xstory_cloze/hi | juletxara/xstory_cloze | hi | | xstory | MultipleChoice |
| 128 | xstory_cloze/id | juletxara/xstory_cloze | id | | xstory | MultipleChoice |
| 129 | xglue/ner | xglue | ner | | xglue_ner | TokenClassification |
| 130 | xglue/pos | xglue | pos | | xglue_pos | TokenClassification |
| 131 | universal_dependencies/nyq_aha/pos | universal_dependencies | nyq_aha | pos | udep__pos | TokenClassification |
| 132 | universal_dependencies/sme_giella/pos | universal_dependencies | sme_giella | pos | udep__pos | TokenClassification |
| 133 | universal_dependencies/no_bokmaal/pos | universal_dependencies | no_bokmaal | pos | udep__pos | TokenClassification |
| 134 | universal_dependencies/no_nynorsk/pos | universal_dependencies | no_nynorsk | pos | udep__pos | TokenClassification |
| 135 | universal_dependencies/no_nynorsklia/pos | universal_dependencies | no_nynorsklia | pos | udep__pos | TokenClassification |
| 136 | universal_dependencies/cu_proiel/pos | universal_dependencies | cu_proiel | pos | udep__pos | TokenClassification |
| 137 | universal_dependencies/fro_srcmf/pos | universal_dependencies | fro_srcmf | pos | udep__pos | TokenClassification |
| 138 | universal_dependencies/orv_rnc/pos | universal_dependencies | orv_rnc | pos | udep__pos | TokenClassification |
| 139 | universal_dependencies/pl_lfg/pos | universal_dependencies | pl_lfg | pos | udep__pos | TokenClassification |
| 140 | universal_dependencies/otk_tonqq/pos | universal_dependencies | otk_tonqq | pos | udep__pos | TokenClassification |
| 141 | universal_dependencies/fa_perdt/pos | universal_dependencies | fa_perdt | pos | udep__pos | TokenClassification |
| 142 | universal_dependencies/fa_seraji/pos | universal_dependencies | fa_seraji | pos | udep__pos | TokenClassification |
| 143 | universal_dependencies/pcm_nsc/pos | universal_dependencies | pcm_nsc | pos | udep__pos | TokenClassification |
| 144 | universal_dependencies/pl_pdb/pos | universal_dependencies | pl_pdb | pos | udep__pos | TokenClassification |
| 145 | universal_dependencies/pl_pud/pos | universal_dependencies | pl_pud | pos | udep__pos | TokenClassification |
| 146 | universal_dependencies/pt_bosque/pos | universal_dependencies | pt_bosque | pos | udep__pos | TokenClassification |
| 147 | universal_dependencies/pt_gsd/pos | universal_dependencies | pt_gsd | pos | udep__pos | TokenClassification |
| 148 | universal_dependencies/pt_pud/pos | universal_dependencies | pt_pud | pos | udep__pos | TokenClassification |
| 149 | universal_dependencies/orv_torot/pos | universal_dependencies | orv_torot | pos | udep__pos | TokenClassification |
| 150 | universal_dependencies/myu_tudet/pos | universal_dependencies | myu_tudet | pos | udep__pos | TokenClassification |
| 151 | universal_dependencies/gv_cadhan/pos | universal_dependencies | gv_cadhan | pos | udep__pos | TokenClassification |
| 152 | universal_dependencies/gun_thomas/pos | universal_dependencies | gun_thomas | pos | udep__pos | TokenClassification |
| 153 | universal_dependencies/koi_uh/pos | universal_dependencies | koi_uh | pos | udep__pos | TokenClassification |
| 154 | universal_dependencies/kpv_ikdp/pos | universal_dependencies | kpv_ikdp | pos | udep__pos | TokenClassification |
| 155 | universal_dependencies/kpv_lattice/pos | universal_dependencies | kpv_lattice | pos | udep__pos | TokenClassification |
| 156 | universal_dependencies/ko_gsd/pos | universal_dependencies | ko_gsd | pos | udep__pos | TokenClassification |
| 157 | universal_dependencies/ko_kaist/pos | universal_dependencies | ko_kaist | pos | udep__pos | TokenClassification |
| 158 | universal_dependencies/ko_pud/pos | universal_dependencies | ko_pud | pos | udep__pos | TokenClassification |
| 159 | universal_dependencies/kmr_mg/pos | universal_dependencies | kmr_mg | pos | udep__pos | TokenClassification |
| 160 | universal_dependencies/la_ittb/pos | universal_dependencies | la_ittb | pos | udep__pos | TokenClassification |
| 161 | universal_dependencies/la_llct/pos | universal_dependencies | la_llct | pos | udep__pos | TokenClassification |
| 162 | universal_dependencies/la_perseus/pos | universal_dependencies | la_perseus | pos | udep__pos | TokenClassification |
| 163 | universal_dependencies/la_proiel/pos | universal_dependencies | la_proiel | pos | udep__pos | TokenClassification |
| 164 | universal_dependencies/lv_lvtb/pos | universal_dependencies | lv_lvtb | pos | udep__pos | TokenClassification |
| 165 | universal_dependencies/lt_alksnis/pos | universal_dependencies | lt_alksnis | pos | udep__pos | TokenClassification |
| 166 | universal_dependencies/lt_hse/pos | universal_dependencies | lt_hse | pos | udep__pos | TokenClassification |
| 167 | universal_dependencies/olo_kkpp/pos | universal_dependencies | olo_kkpp | pos | udep__pos | TokenClassification |
| 168 | universal_dependencies/mt_mudt/pos | universal_dependencies | mt_mudt | pos | udep__pos | TokenClassification |
| 169 | universal_dependencies/ro_nonstandard/pos | universal_dependencies | ro_nonstandard | pos | udep__pos | TokenClassification |
| 170 | universal_dependencies/mr_ufal/pos | universal_dependencies | mr_ufal | pos | udep__pos | TokenClassification |
| 171 | universal_dependencies/gun_dooley/pos | universal_dependencies | gun_dooley | pos | udep__pos | TokenClassification |
| 172 | universal_dependencies/mdf_jr/pos | universal_dependencies | mdf_jr | pos | udep__pos | TokenClassification |
| 173 | universal_dependencies/ro_rrt/pos | universal_dependencies | ro_rrt | pos | udep__pos | TokenClassification |
| 174 | universal_dependencies/ru_taiga/pos | universal_dependencies | ru_taiga | pos | udep__pos | TokenClassification |
| 175 | universal_dependencies/ru_gsd/pos | universal_dependencies | ru_gsd | pos | udep__pos | TokenClassification |
| 176 | universal_dependencies/ta_mwtt/pos | universal_dependencies | ta_mwtt | pos | udep__pos | TokenClassification |
| 177 | universal_dependencies/ta_ttb/pos | universal_dependencies | ta_ttb | pos | udep__pos | TokenClassification |
| 178 | universal_dependencies/te_mtg/pos | universal_dependencies | te_mtg | pos | udep__pos | TokenClassification |
| 179 | universal_dependencies/th_pud/pos | universal_dependencies | th_pud | pos | udep__pos | TokenClassification |
| 180 | universal_dependencies/tpn_tudet/pos | universal_dependencies | tpn_tudet | pos | udep__pos | TokenClassification |
| 181 | universal_dependencies/qtd_sagt/pos | universal_dependencies | qtd_sagt | pos | udep__pos | TokenClassification |
| 182 | universal_dependencies/tr_boun/pos | universal_dependencies | tr_boun | pos | udep__pos | TokenClassification |
| 183 | universal_dependencies/tr_gb/pos | universal_dependencies | tr_gb | pos | udep__pos | TokenClassification |
| 184 | universal_dependencies/tr_imst/pos | universal_dependencies | tr_imst | pos | udep__pos | TokenClassification |
| 185 | universal_dependencies/tr_pud/pos | universal_dependencies | tr_pud | pos | udep__pos | TokenClassification |
| 186 | universal_dependencies/uk_iu/pos | universal_dependencies | uk_iu | pos | udep__pos | TokenClassification |
| 187 | universal_dependencies/hsb_ufal/pos | universal_dependencies | hsb_ufal | pos | udep__pos | TokenClassification |
| 188 | universal_dependencies/ur_udtb/pos | universal_dependencies | ur_udtb | pos | udep__pos | TokenClassification |
| 189 | universal_dependencies/ug_udt/pos | universal_dependencies | ug_udt | pos | udep__pos | TokenClassification |
| 190 | universal_dependencies/vi_vtb/pos | universal_dependencies | vi_vtb | pos | udep__pos | TokenClassification |
| 191 | universal_dependencies/wbp_ufal/pos | universal_dependencies | wbp_ufal | pos | udep__pos | TokenClassification |
| 192 | universal_dependencies/cy_ccg/pos | universal_dependencies | cy_ccg | pos | udep__pos | TokenClassification |
| 193 | universal_dependencies/wo_wtb/pos | universal_dependencies | wo_wtb | pos | udep__pos | TokenClassification |
| 194 | universal_dependencies/yo_ytb/pos | universal_dependencies | yo_ytb | pos | udep__pos | TokenClassification |
| 195 | universal_dependencies/tl_ugnayan/pos | universal_dependencies | tl_ugnayan | pos | udep__pos | TokenClassification |
| 196 | universal_dependencies/ro_simonero/pos | universal_dependencies | ro_simonero | pos | udep__pos | TokenClassification |
| 197 | universal_dependencies/tl_trg/pos | universal_dependencies | tl_trg | pos | udep__pos | TokenClassification |
| 198 | universal_dependencies/sv_talbanken/pos | universal_dependencies | sv_talbanken | pos | udep__pos | TokenClassification |
| 199 | universal_dependencies/ru_pud/pos | universal_dependencies | ru_pud | pos | udep__pos | TokenClassification |
| 200 | universal_dependencies/ru_syntagrus/pos | universal_dependencies | ru_syntagrus | pos | udep__pos | TokenClassification |
| 201 | universal_dependencies/kfm_aha/pos | universal_dependencies | kfm_aha | pos | udep__pos | TokenClassification |
| 202 | universal_dependencies/sa_ufal/pos | universal_dependencies | sa_ufal | pos | udep__pos | TokenClassification |
| 203 | universal_dependencies/sa_vedic/pos | universal_dependencies | sa_vedic | pos | udep__pos | TokenClassification |
| 204 | universal_dependencies/gd_arcosg/pos | universal_dependencies | gd_arcosg | pos | udep__pos | TokenClassification |
| 205 | universal_dependencies/sr_set/pos | universal_dependencies | sr_set | pos | udep__pos | TokenClassification |
| 206 | universal_dependencies/sms_giellagas/pos | universal_dependencies | sms_giellagas | pos | udep__pos | TokenClassification |
| 207 | universal_dependencies/sk_snk/pos | universal_dependencies | sk_snk | pos | udep__pos | TokenClassification |
| 208 | universal_dependencies/sl_ssj/pos | universal_dependencies | sl_ssj | pos | udep__pos | TokenClassification |
| 209 | universal_dependencies/sl_sst/pos | universal_dependencies | sl_sst | pos | udep__pos | TokenClassification |
| 210 | universal_dependencies/soj_aha/pos | universal_dependencies | soj_aha | pos | udep__pos | TokenClassification |
| 211 | universal_dependencies/ajp_madar/pos | universal_dependencies | ajp_madar | pos | udep__pos | TokenClassification |
| 212 | universal_dependencies/es_ancora/pos | universal_dependencies | es_ancora | pos | udep__pos | TokenClassification |
| 213 | universal_dependencies/es_gsd/pos | universal_dependencies | es_gsd | pos | udep__pos | TokenClassification |
| 214 | universal_dependencies/es_pud/pos | universal_dependencies | es_pud | pos | udep__pos | TokenClassification |
| 215 | universal_dependencies/swl_sslc/pos | universal_dependencies | swl_sslc | pos | udep__pos | TokenClassification |
| 216 | universal_dependencies/sv_lines/pos | universal_dependencies | sv_lines | pos | udep__pos | TokenClassification |
| 217 | universal_dependencies/sv_pud/pos | universal_dependencies | sv_pud | pos | udep__pos | TokenClassification |
| 218 | universal_dependencies/gsw_uzh/pos | universal_dependencies | gsw_uzh | pos | udep__pos | TokenClassification |
| 219 | universal_dependencies/kk_ktb/pos | universal_dependencies | kk_ktb | pos | udep__pos | TokenClassification |
| 220 | universal_dependencies/hi_hdtb/pos | universal_dependencies | hi_hdtb | pos | udep__pos | TokenClassification |
| 221 | universal_dependencies/ja_pud/pos | universal_dependencies | ja_pud | pos | udep__pos | TokenClassification |
| 222 | universal_dependencies/zh_gsd/pos | universal_dependencies | zh_gsd | pos | udep__pos | TokenClassification |
| 223 | universal_dependencies/zh_gsdsimp/pos | universal_dependencies | zh_gsdsimp | pos | udep__pos | TokenClassification |
| 224 | universal_dependencies/zh_hk/pos | universal_dependencies | zh_hk | pos | udep__pos | TokenClassification |
| 225 | universal_dependencies/zh_pud/pos | universal_dependencies | zh_pud | pos | udep__pos | TokenClassification |
| 226 | universal_dependencies/ckt_hse/pos | universal_dependencies | ckt_hse | pos | udep__pos | TokenClassification |
| 227 | universal_dependencies/lzh_kyoto/pos | universal_dependencies | lzh_kyoto | pos | udep__pos | TokenClassification |
| 228 | universal_dependencies/cop_scriptorium/pos | universal_dependencies | cop_scriptorium | pos | udep__pos | TokenClassification |
| 229 | universal_dependencies/hr_set/pos | universal_dependencies | hr_set | pos | udep__pos | TokenClassification |
| 230 | universal_dependencies/cs_cac/pos | universal_dependencies | cs_cac | pos | udep__pos | TokenClassification |
| 231 | universal_dependencies/cs_cltt/pos | universal_dependencies | cs_cltt | pos | udep__pos | TokenClassification |
| 232 | universal_dependencies/cs_fictree/pos | universal_dependencies | cs_fictree | pos | udep__pos | TokenClassification |
| 233 | universal_dependencies/cs_pdt/pos | universal_dependencies | cs_pdt | pos | udep__pos | TokenClassification |
| 234 | universal_dependencies/cs_pud/pos | universal_dependencies | cs_pud | pos | udep__pos | TokenClassification |
| 235 | universal_dependencies/da_ddt/pos | universal_dependencies | da_ddt | pos | udep__pos | TokenClassification |
| 236 | universal_dependencies/nl_alpino/pos | universal_dependencies | nl_alpino | pos | udep__pos | TokenClassification |
| 237 | universal_dependencies/nl_lassysmall/pos | universal_dependencies | nl_lassysmall | pos | udep__pos | TokenClassification |
| 238 | universal_dependencies/en_esl/pos | universal_dependencies | en_esl | pos | udep__pos | TokenClassification |
| 239 | universal_dependencies/en_ewt/pos | universal_dependencies | en_ewt | pos | udep__pos | TokenClassification |
| 240 | universal_dependencies/en_gum/pos | universal_dependencies | en_gum | pos | udep__pos | TokenClassification |
| 241 | universal_dependencies/zh_cfl/pos | universal_dependencies | zh_cfl | pos | udep__pos | TokenClassification |
| 242 | universal_dependencies/ca_ancora/pos | universal_dependencies | ca_ancora | pos | udep__pos | TokenClassification |
| 243 | universal_dependencies/yue_hk/pos | universal_dependencies | yue_hk | pos | udep__pos | TokenClassification |
| 244 | universal_dependencies/bxr_bdt/pos | universal_dependencies | bxr_bdt | pos | udep__pos | TokenClassification |
| 245 | universal_dependencies/af_afribooms/pos | universal_dependencies | af_afribooms | pos | udep__pos | TokenClassification |
| 246 | universal_dependencies/krl_kkpp/pos | universal_dependencies | krl_kkpp | pos | udep__pos | TokenClassification |
| 247 | universal_dependencies/akk_riao/pos | universal_dependencies | akk_riao | pos | udep__pos | TokenClassification |
| 248 | universal_dependencies/aqz_tudet/pos | universal_dependencies | aqz_tudet | pos | udep__pos | TokenClassification |
| 249 | universal_dependencies/sq_tsa/pos | universal_dependencies | sq_tsa | pos | udep__pos | TokenClassification |
| 250 | universal_dependencies/am_att/pos | universal_dependencies | am_att | pos | udep__pos | TokenClassification |
| 251 | universal_dependencies/grc_perseus/pos | universal_dependencies | grc_perseus | pos | udep__pos | TokenClassification |
| 252 | universal_dependencies/grc_proiel/pos | universal_dependencies | grc_proiel | pos | udep__pos | TokenClassification |
| 253 | universal_dependencies/apu_ufpa/pos | universal_dependencies | apu_ufpa | pos | udep__pos | TokenClassification |
| 254 | universal_dependencies/en_gumreddit/pos | universal_dependencies | en_gumreddit | pos | udep__pos | TokenClassification |
| 255 | universal_dependencies/ar_nyuad/pos | universal_dependencies | ar_nyuad | pos | udep__pos | TokenClassification |
| 256 | universal_dependencies/ar_pud/pos | universal_dependencies | ar_pud | pos | udep__pos | TokenClassification |
| 257 | universal_dependencies/hy_armtdp/pos | universal_dependencies | hy_armtdp | pos | udep__pos | TokenClassification |
| 258 | universal_dependencies/aii_as/pos | universal_dependencies | aii_as | pos | udep__pos | TokenClassification |
| 259 | universal_dependencies/bm_crb/pos | universal_dependencies | bm_crb | pos | udep__pos | TokenClassification |
| 260 | universal_dependencies/eu_bdt/pos | universal_dependencies | eu_bdt | pos | udep__pos | TokenClassification |
| 261 | universal_dependencies/be_hse/pos | universal_dependencies | be_hse | pos | udep__pos | TokenClassification |
| 262 | universal_dependencies/bho_bhtb/pos | universal_dependencies | bho_bhtb | pos | udep__pos | TokenClassification |
| 263 | universal_dependencies/br_keb/pos | universal_dependencies | br_keb | pos | udep__pos | TokenClassification |
| 264 | universal_dependencies/bg_btb/pos | universal_dependencies | bg_btb | pos | udep__pos | TokenClassification |
| 265 | universal_dependencies/ar_padt/pos | universal_dependencies | ar_padt | pos | udep__pos | TokenClassification |
| 266 | universal_dependencies/en_lines/pos | universal_dependencies | en_lines | pos | udep__pos | TokenClassification |
| 267 | universal_dependencies/akk_pisandub/pos | universal_dependencies | akk_pisandub | pos | udep__pos | TokenClassification |
| 268 | universal_dependencies/en_pronouns/pos | universal_dependencies | en_pronouns | pos | udep__pos | TokenClassification |
| 269 | universal_dependencies/el_gdt/pos | universal_dependencies | el_gdt | pos | udep__pos | TokenClassification |
| 270 | universal_dependencies/he_htb/pos | universal_dependencies | he_htb | pos | udep__pos | TokenClassification |
| 271 | universal_dependencies/qhe_hiencs/pos | universal_dependencies | qhe_hiencs | pos | udep__pos | TokenClassification |
| 272 | universal_dependencies/hi_pud/pos | universal_dependencies | hi_pud | pos | udep__pos | TokenClassification |
| 273 | universal_dependencies/hu_szeged/pos | universal_dependencies | hu_szeged | pos | udep__pos | TokenClassification |
| 274 | universal_dependencies/is_icepahc/pos | universal_dependencies | is_icepahc | pos | udep__pos | TokenClassification |
| 275 | universal_dependencies/id_csui/pos | universal_dependencies | id_csui | pos | udep__pos | TokenClassification |
| 276 | universal_dependencies/id_gsd/pos | universal_dependencies | id_gsd | pos | udep__pos | TokenClassification |
| 277 | universal_dependencies/id_pud/pos | universal_dependencies | id_pud | pos | udep__pos | TokenClassification |
| 278 | universal_dependencies/ga_idt/pos | universal_dependencies | ga_idt | pos | udep__pos | TokenClassification |
| 279 | universal_dependencies/it_isdt/pos | universal_dependencies | it_isdt | pos | udep__pos | TokenClassification |
| 280 | universal_dependencies/it_partut/pos | universal_dependencies | it_partut | pos | udep__pos | TokenClassification |
| 281 | universal_dependencies/it_postwita/pos | universal_dependencies | it_postwita | pos | udep__pos | TokenClassification |
| 282 | universal_dependencies/it_pud/pos | universal_dependencies | it_pud | pos | udep__pos | TokenClassification |
| 283 | universal_dependencies/it_twittiro/pos | universal_dependencies | it_twittiro | pos | udep__pos | TokenClassification |
| 284 | universal_dependencies/it_vit/pos | universal_dependencies | it_vit | pos | udep__pos | TokenClassification |
| 285 | universal_dependencies/ja_bccwj/pos | universal_dependencies | ja_bccwj | pos | udep__pos | TokenClassification |
| 286 | universal_dependencies/ja_gsd/pos | universal_dependencies | ja_gsd | pos | udep__pos | TokenClassification |
| 287 | universal_dependencies/ja_modern/pos | universal_dependencies | ja_modern | pos | udep__pos | TokenClassification |
| 288 | universal_dependencies/got_proiel/pos | universal_dependencies | got_proiel | pos | udep__pos | TokenClassification |
| 289 | universal_dependencies/de_pud/pos | universal_dependencies | de_pud | pos | udep__pos | TokenClassification |
| 290 | universal_dependencies/is_pud/pos | universal_dependencies | is_pud | pos | udep__pos | TokenClassification |
| 291 | universal_dependencies/de_hdt/pos | universal_dependencies | de_hdt | pos | udep__pos | TokenClassification |
| 292 | universal_dependencies/en_pud/pos | universal_dependencies | en_pud | pos | udep__pos | TokenClassification |
| 293 | universal_dependencies/myv_jr/pos | universal_dependencies | myv_jr | pos | udep__pos | TokenClassification |
| 294 | universal_dependencies/de_lit/pos | universal_dependencies | de_lit | pos | udep__pos | TokenClassification |
| 295 | universal_dependencies/et_ewt/pos | universal_dependencies | et_ewt | pos | udep__pos | TokenClassification |
| 296 | universal_dependencies/fo_farpahc/pos | universal_dependencies | fo_farpahc | pos | udep__pos | TokenClassification |
| 297 | universal_dependencies/fo_oft/pos | universal_dependencies | fo_oft | pos | udep__pos | TokenClassification |
| 298 | universal_dependencies/fi_ftb/pos | universal_dependencies | fi_ftb | pos | udep__pos | TokenClassification |
| 299 | universal_dependencies/fi_ood/pos | universal_dependencies | fi_ood | pos | udep__pos | TokenClassification |
| 300 | universal_dependencies/fi_pud/pos | universal_dependencies | fi_pud | pos | udep__pos | TokenClassification |
| 301 | universal_dependencies/fi_tdt/pos | universal_dependencies | fi_tdt | pos | udep__pos | TokenClassification |
| 302 | universal_dependencies/et_edt/pos | universal_dependencies | et_edt | pos | udep__pos | TokenClassification |
| 303 | universal_dependencies/fr_ftb/pos | universal_dependencies | fr_ftb | pos | udep__pos | TokenClassification |
| 304 | universal_dependencies/fr_fqb/pos | universal_dependencies | fr_fqb | pos | udep__pos | TokenClassification |
| 305 | universal_dependencies/de_gsd/pos | universal_dependencies | de_gsd | pos | udep__pos | TokenClassification |
| 306 | universal_dependencies/gl_treegal/pos | universal_dependencies | gl_treegal | pos | udep__pos | TokenClassification |
| 307 | universal_dependencies/gl_ctg/pos | universal_dependencies | gl_ctg | pos | udep__pos | TokenClassification |
| 308 | universal_dependencies/fr_spoken/pos | universal_dependencies | fr_spoken | pos | udep__pos | TokenClassification |
| 309 | universal_dependencies/en_partut/pos | universal_dependencies | en_partut | pos | udep__pos | TokenClassification |
| 310 | universal_dependencies/fr_pud/pos | universal_dependencies | fr_pud | pos | udep__pos | TokenClassification |
| 311 | universal_dependencies/fr_partut/pos | universal_dependencies | fr_partut | pos | udep__pos | TokenClassification |
| 312 | universal_dependencies/fr_sequoia/pos | universal_dependencies | fr_sequoia | pos | udep__pos | TokenClassification |
| 313 | universal_dependencies/fr_gsd/pos | universal_dependencies | fr_gsd | pos | udep__pos | TokenClassification |
| 314 | oasst1_pairwise_rlhf_reward | tasksource/oasst1_pairwise_rlhf_reward | | | oasst_rlhf | MultipleChoice |
| 315 | multilingual-sentiments/all | tyqiangz/multilingual-sentiments | all | | sentiment | Classification |
| 316 | tweet_sentiment_multilingual/arabic | cardiffnlp/tweet_sentiment_multilingual | arabic | | tweet_sentiment | Classification |
| 317 | tweet_sentiment_multilingual/french | cardiffnlp/tweet_sentiment_multilingual | french | | tweet_sentiment | Classification |
| 318 | tweet_sentiment_multilingual/english | cardiffnlp/tweet_sentiment_multilingual | english | | tweet_sentiment | Classification |
| 319 | tweet_sentiment_multilingual/hindi | cardiffnlp/tweet_sentiment_multilingual | hindi | | tweet_sentiment | Classification |
| 320 | tweet_sentiment_multilingual/portuguese | cardiffnlp/tweet_sentiment_multilingual | portuguese | | tweet_sentiment | Classification |
| 321 | tweet_sentiment_multilingual/spanish | cardiffnlp/tweet_sentiment_multilingual | spanish | | tweet_sentiment | Classification |
| 322 | tweet_sentiment_multilingual/all | cardiffnlp/tweet_sentiment_multilingual | all | | tweet_sentiment | Classification |
| 323 | tweet_sentiment_multilingual/german | cardiffnlp/tweet_sentiment_multilingual | german | | tweet_sentiment | Classification |
| 324 | tweet_sentiment_multilingual/italian | cardiffnlp/tweet_sentiment_multilingual | italian | | tweet_sentiment | Classification |
| 325 | amazon_reviews_multi/all_languages | amazon_reviews_multi | all_languages | | review_sentiment | Classification |
| 326 | universal-joy | metaeval/universal-joy | | | emotion | Classification |
| 327 | mms | Brand24/mms | | | mms_sentiment | Classification |
| 328 | mapa | joelito/mapa | | | mapa_fine | TokenClassification |
| 329 | mapa | joelito/mapa | | | mapa_corase | TokenClassification |
| 330 | ACES | nikitam/ACES | | | aces_ranking | MultipleChoice |
| 331 | ACES | nikitam/ACES | | | aces_phenomena | Classification |
| 332 | massive/my-MM | AmazonScience/massive | my-MM | | amazon_intent | Classification |
| 333 | massive/ro-RO | AmazonScience/massive | ro-RO | | amazon_intent | Classification |
| 334 | massive/pt-PT | AmazonScience/massive | pt-PT | | amazon_intent | Classification |
| 335 | massive/pl-PL | AmazonScience/massive | pl-PL | | amazon_intent | Classification |
| 336 | massive/nl-NL | AmazonScience/massive | nl-NL | | amazon_intent | Classification |
| 337 | massive/nb-NO | AmazonScience/massive | nb-NO | | amazon_intent | Classification |
| 338 | massive/es-ES | AmazonScience/massive | es-ES | | amazon_intent | Classification |
| 339 | massive/ms-MY | AmazonScience/massive | ms-MY | | amazon_intent | Classification |
| 340 | massive/mn-MN | AmazonScience/massive | mn-MN | | amazon_intent | Classification |
| 341 | massive/ml-IN | AmazonScience/massive | ml-IN | | amazon_intent | Classification |
| 342 | massive/lv-LV | AmazonScience/massive | lv-LV | | amazon_intent | Classification |
| 343 | massive/ko-KR | AmazonScience/massive | ko-KR | | amazon_intent | Classification |
| 344 | massive/ru-RU | AmazonScience/massive | ru-RU | | amazon_intent | Classification |
| 345 | massive/kn-IN | AmazonScience/massive | kn-IN | | amazon_intent | Classification |
| 346 | massive/ka-GE | AmazonScience/massive | ka-GE | | amazon_intent | Classification |
| 347 | massive/jv-ID | AmazonScience/massive | jv-ID | | amazon_intent | Classification |
| 348 | massive/ja-JP | AmazonScience/massive | ja-JP | | amazon_intent | Classification |
| 349 | massive/it-IT | AmazonScience/massive | it-IT | | amazon_intent | Classification |
| 350 | massive/is-IS | AmazonScience/massive | is-IS | | amazon_intent | Classification |
| 351 | massive/id-ID | AmazonScience/massive | id-ID | | amazon_intent | Classification |
| 352 | massive/hy-AM | AmazonScience/massive | hy-AM | | amazon_intent | Classification |
| 353 | massive/hu-HU | AmazonScience/massive | hu-HU | | amazon_intent | Classification |
| 354 | massive/hi-IN | AmazonScience/massive | hi-IN | | amazon_intent | Classification |
| 355 | massive/he-IL | AmazonScience/massive | he-IL | | amazon_intent | Classification |
| 356 | massive/fr-FR | AmazonScience/massive | fr-FR | | amazon_intent | Classification |
| 357 | massive/km-KH | AmazonScience/massive | km-KH | | amazon_intent | Classification |
| 358 | massive/fi-FI | AmazonScience/massive | fi-FI | | amazon_intent | Classification |
| 359 | massive/sl-SL | AmazonScience/massive | sl-SL | | amazon_intent | Classification |
| 360 | massive/sv-SE | AmazonScience/massive | sv-SE | | amazon_intent | Classification |
| 361 | massive/af-ZA | AmazonScience/massive | af-ZA | | amazon_intent | Classification |
| 362 | massive/am-ET | AmazonScience/massive | am-ET | | amazon_intent | Classification |
| 363 | massive/ar-SA | AmazonScience/massive | ar-SA | | amazon_intent | Classification |
| 364 | massive/az-AZ | AmazonScience/massive | az-AZ | | amazon_intent | Classification |
| 365 | massive/bn-BD | AmazonScience/massive | bn-BD | | amazon_intent | Classification |
| 366 | massive/ca-ES | AmazonScience/massive | ca-ES | | amazon_intent | Classification |
| 367 | massive/cy-GB | AmazonScience/massive | cy-GB | | amazon_intent | Classification |
| 368 | massive/da-DK | AmazonScience/massive | da-DK | | amazon_intent | Classification |
| 369 | massive/de-DE | AmazonScience/massive | de-DE | | amazon_intent | Classification |
| 370 | massive/el-GR | AmazonScience/massive | el-GR | | amazon_intent | Classification |
| 371 | massive/sq-AL | AmazonScience/massive | sq-AL | | amazon_intent | Classification |
| 372 | massive/en-US | AmazonScience/massive | en-US | | amazon_intent | Classification |
| 373 | massive/all | AmazonScience/massive | all | | amazon_intent | Classification |
| 374 | massive/zh-TW | AmazonScience/massive | zh-TW | | amazon_intent | Classification |
| 375 | massive/zh-CN | AmazonScience/massive | zh-CN | | amazon_intent | Classification |
| 376 | massive/vi-VN | AmazonScience/massive | vi-VN | | amazon_intent | Classification |
| 377 | massive/ur-PK | AmazonScience/massive | ur-PK | | amazon_intent | Classification |
| 378 | massive/tr-TR | AmazonScience/massive | tr-TR | | amazon_intent | Classification |
| 379 | massive/tl-PH | AmazonScience/massive | tl-PH | | amazon_intent | Classification |
| 380 | massive/th-TH | AmazonScience/massive | th-TH | | amazon_intent | Classification |
| 381 | massive/te-IN | AmazonScience/massive | te-IN | | amazon_intent | Classification |
| 382 | massive/ta-IN | AmazonScience/massive | ta-IN | | amazon_intent | Classification |
| 383 | massive/sw-KE | AmazonScience/massive | sw-KE | | amazon_intent | Classification |
| 384 | massive/all_1.1 | AmazonScience/massive | all_1.1 | | amazon_intent | Classification |
| 385 | massive/fa-IR | AmazonScience/massive | fa-IR | | amazon_intent | Classification |
| 386 | tydi-as2-balanced | tasksource/tydi-as2-balanced | | | tidy_as2 | Classification |
| 387 | multiconer_v2/Hindi (HI) | MultiCoNER/multiconer_v2 | Hindi (HI) | | multiconer | TokenClassification |
| 388 | multiconer_v2/Multilingual (MULTI) | MultiCoNER/multiconer_v2 | Multilingual (MULTI) | | multiconer | TokenClassification |
| 389 | multiconer_v2/Ukrainian (UK) | MultiCoNER/multiconer_v2 | Ukrainian (UK) | | multiconer | TokenClassification |
| 390 | multiconer_v2/Swedish (SV) | MultiCoNER/multiconer_v2 | Swedish (SV) | | multiconer | TokenClassification |
| 391 | multiconer_v2/Spanish (ES) | MultiCoNER/multiconer_v2 | Spanish (ES) | | multiconer | TokenClassification |
| 392 | multiconer_v2/Bangla (BN) | MultiCoNER/multiconer_v2 | Bangla (BN) | | multiconer | TokenClassification |
| 393 | multiconer_v2/Chinese (ZH) | MultiCoNER/multiconer_v2 | Chinese (ZH) | | multiconer | TokenClassification |
| 394 | multiconer_v2/English (EN) | MultiCoNER/multiconer_v2 | English (EN) | | multiconer | TokenClassification |
| 395 | multiconer_v2/Farsi (FA) | MultiCoNER/multiconer_v2 | Farsi (FA) | | multiconer | TokenClassification |
| 396 | multiconer_v2/Portuguese (PT) | MultiCoNER/multiconer_v2 | Portuguese (PT) | | multiconer | TokenClassification |
| 397 | multiconer_v2/German (DE) | MultiCoNER/multiconer_v2 | German (DE) | | multiconer | TokenClassification |
| 398 | multiconer_v2/Italian (IT) | MultiCoNER/multiconer_v2 | Italian (IT) | | multiconer | TokenClassification |
| 399 | multiconer_v2/French (FR) | MultiCoNER/multiconer_v2 | French (FR) | | multiconer | TokenClassification |
| 400 | mtop | tasksource/mtop | | | mtop | Classification |
| 401 | multilingual-zero-shot-label-nli | tasksource/multilingual-zero-shot-label-nli | | | mlabel_nli | Classification |
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=45", "setuptools_scm[toml]>=6.2"]
build-backend = "setuptools.build_meta"
[tool.setuptools_scm]
================================================
FILE: setup.cfg
================================================
[metadata]
name = tasksource
description = Preprocessings to prepare datasets for a task
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/sileod/tasksource/
classifiers =
Programming Language :: Python :: 3
License :: OSI Approved :: BSD License
Intended Audience :: Developers
[options]
package_dir =
= src
packages = find:
python_requires = >=3.6
install_requires =
dotwiz
funcy
datasets
exrex
magicattr
pandas
numpy
scipy
sorcery
[options.packages.find]
where = src
================================================
FILE: src/tasksource/.ipynb_checkpoints/access-checkpoint.py
================================================
from .preprocess import Preprocessing
import re
import pandas as pd
from . import tasks, recast
from .metadata import dataset_rank
from datasets import load_dataset
import funcy as fc
import os
import copy
from sorcery import dict_of
from functools import cache
import random
class lazy_mtasks:
def __getattr__(self, name):
from . import mtasks
return getattr(mtasks, name)
def __dir__(self):
from . import mtasks
return dir(mtasks)
lmtasks=lazy_mtasks()
def parse_var_name(s):
config_name,task_name = None,None
if '__' in s and '___' not in s: # dataset__task
dataset_name, task_name = s.split('__')
elif '__' not in s.replace('___','') and '___' in s: #dataset___config
dataset_name, config_name = s.split('___')
elif '___' in s and '__' in s.split('___')[1]: #dataset___config__task
dataset_name, config_task=s.split('___')
config_name,task_name = config_task.split('__')
else: # dataset
dataset_name = s
return dataset_name,config_name,task_name
def pretty_name(x):
dn = x.dataset_name.split("/")[-1]
cn = x.config_name if x.config_name else ""
tn = x.task_name if x.task_name else ""
return f"{dn}/{cn}/{tn}".replace('//','/').rstrip('/')
@cache
def list_tasks(tasks_path=f'{os.path.dirname(__file__)}/tasks.py',multilingual=False,instruct=False, excluded=[]):
if multilingual:
tasks_path=tasks_path.replace('/tasks.py','/mtasks.py')
task_order = open(tasks_path).readlines()
task_order = [x.split('=')[0].rstrip() for x in task_order if '=' in x]
task_order = [x for x in task_order if x.isidentifier()]
task_order = fc.flip(dict(enumerate(task_order)))
l = []
_tasks = (lmtasks if multilingual else tasks)
for key in dir(_tasks):
if key not in task_order:
continue
value=getattr(_tasks, key)
if isinstance(value,Preprocessing):
dataset_name, config_name, task_name = parse_var_name(key)
dataset_name = (value.dataset_name if value.dataset_name else dataset_name)
config_name = (value.config_name if value.config_name else config_name)
hasattr(value,key)
l+=[{'dataset_name': dataset_name,
'config_name' : config_name,
'task_name': task_name,
'preprocessing_name': key,
'task_type': value.__class__.__name__,'mapping': value,
'rank':task_order.get(key,None)}]
df=pd.DataFrame(l).explode('config_name')
df = df.sort_values('rank').reset_index(drop=True)
df['id'] = df.apply(lambda x: pretty_name(x), axis=1)
df.insert(0, 'id', df.pop('id'))
del df['rank']
if instruct:
df=df[df.id.map(lambda x: not any(a in x for a in recast.improper_labels))]
df=df[df.id.map(lambda x: not any(x in a for a in excluded))]
return df
#task_df =list_tasks()
#mtask_df =list_tasks(multilingual=True)
def dict_to_query(d=dict(), **kwargs):
d={**d,**kwargs}
return '&'.join([f'`{k}`=="{v}"' for k,v in d.items()])
def load_preprocessing(tasks=tasks, **kwargs):
_tasks_df = list_tasks(multilingual=tasks==lmtasks)
y = _tasks_df.copy().query(dict_to_query(**kwargs)).iloc[0]
preprocessing= copy.copy(getattr(tasks, y.preprocessing_name))
for c in 'dataset_name','config_name':
if not isinstance(getattr(preprocessing,c), str):
setattr(preprocessing,c,getattr(y,c))
return preprocessing
def load_task(id=None, dataset_name=None,config_name=None,task_name=None,preprocessing_name=None,
max_rows=None, max_rows_eval=None, multilingual=False, instruct=False, seed=0, **load_dataset_kwargs):
query = dict_of(id, dataset_name, config_name, task_name,preprocessing_name)
query = {k:v for k,v in query.items() if v}
_tasks = (lmtasks if multilingual else tasks)
preprocessing = load_preprocessing(_tasks, **query)
if "trust_remote_code" not in load_dataset_kwargs:
load_dataset_kwargs["trust_remote_code"] = True
dataset = load_dataset(preprocessing.dataset_name, preprocessing.config_name, **load_dataset_kwargs)
dataset= preprocessing(dataset,max_rows, max_rows_eval)
dataset.task_type = preprocessing.__class__.__name__
if instruct:
dataset=recast.recast_instruct(dataset)
return dataset
================================================
FILE: src/tasksource/.ipynb_checkpoints/preprocess-checkpoint.py
================================================
from collections.abc import Iterable
from dotwiz import DotWiz
from dataclasses import dataclass
from typing import Union
import itertools
import funcy as fc
import exrex
import magicattr
import numpy as np
import copy
import datasets
import time
MAX_MC_OPTIONS = 4
def get_column_names(dataset):
cn = dataset.column_names
if type(cn)==dict:
return set(fc.flatten(cn.values()))
else:
return set(cn)
def sample_dataset(dataset,n=10000, n_eval=1000,seed=0):
for k in dataset:
n_k=(n if k=='train' else n_eval)
if n_k and len(dataset[k])>n_k:
dataset[k]=dataset[k].train_test_split(train_size=n_k,seed=seed)['train']
return dataset
class Preprocessing(DotWiz):
default_splits = ('train','validation','test')
_instances = []
def __post_init__(self):
Preprocessing._instances+=[self]
@staticmethod
def __map_to_target(x,fn=lambda x:None, target=None):
x[target]=fn(x)
return x
def load(self):
return self(datasets.load_dataset(self.dataset_name,self.config_name))
def __call__(self,dataset, max_rows=None, max_rows_eval=None,seed=0):
dataset = self.pre_process(dataset)
# manage splits
for k,v in zip(self.default_splits, self.splits):
if v and k!=v:
dataset[k]=dataset[v]
del dataset[v]
if k in dataset and not v: # obfuscated label
del dataset[k]
dataset = fix_splits(dataset)
for k in list(dataset.keys()):
if k not in self.default_splits:
del dataset[k]
dataset = sample_dataset(dataset, max_rows, max_rows_eval,seed=seed)
# field annotated with a string
substitutions = {v:k for k,v in self.to_dict().items()
if (k and k not in {'splits','dataset_name','config_name'}
and type(v)==str and k!=v)}
dataset=dataset.remove_columns([c for c in substitutions.values() if c in dataset['train'].features and c not in substitutions])
dataset=dataset.rename_columns(substitutions)
# field annotated with a function
for k in self.to_dict().keys():
v=getattr(self, k)
if callable(v) and k not in {"post_process","pre_process","load"}:
dataset=dataset.map(self.__map_to_target,
fn_kwargs={'fn':v,'target':k})
dataset=dataset.remove_columns(
get_column_names(dataset)-set(self.to_dict().keys()))
dataset = fix_labels(dataset)
dataset = fix_splits(dataset) # again: label mapping changed
dataset = self.post_process(dataset)
return dataset
@dataclass
class cat(Preprocessing):
fields:Union[str,list]=None
separator:str=' '
def __call__(self, example=None):
y=[np.char.array(example[f]) + sep
for f,sep in zip(self.fields[::-1],itertools.repeat(self.separator))]
y=list(sum(*y))
if len(y)==1:
y=y[0]
return y
def pretty(f):
class pretty_f(DotWiz):
def __init__(self,*args):
self.__f_arg = f(*args)
for a in args:
setattr(self,'value',a)
def __call__(self, *args,**kwargs):
return self.__f_arg(*args,**kwargs)
def __repr__(self):
return f"{self.__f_arg.__qualname__ .split('.')[0]}({self.value})"
return pretty_f
class dotgetter:
def __init__(self, path=''):
self.path=path
def __bool__(self):
return bool(self.path)
def __getattr__(self, k):
return self.__class__(f'{self.path}.{k}'.lstrip('.'))
def __getitem__(self, i):
return self.__class__(f'{self.path}[{i}]')
def __call__(self, example=None):
return magicattr.get(DotWiz(example), self.path)
def __hash__(self):
return hash(self.path)
@dataclass
class ClassificationFields(Preprocessing):
sentence1:str='sentence1'
sentence2:str='sentence2'
labels:str='labels'
@dataclass
class Seq2SeqLMFields(Preprocessing):
prompt:str='prompt'
output:str='output'
@dataclass
class TokenClassificationFields(Preprocessing):
tokens:str='tokens'
labels:str='labels'
@dataclass
class MultipleChoiceFields(Preprocessing):
inputs:str='input'
choices:Iterable=tuple()
labels:str='labels'
choices_list:str=None
def __post_init__(self):
for i, c in enumerate(self.choices):
setattr(self,f'choice{i}',c)
delattr(self,'choices')
if not self.choices_list:
delattr(self,'choices_list')
def __call__(self,dataset, *args, **kwargs):
dataset = super().__call__(dataset, *args, **kwargs)
if self.choices_list:
dataset = dataset.filter(lambda x: 1<len(x['choices_list']))
n_options = min([len(x) for k in dataset for x in dataset[k]['choices_list']])
n_options = min(MAX_MC_OPTIONS,n_options)
dataset = dataset.map(self.flatten_choice_list, fn_kwargs={'n_options':n_options})
else:
dataset = dataset.map(self.sample_choices, fn_kwargs={'n_options':MAX_MC_OPTIONS})
return dataset
@staticmethod
def flatten_choice_list(x, n_options=None):
n_neg = n_options-1 if n_options else None
choices = x['choices_list']
label=x['labels']
neg = choices[:label] + choices[label+1:]
pos = choices[label]
x['labels']=0
x['choices_list']=[pos]+neg[:n_neg]
for i,o in enumerate(x['choices_list']):
x[f'choice{i}']=o
del x['choices_list']
return x
@staticmethod
def sample_choices(x, n_options=None):
choices = [x[c] for c in x if 'choice' in c]
if not MAX_MC_OPTIONS or len(choices)<=n_options:
return x
n_neg = n_options-1 if n_options else None
label=x['labels']
neg = choices[:label] + choices[label+1:]
pos = choices[label]
x['labels']=0
choices_list=[pos]+neg[:n_neg]
for c in list(x):
if 'choice' in c:
del x[c]
for i,o in enumerate(choices_list):
x[f'choice{i}']=o
return x
@dataclass
class SharedFields:
splits:list=Preprocessing.default_splits
dataset_name:str = None
config_name:str = None
pre_process: callable = fc.identity
post_process: callable = fc.identity
#language:str="en"
@dataclass
class Classification(SharedFields, ClassificationFields): pass
@dataclass
class MultipleChoice(SharedFields, MultipleChoiceFields): pass
@dataclass
class TokenClassification(SharedFields, TokenClassificationFields): pass
@dataclass
class Seq2SeqLM(SharedFields, Seq2SeqLMFields): pass
get=dotgetter()
constant = pretty(fc.constantly)
regen = lambda x: list(exrex.generate(x))
def name(label_name, classes):
return lambda x:classes[x[label_name]]
def fix_splits(dataset):
if len(dataset)==1 and "train" not in dataset:
k = list(dataset)[0]
dataset['train'] = copy.deepcopy(dataset[k])
del dataset[k]
if 'auxiliary_train' in dataset:
del dataset['auxiliary_train']
if 'test' in dataset: # manage obfuscated labels
if 'labels' in dataset['test'].features:
if len(set(fc.flatten(dataset['test'].to_dict()['labels'])))==1:
del dataset['test']
if 'validation' in dataset and 'train' not in dataset:
train_validation = dataset['validation'].train_test_split(0.5, seed=0)
dataset['train'] = train_validation['train']
dataset['validation']=train_validation['test']
if 'validation' in dataset and 'test' not in dataset:
validation_test = dataset['validation'].train_test_split(0.5, seed=0)
dataset['validation'] = validation_test['train']
dataset['test']=validation_test['test']
if 'train' in dataset and 'validation' not in dataset:
train_val = dataset['train'].train_test_split(train_size=0.90, seed=0)
dataset['train'] = train_val['train']
dataset['validation']=train_val['test']
if 'test' in dataset and 'validation' not in dataset:
validation_test = dataset['test'].train_test_split(0.5, seed=0)
dataset['validation'] = validation_test['train']
dataset['test']=validation_test['test']
if 'validation' not in dataset and 'test' not in dataset:
train_val_test = dataset["train"].train_test_split(train_size=0.90, seed=0)
val_test = train_val_test["test"].train_test_split(0.5, seed=0)
dataset["train"] = train_val_test["train"]
dataset["validation"] = val_test["train"]
dataset["test"] = val_test["test"]
return dataset
def fix_labels(dataset, label_key='labels'):
if type(dataset['train'][label_key][0]) in [int,list,float]:
return dataset
labels=set(fc.flatten(dataset[k][label_key] for k in {"train"}))
if set(labels)=={'entailment','neutral','contradiction'}:
order=lambda x:dict(fc.flip(enumerate(['entailment','neutral','contradiction']))).get(x,x)
else:
order=str
labels=sorted(labels, key=order)
dataset=dataset.cast_column(label_key, datasets.ClassLabel(names=labels))
return dataset
def concatenate_dataset_dict(l):
"""Concatenate a list of DatastDict objects sharing same splits and columns."""
keys=l[0].keys()
return datasets.DatasetDict({k: datasets.concatenate_datasets([x[k] for x in l]) for k in keys})
================================================
FILE: src/tasksource/.ipynb_checkpoints/recast-checkpoint.py
================================================
import random
from datasets import DatasetDict, Dataset
from sorcery import dict_of
import string
improper_labels =['recast/recast_kg_relations','linguisticprobing',"lex_glue/scotus",'lexical_relation_classification/ROOT09',"pragmeval/squinky","pragmeval/emobank",'pragmeval/persuasiveness']
improper_labels += ['glue/stsb', 'sick/relatedness', 'joci', 'utilitarianism', 'amazon_counterfactual/en', 'toxic_conversations', 'ethos/multilabel', 'lex_glue/eurlex', 'lex_glue/unfair_tos', 'app_reviews', 'humicroedit/subtask-1', 'stackoverflow-questions', 'go_emotions/simplified', 'google_wellformed_query', 'has_part', 'blog_authorship_corpus/age', 'promptCoherence', 'Sarcasm_News_Headline', 'auditor_review/demo-org--auditor_review', 'Dynasent_Disagreement', 'Politeness_Disagreement', 'SBIC_Disagreement', 'SChem_Disagreement', 'Dilemmas_Disagreement', 'sts-companion', 'acceptability-prediction', 'chaos-mnli-ambiguity', 'headline_cause/en_simple', 'oasst1_dense_flat', 'civil_comments']
improper_labels += ['stsb_multi_mt','MLMA_hate_speech','icl-symbol-tuning-instruct','zero-shot-label-nli']
improper_labels += ['essay-scoring','english-grading','HelpSteer','oasst2']
def render_options(options):
options = [f'"{x}"' for x in options]
return f"{', '.join(options[:-1])} or {options[-1]}"
def render_classification(text,options,answer):
example = 'text_A→text_B' if text.startswith('text_A:') else 'the following'
inputs = f'With no explanation, label {example} with either {render_options(options)}.\n{text}'
targets = f"{answer}."
return dict_of(inputs,targets)
def render_token_classification(tokens,options,labels):
prefix = f'With no explanation, label each line with {render_options(options)} preceded by ":".\n'
inputs = prefix+"\n".join(tokens)
targets = "\n".join([':'.join(x) for x in zip(tokens,labels)])
return dict_of(inputs,targets)
def render_multiple_choice(prompt, options, labels):
inputs=(prompt+'\n' if prompt else '')
letters = string.ascii_uppercase[:len(options)]
inputs=f'With no explanation, chose the best option from {render_options(letters)}. {inputs}'
for letter, option in zip(letters, options):
inputs+=f'\n{letter}: {option}'
targets = f'{letters[labels]}.'
return dict_of(inputs, targets)
def negative_sample_options(y, labels,N=4):
if len(labels)<N:
return labels
else:
return [y]+random.sample([x for x in labels if x!=y], N-1)
def shuffle_choices(x):
choices = sorted([k for k in x if 'choice' in k])
choices_texts = [x[c] for c in choices]
correct_choice =choices_texts[x['labels']]
random.shuffle(choices_texts)
for c, ct in zip(choices, choices_texts):
x[c]=ct
x["labels"]=choices_texts.index(correct_choice)
return x
def recast_dataset_classification_to_mc(dataset,sep="[SEP]",N=4):
def recast_split(d,N=N):
labels = d.features['labels']
df=d.to_pandas()
df['inputs'] = df.sentence1
if "sentence2" in df:
df['inputs'] +=sep + df.sentence2
N=min(N, len(labels.names))
df['choices']=df.apply(lambda x:negative_sample_options(labels.int2str(x['labels']), labels.names,N),axis=1)
df['labels']=df.apply(lambda x:x['choices'].index(labels.int2str(x['labels'])),axis=1)
for i in range(N):
df[f'choice{i}']= "This example is " + df.choices.map(lambda x:x[i])
choices = [f'choice{i}' for i in range(N)]
return Dataset.from_pandas(df[['inputs',*choices,'labels']],preserve_index=False)
return DatasetDict({k: recast_split(v) for k,v in dataset.items()})
def recast_instruct(dataset):
features = dataset['train'].features
labels = features['labels']
if "sentence1" in features:
task_type='Classification'
if "choice0" in features:
task_type = "MultipleChoice"
if "tokens" in features:
task_type = "TokenClassification"
def recast_MultipleChoice(x):
x=shuffle_choices(x)
choices = sorted([k for k in x if 'choice' in k])
if all([x[c] in x['inputs'] for c in choices]):
return {"inputs":x['inputs'], 'targets': x[f"choice{x['labels']}"].strip()+"."}
else:
return render_multiple_choice(x['inputs'],[x[c] for c in choices],x['labels'])
def recast_TokenClassification(x):
distractors = list(labels.feature.names)
x_labels = [labels.feature.int2str(y) for y in x['labels']]
labels_set= list({labels.feature.int2str(y) for y in x['labels']})
options=list(dict.fromkeys(labels_set+distractors))[:max(len(labels_set),10)]
return render_token_classification(x['tokens'],options,x_labels)
def recast_Classification(x):
if 'sentence2' in x:
text=f"text_A: {x['sentence1']}\ntext_B: {x['sentence2']}"
else:
text=x['sentence1']
answer=labels.int2str(x['labels']).strip()
options= negative_sample_options(answer, labels._int2str)
return render_classification(text, options, answer)
dataset = dataset.map(eval(f"recast_{task_type}"))
dataset = dataset.remove_columns([k for k in features if k not in ['inputs','targets']])
return dataset
================================================
FILE: src/tasksource/.ipynb_checkpoints/tasks-checkpoint.py
================================================
from .preprocess import cat, get, regen, name, constant, Classification, TokenClassification, MultipleChoice
from .metadata import bigbench_discriminative_english, blimp_hard, imppres_presupposition, imppres_implicature, udep_en_configs, udep_en_labels
from datasets import get_dataset_config_names, Sequence, ClassLabel, Dataset, DatasetDict
# variable name: dataset___config__task
###################### NLI/paraphrase ###############################
glue___mnli = Classification(sentence1="premise", sentence2="hypothesis", labels="label", splits=["train", None, "validation_matched"])
glue___qnli = Classification("question","sentence", labels="label")
glue___rte = Classification(sentence1="sentence1", sentence2="sentence2", labels="label")
glue___wnli = Classification(sentence1="sentence1", sentence2="sentence2", labels="label")
#glue___ax = Classification(sentence1="premise", sentence2="hypothesis", labels="label", splits=["test", None, None]) # fully masked
glue___mrpc = Classification(sentence1="sentence1", sentence2="sentence2", labels="label")
glue___qqp = Classification(sentence1="question1", sentence2="question2", labels="label")
glue___stsb = Classification(sentence1="sentence1", sentence2="sentence2", labels="label")
super_glue___boolq = Classification(sentence1="question", labels="label")
super_glue___cb = Classification(sentence1="premise", sentence2="hypothesis", labels="label")
super_glue___multirc = Classification(
cat(["paragraph", "question"]),
'answer',
labels='label'
)
#super_glue___rte = Classification(sentence1="premise", sentence2="hypothesis", labels="label") # in glue
super_glue___wic = Classification(
sentence1=cat(["word","sentence1"], " : "),
sentence2=cat(["word","sentence2"], " : "),
labels='label'
)
super_glue___axg = Classification(sentence1="premise", sentence2="hypothesis", labels="label", splits=["test", None, None])
anli__a1 = Classification('premise','hypothesis','label', splits=['train_r1','dev_r1','test_r1'])
anli__a2 = Classification('premise','hypothesis','label', splits=['train_r2','dev_r2','test_r2'])
anli__a3 = Classification('premise','hypothesis','label', splits=['train_r3','dev_r3','test_r3'])
babi_nli = Classification("premise", "hypothesis", "label",
dataset_name="tasksource/babi_nli",
config_name=set(get_dataset_config_names("tasksource/babi_nli"))-{"agents-motivations"}
) # agents-motivations task is not as clear-cut as the others
sick__label = Classification('sentence_A','sentence_B','label')
sick__relatedness = Classification('sentence_A','sentence_B','relatedness_score')
sick__entailment_AB = Classification('sentence_A','sentence_B','entailment_AB')
#sick__entailment_BA = Classification('sentence_A','sentence_B','entailment_BA')
def remove_neg_1(dataset):
return dataset.filter(lambda x:x['labels']!=-1)
snli = Classification(sentence1="premise", sentence2="hypothesis", labels="label",
post_process=remove_neg_1)
scitail = Classification("sentence1","sentence2","gold_label",config_name="snli_format")
hans = Classification(sentence1="premise", sentence2="hypothesis", labels="label")
wanli = Classification('premise','hypothesis','gold', dataset_name="alisawuffles/WANLI")
recast_nli = Classification(sentence1="context", sentence2="hypothesis", labels="label", dataset_name="tasksource/recast",
config_name=['recast_kg_relations', 'recast_puns', 'recast_factuality', 'recast_verbnet',
'recast_verbcorner', 'recast_ner', 'recast_sentiment', 'recast_megaveridicality'])
probability_words_nli = Classification(sentence1="context", sentence2="hypothesis", labels="label",
dataset_name="sileod/probability_words_nli",
config_name=["reasoning_1hop","reasoning_2hop","usnli"])
nan_nli = Classification("premise", "hypothesis", "label", dataset_name="joey234/nan-nli")
nli_fever = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/nli_fever", splits=["train","dev",None])
breaking_nli = Classification("sentence1","sentence2","label",
dataset_name="pietrolesci/breaking_nli", splits=["full",None,None])
conj_nli = Classification("premise","hypothesis","label",post_process=remove_neg_1,
dataset_name="pietrolesci/conj_nli",splits=['train','dev',None])
fracas = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/fracas")
dialogue_nli = Classification("sentence1","sentence2","label",
dataset_name="pietrolesci/dialogue_nli")
mpe_nli = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/mpe",
splits=["train","dev","test"])
dnc_nli = Classification("context","hypothesis","label",
dataset_name="pietrolesci/dnc")
# gpt3_nli = Classification("text_a","text_b","label",dataset_name="pietrolesci/gpt3_nli") # not sound enough
recast_white__fnplus = Classification("text","hypothesis","label",
dataset_name="pietrolesci/recast_white",splits=['fnplus',None,None])
recast_white__sprl = Classification("text","hypothesis","label",
dataset_name="pietrolesci/recast_white",splits=['sprl',None,None])
recast_white__dpr = Classification("text","hypothesis","label",
dataset_name="pietrolesci/recast_white",splits=['dpr',None,None])
joci = Classification("context","hypothesis",
labels=lambda x: [None, "impossible", "technically possible", "plausible", "likely", "very likely"][x["original_label"]],
pre_process=lambda ds:ds.filter(lambda x:x['original_label']!=0),
dataset_name="pietrolesci/joci",splits=['full',None,None])
#enfever_nli = Classification("evidence","claim","label", dataset_name="ctu-aic/enfever_nli")
robust_nli__IS_CS = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["IS_CS",None,None])
robust_nli__LI_LI = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["LI_LI",None,None])
robust_nli__ST_WO = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["ST_WO",None,None])
robust_nli__PI_SP = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["PI_SP",None,None])
robust_nli__PI_CD = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["PI_CD",None,None])
robust_nli__ST_SE = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["ST_SE",None,None])
robust_nli__ST_NE = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["ST_NE",None,None])
robust_nli__ST_LM = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli", splits=["ST_LM",None,None])
robust_nli_is_sd = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli_is_sd")
robust_nli_li_ts = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/robust_nli_li_ts")
gen_debiased_nli__snli_seq_z = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/gen_debiased_nli", splits=["snli_seq_z",None,None])
gen_debiased_nli__snli_z_aug = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/gen_debiased_nli", splits=["snli_z_aug",None,None])
gen_debiased_nli__snli_par_z = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/gen_debiased_nli", splits=["snli_par_z",None,None])
gen_debiased_nli__mnli_par_z = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/gen_debiased_nli", splits=["mnli_par_z",None,None])
gen_debiased_nli__mnli_z_aug = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/gen_debiased_nli", splits=["mnli_z_aug",None,None])
gen_debiased_nli__mnli_seq_z = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/gen_debiased_nli", splits=["mnli_seq_z",None,None])
add_one_rte = Classification("premise","hypothesis","label",
dataset_name="pietrolesci/add_one_rte",splits=["train","dev","test"])
def _imppres_post_process(ds,prefix=''):
# imppres entailment definition is either purely semantic or purely pragmatic
# because of that, we assign differentiate the labels from anli/mnli notation
return ds.cast_column('labels', ClassLabel(
names=[f'{prefix}_entailment',f'{prefix}_neutral',f'{prefix}_contradiction']))
imppres__presupposition = imppres__prag = Classification("premise","hypothesis","gold_label",
dataset_name="tasksource/imppres", config_name=imppres_presupposition,
post_process=_imppres_post_process)
imppres__prag = Classification("premise","hypothesis","gold_label_prag",
dataset_name="tasksource/imppres", config_name=imppres_implicature,
post_process=lambda x: _imppres_post_process(x,'pragmatic'))
imppres__log = Classification("premise","hypothesis","gold_label_log",
dataset_name="tasksource/imppres", config_name=imppres_implicature,
post_process=lambda x: _imppres_post_process(x,'logical'))
#glue__diagnostics = Classification("premise","hypothesis","label",
# dataset_name="pietrolesci/glue_diagnostics",splits=["test",None,None])
hlgd = Classification("headline_a", "headline_b", labels="label")
paws___labeled_final = Classification("sentence1", "sentence2", name('label',['not_paraphrase','paraphrase']))
paws___labeled_swap = Classification("sentence1", "sentence2", name('label',['not_paraphrase','paraphrase']), splits=["train", None, None])
#paws___unlabeled_final = Classification("sentence1", "sentence2", "label")
#quora = Classification(get.questions.text[0], get.questions.text[1], 'is_duplicate') # in glue
medical_questions_pairs = Classification("question_1","question_2", name("label",['not similar','similar']))
###################### Token Classification #########################
conll2003__pos_tags = TokenClassification(tokens="tokens", labels='pos_tags')
conll2003__chunk_tags = TokenClassification(tokens="tokens", labels='chunk_tags')
conll2003__ner_tags = TokenClassification(tokens="tokens", labels='ner_tags')
#tner___tweebank_ner = TokenClassification(tokens="tokens", labels="tags")
######################## Multiple choice ###########################
model_written_evals = MultipleChoice('question', choices=['answer_matching_behavior','answer_not_matching_behavior'], labels=constant(0),
dataset_name="Anthropic/model-written-evals")
truthful_qa___multiple_choice = MultipleChoice(
"question",
choices_list=get.mc1_targets.choices,
labels=constant(0)
)
fig_qa = MultipleChoice(
"startphrase",
choices=["ending1","ending2"],
labels="labels",
dataset_name="nightingal3/fig-qa",
splits=["train","validation",None]
)
bigbench = MultipleChoice(
'inputs',
choices_list='multiple_choice_targets',
labels=lambda x:x['multiple_choice_scores'].index(1) if 1 in ['multiple_choice_scores'] else -1,
dataset_name='tasksource/bigbench',
config_name=bigbench_discriminative_english - {"social_i_qa","intersect_geometry"} # english multiple choice tasks, minus duplicates
)
#"goal_step_wikihow"
blimp_hard = MultipleChoice(inputs=constant(''),
choices=['sentence_good','sentence_bad'],
labels=constant(0),
dataset_name="blimp",
config_name=blimp_hard # tasks where GPT2 is at least 10% below human accuracy
)
cos_e = MultipleChoice('question',
choices_list='choices',
labels= lambda x: x['choices_list'].index(x['answer']),
config_name='v1.0')
cosmos_qa = MultipleChoice(cat(['context','question']),regen('answer[0-3]'),'label')
dream = MultipleChoice(
lambda x:"\n".join(x['dialogue']+[x['question']]),
choices_list='choice',
labels=lambda x:x['choices_list'].index(x['answer'])
)
openbookqa = MultipleChoice(
'question_stem',
choices_list=get.choices.text,
labels='answerKey'
)
qasc = MultipleChoice(
'question',
choices_list=get.choices.text,
labels=lambda x: "ABCDEFGH".index(x['answerKey']),
splits=['train','validation',None]
)
quartz = MultipleChoice(
'question',
choices_list=get.choices.text,
labels='answerKey'
)
quail = MultipleChoice(
cat(['context','question']),
choices_list='answers',
labels='correct_answer_id'
)
head_qa___en = MultipleChoice("qtext",
choices_list = lambda x:[a['atext'] for a in x["answers"]],
labels = lambda x:[a['aid'] for a in x["answers"]].index(x["ra"])
)
sciq = MultipleChoice(
'question',
['correct_answer']+regen('distractor[1-3]'),
labels=constant(0))
social_i_qa = MultipleChoice(
'question',
['answerA','answerB','answerC'],
'label')
wiki_hop___original = MultipleChoice(
'question',
choices_list='candidates',
labels=lambda x:x['choices_list'].index(x["answer"]))
wiqa = MultipleChoice('question_stem',
choices_list = lambda x: x['choices']['text'],
labels='answer_label_as_choice')
piqa = MultipleChoice('goal', choices=['sol1','sol2'], labels='label')
hellaswag = MultipleChoice('ctx_a',
choices_list=lambda x: [f'{x["ctx_b"]}{e}' for e in x["endings"]],
labels='label', splits=['train','validation',None])
super_glue___copa = MultipleChoice('premise',['choice1','choice2'],'label')
balanced_copa = MultipleChoice('premise',['choice1','choice2'],'label',
dataset_name="pkavumba/balanced-copa")
e_care = MultipleChoice('premise',['choice1','choice2'],'label',
dataset_name="12ml/e-CARE")
art = MultipleChoice(cat(['hypothesis_1','hypothesis_2']),
['observation_1','observation_2'],
labels=lambda x:x['label']-1,
splits=['train','validation',None]
)
mmlu = MultipleChoice('question',labels='answer',choices_list='choices',splits=['validation','dev','test'],
dataset_name="tasksource/mmlu",
config_name=get_dataset_config_names("tasksource/mmlu")
)
winogrande = MultipleChoice('sentence',['option1','option2'],'answer',config_name='winogrande_xl',
splits=['train','validation',None])
codah = MultipleChoice('question_propmt',choices_list='candidate_answers',labels='correct_answer_idx',config_name='codah')
ai2_arc__challenge = MultipleChoice('question',
choices_list=get.choices.text,
labels=lambda x: get.choices.label(x).index(x["answerKey"]),
config_name=["ARC-Challenge","ARC-Easy"])
definite_pronoun_resolution = MultipleChoice(
inputs=cat(["sentence","pronoun"],' : '),
choices_list='candidates',
labels="label",
splits=['train',None,'test'])
swag___regular=MultipleChoice(cat(["sent1","sent2"]),regen("ending[0-3]"),"label")
def _split_choices(s):
import re
return [x.rstrip(', ') for x in re.split(r'[a-e] \) (.*?)',s) if x.strip(', ')]
math_qa = MultipleChoice(
'Problem',
choices_list = lambda x: _split_choices(x['options']),
labels = lambda x:'abcde'.index(x['correct'])
)
#aqua_rat___tokenized = MultipleChoice("question",choices_list="options",labels=lambda x:"ABCDE".index(x['correct'])) in math_qa
######################## Classification (other) ########################
glue___cola = Classification(sentence1="sentence", labels="label")
glue___sst2 = Classification(sentence1="sentence", labels="label")
utilitarianism = Classification("comparison",labels="label",
dataset_name="metaeval/utilitarianism")
amazon_counterfactual = Classification(
"text", labels="label",
dataset_name="mteb/amazon_counterfactual",
config_name="en")
insincere_questions = Classification(
"text", labels="label_text",
dataset_name="SetFit/insincere-questions")
toxic_conversations = Classification(
"text", labels="label",
dataset_name="SetFit/toxic_conversations")
turingbench = Classification("Generation",labels="label",
dataset_name="turingbench/TuringBench",
splits=["train","validation",None])
trec = Classification(sentence1="text", labels="fine_label")
tals_vitaminc = Classification('claim','evidence','label', dataset_name="tals/vitaminc")
hope_edi = Classification("text", labels="label", splits=["train", "validation", None], config_name=["english"])
#fever___v1_0 = Classification(sentence1="claim", labels="label", splits=["train", "paper_dev", "paper_test"], dataset_name="fever", config_name="v1.0")
#fever___v2_0 = Classification(sentence1="claim", labels="label", splits=[None, "validation", None], dataset_name="fever", config_name="v2.0")
rumoureval_2019 = Classification(
sentence1="source_text",
sentence2=lambda x: str(x["reply_text"]),
labels="label", dataset_name="strombergnlp/rumoureval_2019", config_name="RumourEval2019",
post_process=lambda ds:ds.filter(lambda x:x['labels']!=None)
)
ethos___binary = Classification(sentence1="text", labels="label", splits=["train", None, None])
ethos___multilabel = Classification(
'text',
labels=lambda x: [x[c] for c in
['violence', 'gender', 'race', 'national_origin', 'disability', 'religion', 'sexual_orientation','directed_vs_generalized']
],
splits=["train", None, None]
)
tweet_eval = Classification(sentence1="text", labels="label",
config_name=["emoji", "emotion", "hate", "irony", "offensive", "sentiment"])
def stance_kwargs(topic):
return {
"sentence1": constant(f'Topic: {topic}. \n Opinion:\n'),
"sentence2": "text",
"labels": "label",
"config_name": f"stance_{topic.lower()}",
"dataset_name": "tweet_eval"
}
tweet_eval_abortion = Classification(**stance_kwargs("abortion"))
tweet_eval_atheism = Classification(**stance_kwargs("atheism"))
tweet_eval_climate = Classification(**stance_kwargs("climate"))
tweet_eval_feminist = Classification(**stance_kwargs("feminist"))
tweet_eval_hillary = Classification(**stance_kwargs("Hillary"))
discovery = Classification("sentence1", "sentence2", labels="label", config_name=["discovery"])
pragmeval_1 = Classification("sentence",labels="label",
dataset_name="pragmeval",
config_name= ["emobank-arousal", "emobank-dominance", "emobank-valence", "squinky-formality", "squinky-implicature",
"squinky-informativeness","switchboard","mrda","verifiability"])
pragmeval_2 = Classification("sentence1","sentence2",labels="label",
dataset_name="pragmeval",
config_name= ["emergent", "gum", "pdtb", "persuasiveness-claimtype",
"persuasiveness-eloquence", "persuasiveness-premisetype", "persuasiveness-relevance", "persuasiveness-specificity",
"persuasiveness-strength", "sarcasm","stac"])
silicone = Classification("Utterance",labels="Label",
config_name=['dyda_da', 'dyda_e', 'iemocap', 'maptask', 'meld_e', 'meld_s', 'oasis', 'sem'] # +['swda', 'mrda'] # in pragmeval
)
lex_glue___eurlex = Classification(sentence1="text", labels="labels")
lex_glue___scotus = Classification(sentence1="text", labels="label")
lex_glue___ledgar = Classification(sentence1="text", labels="label")
lex_glue___unfair_tos = Classification(sentence1="text", labels="labels")
lex_glue___case_hold = MultipleChoice("context", choices_list='endings', labels="label")
language_identification = Classification("text",labels="labels", dataset_name="papluca/language-identification")
################ Automatically generated (verified)##########
imdb = Classification(sentence1="text", labels="label", splits=["train", None, "test"])
rotten_tomatoes = Classification(sentence1="text", labels="label")
ag_news = Classification(sentence1="text", labels="label", splits=["train", None, "test"])
yelp_review_full = Classification(sentence1="text", labels="label", splits=["train", None, "test"], config_name=["yelp_review_full"])
financial_phrasebank = Classification(sentence1="sentence", labels="label", splits=["train", None, None],
config_name=["sentences_allagree"])
poem_sentiment = Classification(sentence1="verse_text", labels="label")
#emotion = Classification(sentence1="text", labels="label") # file not found
dbpedia_14 = Classification(sentence1="content", labels="label", splits=["train", None, "test"], config_name=["dbpedia_14"])
amazon_polarity = Classification(sentence1="content", labels="label", splits=["train", None, "test"], config_name=["amazon_polarity"])
app_reviews = Classification("review", labels="star", splits=["train", None, None])
# multi_nli = Classification(sentence1="premise", sentence2="hypothesis", labels="label", splits=["train", "validation_matched", None]) #glue
hate_speech18 = Classification(sentence1="text", labels="label", splits=["train", None, None])
sms_spam = Classification(sentence1="sms", labels="label", splits=["train", None, None])
humicroedit___subtask_1 = Classification("original", "edit", labels="meanGrade", dataset_name="humicroedit", config_name="subtask-1")
humicroedit___subtask_2 = Classification(
sentence1=cat(['original1','edit1'],' : '),
sentence2=cat(['original2','edit2'],' : '),
labels="label", dataset_name="humicroedit", config_name="subtask-2")
snips_built_in_intents = Classification(sentence1="text", labels="label", splits=["train", None, None])
banking77 = Classification(sentence1="text", labels="label", splits=["train", None, "test"])
hate_speech_offensive = Classification(sentence1="tweet", labels="class", splits=["train", None, None])
yahoo_answers_topics = Classification(
"question_title","question_content",labels="topic")
stackoverflow_questions=Classification("title","body",labels="label",
dataset_name="pacovaldez/stackoverflow-questions")
#hyperpartisan_news_detection___byarticle = Classification(sentence1="text", labels="hyperpartisan", splits=["train", None, None]) # files too heavy
#hyperpartisan_news_detection___bypublisher = Classification(sentence1="text", labels="hyperpartisan", splits=["train","validation", None]) # files too heavy
hyperpartisan_news = Classification(
"text",
labels=lambda x: {'true':'hyperpartisan','false':'not_hyperpartisan'}.get(x["label"]),
dataset_name="zapsdcn/hyperpartisan_news")
scierc = Classification("text",labels="label",dataset_name="zapsdcn/sciie")
citation_intent = Classification("text",labels="label",dataset_name="zapsdcn/citation_intent")
#go_emotions___raw = Classification(sentence1="text", splits=["train", None, None])
go_emotions___simplified = Classification(sentence1="text", labels="labels")
#boolq = Classification(sentence1="question", splits=["train", "validation", None]) # in superglue
#ecthr_cases___alleged_violation_prediction = Classification(labels="labels", dataset_name="ecthr_cases", config_name="alleged-violation-prediction")
#ecthr_cases___violation_prediction = Classification(labels="labels", dataset_name="ecthr_cases", config_name="violation-prediction")
# too long
scicite = Classification(sentence1="string", labels="label",dataset_name="allenai/scicite")
liar = Classification(sentence1="statement", labels="label")
relbert_lexical_relation_classification = Classification(sentence1="head", sentence2="tail", labels="relation",
dataset_name="relbert/lexical_relation_classification",
config_name=["BLESS","CogALexV","EVALution","K&H+N","ROOT09"])
linguisticprobing = Classification("sentence", labels="label", dataset_name="tasksource/linguisticprobing",
config_name=['subj_number',
'obj_number',
'past_present',
'sentence_length',
'top_constituents',
'tree_depth',
'coordination_inversion',
'odd_man_out',
'bigram_shift']#+['word_content'] #too many labels
)
crowdflower = Classification("text", labels="label",
splits=["train", None, None], dataset_name="tasksource/crowdflower",
config_name=['sentiment_nuclear_power',
'tweet_global_warming',
'airline-sentiment',
'corporate-messaging',
'economic-news',
'political-media-audience',
'political-media-bias',
'political-media-message',
'text_emotion']
)
ethics___commonsense = Classification(sentence1="text", labels="label", dataset_name="metaeval/ethics", config_name="commonsense")
ethics___deontology = Classification(sentence1="text", labels="label", dataset_name="metaeval/ethics", config_name="deontology")
ethics___justice = Classification(sentence1="text", labels="label", dataset_name="metaeval/ethics", config_name="justice")
ethics___virtue = Classification(sentence1="sentence1", sentence2="sentence2", labels="label", dataset_name="metaeval/ethics", config_name="virtue")
emo = Classification(sentence1="text", labels="label", splits=["train", None, "test"], config_name=["emo2019"])
google_wellformed_query = Classification(sentence1="content", labels="rating")
tweets_hate_speech_detection = Classification(sentence1="tweet", labels="label", splits=["train", None, None])
#adv_glue___adv_sst2 = Classification(sentence1="sentence", labels="label", splits=["validation", None, None])
#adv_glue___adv_qqp = Classification(sentence1="question1", sentence2="question2", labels="label", splits=["validation", None, None])
#adv_glue___adv_mnli = Classification(sentence1="premise", sentence2="hypothesis", labels="label", splits=["validation", None, None])
#adv_glue___adv_mnli_mismatched = Classification(sentence1="premise", sentence2="hypothesis", labels="label", splits=["validation", None, None])
#adv_glue___adv_qnli = Classification(sentence1="question", labels="label", splits=["validation", None, None])
#adv_glue___adv_rte = Classification(sentence1="sentence1", sentence2="sentence2", labels="label", splits=["validation", None, None])
has_part = Classification("arg1","arg2", labels="score", splits=["train", None, None])
wnut_17 = TokenClassification(tokens="tokens", labels="ner_tags", config_name=["wnut_17"])
ncbi_disease = TokenClassification(tokens="tokens", labels="ner_tags", config_name=["ncbi_disease"])
acronym_identification = TokenClassification(labels="labels", tokens="tokens")
jnlpba = TokenClassification(tokens="tokens", labels="ner_tags", splits=["train", "validation", None], config_name=["jnlpba"])
#species_800 = TokenClassification(tokens="tokens", labels="ner_tags", config_name=["species_800"]) missing files
SpeedOfMagic_ontonotes_english = TokenClassification(tokens="tokens", labels="ner_tags", dataset_name="SpeedOfMagic/ontonotes_english", config_name="SpeedOfMagic--ontonotes_english")
blog_authorship_corpus__gender = Classification(sentence1="text",labels="gender")
blog_authorship_corpus__age = Classification(sentence1="text",labels="age")
#blog_authorship_corpus__horoscope = Classification(sentence1="text",labels="horoscope")
blog_authorship_corpus__job = Classification(sentence1="text",labels="job")
launch_open_question_type = Classification(sentence1="question", labels="resolve_type", dataset_name="launch/open_question_type")
health_fact = Classification(sentence1="claim", labels="label",
pre_process = lambda ds:ds.filter(lambda x:x['label'] not in {-1})
)
commonsense_qa = MultipleChoice(
"question",
choices_list=get.choices.text,
labels=lambda x: "ABCDE".index(x["answerKey"]),
splits=["train","validation",None]
)
mc_taco = Classification(
lambda x: f'{x["sentence"]} {x["question"]} {x["answer"]}',
labels="label",
splits=[ "validation",None,"test"]
)
ade_corpus_v2___Ade_corpus_v2_classification = Classification("text",labels="label")
discosense = MultipleChoice("context",choices=regen("option\_[0-3]"),labels="label",
dataset_name="prajjwal1/discosense")
circa = Classification(
sentence1=cat(["context","question-X"]),
sentence2="answer-Y",
labels="goldstandard2", post_process=remove_neg_1)
#code_x_glue_cc_defect_detection = Classification("func", labels="target")
#code_x_glue_cc_clone_detection_big_clone_bench = Classification("func1", "func2", "label") # in bigbench + too heavy (100g)
#code_x_glue_cc_code_refinement = MultipleChoice(
# constant(""), choices=["buggy","fixed"], labels=constant(0),
# config_name="medium")
#effective_feedback_student_writing = Classification("discourse_text",
#labels="discourse_effectiveness",dataset_name="YaHi/EffectiveFeedbackStudentWriting")
# discontinued /!\
#promptSentiment = Classification("text",labels="label",dataset_name="Ericwang/promptSentiment")
#promptNLI = Classification("premise","hypothesis",labels="label",dataset_name="Ericwang/promptNLI")
#promptSpoke = Classification("text",labels="label",dataset_name="Ericwang/promptSpoke")
#promptProficiency = Classification("text",labels="label",dataset_name="Ericwang/promptProficiency")
#promptGrammar = Classification("text",labels="label",dataset_name="Ericwang/promptGrammar")
#promptCoherence = Classification("text",labels="label",dataset_name="Ericwang/promptCoherence")
phrase_similarity = Classification(
sentence1=cat(["phrase1","sentence1"], " : "),
sentence2=cat(["phrase2","sentence2"], " : "),
labels='label',
dataset_name="PiC/phrase_similarity"
)
exaggeration_detection = Classification(
sentence1="press_release_conclusion",
sentence2="abstract_conclusion",
labels="exaggeration_label",
dataset_name="copenlu/scientific-exaggeration-detection"
)
quarel = Classification(
"question",
labels=lambda x: "AB"[x["answer_index"]]
)
mwong_fever_evidence_related = Classification(sentence1="claim", sentence2="evidence", labels=name("labels",['unrelated','related']),
splits=["train", "valid", "test"], dataset_name="mwong/fever-evidence-related")
numer_sense = Classification("sentence",labels="target",splits=["train",None,None])
dynasent__r1 = Classification("sentence", labels="gold_label",
dataset_name="dynabench/dynasent", config_name="dynabench.dynasent.r1.all")
dynasent__r2 = Classification("sentence", labels="gold_label",
dataset_name="dynabench/dynasent", config_name="dynabench.dynasent.r2.all")
sarcasm_news = Classification("headline", labels="is_sarcastic",
dataset_name="raquiba/Sarcasm_News_Headline")
sem_eval_2010_task_8 = Classification("sentence",labels="relation")
auditor_review = Classification(sentence1="sentence",
labels=name("label",['negative','neutral','positive']),
dataset_name="demo-org/auditor_review")
medmcqa = MultipleChoice("question", choices=regen('op[a-d]'),labels='cop')
dynasent_disagreement = Classification("text", labels="binary_disagreement", dataset_name="RuyuanWan/Dynasent_Disagreement")
politeness_disagreement = Classification("text", labels="binary_disagreement", dataset_name="RuyuanWan/Politeness_Disagreement")
sbic_disagreement = Classification("text", labels="binary_disagreement", dataset_name="RuyuanWan/SBIC_Disagreement")
schem_disagreement = Classification("text", labels="binary_disagreement", dataset_name="RuyuanWan/SChem_Disagreement")
dilemmas_disagreement = Classification("text", labels="binary_disagreement", dataset_name="RuyuanWan/Dilemmas_Disagreement")
logiqa = MultipleChoice(
cat(["context","query"]),
choices_list = 'options',
labels = "correct_option",
dataset_name="lucasmccabe/logiqa"
)
#proto_qa = MultipleChoice(
# "question",
# choices_list=lambda x:x['answer-clusters']['answers'],
# labels=lambda x: x['answer-clusters']['count'].index(max(x['answer-clusters']['count'])),
# config_name='proto_qa'
#)
wiki_qa = Classification("question","answer", name("label",['False','True']))
cycic_classification = Classification("question",labels=name("correct_answer",['False','True']),
dataset_name = "tasksource/cycic_classification")
cycic_mc = MultipleChoice("question", choices=regen('answer\_option[0-4]'), labels="correct_answer",
dataset_name = "tasksource/cycic_multiplechoice")
def _preprocess_chatgpt_detection(ex):
import random
label=random.random()<0.5
ex['label']=int(label)
ex['answer']=[str(ex['human_answers'][0]),str(ex['chatgpt_answers'][0])][label]
return ex
#chatgpt_detection = Classification("question","answer","label",
# dataset_name = 'Hello-SimpleAI/HC3', config_name="all",
# pre_process=lambda dataset:dataset.map(_preprocess_chatgpt_detection))
sts_companion = Classification("sentence1","sentence2","label",
dataset_name="tasksource/sts-companion")
commonsense_qa_2 = Classification("question",labels="answer",
dataset_name="tasksource/commonsense_qa_2.0")
ling_nli = Classification("premise","hypothesis","label",dataset_name="tasksource/lingnli")
monotonicity_entailment = Classification("sentence1", "sentence2", "gold_label",
dataset_name="tasksource/monotonicity-entailment")
arct = MultipleChoice(cat(["reason","claim"]),choices=["warrant0","warrant1"],
labels="correctLabelW0orW1", dataset_name="tasksource/arct")
scinli = Classification("sentence1", "sentence2", labels="label",
post_process=lambda x:x.shuffle(seed=0),
dataset_name="tasksource/scinli")
naturallogic = Classification(" sent1 "," sent2 "," new_label ",dataset_name="tasksource/naturallogic")
onestop_qa = MultipleChoice(cat(["paragraph","question"]),choices_list="answers",
labels=constant(0))
moral_stories = MultipleChoice(cat(["situation","intention"]),
choices=['moral_action',"immoral_action"],labels=constant(0),
dataset_name="demelin/moral_stories", config_name="full")
prost = MultipleChoice(cat(["context","ex_question"]), choices=['A','B','C','D'],labels="label",
dataset_name="corypaik/prost")
dyna_hate = Classification("text",labels="label",dataset_name="aps/dynahate",splits=['train',None,None])
syntactic_augmentation_nli = Classification('sentence1',"sentence2","gold_label",dataset_name="metaeval/syntactic-augmentation-nli")
autotnli = Classification("premises", "hypothesis", "label", dataset_name="tasksource/autotnli")
#equate = Classification("sentence1", "sentence2", "gold_label",dataset_name="metaeval/equate")
conqada = Classification("sentence1","sentence2","label",dataset_name="lasha-nlp/CONDAQA",
pre_process = lambda ds:ds.filter(lambda x:x['label'] in {"DON'T KNOW","YES","NO"})
)
webgbpt_comparisons = MultipleChoice(get.question.full_text, choices=['answer_0','answer_1'],
labels=lambda x:int(x['score_1']>0),
dataset_name="openai/webgpt_comparisons")
synthetic_instruct = MultipleChoice('prompt', choices=['chosen', 'rejected'],
labels=constant(0), dataset_name="Dahoas/synthetic-instruct-gptj-pairwise")
scruples = Classification("text",labels="binarized_label",dataset_name="metaeval/scruples")
wouldyourather = MultipleChoice(constant('Most people would rather:'), choices=['option_a','option_b'],
labels= lambda x: int(x['votes_a']<x['votes_b']),
dataset_name="metaeval/wouldyourather")
#attempto_nli = Classification("premise","hypothesis",
# lambda x:f'race-{x["race_label"]}',
# dataset_name="sileod/attempto-nli")
defeasible_nli = Classification(cat(["Premise","Hypothesis"]),"Update",labels="UpdateType",
dataset_name="metaeval/defeasible-nli",config_name=['atomic', 'snli'])
#defeasible_nli_social = Classification(cat(["SocialChemROT","Hypothesis"]),"Update",labels="UpdateType",
# dataset_name="metaeval/defeasible-nli",config_name='social')
help_nli = Classification("ori_sentence","new_sentence","gold_label",
dataset_name="tasksource/help-nli")
nli_veridicality_transitivity = Classification("sentence1","sentence2","gold_label",
dataset_name="metaeval/nli-veridicality-transitivity")
lonli = Classification("premise","hypothesis","label",
dataset_name="tasksource/lonli")
dadc_limit = Classification("sentence1","sentence2","label",
dataset_name="tasksource/dadc-limit-nli")
flute = Classification("premise","hypothesis","label",
dataset_name="ColumbiaNLP/FLUTE")
strategy_qa = Classification('question',labels='answer',
dataset_name="tasksource/strategy-qa",splits=['train',None,None])
summarize_from_feedback = MultipleChoice(get.info.post,
choices_list=lambda x: [x['summaries'][0]['text'],x['summaries'][1]['text']],
labels="choice",
dataset_name="openai/summarize_from_feedback", config_name="comparisons",
pre_process = lambda ds:ds.filter(lambda x: type(get.info.post(x))==str)
)
folio = Classification("premises","conclusion",
labels=lambda x:{'False':'contradiction','True':'entailment', 'Uncertain':'neutral'}.get(x["label"]),
dataset_name="tasksource/folio")
tomi_nli = Classification("premise","hypothesis","label",
dataset_name="tasksource/tomi-nli")
avicenna = Classification("Premise 1","Premise 2","Syllogistic relation",
dataset_name="tasksource/avicenna")
shp = MultipleChoice("history",
choices=['human_ref_A','human_ref_B'],
labels="labels",
dataset_name="stanfordnlp/SHP")
medqa_usmle = MultipleChoice('sent1',choices=regen('ending[0-3]'),labels='label',
dataset_name="GBaker/MedQA-USMLE-4-options-hf")
wikimedqa = MultipleChoice("text",choices=regen('option\_[0-7]'),labels='label',
dataset_name="sileod/wikimedqa",
config_name=["medwiki"])
cicero = MultipleChoice(lambda x: " ".join(x['Dialogue']),
choices_list="Choices", labels=lambda x:x['Human Written Answer'][0],
dataset_name="declare-lab/cicero")
creak = Classification("sentence",labels="label",
dataset_name='amydeng2000/CREAK')
mutual = MultipleChoice("article",choices_list="options",
labels=lambda x: "ABCD".index(x['answers']),
dataset_name="tasksource/mutual",splits=["train",None,None])
neqa = MultipleChoice('prompt',choices_list='classes',labels="answer_index",
dataset_name="inverse-scaling/NeQA")
quote_repetition = MultipleChoice('prompt',choices_list='classes',labels="answer_index",
dataset_name="inverse-scaling/quote-repetition")
redefine_math = MultipleChoice('prompt',choices_list='classes',labels="answer_index",
dataset_name="inverse-scaling/redefine-math")
puzzte = Classification("puzzle_text","question","answer",
dataset_name="tasksource/puzzte")
implicatures = MultipleChoice(cat(['context','response'],"\n"),
choices=['correct_implicature','incorrect_implicature'],
labels=constant(0),
dataset_name='tasksource/implicatures')
race = MultipleChoice(cat(['question','article'],'\n'), choices_list='options',
labels=lambda x:'ABCDE'.index(x['answer']),
config_name=['middle','high'])
race_c = MultipleChoice(cat(['question','article'],'\n'),choices_list='option',labels='label',
dataset_name='tasksource/race-c')
spartqa_yn=Classification("story","question","answer",
dataset_name="tasksource/spartqa-yn")
spartqa_mc=MultipleChoice(cat(["story","question"]),choices_list="candidate_answers",labels="answer",
dataset_name="tasksource/spartqa-mchoice")
temporal_nli = Classification("Premise","Hypothesis","Label",
dataset_name="tasksource/temporal-nli")
riddle_sense = MultipleChoice("question", choices_list=get.choices.text,
labels=lambda x : "ABCDE".index(x['answerKey']))
clcd = Classification(
"sentence1","sentence2","label",
dataset_name="tasksource/clcd-english")
twentyquestions = Classification("question","subject","answer",dataset_name="maximedb/twentyquestions")
reclor = MultipleChoice(cat(["context","question"]),choices_list="answers",labels="label",
dataset_name="metaeval/reclor",splits=['train','validation',None])
c_aug_imdb = Classification("Text",labels="Sentiment",
dataset_name='tasksource/counterfactually-augmented-imdb')
c_aug_snli = Classification("sentence1","sentence2","gold_label",
dataset_name='tasksource/counterfactually-augmented-snli')
cnli = Classification("premise","hypothesis","label",
dataset_name='metaeval/cnli')
perturbed_boolq = Classification("question",labels="hard_label",
dataset_name='tasksource/boolq-natural-perturbations')
#mega_acceptability = Classification("sentence",labels="average",
# dataset_name='metaeval/mega-acceptability-v2')
graded_acceptability = Classification("text",labels="normalized_score",
dataset_name="metaeval/acceptability-prediction")
equate = Classification("sentence1","sentence2","gold_label",
dataset_name='metaeval/equate')
science_qa = MultipleChoice("question",choices_list="choices",labels="answer",
dataset_name="tasksource/ScienceQA_text_only")
ekar=MultipleChoice("question",choices_list=get.choices.text,
labels=lambda x:"ABCD".index(x['answerKey']),
dataset_name="Jiangjie/ekar_english")
implicit_hate = Classification("post",labels="class",
dataset_name="tasksource/implicit-hate-stg1")
nli_unambiguity = Classification("premise","hypothesis","gini",
dataset_name="metaeval/chaos-mnli-ambiguity")
headline_cause = Classification('left_title','right_title','label',
dataset_name='IlyaGusev/headline_cause',config_name='en_simple')
logiqa_2 = Classification("premise","hypothesis","label",dataset_name="tasksource/logiqa-2.0-nli")
_oasst = dict(dataset_name="tasksource/oasst2_dense_flat",
pre_process = lambda ds:ds.filter(lambda x:x['lang']=='en'))
oasst1__quality = Classification("parent_text","text",labels="quality",**_oasst)
oasst1__toxicity = Classification("parent_text","text",labels="toxicity",**_oasst)
oasst1__helpfulness = Classification("parent_text","text",labels="helpfulness",**_oasst)
mindgames = Classification("premise","hypothesis","label",dataset_name="sileod/mindgames")
def _udep_post_process(ds):
return ds.cast_column('labels', Sequence(ClassLabel(names=udep_en_labels)))
udep__deprel = TokenClassification('tokens',lambda x:[udep_en_labels.index(a) for a in x['deprel']],
config_name=udep_en_configs,dataset_name="universal_dependencies",post_process=_udep_post_process)
ambient= Classification("premise","hypothesis","hypothesis_ambiguous",dataset_name="metaeval/ambient")
path_naturalness = MultipleChoice(constant(""),choices=['choice1','choice2'],labels="label",
dataset_name="metaeval/path-naturalness-prediction")
civil_comments__toxicity = Classification("text",labels="toxicity")
civil_comments__severe_toxicity = Classification("text",labels="severe_toxicity")
civil_comments__obscene = Classification("text",labels="obscene")
civil_comments__threat = Classification("text",labels="threat")
civil_comments__insult = Classification("text",labels="insult")
civil_comments__identity_attack = Classification("text",labels="identity_attack")
civil_comments__sexual_explicit = Classification("text",labels="sexual_explicit")
cloth = MultipleChoice("sentence", choices_list=lambda x:[x["answer"]]+x["distractors"],labels=constant(0), dataset_name="AndyChiang/cloth")
dgen = MultipleChoice("sentence", choices_list=lambda x:[x["answer"]]+x["distractors"],labels=constant(0), dataset_name="AndyChiang/dgen")
i2d2 = Classification("sentence1",labels=name('label',['False','True']), dataset_name="tasksource/I2D2")
arg_me = Classification('argument','conclusion','stance', dataset_name="webis/args_me")
valueeval_stance = Classification("Premise","Conclusion","Stance", dataset_name="webis/Touche23-ValueEval")
starcon = Classification('argument','topic','label',dataset_name="tasksource/starcon")
banking77 = Classification("text",labels="label",dataset_name="PolyAI/banking77")
control = Classification('premise','hypothesis',"label",dataset_name="tasksource/ConTRoL-nli")
tracie = Classification("premise","hypothesis","answer",dataset_name='tasksource/tracie')
sherliic = Classification("premise","hypothesis","label",dataset_name='tasksource/sherliic')
sen_making__1 = MultipleChoice(constant('Chose most plausible:'), choices=['sentence0','sentence1'],labels='false',
dataset_name="tasksource/sen-making")
sen_making__2 = MultipleChoice(lambda x: [x['sentence0'],x['sentence1']][x['false']] + '\n is not plausible because :',
choices=['A','B','C'],labels=lambda x: 'ABC'.index(x['reason']), dataset_name="tasksource/sen-making")
winowhy = Classification('sentence', lambda x: f'In "{x["wnli_sent1"]}", {x["wnli_sent2"]}',
labels=name('label',['False','True']), dataset_name="tasksource/winowhy")
#for CFG in "cognitive-bias", "fake-news", "gender-bias", "hate-speech", "linguistic-bias", "political-bias", "racial-bias", "text-level-bias":
# print(f"mbib__{CFG.replace('-','_')} = Classification('text',labels=name('label',['not {CFG}','{CFG}']), dataset_name='mediabiasgroup/mbib-base', config_name='{CFG}')")
"""
mbib_cognitive_bias = Classification('text',labels=name('label',['not cognitive-bias','cognitive-bias']), dataset_name='mediabiasgroup/mbib-base', config_name='cognitive-bias')
mbib_fake_news = Classification('text',labels=name('label',['not fake-news','fake-news']), dataset_name='mediabiasgroup/mbib-base', config_name='fake-news')
mbib_gender_bias = Classification('text',labels=name('label',['not gender-bias','gender-bias']), dataset_name='mediabiasgroup/mbib-base', config_name='gender-bias')
mbib_hate_speech = Classification('text',labels=name('label',['not hate-speech','hate-speech']), dataset_name='mediabiasgroup/mbib-base', config_name='hate-speech')
mbib_linguistic_bias = Classification('text',labels=name('label',['not linguistic-bias','linguistic-bias']), dataset_name='mediabiasgroup/mbib-base', config_name='linguistic-bias')
mbib_political_bias = Classification('text',labels=name('label',['not political-bias','political-bias']), dataset_name='mediabiasgroup/mbib-base', config_name='political-bias')
mbib_racial_bias = Classification('text',labels=name('label',['not racial-bias','racial-bias']), dataset_name='mediabiasgroup/mbib-base', config_name='racial-bias')
mbib_text_level_bias = Classification('text',labels=name('label',['not text-level-bias','text-level-bias']), dataset_name='mediabiasgroup/mbib-base', config_name='text-level-bias')
"""
robustLR = Classification("context","statement","label", dataset_name="tasksource/robustLR")
cluttr = Classification("story","query", "target_text",dataset_name="CLUTRR/v1", config_name="gen_train234_test2to10")
logical_fallacy = Classification("source_article", labels="logical_fallacies", dataset_name="tasksource/logical-fallacy")
parade = Classification("Definition1","Definition2", labels=name('Binary labels',["not-paraphrase","paraphrase"]), dataset_name="tasksource/parade")
cladder = Classification("given_info", "question", "answer",dataset_name="tasksource/cladder")
subjectivity = Classification("Sentence",labels="Label",dataset_name="tasksource/subjectivity")
moh = Classification("context","expression","label", dataset_name="tasksource/MOH")
vuac = Classification("context","expression","label", dataset_name="tasksource/VUAC")
trofi = Classification("context","expression","label", dataset_name="tasksource/TroFi", splits=['train',None,'test'])
sharc_classification = Classification("snippet", lambda x:f'{x["scenario"]}\n{x["question"]}',
labels=lambda x:x["answer"] if x['answer'] in {"Yes","No","Irrelevant"} else "Clarification needed",
dataset_name='sharc_modified',config_name='mod')
conceptrules_v2 = Classification("context", "text", "label", dataset_name="tasksource/conceptrules_v2")
scidtb = Classification("unit1_txt","unit2_txt","label", dataset_name="metaeval/disrpt",config_name='eng.dep.scidtb.rels')
chunking = TokenClassification("tokens","chunk_tags", dataset_name="conll2000")
few_nerd = TokenClassification("tokens","fine_ner_tags",dataset_name="DFKI-SLT/few-nerd",config_name='supervised')
finer = TokenClassification('tokens','ner_tags',dataset_name='nlpaueb/finer-139')
label_nli = Classification("premise","hypothesis","labels",dataset_name='tasksource/zero-shot-label-nli')
com2sense = Classification("sent",labels="label",dataset_name="tasksource/com2sense",splits=['train',"validation",None])
scone = Classification('sentence1_edited','sentence2_edited','gold_label_edited',dataset_name="tasksource/scone")
winodict = MultipleChoice(cat(['definition','sentence']),['option1','option2'],'label',dataset_name='tasksource/winodict')
fool_me_twice = Classification(
lambda x: " ".join(a['text'] for a in x['gold_evidence']),
'text', 'label', dataset_name='tasksource/fool-me-twice')
monli = Classification("sentence1","sentence2","gold_label", dataset_name="tasksource/monli")
causality = Classification('premise','hypothesis','relation', dataset_name='tasksource/corr2cause')
lsat = MultipleChoice(cat(['passage','question']), choices_list='references',labels='gold_index',dataset_name='lighteval/lsat_qa',config_name='all')
apt = Classification('text_a','text_b',name('labels',['not_paraphrase','paraphrase']),dataset_name='tasksource/apt')
#xsum_factuality = Classification("summary",labels="is_factual")
financial_sentiment = Classification("text",labels=name('label',['Bearish','Bullish','Neutral']),
dataset_name="zeroshot/twitter-financial-news-sentiment")
def _icl_rand(x):
import random
return random.Random(x['sentence1'][:50]).randint(0,1) #deterministic label for each input
icl = Classification("inputs", lambda x: x['symbols'][_icl_rand(x)],
labels=lambda x: str(x['symbols'][_icl_rand(x)]==x['targets']),
dataset_name="tasksource/icl-symbol-tuning-instruct",
pre_process=lambda ds:ds.filter(lambda x:len(x['inputs'])<500*4), # 500 tokens of 4 char
)
space_nli = Classification("premises","hypothesis","label",dataset_name="tasksource/SpaceNLI")
propsegment = Classification("hypothesis","premise",
labels = lambda x:{'n':'neutral','e':'entailment','c':'contradiction'}[x['label']],
dataset_name="sihaochen/propsegment",config_name='nli')
hatemoji = Classification('text',labels=name("label_gold", ['not-hate-speech','hate-speech']),
dataset_name="HannahRoseKirk/HatemojiBuild")
regset = Classification("context",labels="answer",dataset_name='tasksource/regset')
esci = Classification('query','product_text','esci_label',
dataset_name="tasksource/esci",
pre_process=lambda ds:ds.filter(lambda x:x['product_locale']=='us'))
def _preprocess_chatbot_arena(ds):
ds=ds.filter(lambda x:x['winner'] in ["model_a","model_b"])
ds=ds.filter(lambda x:x['language']=="English")
def _unroll(x):
f=lambda x:"\n".join([f"{turn['role']}:\n{turn['content']}" for turn in x])
x['conversation_a'] = f(x['conversation_a'])
x['conversation_b'] = f(x['conversation_b'])
return x
ds=ds.map(_unroll)
return ds
chatbot_arena = MultipleChoice(constant(""),
choices=["conversation_a","conversation_b"],
labels=lambda x: ["model_a","model_b"].index(x["winner"]),
dataset_name="lmsys/chatbot_arena_conversations",
pre_process=_preprocess_chatbot_arena)
dnd_intent = Classification("examples",labels="label_names",
dataset_name='neurae/dnd_style_intents')
fld = Classification("context","hypothesis", "proof_label",
dataset_name="hitachi-nlp/FLD.v2",config_name="default")
flds = Classification("context","hypothesis", "proof_label",
dataset_name="hitachi-nlp/FLD.v2",config_name="star")
sdoh_nli = Classification("premise","hypothesis",labels=lambda x:{True:"entailment",False:"not_entailment"}[x['label']],
dataset_name="tasksource/SDOH-NLI")
scifact_entailment = Classification(lambda x:"\n".join(x["abstract"]),"claim",
labels=lambda x:x['verdict'].replace('NEI','NEUTRAL').lower(),
dataset_name="allenai/scifact_entailment")
feasibilityQA = Classification(cat(['knowledge','premise']),'hypothesis','binary_classification_label',
dataset_name="tasksource/feasibilityQA")
simple_pair = Classification("premise","hypothesis","label", dataset_name="tasksource/simple_pair")
adjective_scale_probe = Classification("premise","hypothesis","label", dataset_name="tasksource/AdjectiveScaleProbe-nli")
repectively_nli = Classification("premise","hypothesis","label",dataset_name="tasksource/resnli")
spartun=MultipleChoice(cat(["story","question"]),choices_list="candidate_answers",
labels=lambda x: [c.lower() for c in x['choices_list']].index(x["answer"][0].lower()),
pre_process=lambda ds:ds.filter(lambda x:len(x['answer'])==1),
dataset_name="tasksource/SpaRTUN")
resq=MultipleChoice(cat(["story","question"]),choices_list="candidate_answers",
labels=lambda x: [c.lower() for c in x['choices_list']].index(x["answer"][0].lower()),
pre_process=lambda ds:ds.filter(lambda x:len(x['answer'])==1),
dataset_name="tasksource/ReSQ")
semantic_fragments_nli = Classification("sentence1","sentence2","gold_label",
dataset_name="tasksource/semantic_fragments_nli")
moritz_zs_nli = Classification('text','hypothesis','labels',
pre_process=lambda ds:ds.filter(lambda x:x['task_name'] not in ["mnli", "anli", "fevernli", "wanli", "lingnli"]),
dataset_name="MoritzLaurer/dataset_train_nli"
)
stepgame = Classification('story','question','label',dataset_name="tasksource/stepgame")
def _nlgraph_binarize(x):
a=x['answer'].lower()
if "yes" in a: return "True"
if "no" in a: return "False"
assert False
nlgraph = Classification('question',labels=_nlgraph_binarize,
pre_process=lambda ds:ds.filter(lambda x:x['task'] in "connectivity cycle hamilton"),
dataset_name="tasksource/nlgraph")
oasst_rlhf = MultipleChoice("prompt",choices=['chosen','rejected'],labels=constant(0),
dataset_name="tasksource/oasst2_pairwise_rlhf_reward")
anthropic_rlhf_helpfulness = MultipleChoice(constant('Most helpful assistant answer:'), ['chosen','rejected'], constant(0),
dataset_name="tasksource/hh-rlhf",config_name=["helpful-base", "helpful-online", "helpful-rejection-sampled"])
anthropic_rlhf_harmless = MultipleChoice(constant('Most harmless assistant answer:'), ['chosen','rejected'], constant(0),
dataset_name="tasksource/hh-rlhf",config_name="harmless-base")
ruletaker = Classification(
lambda x: 'What is not explicitly stated as true is considered false. \n' +x["context"], #closed world assumption
"question","label",dataset_name="tasksource/ruletaker")
para_rules = Classification(
lambda x: 'What is not explicitly stated as true is considered false. \n' +x["context"], #closed world assumption
"question", labels=name("label",["False","True"]),
dataset_name="qbao775/PARARULE-Plus")
proofwriter_deduction = Classification("theory","question","answer",
dataset_name="tasksource/proofwriter") #open world assumption
logical_entailment = Classification("A","B","label",dataset_name='tasksource/logical-entailment')
nope = Classification('premise','hypothesis',
labels=lambda x:dict(E='entailment',N='neutral',C='contradiction').get(x['label'],x['label']),
dataset_name='tasksource/nope')
logicNLI = Classification('premise','hypothesis','label',dataset_name='tasksource/LogicNLI')
contract_nli__seg = Classification("premise","hypothesis","label", dataset_name="kiddothe2b/contract-nli",config_name="contractnli_a")
contract_nli__full = Classification("premise","hypothesis","label", dataset_name="kiddothe2b/contract-nli",config_name="contractnli_b")
nli4ct = Classification(lambda x: "\n".join(x['Primary_evidence']),'Statement',"Label",
dataset_name="AshtonIsNotHere/nli4ct_semeval2024",splits=['train','dev',None])
lsat_ar = MultipleChoice(
cat(['context','question']),
choices_list='answers',labels="label",
dataset_name="tasksource/lsat-ar")
lsat_rc = MultipleChoice(
cat(['context','question']),
choices_list='answers',labels="label",
dataset_name="tasksource/lsat-rc")
biosift_nli = Classification("Abstract","Hypothesis",
labels=lambda x: {True:"entailment",False:"not-entailment"}[bool(x['Entailment'])],
dataset_name="AshtonIsNotHere/biosift-nli")
brainteasers = MultipleChoice("question",
choices_list=lambda x:eval(x["choice_list"]),
labels="label",
dataset_name="tasksource/brainteasers",config_name=['WP','SP'])
#GATED !
#toxigen = Classification("text",labels="toxicity_human", dataset_name="skg/toxigen-data")
persuasiveness = Classification("claim","argument",labels="persuasiveness_metric",dataset_name="Anthropic/persuasion")
#ste_wic = Classification(cat("text_1","text_2"),
# lambda x:f"{x['target']} means the same thing in these texts",
# "gold_label_binary",
# dataset_name="cardiffnlp/super_tweeteval", config_name="tempo_wic",splits=['train','validation',None])
#ste_nerd = Classification("text",
# lambda x:f"definition of {x['target']} here is 'x{['definition']}'",
# "gold_label_binary",
# dataset_name="cardiffnlp/super_tweeteval", config_name="tweet_nerd",splits=['train','validation',None])
#ste_sim = Classification("text_1","text_2",lambda x:x['gold_score']/5,
# dataset_name="cardiffnlp/super_tweeteval",config_name="tweet_similarity",splits=['train','validation',None])
#ste_intimacy = Classification("text_1",labels=lambda x:x['gold_score']/5,
# dataset_name="cardiffnlp/super_tweeteval",config_name="tweet_intimacy")
#ccdv/patent-classification|abstract text label
ambigNQ = Classification("question",labels=lambda x:{True:"ambiguous", False:"not ambiguous"}.get(x["ambig"]),
dataset_name="erbacher/AmbigNQ-clarifying-question")
siga_nli = Classification("premise","statement","label",dataset_name="tasksource/SIGA-nli")
unigram_fol = Classification("premise","hypothesis","label",dataset_name='unigram/FOL-nli')
#gs_goal = MultipleChoice("sent2",regen("ending[0-3]"),"label",
# dataset_name="tasksource/goal-step-wikihow",config_name="goal")
#gs_step = MultipleChoice("sent2",regen("ending[0-3]"),"label",
# dataset_name="tasksource/goal-step-wikihow",config_name="step")
gs_order = MultipleChoice("sent2",regen("ending[0-1]"),"label",
dataset_name="tasksource/goal-step-wikihow",config_name="order")
paradise = MultipleChoice("sent2",regen("ending[0-3]"),"label",
dataset_name="GGLab/PARADISE")
docnli = Classification("premise","hypothesis","label",dataset_name="tasksource/doc-nli")
mctest_nli = Classification("premise","hypothesis","label",dataset_name="tasksource/mctest-nli")
patent_phrase_similarity = Classification("anchor","target","label",dataset_name="tasksource/patent-phrase-similarity")
nlsat = Classification('sentence',labels='label',dataset_name="tasksource/natural-language-satisfiability")
idioms_nli = Classification('premise','hypothesis','label',dataset_name="tasksource/idioms-nli")
lifeycle_entailment = Classification("premise","hypothesis","label",dataset_name='tasksource/lifecycle-entailment')
helpsteer__helpfulness = Classification("prompt", "response", "helpfulness", dataset_name="nvidia/HelpSteer")
helpsteer__correctness = Classification("prompt", "response", "correctness", dataset_name="nvidia/HelpSteer")
helpsteer__coherence = Classification("prompt", "response", "coherence", dataset_name="nvidia/HelpSteer")
helpsteer__complexity = Classification("prompt", "response", "complexity", dataset_name="nvidia/HelpSteer")
helpsteer__verbosity = Classification("prompt", "response", "verbosity", dataset_name="nvidia/HelpSteer")
helpsteer_2__helpfulness = Classification("prompt","response","helpfulness",dataset_name="nvidia/HelpSteer2")
helpsteer_2__correctness = Classification("prompt", "response", "correctness", dataset_name="nvidia/HelpSteer2")
helpsteer_2__coherence = Classification("prompt", "response", "coherence", dataset_name="nvidia/HelpSteer2")
helpsteer_2__complexity = Classification("prompt", "response", "complexity", dataset_name="nvidia/HelpSteer2")
helpsteer_2__verbosity = Classification("prompt", "response", "verbosity", dataset_name="nvidia/HelpSteer2")
msci_nli = Classification('sentence1','sentence2','label',dataset_name='sadat2307/MSciNLI')
#lex_glue___ecthr_a = Classification(sentence1="text", labels="labels",dataset_name="coastalcph/lex_glue",config_name="ecthr_a") # too long
#lex_glue___ecthr_b = Classification(sentence1="text", labels="labels") # too long
ultrafeedback = MultipleChoice("question", choices=['response_j','response_k'],labels=constant(0), dataset_name="pushpdeep/UltraFeedback-paired")
essay_scoring = Classification("full_text",labels="score",dataset_name='tasksource/AES2-essay-scoring')
#argument_feedback = Classification("discourse_text",labels="discourse_effectiveness", dataset_name="tasksource/argument-feedback")
eg = lambda x: Classification("full_text", labels=lambda y:int(y[x]), dataset_name="tasksource/english-grading")
grading__cohesion = eg('cohesion')
grading__syntax = eg('syntax')
grading__vocabulary = eg('vocabulary')
grading__phraseology = eg('phraseology')
grading__grammar = eg('grammar')
grading__conventions = eg('conventions')
wice = Classification(lambda x: "\n".join(x['evidence']),'claim','label',
dataset_name='tasksource/wice')
hover = Classification("evidence","claim","label",
dataset_name="Dzeniks/hover")
hover__nli = Classification("evidence","claim",name("label",["entailment","neutral","contradiction"]),
dataset_name="Dzeniks/hover-3way")
tasksource_dpo = MultipleChoice("prompt",choices=['chosen','rejected'],labels=constant(0),
dataset_name="tasksource/tasksource_dpo_pairs")
seahorse = Classification('article',cat(["summary", "question"]),'answer',
dataset_name="tasksource/seahorse_summarization_evaluation")
mip = Classification("prompt",labels="y",
dataset_name="sileod/missing-item-prediction",config_name="contrastive")
jigsaw_toxicity = Classification('comment_text',labels=name("toxic",["notthate","hate"]),
dataset_name="tasksource/jigsaw_toxicity")
pol_nli = Classification("premise","hypothesis",labels=name('entailment',['entailment','not_entailment']),
dataset_name="mlburnham/Pol_NLI")
synthetic_retrieval_nli = Classification('premise','hypothesis','label',dataset_name='tasksource/synthetic-retrieval-NLI',
config_name=["binary","count","position"],
pre_process=lambda ds:ds.filter(lambda x:x['n']<=2048))
issue_similarity = Classification("text1","text2","label",
dataset_name="WhereIsAI/github-issue-similarity")
#nli_l2 = Classification("sentence1","sentence2","labels",
# dataset_name="tasksource/merged-2l-nli")
#nli_l3 = Classification("sentence1","sentence2","labels",
# dataset_name="tasksource/merged-3l-nli")
================================================
FILE: src/tasksource/__init__.py
================================================
from .tasks import *
from .preprocess import *
from .access import *
================================================
FILE: src/tasksource/access.py
================================================
from .preprocess import Preprocessing
import re
import pandas as pd
from . import tasks, recast
from .metadata import dataset_rank
from datasets import load_dataset
import funcy as fc
import os
import copy
from sorcery import dict_of
from functools import cache
import random
class lazy_mtasks:
def __getattr__(self, name):
from . import mtasks
return getattr(mtasks, name)
def __dir__(self):
from . import mtasks
return dir(mtasks)
lmtasks=lazy_mtasks()
def parse_var_name(s):
config_name,task_name = None,None
if '__' in s and '___' not in s: # dataset__task
dataset_name, task_name = s.split('__')
elif '__' not in s.replace('___','') and '___' in s: #dataset___config
dataset_name, config_name = s.split('___')
elif '___' in s and '__' in s.split('___')[1]: #dataset___config__task
dataset_name, config_task=s.split('___')
config_name,task_name = config_task.split('__')
else: # dataset
dataset_name = s
return dataset_name,config_name,task_name
def pretty_name(x):
dn = x.dataset_name.split("/")[-1]
cn = x.config_name if x.config_name else ""
tn = x.task_name if x.task_name else ""
return f"{dn}/{cn}/{tn}".replace('//','/').rstrip('/')
@cache
def list_tasks(tasks_path=f'{os.path.dirname(__file__)}/tasks.py',multilingual=False,instruct=False, excluded=[]):
if multilingual:
tasks_path=tasks_path.replace('/tasks.py','/mtasks.py')
task_order = open(tasks_path).readlines()
task_order = [x.split('=')[0].rstrip() for x in task_order if '=' in x]
task_order = [x for x in task_order if x.isidentifier()]
task_order = fc.flip(dict(enumerate(task_order)))
l = []
_tasks = (lmtasks if multilingual else tasks)
for key in dir(_tasks):
if key not in task_order:
continue
value=getattr(_tasks, key)
if isinstance(value,Preprocessing):
dataset_name, config_name, task_name = parse_var_name(key)
dataset_name = (value.dataset_name if value.dataset_name else dataset_name)
config_name = (value.config_name if value.config_name else config_name)
hasattr(value,key)
l+=[{'dataset_name': dataset_name,
'config_name' : config_name,
'task_name': task_name,
'preprocessing_name': key,
'task_type': value.__class__.__name__,'mapping': value,
'rank':task_order.get(key,None)}]
df=pd.DataFrame(l).explode('config_name')
df = df.sort_values('rank').reset_index(drop=True)
df['id'] = df.apply(lambda x: pretty_name(x), axis=1)
df.insert(0, 'id', df.pop('id'))
del df['rank']
if instruct:
df=df[df.id.map(lambda x: not any(a in x for a in recast.improper_labels))]
df=df[df.id.map(lambda x: not any(x in a for a in excluded))]
return df
#task_df =list_tasks()
#mtask_df =list_tasks(multilingual=True)
def dict_to_query(d=dict(), **kwargs):
d={**d,**kwargs}
return '&'.join([f'`{k}`=="{v}"' for k,v in d.items()])
def load_preprocessing(tasks=tasks, **kwargs):
_tasks_df = list_tasks(multilingual=tasks==lmtasks)
y = _tasks_df.copy().query(dict_to_query(**kwargs)).iloc[0]
preprocessing= copy.copy(getattr(tasks, y.preprocessing_name))
for c in 'dataset_name','config_name':
if not isinstance(getattr(preprocessing,c), str):
setattr(preprocessing,c,getattr(y,c))
return preprocessing
def load_task(id=None, dataset_name=None,config_name=None,task_name=None,preprocessing_name=None,
max
gitextract__ri1waap/ ├── .github/ │ ├── scripts/ │ │ └── release.py │ └── workflows/ │ ├── python-publish.yml │ └── release.yml ├── .gitignore ├── CITATION.cff ├── LICENSE ├── README.md ├── mtasks.md ├── pyproject.toml ├── setup.cfg ├── src/ │ └── tasksource/ │ ├── .ipynb_checkpoints/ │ │ ├── access-checkpoint.py │ │ ├── preprocess-checkpoint.py │ │ ├── recast-checkpoint.py │ │ └── tasks-checkpoint.py │ ├── __init__.py │ ├── access.py │ ├── metadata/ │ │ ├── __init__.py │ │ ├── bigbench_groups.py │ │ ├── blimp_groups.py │ │ ├── original.txt │ │ └── popularity.py │ ├── mtasks.py │ ├── preprocess.py │ ├── recast.py │ └── tasks.py └── tasks.md
SYMBOL INDEX (126 symbols across 10 files)
FILE: .github/scripts/release.py
function get_last_version (line 6) | def get_last_version() -> str:
function bump_patch_number (line 22) | def bump_patch_number(version_number: str) -> str:
function create_new_patch_release (line 28) | def create_new_patch_release():
FILE: src/tasksource/.ipynb_checkpoints/access-checkpoint.py
class lazy_mtasks (line 15) | class lazy_mtasks:
method __getattr__ (line 16) | def __getattr__(self, name):
method __dir__ (line 20) | def __dir__(self):
function parse_var_name (line 25) | def parse_var_name(s):
function pretty_name (line 38) | def pretty_name(x):
function list_tasks (line 45) | def list_tasks(tasks_path=f'{os.path.dirname(__file__)}/tasks.py',multil...
function dict_to_query (line 84) | def dict_to_query(d=dict(), **kwargs):
function load_preprocessing (line 88) | def load_preprocessing(tasks=tasks, **kwargs):
function load_task (line 97) | def load_task(id=None, dataset_name=None,config_name=None,task_name=None...
FILE: src/tasksource/.ipynb_checkpoints/preprocess-checkpoint.py
function get_column_names (line 16) | def get_column_names(dataset):
function sample_dataset (line 24) | def sample_dataset(dataset,n=10000, n_eval=1000,seed=0):
class Preprocessing (line 31) | class Preprocessing(DotWiz):
method __post_init__ (line 35) | def __post_init__(self):
method __map_to_target (line 39) | def __map_to_target(x,fn=lambda x:None, target=None):
method load (line 43) | def load(self):
method __call__ (line 46) | def __call__(self,dataset, max_rows=None, max_rows_eval=None,seed=0):
class cat (line 87) | class cat(Preprocessing):
method __call__ (line 91) | def __call__(self, example=None):
function pretty (line 100) | def pretty(f):
class dotgetter (line 114) | class dotgetter:
method __init__ (line 115) | def __init__(self, path=''):
method __bool__ (line 118) | def __bool__(self):
method __getattr__ (line 121) | def __getattr__(self, k):
method __getitem__ (line 124) | def __getitem__(self, i):
method __call__ (line 127) | def __call__(self, example=None):
method __hash__ (line 130) | def __hash__(self):
class ClassificationFields (line 135) | class ClassificationFields(Preprocessing):
class Seq2SeqLMFields (line 141) | class Seq2SeqLMFields(Preprocessing):
class TokenClassificationFields (line 146) | class TokenClassificationFields(Preprocessing):
class MultipleChoiceFields (line 151) | class MultipleChoiceFields(Preprocessing):
method __post_init__ (line 156) | def __post_init__(self):
method __call__ (line 163) | def __call__(self,dataset, *args, **kwargs):
method flatten_choice_list (line 176) | def flatten_choice_list(x, n_options=None):
method sample_choices (line 190) | def sample_choices(x, n_options=None):
class SharedFields (line 208) | class SharedFields:
class Classification (line 218) | class Classification(SharedFields, ClassificationFields): pass
class MultipleChoice (line 221) | class MultipleChoice(SharedFields, MultipleChoiceFields): pass
class TokenClassification (line 224) | class TokenClassification(SharedFields, TokenClassificationFields): pass
class Seq2SeqLM (line 227) | class Seq2SeqLM(SharedFields, Seq2SeqLMFields): pass
function name (line 233) | def name(label_name, classes):
function fix_splits (line 236) | def fix_splits(dataset):
function fix_labels (line 280) | def fix_labels(dataset, label_key='labels'):
function concatenate_dataset_dict (line 292) | def concatenate_dataset_dict(l):
FILE: src/tasksource/.ipynb_checkpoints/recast-checkpoint.py
function render_options (line 13) | def render_options(options):
function render_classification (line 17) | def render_classification(text,options,answer):
function render_token_classification (line 23) | def render_token_classification(tokens,options,labels):
function render_multiple_choice (line 29) | def render_multiple_choice(prompt, options, labels):
function negative_sample_options (line 38) | def negative_sample_options(y, labels,N=4):
function shuffle_choices (line 44) | def shuffle_choices(x):
function recast_dataset_classification_to_mc (line 54) | def recast_dataset_classification_to_mc(dataset,sep="[SEP]",N=4):
function recast_instruct (line 76) | def recast_instruct(dataset):
FILE: src/tasksource/.ipynb_checkpoints/tasks-checkpoint.py
function remove_neg_1 (line 51) | def remove_neg_1(dataset):
function _imppres_post_process (line 149) | def _imppres_post_process(ds,prefix=''):
function _split_choices (line 331) | def _split_choices(s):
function stance_kwargs (line 397) | def stance_kwargs(topic):
function _preprocess_chatgpt_detection (line 691) | def _preprocess_chatgpt_detection(ex):
function _udep_post_process (line 915) | def _udep_post_process(ds):
function _icl_rand (line 1026) | def _icl_rand(x):
function _preprocess_chatbot_arena (line 1051) | def _preprocess_chatbot_arena(ds):
function _nlgraph_binarize (line 1112) | def _nlgraph_binarize(x):
FILE: src/tasksource/access.py
class lazy_mtasks (line 15) | class lazy_mtasks:
method __getattr__ (line 16) | def __getattr__(self, name):
method __dir__ (line 20) | def __dir__(self):
function parse_var_name (line 25) | def parse_var_name(s):
function pretty_name (line 38) | def pretty_name(x):
function list_tasks (line 45) | def list_tasks(tasks_path=f'{os.path.dirname(__file__)}/tasks.py',multil...
function dict_to_query (line 84) | def dict_to_query(d=dict(), **kwargs):
function load_preprocessing (line 88) | def load_preprocessing(tasks=tasks, **kwargs):
function load_task (line 97) | def load_task(id=None, dataset_name=None,config_name=None,task_name=None...
FILE: src/tasksource/mtasks.py
function all (line 5) | def all(dataset_name):
function concatenate_configs (line 13) | def concatenate_configs(dataset):
function udep_post_process (line 102) | def udep_post_process(ds):
FILE: src/tasksource/preprocess.py
function get_column_names (line 16) | def get_column_names(dataset):
function sample_dataset (line 24) | def sample_dataset(dataset,n=10000, n_eval=1000,seed=0):
class Preprocessing (line 31) | class Preprocessing(DotWiz):
method __post_init__ (line 35) | def __post_init__(self):
method __map_to_target (line 39) | def __map_to_target(x,fn=lambda x:None, target=None):
method load (line 43) | def load(self):
method __call__ (line 46) | def __call__(self,dataset, max_rows=None, max_rows_eval=None,seed=0):
class cat (line 87) | class cat(Preprocessing):
method __call__ (line 91) | def __call__(self, example=None):
function pretty (line 100) | def pretty(f):
class dotgetter (line 114) | class dotgetter:
method __init__ (line 115) | def __init__(self, path=''):
method __bool__ (line 118) | def __bool__(self):
method __getattr__ (line 121) | def __getattr__(self, k):
method __getitem__ (line 124) | def __getitem__(self, i):
method __call__ (line 127) | def __call__(self, example=None):
method __hash__ (line 130) | def __hash__(self):
class ClassificationFields (line 135) | class ClassificationFields(Preprocessing):
class Seq2SeqLMFields (line 141) | class Seq2SeqLMFields(Preprocessing):
class TokenClassificationFields (line 146) | class TokenClassificationFields(Preprocessing):
class MultipleChoiceFields (line 151) | class MultipleChoiceFields(Preprocessing):
method __post_init__ (line 156) | def __post_init__(self):
method __call__ (line 163) | def __call__(self,dataset, *args, **kwargs):
method flatten_choice_list (line 176) | def flatten_choice_list(x, n_options=None):
method sample_choices (line 190) | def sample_choices(x, n_options=None):
class SharedFields (line 208) | class SharedFields:
class Classification (line 218) | class Classification(SharedFields, ClassificationFields): pass
class MultipleChoice (line 221) | class MultipleChoice(SharedFields, MultipleChoiceFields): pass
class TokenClassification (line 224) | class TokenClassification(SharedFields, TokenClassificationFields): pass
class Seq2SeqLM (line 227) | class Seq2SeqLM(SharedFields, Seq2SeqLMFields): pass
function name (line 233) | def name(label_name, classes):
function fix_splits (line 236) | def fix_splits(dataset):
function fix_labels (line 280) | def fix_labels(dataset, label_key='labels'):
function concatenate_dataset_dict (line 292) | def concatenate_dataset_dict(l):
FILE: src/tasksource/recast.py
function render_options (line 13) | def render_options(options):
function render_classification (line 17) | def render_classification(text,options,answer):
function render_token_classification (line 23) | def render_token_classification(tokens,options,labels):
function render_multiple_choice (line 29) | def render_multiple_choice(prompt, options, labels):
function negative_sample_options (line 38) | def negative_sample_options(y, labels,N=4):
function shuffle_choices (line 44) | def shuffle_choices(x):
function recast_dataset_classification_to_mc (line 54) | def recast_dataset_classification_to_mc(dataset,sep="[SEP]",N=4):
function recast_instruct (line 76) | def recast_instruct(dataset):
FILE: src/tasksource/tasks.py
function remove_neg_1 (line 51) | def remove_neg_1(dataset):
function _imppres_post_process (line 149) | def _imppres_post_process(ds,prefix=''):
function _split_choices (line 331) | def _split_choices(s):
function stance_kwargs (line 397) | def stance_kwargs(topic):
function _preprocess_chatgpt_detection (line 691) | def _preprocess_chatgpt_detection(ex):
function _udep_post_process (line 915) | def _udep_post_process(ds):
function _icl_rand (line 1026) | def _icl_rand(x):
function _preprocess_chatbot_arena (line 1051) | def _preprocess_chatbot_arena(ds):
function _nlgraph_binarize (line 1112) | def _nlgraph_binarize(x):
Condensed preview — 26 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (519K chars).
[
{
"path": ".github/scripts/release.py",
"chars": 1328,
"preview": "#!/usr/bin/env python3\nimport json\nimport subprocess\n\n\ndef get_last_version() -> str:\n \"\"\"Return the version number o"
},
{
"path": ".github/workflows/python-publish.yml",
"chars": 431,
"preview": "name: Publish to PyPI.org\non:\n release:\n types: [published]\njobs:\n pypi:\n runs-on: ubuntu-latest\n steps:\n "
},
{
"path": ".github/workflows/release.yml",
"chars": 319,
"preview": "name: Create a new patch release\non: workflow_dispatch\njobs:\n github:\n runs-on: ubuntu-latest\n steps:\n - nam"
},
{
"path": ".gitignore",
"chars": 702,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\n"
},
{
"path": "CITATION.cff",
"chars": 331,
"preview": "cff-version: 1.1.0\nmessage: \"If you use this work, please cite it as below.\"\nauthors:\n - family-names: \"Sileo\"\n give"
},
{
"path": "LICENSE",
"chars": 18646,
"preview": "Attribution 4.0 International\n\n=======================================================================\n\nCreative Commons"
},
{
"path": "README.md",
"chars": 4795,
"preview": "## tasksource ![](https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/5fc0bcb41160c47d1"
},
{
"path": "mtasks.md",
"chars": 85648,
"preview": "| | id | dataset_name | con"
},
{
"path": "pyproject.toml",
"chars": 137,
"preview": "[build-system]\nrequires = [\"setuptools>=45\", \"setuptools_scm[toml]>=6.2\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool"
},
{
"path": "setup.cfg",
"chars": 581,
"preview": " [metadata]\nname = tasksource\ndescription = Preprocessings to prepare datasets for a task\nlong_description = file: READM"
},
{
"path": "src/tasksource/.ipynb_checkpoints/access-checkpoint.py",
"chars": 4385,
"preview": "from .preprocess import Preprocessing\nimport re\nimport pandas as pd\nfrom . import tasks, recast\nfrom .metadata import da"
},
{
"path": "src/tasksource/.ipynb_checkpoints/preprocess-checkpoint.py",
"chars": 9660,
"preview": "from collections.abc import Iterable\nfrom dotwiz import DotWiz\nfrom dataclasses import dataclass\nfrom typing import Unio"
},
{
"path": "src/tasksource/.ipynb_checkpoints/recast-checkpoint.py",
"chars": 5281,
"preview": "import random\nfrom datasets import DatasetDict, Dataset\nfrom sorcery import dict_of\nimport string\n\nimproper_labels =['re"
},
{
"path": "src/tasksource/.ipynb_checkpoints/tasks-checkpoint.py",
"chars": 61919,
"preview": "from .preprocess import cat, get, regen, name, constant, Classification, TokenClassification, MultipleChoice\nfrom .metad"
},
{
"path": "src/tasksource/__init__.py",
"chars": 69,
"preview": "from .tasks import *\nfrom .preprocess import *\nfrom .access import *\n"
},
{
"path": "src/tasksource/access.py",
"chars": 4385,
"preview": "from .preprocess import Preprocessing\nimport re\nimport pandas as pd\nfrom . import tasks, recast\nfrom .metadata import da"
},
{
"path": "src/tasksource/metadata/__init__.py",
"chars": 6152,
"preview": "from .bigbench_groups import *\nfrom .blimp_groups import *\nfrom .popularity import *\n\nimppres_presupposition=['presuppos"
},
{
"path": "src/tasksource/metadata/bigbench_groups.py",
"chars": 3612,
"preview": "bigbench_discriminative = set(\"\"\"abstract_narrative_understanding\r\nanachronisms\r\nanalogical_similarity\r\nanalytic_entailm"
},
{
"path": "src/tasksource/metadata/blimp_groups.py",
"chars": 2721,
"preview": "import pandas as pd\n\ndfh=pd.read_csv('https://raw.githubusercontent.com/alexwarstadt/blimp/master/raw_results/summary/hu"
},
{
"path": "src/tasksource/metadata/original.txt",
"chars": 4569,
"preview": "WANLI\nrecast/recast_verbnet\nrecast/recast_verbcorner\nrecast/recast_ner\nrecast/recast_sentiment\nrecast/recast_puns\nrecast"
},
{
"path": "src/tasksource/metadata/popularity.py",
"chars": 25139,
"preview": "dataset_rank = {'glue': 0,\r\n 'super_glue': 12,\r\n 'tweet_eval': 23,\r\n 'blimp': 34,\r\n 'imdb': 101,\r\n 'wikitext': 102,\r\n 's"
},
{
"path": "src/tasksource/mtasks.py",
"chars": 6995,
"preview": "from .preprocess import cat, get,name, regen, constant, Classification, TokenClassification, MultipleChoice\nfrom .metada"
},
{
"path": "src/tasksource/preprocess.py",
"chars": 9660,
"preview": "from collections.abc import Iterable\nfrom dotwiz import DotWiz\nfrom dataclasses import dataclass\nfrom typing import Unio"
},
{
"path": "src/tasksource/recast.py",
"chars": 5281,
"preview": "import random\nfrom datasets import DatasetDict, Dataset\nfrom sorcery import dict_of\nimport string\n\nimproper_labels =['re"
},
{
"path": "src/tasksource/tasks.py",
"chars": 61919,
"preview": "from .preprocess import cat, get, regen, name, constant, Classification, TokenClassification, MultipleChoice\nfrom .metad"
},
{
"path": "tasks.md",
"chars": 178623,
"preview": "| | id | dataset_name "
}
]
About this extraction
This page contains the full source code of the sileod/tasksource GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 26 files (491.5 KB), approximately 102.1k tokens, and a symbol index with 126 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.