SYMBOL INDEX (963 symbols across 134 files)

FILE: docs/source/conf.py
  function skip (line 231) | def skip(app, what, name, obj, would_skip, options):
  function setup (line 237) | def setup(app):

FILE: examples/question_answering/bert_run_squad_azureml.py
  class SquadExample (line 52) | class SquadExample(object):
    method __init__ (line 55) | def __init__(self,
    method __str__ (line 69) | def __str__(self):
    method __repr__ (line 72) | def __repr__(self):
  class InputFeatures (line 85) | class InputFeatures(object):
    method __init__ (line 88) | def __init__(self,
  function read_squad_examples (line 113) | def read_squad_examples(input_file, is_training):
  function convert_examples_to_features (line 182) | def convert_examples_to_features(examples, tokenizer, max_seq_length,
  function _improve_answer_span (line 334) | def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
  function _check_is_max_context (line 371) | def _check_is_max_context(doc_spans, cur_span_index, position):
  function write_predictions (line 413) | def write_predictions(all_examples, all_features, all_results, n_best_size,
  function get_final_text (line 547) | def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=...
  function _get_best_indexes (line 643) | def _get_best_indexes(logits, n_best_size):
  function _compute_softmax (line 655) | def _compute_softmax(scores):
  function main (line 678) | def main():

FILE: examples/sentence_similarity/gensen_train.py
  function metric_average (line 49) | def metric_average(value, name):
  function setup_horovod (line 61) | def setup_horovod(model, learning_rate):
  function setup_logging (line 92) | def setup_logging(config):
  function log_config (line 107) | def log_config(config):
  function evaluate (line 126) | def evaluate(
  function evaluate_nli (line 223) | def evaluate_nli(nli_iterator, model, batch_size, n_gpus):
  function train (line 274) | def train(config, data_folder, learning_rate=0.0001, max_epoch=None):
  function read_config (line 616) | def read_config(json_file):

FILE: examples/sentence_similarity/gensen_wrapper.py
  class GenSenClassifier (line 15) | class GenSenClassifier:
    method __init__ (line 27) | def __init__(
    method _validate_params (line 44) | def _validate_params(self):
    method _get_gensen_tokens (line 63) | def _get_gensen_tokens(self, train_df=None, dev_df=None, test_df=None):
    method _read_config (line 81) | def _read_config(config_file):
    method _create_multiseq2seq_model (line 94) | def _create_multiseq2seq_model(self):
    method fit (line 107) | def fit(self, train_df, dev_df, test_df):
    method predict (line 128) | def predict(self, sentences):

FILE: examples/text_summarization/abstractive_summarization_bertsum_cnndm_distributed_train.py
  function main (line 168) | def main():
  function main_worker (line 199) | def main_worker(

FILE: examples/text_summarization/abstractive_summarization_unilm_cnndm.py
  function main (line 54) | def main():

FILE: examples/text_summarization/extractive_summarization_cnndm_distributed_train.py
  function cleanup (line 141) | def cleanup():
  function main (line 150) | def main():
  function main_worker (line 173) | def main_worker(local_rank, ngpus_per_node, summarizer, args):

FILE: setup.py
  function read (line 15) | def read(*names, **kwargs):

FILE: tests/conftest.py
  function scripts (line 28) | def scripts():
  function notebooks (line 47) | def notebooks():
  function tmp (line 146) | def tmp(tmp_path_factory):
  function tmp_module (line 155) | def tmp_module(tmp_path_factory):
  function ner_test_data (line 164) | def ner_test_data():
  function qa_test_df (line 256) | def qa_test_df():
  function pytest_addoption (line 308) | def pytest_addoption(parser):
  function subscription_id (line 321) | def subscription_id(request):
  function resource_group (line 326) | def resource_group(request):
  function workspace_name (line 331) | def workspace_name(request):
  function workspace_region (line 336) | def workspace_region(request):
  function cluster_name (line 341) | def cluster_name(request):
  function bert_english_tokenizer (line 346) | def bert_english_tokenizer():
  function xlnet_english_tokenizer (line 351) | def xlnet_english_tokenizer():
  function teardown_service (line 356) | def teardown_service(subscription_id, resource_group, workspace_name, wo...

FILE: tests/integration/test_ddp_summarization.py
  function test_ddp_extractive_summarization_cnndm_transformers (line 10) | def test_ddp_extractive_summarization_cnndm_transformers(scripts, tmp):
  function test_ddp_abstractive_summarization_cnndm_transformers (line 52) | def test_ddp_abstractive_summarization_cnndm_transformers(scripts, tmp):

FILE: tests/integration/test_gpu_utils.py
  function test_machine_is_gpu_machine (line 10) | def test_machine_is_gpu_machine():

FILE: tests/integration/test_notebooks_abstractive_summarization_bertsumabs.py
  function test_abstractive_summarization_bertsumabs_cnndm (line 15) | def test_abstractive_summarization_bertsumabs_cnndm(notebooks, tmp):

FILE: tests/integration/test_notebooks_embeddings.py
  function test_embedding_trainer_runs (line 12) | def test_embedding_trainer_runs(notebooks):

FILE: tests/integration/test_notebooks_entailment.py
  function test_entailment_multinli_bert (line 17) | def test_entailment_multinli_bert(notebooks, tmp):
  function test_entailment_xnli_bert_azureml (line 42) | def test_entailment_xnli_bert_azureml(

FILE: tests/integration/test_notebooks_extractive_summarization.py
  function test_extractive_summarization_cnndm_transformers (line 14) | def test_extractive_summarization_cnndm_transformers(notebooks, tmp):
  function test_extractive_summarization_cnndm_transformers_processed (line 41) | def test_extractive_summarization_cnndm_transformers_processed(notebooks...

FILE: tests/integration/test_notebooks_interpretability.py
  function test_deep_and_unified_understanding (line 13) | def test_deep_and_unified_understanding(notebooks):

FILE: tests/integration/test_notebooks_minilm_abstractive_summarization.py
  function test_minilm_abstractive_summarization (line 15) | def test_minilm_abstractive_summarization(notebooks, tmp):
  function test_minilm_abstractive_summarization (line 40) | def test_minilm_abstractive_summarization(notebooks, tmp):

FILE: tests/integration/test_notebooks_named_entity_recognition.py
  function test_ner_wikigold_bert (line 13) | def test_ner_wikigold_bert(notebooks, tmp):

FILE: tests/integration/test_notebooks_question_answering.py
  function test_question_answering_squad_transformers (line 13) | def test_question_answering_squad_transformers(notebooks, tmp):
  function test_bidaf_deep_dive (line 38) | def test_bidaf_deep_dive(
  function test_bidaf_quickstart (line 65) | def test_bidaf_quickstart(
  function test_bert_qa_runs (line 88) | def test_bert_qa_runs(notebooks, subscription_id, resource_group, worksp...

FILE: tests/integration/test_notebooks_sentence_similarity.py
  function baseline_results (line 15) | def baseline_results():
  function test_similarity_embeddings_baseline_runs (line 37) | def test_similarity_embeddings_baseline_runs(notebooks, baseline_results):
  function test_gensen_local (line 50) | def test_gensen_local(notebooks):
  function test_bert_encoder (line 73) | def test_bert_encoder(notebooks, tmp):
  function test_bert_senteval (line 87) | def test_bert_senteval(
  function test_similarity_embeddings_baseline_runs (line 116) | def test_similarity_embeddings_baseline_runs(notebooks, baseline_results):
  function test_automl_local_deployment_aci (line 127) | def test_automl_local_deployment_aci(
  function test_gensen_aml_deep_dive (line 154) | def test_gensen_aml_deep_dive(notebooks):
  function test_automl_with_pipelines_deployment_aks (line 181) | def test_automl_with_pipelines_deployment_aks(notebooks):

FILE: tests/integration/test_notebooks_text_classification.py
  function test_tc_mnli_transformers (line 18) | def test_tc_mnli_transformers(notebooks, tmp):
  function test_tc_bert_azureml (line 43) | def test_tc_bert_azureml(
  function test_multi_languages_transformer (line 80) | def test_multi_languages_transformer(notebooks, tmp):

FILE: tests/integration/test_notebooks_unilm_abstractive_summarization.py
  function test_unilm_abstractive_summarization (line 15) | def test_unilm_abstractive_summarization(notebooks, tmp):
  function test_unilm_abstractive_summarization (line 41) | def test_unilm_abstractive_summarization(notebooks, tmp):

FILE: tests/notebooks_common.py
  function path_notebooks (line 12) | def path_notebooks():

FILE: tests/smoke/test_dataset.py
  function test_msrpc_download (line 12) | def test_msrpc_download(tmp_path):
  function test_msrpc_load_df (line 20) | def test_msrpc_load_df(tmp_path):
  function test_xnli (line 27) | def test_xnli(tmp_path):

FILE: tests/smoke/test_gpu_utils.py
  function test_machine_is_gpu_machine (line 10) | def test_machine_is_gpu_machine():

FILE: tests/smoke/test_word_embeddings.py
  function test_load_pretrained_vectors_word2vec (line 22) | def test_load_pretrained_vectors_word2vec(tmp_path):
  function test_load_pretrained_vectors_glove (line 33) | def test_load_pretrained_vectors_glove(tmp_path):
  function test_load_pretrained_vectors_fasttext (line 44) | def test_load_pretrained_vectors_fasttext(tmp_path):

FILE: tests/unit/test_abstractive_summarization_bertsum.py
  function source_data (line 21) | def source_data():
  function target_data (line 35) | def target_data():
  function test_dataset_for_bertsumabs (line 52) | def test_dataset_for_bertsumabs(tmp_module):
  function test_train_model (line 87) | def test_train_model(tmp_module, test_dataset_for_bertsumabs, batch_size...
  function test_finetuned_model (line 135) | def test_finetuned_model(

FILE: tests/unit/test_abstractive_summarization_seq2seq.py
  function s2s_test_data (line 28) | def s2s_test_data():
  function test_S2SAbstractiveSummarizer (line 87) | def test_S2SAbstractiveSummarizer(s2s_test_data, tmp, model_name):
  function test_S2SAbsSumProcessor (line 151) | def test_S2SAbsSumProcessor(s2s_test_data, tmp):
  function test_S2SConfig (line 229) | def test_S2SConfig(tmp):

FILE: tests/unit/test_bert_common.py
  function test_tokenize (line 9) | def test_tokenize(bert_english_tokenizer):
  function test_tokenize_ner (line 19) | def test_tokenize_ner(ner_test_data, bert_english_tokenizer):
  function test_create_data_loader (line 72) | def test_create_data_loader(ner_test_data):

FILE: tests/unit/test_bert_encoder.py
  function data (line 10) | def data():
  function test_encoder (line 13) | def test_encoder(tmp, data):

FILE: tests/unit/test_bert_sentence_encoding.py
  function data (line 12) | def data():
  function test_sentence_encoding (line 21) | def test_sentence_encoding(tmp, data):

FILE: tests/unit/test_common_pytorch_utils.py
  function model (line 19) | def model():
  function test_get_device_cpu (line 23) | def test_get_device_cpu():
  function test_machine_is_gpu_machine (line 35) | def test_machine_is_gpu_machine():
  function test_get_device_gpu (line 40) | def test_get_device_gpu():
  function test_get_device_all_gpus (line 52) | def test_get_device_all_gpus():
  function test_get_device_local_rank (line 60) | def test_get_device_local_rank():
  function test_get_device_local_rank_cpu (line 68) | def test_get_device_local_rank_cpu():
  function test_move_to_device_cpu (line 75) | def test_move_to_device_cpu(model):
  function test_move_to_device_cpu_parallelized (line 82) | def test_move_to_device_cpu_parallelized(model):
  function test_move_to_device_exception_not_torch_device (line 92) | def test_move_to_device_exception_not_torch_device(model):
  function test_move_to_device_exception_wrong_type (line 98) | def test_move_to_device_exception_wrong_type(model):
  function test_move_to_device_exception_gpu_model_on_cpu_machine (line 108) | def test_move_to_device_exception_gpu_model_on_cpu_machine(model):
  function test_parallelize_model_exception_cuda_zero_gpus (line 115) | def test_parallelize_model_exception_cuda_zero_gpus(model):
  function test_parallelize_model (line 123) | def test_parallelize_model(model):

FILE: tests/unit/test_data_loaders.py
  function csv_file (line 21) | def csv_file(tmpdir):
  function json_file (line 39) | def json_file(tmpdir):
  function test_dask_csv_rnd_loader (line 53) | def test_dask_csv_rnd_loader(csv_file):
  function test_dask_csv_seq_loader (line 75) | def test_dask_csv_seq_loader(csv_file):
  function test_dask_json_rnd_loader (line 95) | def test_dask_json_rnd_loader(json_file):
  function test_dask_json_seq_loader (line 117) | def test_dask_json_seq_loader(json_file):

FILE: tests/unit/test_dataset.py
  function ner_utils_test_data (line 23) | def ner_utils_test_data(scope="module"):
  function test_maybe_download (line 78) | def test_maybe_download():
  function test_msrpc (line 90) | def test_msrpc():
  function test_wikigold (line 95) | def test_wikigold(tmp_path):
  function test_ner_utils (line 114) | def test_ner_utils(ner_utils_test_data):
  function test_xnli (line 119) | def test_xnli(tmp_path):
  function test_snli (line 126) | def test_snli(tmp_path):
  function test_squad (line 135) | def test_squad(tmp_path):
  function test_CNNDMSummarizationDatasetOrg (line 157) | def test_CNNDMSummarizationDatasetOrg(tmp):

FILE: tests/unit/test_dataset_pytorch.py
  function test_QADataset (line 4) | def test_QADataset(qa_test_df):

FILE: tests/unit/test_distributed_sampler.py
  function test_sampler (line 8) | def test_sampler():

FILE: tests/unit/test_eval_classification.py
  function test_compute (line 9) | def test_compute():

FILE: tests/unit/test_eval_compute_rouge.py
  function rouge_test_data (line 29) | def rouge_test_data():
  function test_compute_rouge_perl (line 128) | def test_compute_rouge_perl(rouge_test_data):
  function test_compute_rouge_python (line 144) | def test_compute_rouge_python(rouge_test_data):
  function test_compute_rouge_python_hi (line 160) | def test_compute_rouge_python_hi(rouge_test_data):
  function test_compute_rouge_perl_file (line 176) | def test_compute_rouge_perl_file(rouge_test_data, tmp):
  function test_compute_rouge_python_file (line 200) | def test_compute_rouge_python_file(rouge_test_data, tmp):

FILE: tests/unit/test_extractive_summarization.py
  function source_data (line 18) | def source_data():
  function target_data (line 27) | def target_data():
  function data (line 38) | def data(tmp_module):
  function test_bert_training (line 70) | def test_bert_training(data, tmp_module):

FILE: tests/unit/test_gensen_utils.py
  function test_gensen_preprocess (line 12) | def test_gensen_preprocess(tmp_path):
  function test_data_iterator (line 54) | def test_data_iterator():

FILE: tests/unit/test_interpreter.py
  function fixed_length_Phi (line 18) | def fixed_length_Phi(x):
  function variable_length_Phi (line 22) | def variable_length_Phi(function):
  function fixed_length_interp (line 27) | def fixed_length_interp():
  function variable_length_interp (line 34) | def variable_length_interp():
  function test_fixed_length_regularization (line 43) | def test_fixed_length_regularization():
  function test_variable_length_regularization (line 54) | def test_variable_length_regularization():
  function test_initialize_interpreter (line 73) | def test_initialize_interpreter():
  function test_train_fixed_length_interp (line 82) | def test_train_fixed_length_interp(fixed_length_interp):
  function test_train_variable_length_interp (line 94) | def test_train_variable_length_interp(variable_length_interp):
  function test_interpreter_get_simga (line 106) | def test_interpreter_get_simga(fixed_length_interp):

FILE: tests/unit/test_models_transformers_question_answering.py
  function qa_test_data (line 23) | def qa_test_data(qa_test_df, tmp_module):
  function test_QAProcessor (line 145) | def test_QAProcessor(qa_test_data, tmp_module):
  function test_AnswerExtractor (line 195) | def test_AnswerExtractor(qa_test_data, tmp_module):
  function test_postprocess_bert_answer (line 239) | def test_postprocess_bert_answer(qa_test_data, tmp_module):
  function test_postprocess_xlnet_answer (line 274) | def test_postprocess_xlnet_answer(qa_test_data, tmp_module):

FILE: tests/unit/test_notebooks_cpu.py
  function test_bert_encoder (line 12) | def test_bert_encoder(notebooks, tmp):

FILE: tests/unit/test_notebooks_gpu.py
  function test_bert_encoder (line 13) | def test_bert_encoder(notebooks, tmp):

FILE: tests/unit/test_preprocess.py
  function df_sentences (line 12) | def df_sentences():
  function test_to_lowercase_all (line 31) | def test_to_lowercase_all(df_sentences):
  function test_to_lowercase_subset (line 38) | def test_to_lowercase_subset(df_sentences):
  function test_to_spacy_tokens (line 45) | def test_to_spacy_tokens(df_sentences):
  function test_rm_spacy_stopwords (line 62) | def test_rm_spacy_stopwords(df_sentences):
  function test_to_nltk_tokens (line 73) | def test_to_nltk_tokens(df_sentences):
  function test_rm_nltk_stopwords (line 90) | def test_rm_nltk_stopwords(df_sentences):
  function test_convert_to_unicode (line 101) | def test_convert_to_unicode():

FILE: tests/unit/test_timer.py
  function t (line 14) | def t():
  function test_no_time (line 18) | def test_no_time(t):
  function test_stop_before_start (line 23) | def test_stop_before_start(t):
  function test_interval_before_stop (line 28) | def test_interval_before_stop(t):
  function test_timer (line 34) | def test_timer(t):
  function test_timer_format (line 48) | def test_timer_format(t):

FILE: tests/unit/test_transformers_sequence_classification.py
  function data (line 15) | def data():
  function test_classifier (line 20) | def test_classifier(data, tmpdir):
  function test_classifier_gpu_train_cpu_predict (line 37) | def test_classifier_gpu_train_cpu_predict(data, tmpdir):

FILE: tests/unit/test_transformers_token_classification.py
  function test_token_classifier_fit_predict (line 15) | def test_token_classifier_fit_predict(tmpdir, ner_test_data):

FILE: tools/remove_pixelserver.py
  function remove_pixelserver_from_notebook (line 15) | def remove_pixelserver_from_notebook(file_path):
  function get_all_notebook_files (line 52) | def get_all_notebook_files():
  function main (line 69) | def main():

FILE: utils_nlp/azureml/azureml_bert_util.py
  function warmup_linear (line 35) | def warmup_linear(x, warmup=0.002):
  function adjust_gradient_accumulation_steps (line 41) | def adjust_gradient_accumulation_steps(x, initial_steps, target_steps, w...
  class DistributedCommunicator (line 45) | class DistributedCommunicator:
    method __init__ (line 48) | def __init__(self, accumulation_step=1):
    method register_model (line 67) | def register_model(self, model, fp16):
    method _allreduce_tensor (line 91) | def _allreduce_tensor(self, p):
    method _make_hook (line 103) | def _make_hook(self, p):
    method synchronize (line 110) | def synchronize(self):
    method set_accumulation_step (line 142) | def set_accumulation_step(self, accumulation_step):

FILE: utils_nlp/azureml/azureml_utils.py
  function get_auth (line 16) | def get_auth():
  function get_or_create_workspace (line 31) | def get_or_create_workspace(
  function get_or_create_amlcompute (line 87) | def get_or_create_amlcompute(
  function get_output_files (line 139) | def get_output_files(run, output_path, file_names=None):

FILE: utils_nlp/common/pytorch_utils.py
  function get_device (line 11) | def get_device(num_gpus=None, gpu_ids=None, local_rank=-1):
  function move_model_to_device (line 30) | def move_model_to_device(model, device):
  function parallelize_model (line 44) | def parallelize_model(model, device, num_gpus=None, gpu_ids=None, local_...
  function dataloader_from_dataset (line 103) | def dataloader_from_dataset(
  function compute_training_steps (line 135) | def compute_training_steps(
  function get_amp (line 167) | def get_amp(fp16):

FILE: utils_nlp/common/timer.py
  class Timer (line 9) | class Timer(object):
    method __init__ (line 29) | def __init__(self):
    method __enter__ (line 34) | def __enter__(self):
    method __exit__ (line 38) | def __exit__(self, *args):
    method __str__ (line 41) | def __str__(self):
    method start (line 44) | def start(self):
    method stop (line 49) | def stop(self):
    method interval (line 62) | def interval(self):

FILE: utils_nlp/dataset/__init__.py
  class Split (line 11) | class Split(str, Enum):

FILE: utils_nlp/dataset/bbc_hindi.py
  function load_pandas_df (line 27) | def load_pandas_df(local_cache_path=TemporaryDirectory().name):
  function load_tc_dataset (line 59) | def load_tc_dataset(
  function get_label_values (line 172) | def get_label_values(label_encoder, label_ids):

FILE: utils_nlp/dataset/cnndm.py
  function _clean (line 46) | def _clean(x):
  function _remove_ttags (line 52) | def _remove_ttags(line):
  function _target_sentence_tokenization (line 60) | def _target_sentence_tokenization(line):
  function join (line 64) | def join(sentences):
  function CNNDMSummarizationDataset (line 68) | def CNNDMSummarizationDataset(*args, **kwargs):
  class CNNDMBertSumProcessedData (line 147) | class CNNDMBertSumProcessedData:
    method download (line 153) | def download(local_path=".data"):
  function detokenize (line 168) | def detokenize(line):
  function CNNDMSummarizationDatasetOrg (line 182) | def CNNDMSummarizationDatasetOrg(

FILE: utils_nlp/dataset/dac.py
  function load_pandas_df (line 31) | def load_pandas_df(local_cache_path=None, num_rows=None):
  function load_tc_dataset (line 50) | def load_tc_dataset(
  function get_label_values (line 162) | def get_label_values(label_encoder, label_ids):

FILE: utils_nlp/dataset/data_loaders.py
  class DaskCSVLoader (line 10) | class DaskCSVLoader:
    method __init__ (line 15) | def __init__(self, file_path, sep=",", header="infer", block_size=10e6...
    method get_random_batches (line 36) | def get_random_batches(self, num_batches, batch_size):
    method get_sequential_batches (line 55) | def get_sequential_batches(self, batch_size):
  class DaskJSONLoader (line 69) | class DaskJSONLoader:
    method __init__ (line 74) | def __init__(self, file_path, block_size=10e6, random_seed=None, lines...
    method get_random_batches (line 92) | def get_random_batches(self, num_batches, batch_size):
    method get_sequential_batches (line 111) | def get_sequential_batches(self, batch_size, num_batches=None):

FILE: utils_nlp/dataset/msrpc.py
  function download_msrpc (line 24) | def download_msrpc(download_dir):
  function load_pandas_df (line 41) | def load_pandas_df(local_cache_path=None, dataset_type="train"):

FILE: utils_nlp/dataset/multinli.py
  function download_file_and_extract (line 35) | def download_file_and_extract(
  function download_tsv_files_and_extract (line 54) | def download_tsv_files_and_extract(local_cache_path: str = ".") -> None:
  function load_pandas_df (line 79) | def load_pandas_df(local_cache_path=".", file_split="train"):
  function get_generator (line 99) | def get_generator(
  function load_tc_dataset (line 134) | def load_tc_dataset(
  function get_label_values (line 260) | def get_label_values(label_encoder, label_ids):

FILE: utils_nlp/dataset/ner_utils.py
  function preprocess_conll (line 7) | def preprocess_conll(text, sep="\t"):
  function read_conll_file (line 48) | def read_conll_file(file_path, sep="\t", encoding=None):

FILE: utils_nlp/dataset/preprocess.py
  function to_lowercase_all (line 15) | def to_lowercase_all(df):
  function to_lowercase (line 28) | def to_lowercase(df, column_names=[]):
  function to_spacy_tokens (line 47) | def to_spacy_tokens(
  function rm_spacy_stopwords (line 74) | def rm_spacy_stopwords(
  function to_nltk_tokens (line 108) | def to_nltk_tokens(
  function rm_nltk_stopwords (line 132) | def rm_nltk_stopwords(
  function convert_to_unicode (line 162) | def convert_to_unicode(input_text, encoding="utf-8"):

FILE: utils_nlp/dataset/sentence_selection.py
  function _get_ngrams (line 11) | def _get_ngrams(n, text):
  function _get_word_ngrams (line 27) | def _get_word_ngrams(n, sentences):
  function cal_rouge (line 40) | def cal_rouge(evaluated_ngrams, reference_ngrams):
  function combination_selection (line 61) | def combination_selection(doc_sent_list, abstract_sent_list, summary_size):
  function greedy_selection (line 95) | def greedy_selection(doc_sent_list, abstract_sent_list, summary_size):

FILE: utils_nlp/dataset/snli.py
  function load_pandas_df (line 28) | def load_pandas_df(local_cache_path=None, file_split=Split.TRAIN, file_t...
  function _maybe_download_and_extract (line 58) | def _maybe_download_and_extract(zip_path, file_split, file_type):
  function download_snli (line 90) | def download_snli(dest_path):
  function extract_snli (line 104) | def extract_snli(zip_path, source_path, dest_path):
  function clean_cols (line 118) | def clean_cols(df):
  function clean_rows (line 151) | def clean_rows(df, label_col=LABEL_COL):
  function clean_df (line 168) | def clean_df(df, label_col=LABEL_COL):
  function load_azureml_df (line 175) | def load_azureml_df(local_cache_path=None, file_split=Split.TRAIN, file_...

FILE: utils_nlp/dataset/squad.py
  function load_pandas_df (line 26) | def load_pandas_df(local_cache_path=".", squad_version="v1.1", file_spli...

FILE: utils_nlp/dataset/stsbenchmark.py
  function load_pandas_df (line 21) | def load_pandas_df(data_path, file_split=DEFAULT_FILE_SPLIT):
  function _maybe_download_and_extract (line 38) | def _maybe_download_and_extract(sts_file, base_data_path):
  function _download_sts (line 47) | def _download_sts(dirpath):
  function _extract_sts (line 63) | def _extract_sts(tarpath, target_dirpath=".", tmode="r"):
  function _load_sts (line 84) | def _load_sts(src_file_path):
  function clean_sts (line 121) | def clean_sts(df):

FILE: utils_nlp/dataset/url_utils.py
  function maybe_download (line 21) | def maybe_download(url, filename=None, work_directory=".", expected_byte...
  function maybe_download_googledrive (line 63) | def maybe_download_googledrive(
  function extract_tar (line 94) | def extract_tar(file_path, dest_path="."):
  function extract_zip (line 108) | def extract_zip(file_path, dest_path="."):
  function download_path (line 123) | def download_path(path):

FILE: utils_nlp/dataset/wikigold.py
  function load_train_test_dfs (line 31) | def load_train_test_dfs(local_cache_path="./", test_fraction=0.5, random...
  function get_unique_labels (line 82) | def get_unique_labels():
  function load_dataset (line 87) | def load_dataset(

FILE: utils_nlp/dataset/xnli.py
  function load_pandas_df (line 21) | def load_pandas_df(local_cache_path=".", file_split="dev", language="zh"):

FILE: utils_nlp/dataset/xnli_torch_dataset.py
  function _load_pandas_df (line 27) | def _load_pandas_df(cache_dir, file_split, language, data_percent_used):
  function _tokenize (line 34) | def _tokenize(tok_language, to_lowercase, cache_dir, df):
  function _fit_train_labels (line 47) | def _fit_train_labels(df):
  class XnliDataset (line 54) | class XnliDataset(data.Dataset):
    method __init__ (line 55) | def __init__(
    method __len__ (line 109) | def __len__(self):
    method __getitem__ (line 113) | def __getitem__(self, index):

FILE: utils_nlp/eval/SentEval/senteval/binary.py
  class BinaryClassifierEval (line 21) | class BinaryClassifierEval(object):
    method __init__ (line 22) | def __init__(self, pos, neg, seed=1111):
    method do_prepare (line 27) | def do_prepare(self, params, prepare):
    method loadFile (line 33) | def loadFile(self, fpath):
    method run (line 37) | def run(self, params, batcher):
  class CREval (line 63) | class CREval(BinaryClassifierEval):
    method __init__ (line 64) | def __init__(self, task_path, seed=1111):
  class MREval (line 71) | class MREval(BinaryClassifierEval):
    method __init__ (line 72) | def __init__(self, task_path, seed=1111):
  class SUBJEval (line 79) | class SUBJEval(BinaryClassifierEval):
    method __init__ (line 80) | def __init__(self, task_path, seed=1111):
  class MPQAEval (line 87) | class MPQAEval(BinaryClassifierEval):
    method __init__ (line 88) | def __init__(self, task_path, seed=1111):

FILE: utils_nlp/eval/SentEval/senteval/engine.py
  class SE (line 26) | class SE(object):
    method __init__ (line 27) | def __init__(self, params, batcher, prepare=None):
    method eval (line 56) | def eval(self, name):

FILE: utils_nlp/eval/SentEval/senteval/mrpc.py
  class MRPCEval (line 23) | class MRPCEval(object):
    method __init__ (line 24) | def __init__(self, task_path, seed=1111):
    method do_prepare (line 33) | def do_prepare(self, params, prepare):
    method loadFile (line 40) | def loadFile(self, fpath):
    method run (line 54) | def run(self, params, batcher):

FILE: utils_nlp/eval/SentEval/senteval/probing.py
  class PROBINGEval (line 23) | class PROBINGEval(object):
    method __init__ (line 24) | def __init__(self, task, task_path, seed=1111):
    method do_prepare (line 36) | def do_prepare(self, params, prepare):
    method loadFile (line 41) | def loadFile(self, fpath):
    method run (line 57) | def run(self, params, batcher):
  class LengthEval (line 104) | class LengthEval(PROBINGEval):
    method __init__ (line 105) | def __init__(self, task_path, seed=1111):
  class WordContentEval (line 110) | class WordContentEval(PROBINGEval):
    method __init__ (line 111) | def __init__(self, task_path, seed=1111):
  class DepthEval (line 119) | class DepthEval(PROBINGEval):
    method __init__ (line 120) | def __init__(self, task_path, seed=1111):
  class TopConstituentsEval (line 125) | class TopConstituentsEval(PROBINGEval):
    method __init__ (line 126) | def __init__(self, task_path, seed=1111):
  class BigramShiftEval (line 131) | class BigramShiftEval(PROBINGEval):
    method __init__ (line 132) | def __init__(self, task_path, seed=1111):
  class TenseEval (line 143) | class TenseEval(PROBINGEval):
    method __init__ (line 144) | def __init__(self, task_path, seed=1111):
  class SubjNumberEval (line 149) | class SubjNumberEval(PROBINGEval):
    method __init__ (line 150) | def __init__(self, task_path, seed=1111):
  class ObjNumberEval (line 155) | class ObjNumberEval(PROBINGEval):
    method __init__ (line 156) | def __init__(self, task_path, seed=1111):
  class OddManOutEval (line 161) | class OddManOutEval(PROBINGEval):
    method __init__ (line 162) | def __init__(self, task_path, seed=1111):
  class CoordinationInversionEval (line 167) | class CoordinationInversionEval(PROBINGEval):
    method __init__ (line 168) | def __init__(self, task_path, seed=1111):

FILE: utils_nlp/eval/SentEval/senteval/rank.py
  class ImageCaptionRetrievalEval (line 26) | class ImageCaptionRetrievalEval(object):
    method __init__ (line 27) | def __init__(self, task_path, seed=1111):
    method do_prepare (line 35) | def do_prepare(self, params, prepare):
    method loadFile (line 41) | def loadFile(self, fpath):
    method run (line 68) | def run(self, params, batcher):

FILE: utils_nlp/eval/SentEval/senteval/sick.py
  class SICKRelatednessEval (line 25) | class SICKRelatednessEval(object):
    method __init__ (line 26) | def __init__(self, task_path, seed=1111):
    method do_prepare (line 34) | def do_prepare(self, params, prepare):
    method loadFile (line 42) | def loadFile(self, fpath):
    method run (line 58) | def run(self, params, batcher):
    method encode_labels (line 123) | def encode_labels(self, labels, nclass=5):
  class SICKEntailmentEval (line 137) | class SICKEntailmentEval(SICKRelatednessEval):
    method __init__ (line 138) | def __init__(self, task_path, seed=1111):
    method loadFile (line 146) | def loadFile(self, fpath):
    method run (line 162) | def run(self, params, batcher):

FILE: utils_nlp/eval/SentEval/senteval/snli.py
  class SNLIEval (line 23) | class SNLIEval(object):
    method __init__ (line 24) | def __init__(self, taskpath, seed=1111):
    method do_prepare (line 62) | def do_prepare(self, params, prepare):
    method loadFile (line 65) | def loadFile(self, fpath):
    method run (line 70) | def run(self, params, batcher):

FILE: utils_nlp/eval/SentEval/senteval/sst.py
  class SSTEval (line 22) | class SSTEval(object):
    method __init__ (line 23) | def __init__(self, task_path, nclasses=2, seed=1111):
    method do_prepare (line 37) | def do_prepare(self, params, prepare):
    method loadFile (line 42) | def loadFile(self, fpath):
    method run (line 57) | def run(self, params, batcher):

FILE: utils_nlp/eval/SentEval/senteval/sts.py
  class STSEval (line 26) | class STSEval(object):
    method loadFile (line 27) | def loadFile(self, fpath):
    method do_prepare (line 52) | def do_prepare(self, params, prepare):
    method run (line 59) | def run(self, params, batcher):
  class STS12Eval (line 107) | class STS12Eval(STSEval):
    method __init__ (line 108) | def __init__(self, taskpath, seed=1111):
  class STS13Eval (line 116) | class STS13Eval(STSEval):
    method __init__ (line 118) | def __init__(self, taskpath, seed=1111):
  class STS14Eval (line 125) | class STS14Eval(STSEval):
    method __init__ (line 126) | def __init__(self, taskpath, seed=1111):
  class STS15Eval (line 134) | class STS15Eval(STSEval):
    method __init__ (line 135) | def __init__(self, taskpath, seed=1111):
  class STS16Eval (line 143) | class STS16Eval(STSEval):
    method __init__ (line 144) | def __init__(self, taskpath, seed=1111):
  class STSBenchmarkEval (line 152) | class STSBenchmarkEval(SICKRelatednessEval):
    method __init__ (line 153) | def __init__(self, task_path, seed=1111):
    method loadFile (line 161) | def loadFile(self, fpath):

FILE: utils_nlp/eval/SentEval/senteval/tools/classifier.py
  class PyTorchClassifier (line 24) | class PyTorchClassifier(object):
    method __init__ (line 25) | def __init__(self, inputdim, nclasses, l2reg=0., batch_size=64, seed=1...
    method prepare_split (line 38) | def prepare_split(self, X, y, validation_data=None, validation_split=N...
    method fit (line 60) | def fit(self, X, y, validation_data=None, validation_split=None,
    method trainepoch (line 85) | def trainepoch(self, X, y, epoch_size=1):
    method score (line 111) | def score(self, devX, devy):
    method predict (line 130) | def predict(self, devX):
    method predict_proba (line 144) | def predict_proba(self, devX):
  class MLP (line 162) | class MLP(PyTorchClassifier):
    method __init__ (line 163) | def __init__(self, params, inputdim, nclasses, l2reg=0., batch_size=64,

FILE: utils_nlp/eval/SentEval/senteval/tools/ranking.py
  class COCOProjNet (line 23) | class COCOProjNet(nn.Module):
    method __init__ (line 24) | def __init__(self, config):
    method forward (line 36) | def forward(self, img, sent, imgc, sentc):
    method proj_sentence (line 66) | def proj_sentence(self, sent):
    method proj_image (line 71) | def proj_image(self, img):
  class PairwiseRankingLoss (line 77) | class PairwiseRankingLoss(nn.Module):
    method __init__ (line 81) | def __init__(self, margin):
    method forward (line 85) | def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
  class ImageSentenceRankingPytorch (line 95) | class ImageSentenceRankingPytorch(object):
    method __init__ (line 97) | def __init__(self, train, valid, test, config):
    method prepare_data (line 126) | def prepare_data(self, trainTxt, trainImg, devTxt, devImg,
    method run (line 137) | def run(self):
    method trainepoch (line 226) | def trainepoch(self, trainTxt, trainImg, devTxt, devImg, nepoches=1):
    method t2i (line 274) | def t2i(self, images, captions):
    method i2t (line 314) | def i2t(self, images, captions):

FILE: utils_nlp/eval/SentEval/senteval/tools/relatedness.py
  class RelatednessPytorch (line 23) | class RelatednessPytorch(object):
    method __init__ (line 25) | def __init__(self, train, valid, test, devscores, config):
    method prepare_data (line 59) | def prepare_data(self, trainX, trainy, devX, devy, testX, testy):
    method run (line 70) | def run(self):
    method trainepoch (line 103) | def trainepoch(self, X, y, nepoches=1):
    method predict_proba (line 124) | def predict_proba(self, devX):

FILE: utils_nlp/eval/SentEval/senteval/tools/validation.py
  function get_classif_name (line 28) | def get_classif_name(classifier_config, usepytorch):
  class InnerKFoldClassifier (line 39) | class InnerKFoldClassifier(object):
    method __init__ (line 43) | def __init__(self, X, y, config):
    method run (line 57) | def run(self):
  class KFoldClassifier (line 110) | class KFoldClassifier(object):
    method __init__ (line 114) | def __init__(self, train, test, config):
    method run (line 126) | def run(self):
  class SplitClassifier (line 184) | class SplitClassifier(object):
    method __init__ (line 188) | def __init__(self, X, y, config):
    method run (line 202) | def run(self):

FILE: utils_nlp/eval/SentEval/senteval/trec.py
  class TRECEval (line 22) | class TRECEval(object):
    method __init__ (line 23) | def __init__(self, task_path, seed=1111):
    method do_prepare (line 29) | def do_prepare(self, params, prepare):
    method loadFile (line 33) | def loadFile(self, fpath):
    method run (line 46) | def run(self, params, batcher):

FILE: utils_nlp/eval/SentEval/senteval/utils.py
  function create_dictionary (line 16) | def create_dictionary(sentences):
  function cosine (line 38) | def cosine(u, v):
  class dotdict (line 42) | class dotdict(dict):
  function get_optimizer (line 49) | def get_optimizer(s):

FILE: utils_nlp/eval/classification.py
  function eval_classification (line 21) | def eval_classification(actual, predicted, round_decimals=4):
  function compute_correlation_coefficients (line 38) | def compute_correlation_coefficients(x, y=None):
  function plot_confusion_matrix (line 58) | def plot_confusion_matrix(

FILE: utils_nlp/eval/evaluate_squad.py
  function normalize_answer (line 15) | def normalize_answer(s):
  function f1_score (line 34) | def f1_score(prediction, ground_truth):
  function exact_match_score (line 47) | def exact_match_score(prediction, ground_truth):
  function metric_max_over_ground_truths (line 51) | def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
  function evaluate (line 59) | def evaluate(dataset, predictions):

FILE: utils_nlp/eval/evaluate_summarization.py
  function get_rouge (line 10) | def get_rouge(predictions, targets, temp_dir, random_seed=42):

FILE: utils_nlp/eval/question_answering.py
  function get_raw_scores (line 11) | def get_raw_scores(qa_ids, actuals, preds):
  function find_best_thresh (line 100) | def find_best_thresh(preds, scores, na_probs, qid_to_has_ans, unanswerab...
  function find_all_best_thresh (line 176) | def find_all_best_thresh(
  function evaluate_qa (line 210) | def evaluate_qa(

FILE: utils_nlp/eval/rouge/compute_rouge.py
  function compute_rouge_perl (line 14) | def compute_rouge_perl(cand, ref, is_input_files=False, verbose=False):
  function compute_rouge_python (line 81) | def compute_rouge_python(cand, ref, is_input_files=False, language="en"):

FILE: utils_nlp/eval/rouge/rouge_ext.py
  class RougeExt (line 30) | class RougeExt(Rouge):
    method __init__ (line 58) | def __init__(
    method tokenize_text (line 190) | def tokenize_text(self, text):
    method split_into_sentences (line 203) | def split_into_sentences(self, text):
    method stem_tokens (line 217) | def stem_tokens(self, tokens):
    method _split_into_words (line 232) | def _split_into_words(self, sentences):
    method _get_word_ngrams_and_length (line 248) | def _get_word_ngrams_and_length(self, n, sentences):
    method _get_unigrams (line 266) | def _get_unigrams(self, sentences):
    method _compute_ngrams (line 284) | def _compute_ngrams(self, evaluated_sentences, reference_sentences, n):
    method _compute_ngrams_lcs (line 322) | def _compute_ngrams_lcs(self, evaluated_sentences, reference_sentences...
    method _preprocess_summary_as_a_whole (line 461) | def _preprocess_summary_as_a_whole(self, summary):
    method _preprocess_summary_per_sentence (line 532) | def _preprocess_summary_per_sentence(self, summary):

FILE: utils_nlp/eval/senteval.py
  class SentEvalConfig (line 7) | class SentEvalConfig:
    method __init__ (line 16) | def __init__(self, model_params, senteval_params):
    method model_params (line 27) | def model_params(self):
    method model_params (line 31) | def model_params(self, model_params):
    method append_senteval_params (line 34) | def append_senteval_params(self, params):

FILE: utils_nlp/interpreter/Interpreter.py
  function calculate_regularization (line 14) | def calculate_regularization(sampled_x, Phi, reduced_axes=None, device=N...
  class Interpreter (line 47) | class Interpreter(nn.Module):
    method __init__ (line 64) | def __init__(self, x, Phi, scale=0.5, rate=0.1, regularization=None, w...
    method forward (line 113) | def forward(self):
    method optimize (line 136) | def optimize(self, iteration=5000, lr=0.01, show_progress=False):
    method get_sigma (line 161) | def get_sigma(self):
    method visualize (line 171) | def visualize(self):

FILE: utils_nlp/language_utils/hi/hindi_stemmer.py
  function hi_stem (line 87) | def hi_stem(word):

FILE: utils_nlp/models/bert/common.py
  class Language (line 32) | class Language(str, Enum):
  class Tokenizer (line 45) | class Tokenizer:
    method __init__ (line 46) | def __init__(self, language=Language.ENGLISH, to_lower=False, cache_di...
    method tokenize (line 60) | def tokenize(self, text):
    method _truncate_seq_pair (line 76) | def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
    method preprocess_classification_tokens (line 103) | def preprocess_classification_tokens(self, tokens, max_len=BERT_MAX_LEN):
    method preprocess_encoder_tokens (line 159) | def preprocess_encoder_tokens(self, tokens, max_len=BERT_MAX_LEN):
    method tokenize_ner (line 216) | def tokenize_ner(
  function create_data_loader (line 369) | def create_data_loader(
  class TextDataset (line 418) | class TextDataset(Dataset):
    method __init__ (line 425) | def __init__(self, filename):
    method __len__ (line 436) | def __len__(self):
    method _cast (line 441) | def _cast(row):
    method __getitem__ (line 444) | def __getitem__(self, index):
  function get_dataset_multiple_files (line 470) | def get_dataset_multiple_files(files):

FILE: utils_nlp/models/bert/sequence_classification.py
  class BERTSequenceClassifier (line 25) | class BERTSequenceClassifier:
    method __init__ (line 28) | def __init__(self, language=Language.ENGLISH, num_labels=2, cache_dir=...
    method cuda (line 53) | def cuda(self):
    method fit (line 59) | def fit(
    method predict (line 200) | def predict(

FILE: utils_nlp/models/bert/sequence_classification_distributed.py
  class BERTSequenceClassifier (line 30) | class BERTSequenceClassifier:
    method __init__ (line 33) | def __init__(
    method create_optimizer (line 89) | def create_optimizer(
    method create_data_loader (line 139) | def create_data_loader(self, dataset, batch_size=32, mode="train", **k...
    method save_model (line 168) | def save_model(self):
    method fit (line 191) | def fit(
    method predict (line 289) | def predict(self, test_loader, num_gpus=None, probabilities=False):

FILE: utils_nlp/models/bert/sequence_encoding.py
  class PoolingStrategy (line 25) | class PoolingStrategy(str, Enum):
  class BERTSentenceEncoder (line 33) | class BERTSentenceEncoder:
    method __init__ (line 36) | def __init__(
    method layer_index (line 84) | def layer_index(self):
    method layer_index (line 88) | def layer_index(self, layer_index):
    method cuda (line 95) | def cuda(self):
    method pooling_strategy (line 102) | def pooling_strategy(self):
    method pooling_strategy (line 106) | def pooling_strategy(self, pooling_strategy):
    method get_hidden_states (line 109) | def get_hidden_states(self, text, batch_size=32):
    method pool (line 184) | def pool(self, df):
    method encode (line 241) | def encode(self, text, batch_size=32, as_numpy=False):

FILE: utils_nlp/models/bert/token_classification.py
  class BERTTokenClassifier (line 24) | class BERTTokenClassifier:
    method __init__ (line 27) | def __init__(self, language=Language.ENGLISH, num_labels=2, cache_dir=...
    method cuda (line 62) | def cuda(self):
    method _get_optimizer (line 68) | def _get_optimizer(self, learning_rate, num_train_optimization_steps, ...
    method fit (line 101) | def fit(
    method predict (line 192) | def predict(
  function create_label_map (line 279) | def create_label_map(label_list, trailing_piece_tag="X"):
  function postprocess_token_labels (line 288) | def postprocess_token_labels(

FILE: utils_nlp/models/gensen/create_gensen_model.py
  function create_multiseq2seq_model (line 13) | def create_multiseq2seq_model(

FILE: utils_nlp/models/gensen/gensen.py
  class Encoder (line 18) | class Encoder(nn.Module):
    method __init__ (line 24) | def __init__(
    method set_pretrained_embeddings (line 51) | def set_pretrained_embeddings(self, embedding_matrix):
    method forward (line 89) | def forward(self, input, lengths, return_all=False, pool="last"):
  class GenSen (line 126) | class GenSen(nn.Module):
    method __init__ (line 132) | def __init__(self, *args, **kwargs):
    method vocab_expansion (line 136) | def vocab_expansion(self, task_vocab):
    method get_representation (line 146) | def get_representation(
  class GenSenSingle (line 190) | class GenSenSingle(nn.Module):
    method __init__ (line 196) | def __init__(
    method _load_params (line 223) | def _load_params(self):
    method first_expansion (line 278) | def first_expansion(self):
    method vocab_expansion (line 312) | def vocab_expansion(self, task_vocab):
    method get_minibatch (line 371) | def get_minibatch(self, sentences, tokenize=False, add_start_end=True):
    method get_representation (line 425) | def get_representation(

FILE: utils_nlp/models/gensen/multi_task_model.py
  class MultitaskModel (line 13) | class MultitaskModel(nn.Module):
    method __init__ (line 21) | def __init__(
    method init_weights (line 103) | def init_weights(self):
    method set_pretrained_embeddings (line 112) | def set_pretrained_embeddings(self, embedding_matrix):
    method forward (line 134) | def forward(
    method decode (line 265) | def decode(self, logits):
    method get_hidden (line 274) | def get_hidden(self, input_src, src_lengths, strategy="last"):

FILE: utils_nlp/models/gensen/preprocess_utils.py
  function _preprocess (line 9) | def _preprocess(split_map, data_path, column_names):
  function _split_and_cleanup (line 66) | def _split_and_cleanup(split_map, data_path):
  function gensen_preprocess (line 107) | def gensen_preprocess(train_tok, dev_tok, test_tok, data_path):

FILE: utils_nlp/models/gensen/utils.py
  class DataIterator (line 21) | class DataIterator(object):
    method _trim_vocab (line 25) | def _trim_vocab(vocab, vocab_size):
    method construct_vocab (line 66) | def construct_vocab(
  class BufferedDataIterator (line 97) | class BufferedDataIterator(DataIterator):
    method __init__ (line 100) | def __init__(
    method _reset_filepointer (line 160) | def _reset_filepointer(self, idx):
    method fetch_buffer (line 170) | def fetch_buffer(self, idx, reset=True):
    method build_vocab (line 214) | def build_vocab(self):
    method shuffle_dataset (line 268) | def shuffle_dataset(self, idx):
    method get_parallel_minibatch (line 276) | def get_parallel_minibatch(
  class NLIIterator (line 376) | class NLIIterator(DataIterator):
    method __init__ (line 379) | def __init__(
    method shuffle_dataset (line 434) | def shuffle_dataset(self):
    method get_parallel_minibatch (line 438) | def get_parallel_minibatch(self, index, batch_size, sent_type="train"):
  function get_validation_minibatch (line 541) | def get_validation_minibatch(
  function compute_validation_loss (line 622) | def compute_validation_loss(

FILE: utils_nlp/models/glove/src/cooccur.c
  type real (line 37) | typedef double real;
  type CREC (line 39) | typedef struct cooccur_rec {
  type CRECID (line 45) | typedef struct cooccur_rec_id {
  type HASHREC (line 52) | typedef struct hashrec {
  function scmp (line 68) | int scmp( char *s1, char *s2 ) {
  function bitwisehash (line 76) | unsigned int bitwisehash(char *word, int tsize, unsigned int seed) {
  function HASHREC (line 85) | HASHREC ** inithashtable() {
  function HASHREC (line 94) | HASHREC *hashsearch(HASHREC **ht, char *w) {
  function hashinsert (line 107) | void hashinsert(HASHREC **ht, char *w, long long id) {
  function get_word (line 134) | int get_word(char *word, FILE *fin) {
  function write_chunk (line 167) | int write_chunk(CREC *cr, long long length, FILE *fout) {
  function compare_crec (line 186) | int compare_crec(const void *a, const void *b) {
  function compare_crecid (line 194) | int compare_crecid(CRECID a, CRECID b) {
  function swap_entry (line 201) | void swap_entry(CRECID *pq, int i, int j) {
  function insert (line 208) | void insert(CRECID *pq, CRECID new, int size) {
  function delete (line 218) | void delete(CRECID *pq, int size) {
  function merge_write (line 240) | int merge_write(CRECID new, CRECID *old, FILE *fout) {
  function merge_files (line 251) | int merge_files(int num) {
  function get_cooccurrence (line 308) | int get_cooccurrence() {
  function find_arg (line 451) | int find_arg(char *str, int argc, char **argv) {
  function main (line 465) | int main(int argc, char **argv) {

FILE: utils_nlp/models/glove/src/glove.c
  type real (line 36) | typedef double real;
  type CREC (line 38) | typedef struct cooccur_rec {
  function scmp (line 61) | int scmp( char *s1, char *s2 ) {
  function initialize_parameters (line 66) | void initialize_parameters() {
  function real (line 94) | inline real check_nan(real update) {
  function save_params (line 178) | int save_params(int nb_iter) {
  function train_glove (line 293) | int train_glove() {
  function find_arg (line 353) | int find_arg(char *str, int argc, char **argv) {
  function main (line 367) | int main(int argc, char **argv) {

FILE: utils_nlp/models/glove/src/shuffle.c
  type real (line 31) | typedef double real;
  type CREC (line 33) | typedef struct cooccur_rec {
  function scmp (line 45) | int scmp( char *s1, char *s2 ) {
  function rand_long (line 52) | static long rand_long(long n) {
  function write_chunk (line 62) | int write_chunk(CREC *array, long size, FILE *fout) {
  function shuffle (line 69) | void shuffle(CREC *array, long n) {
  function shuffle_merge (line 81) | int shuffle_merge(int num) {
  function shuffle_by_chunks (line 129) | int shuffle_by_chunks() {
  function find_arg (line 177) | int find_arg(char *str, int argc, char **argv) {
  function main (line 191) | int main(int argc, char **argv) {

FILE: utils_nlp/models/glove/src/vocab_count.c
  type VOCAB (line 37) | typedef struct vocabulary {
  type HASHREC (line 42) | typedef struct hashrec {
  function scmp (line 54) | int scmp( char *s1, char *s2 ) {
  function CompareVocabTie (line 61) | int CompareVocabTie(const void *a, const void *b) {
  function CompareVocab (line 69) | int CompareVocab(const void *a, const void *b) {
  function bitwisehash (line 78) | unsigned int bitwisehash(char *word, int tsize, unsigned int seed) {
  function HASHREC (line 87) | HASHREC ** inithashtable() {
  function hashinsert (line 96) | void hashinsert(HASHREC **ht, char *w) {
  function get_word (line 135) | int get_word(char *word, FILE *fin) {
  function get_counts (line 167) | int get_counts() {
  function find_arg (line 226) | int find_arg(char *str, int argc, char **argv) {
  function main (line 240) | int main(int argc, char **argv) {

FILE: utils_nlp/models/pretrained_embeddings/fasttext.py
  function _extract_fasttext_vectors (line 15) | def _extract_fasttext_vectors(zip_path, dest_path="."):
  function _download_fasttext_vectors (line 37) | def _download_fasttext_vectors(download_dir, file_name="wiki.simple.zip"):
  function _maybe_download_and_extract (line 59) | def _maybe_download_and_extract(dest_path, file_name):
  function load_pretrained_vectors (line 84) | def load_pretrained_vectors(dest_path, file_name="wiki.simple.bin"):

FILE: utils_nlp/models/pretrained_embeddings/glove.py
  function _extract_glove_vectors (line 17) | def _extract_glove_vectors(zip_path, dest_path="."):
  function _download_glove_vectors (line 39) | def _download_glove_vectors(download_dir, file_name="glove.840B.300d.zip"):
  function _maybe_download_and_extract (line 57) | def _maybe_download_and_extract(dest_path, file_name):
  function download_and_extract (line 82) | def download_and_extract(dir_path, file_name="glove.840B.300d.txt"):
  function load_pretrained_vectors (line 96) | def load_pretrained_vectors(

FILE: utils_nlp/models/pretrained_embeddings/word2vec.py
  function _extract_word2vec_vectors (line 15) | def _extract_word2vec_vectors(zip_path, dest_filepath):
  function _download_word2vec_vectors (line 34) | def _download_word2vec_vectors(
  function _maybe_download_and_extract (line 54) | def _maybe_download_and_extract(dest_path, file_name):
  function load_pretrained_vectors (line 79) | def load_pretrained_vectors(

FILE: utils_nlp/models/pytorch_modules/conditional_gru.py
  class ConditionalGRU (line 11) | class ConditionalGRU(nn.Module):
    method __init__ (line 14) | def __init__(self, input_dim, hidden_dim, dropout=0.0):
    method reset_parameters (line 33) | def reset_parameters(self):
    method forward (line 39) | def forward(self, input, hidden, ctx):

FILE: utils_nlp/models/transformers/abstractive_summarization_bertsum.py
  function fit_to_block_size (line 37) | def fit_to_block_size(sequence, block_size, pad_token_id):
  function build_mask (line 56) | def build_mask(sequence, pad_token_id):
  function compute_token_type_ids (line 74) | def compute_token_type_ids(batch, separator_token_id):
  class BertSumAbsProcessor (line 103) | class BertSumAbsProcessor:
    method __init__ (line 107) | def __init__(
    method list_supported_models (line 156) | def list_supported_models():
    method model_name (line 160) | def model_name(self):
    method model_name (line 164) | def model_name(self, value):
    method get_inputs (line 175) | def get_inputs(batch, device, model_name, train_mode=True):
    method collate (line 215) | def collate(self, data, block_size, device, train_mode=True):
    method preprocess (line 301) | def preprocess(self, story_lines, summary_lines=None):
  function validate (line 349) | def validate(summarizer, validate_dataset):
  class BertSumAbs (line 380) | class BertSumAbs(Transformer):
    method __init__ (line 384) | def __init__(
    method list_supported_models (line 444) | def list_supported_models():
    method fit (line 447) | def fit(
    method predict (line 653) | def predict(
    method save_model (line 799) | def save_model(self, global_step=None, full_name=None):

FILE: utils_nlp/models/transformers/abstractive_summarization_seq2seq.py
  function _get_model_type (line 77) | def _get_model_type(model_name):
  function detokenize (line 88) | def detokenize(tk_list):
  class S2SAbsSumDataset (line 98) | class S2SAbsSumDataset(Dataset):
    method __init__ (line 104) | def __init__(self, features):
    method __getitem__ (line 107) | def __getitem__(self, idx):
    method __len__ (line 110) | def __len__(self):
  class S2SAbsSumProcessor (line 114) | class S2SAbsSumProcessor:
    method __init__ (line 130) | def __init__(
    method list_supported_models (line 142) | def list_supported_models():
    method get_inputs (line 146) | def get_inputs(cls, batch, device, model_name):
    method create_s2s_dataset (line 162) | def create_s2s_dataset(
    method s2s_dataset_from_iterable_sum_ds (line 245) | def s2s_dataset_from_iterable_sum_ds(
    method s2s_dataset_from_sum_ds (line 290) | def s2s_dataset_from_sum_ds(
    method s2s_dataset_from_json_or_file (line 331) | def s2s_dataset_from_json_or_file(
  class S2SConfig (line 379) | class S2SConfig:
    method __init__ (line 412) | def __init__(
    method save_to_json (line 439) | def save_to_json(self, json_file):
    method load_from_json (line 444) | def load_from_json(cls, json_file):
  class S2SAbstractiveSummarizer (line 451) | class S2SAbstractiveSummarizer(Transformer):
    method __init__ (line 452) | def __init__(
    method list_supported_models (line 580) | def list_supported_models():
    method fit (line 583) | def fit(
    method predict (line 778) | def predict(
    method save_model (line 1055) | def save_model(self, output_dir, global_step, fp16):
  function load_and_cache_examples (line 1074) | def load_and_cache_examples(

FILE: utils_nlp/models/transformers/bertsum/adam.py
  class Adam (line 11) | class Adam(Optimizer):
    method __init__ (line 35) | def __init__(
    method __setstate__ (line 57) | def __setstate__(self, state):
    method step (line 62) | def step(self, closure=None):

FILE: utils_nlp/models/transformers/bertsum/beam.py
  class Beam (line 11) | class Beam(object):
    method __init__ (line 25) | def __init__(
    method get_current_state (line 77) | def get_current_state(self):
    method get_current_origin (line 81) | def get_current_origin(self):
    method advance (line 85) | def advance(self, word_probs, attn_out):
    method done (line 160) | def done(self):
    method sort_finished (line 163) | def sort_finished(self, minimum=None):
    method get_hyp (line 178) | def get_hyp(self, timestep, k):
  class GNMTGlobalScorer (line 190) | class GNMTGlobalScorer(object):
    method __init__ (line 200) | def __init__(self, alpha, length_penalty):
    method score (line 207) | def score(self, beam, logprobs):

FILE: utils_nlp/models/transformers/bertsum/data_loader.py
  class IterableDistributedSampler (line 10) | class IterableDistributedSampler(object):
    method __init__ (line 21) | def __init__(self, world_size=1, rank=0, local_rank=-1):
    method iter (line 26) | def iter(self, iterable):
  class ChunkDataLoader (line 35) | class ChunkDataLoader(object):
    method __init__ (line 47) | def __init__(self, datasets, batch_size, shuffle, is_labeled, sampler):
    method eachiter (line 56) | def eachiter(self):
    method __iter__ (line 63) | def __iter__(self):
    method _next_dataset_iterator (line 66) | def _next_dataset_iterator(self, dataset_iter):
  class Batch (line 87) | class Batch(object):
    method _pad (line 88) | def _pad(self, data, pad_id, width=-1):
    method __init__ (line 94) | def __init__(self, data=None, is_labeled=False):
    method to (line 131) | def to(self, device):
    method __len__ (line 149) | def __len__(self):
  function create_batch_with_size (line 153) | def create_batch_with_size(data, batch_size):
  function simple_batch_size_fn (line 169) | def simple_batch_size_fn(new, count):
  class DataIterator (line 182) | class DataIterator(object):
    method __init__ (line 183) | def __init__(self, dataset, batch_size, is_labeled=False, shuffle=True...
    method data (line 195) | def data(self):
    method preprocess (line 201) | def preprocess(self, ex, is_labeled):
    method batch_buffer (line 220) | def batch_buffer(self, data, batch_size):
    method create_batches (line 239) | def create_batches(self):
    method __iter__ (line 256) | def __iter__(self):

FILE: utils_nlp/models/transformers/bertsum/dataset.py
  function get_dataset (line 9) | def get_dataset(file):
  class ExtSumProcessedIterableDataset (line 13) | class ExtSumProcessedIterableDataset(IterableDataset):
    method __init__ (line 17) | def __init__(self, file_list, is_shuffle=False):
    method get_stream (line 30) | def get_stream(self):
    method __iter__ (line 42) | def __iter__(self):
  class ExtSumProcessedDataset (line 46) | class ExtSumProcessedDataset(Dataset):
    method __init__ (line 50) | def __init__(self, file_list, is_shuffle=False):
    method __len__ (line 66) | def __len__(self):
    method __getitem__ (line 69) | def __getitem__(self, idx):

FILE: utils_nlp/models/transformers/bertsum/decoder.py
  class TransformerDecoderLayer (line 18) | class TransformerDecoderLayer(nn.Module):
    method __init__ (line 30) | def __init__(self, d_model, heads, d_ff, dropout):
    method forward (line 45) | def forward(
    method _get_attn_subsequent_mask (line 105) | def _get_attn_subsequent_mask(self, size):
  class TransformerDecoder (line 123) | class TransformerDecoder(nn.Module):
    method __init__ (line 153) | def __init__(self, num_layers, d_model, heads, d_ff, dropout, embeddin...
    method forward (line 172) | def forward(
    method init_decoder_state (line 252) | def init_decoder_state(self, src, memory_bank, with_cache=False):
  class TransformerDecoderState (line 260) | class TransformerDecoderState(DecoderState):
    method __init__ (line 263) | def __init__(self, src):
    method _all (line 275) | def _all(self):
    method detach (line 284) | def detach(self):
    method update_state (line 291) | def update_state(self, new_input, previous_layer_inputs):
    method _init_cache (line 297) | def _init_cache(self, memory_bank, num_layers):
    method repeat_beam_size_times (line 306) | def repeat_beam_size_times(self, beam_size):
    method map_batch_fn (line 310) | def map_batch_fn(self, fn):

FILE: utils_nlp/models/transformers/bertsum/encoder.py
  class Classifier (line 13) | class Classifier(nn.Module):
    method __init__ (line 14) | def __init__(self, hidden_size):
    method forward (line 19) | def forward(self, x, mask_cls):
  class PositionalEncoding (line 25) | class PositionalEncoding(nn.Module):
    method __init__ (line 26) | def __init__(self, dropout, dim, max_len=5000):
    method forward (line 40) | def forward(self, emb, step=None):
    method get_emb (line 50) | def get_emb(self, emb):
  class TransformerEncoderLayer (line 54) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 55) | def __init__(self, d_model, heads, d_ff, dropout):
    method forward (line 63) | def forward(self, iter, query, inputs, mask):
  class ExtTransformerEncoder (line 75) | class ExtTransformerEncoder(nn.Module):
    method __init__ (line 76) | def __init__(self, d_model, d_ff, heads, dropout, num_inter_layers=0):
    method forward (line 92) | def forward(self, top_vecs, mask):
  class RNNEncoder (line 111) | class RNNEncoder(nn.Module):
    method __init__ (line 113) | def __init__(self, bidirectional, num_layers, input_size,
    method forward (line 130) | def forward(self, x, mask):

FILE: utils_nlp/models/transformers/bertsum/loss.py
  function abs_loss (line 21) | def abs_loss(generator, symbols, vocab_size, train=True, label_smoothing...
  class LossComputeBase (line 32) | class LossComputeBase(nn.Module):
    method __init__ (line 52) | def __init__(self, generator, pad_id):
    method _make_shard_state (line 57) | def _make_shard_state(self, batch, output, attns=None):
    method _compute_loss (line 71) | def _compute_loss(self, batch, output, target, **kwargs):
    method monolithic_compute_loss (line 84) | def monolithic_compute_loss(self, output, target, number_tokens):
    method sharded_compute_loss (line 104) | def sharded_compute_loss(self, batch, output, shard_size, normalization):
    method _stats (line 142) | def _stats(self, loss, scores, target):
    method _bottle (line 159) | def _bottle(self, _v):
    method _unbottle (line 162) | def _unbottle(self, _v, batch_size):
  class LabelSmoothingLoss (line 166) | class LabelSmoothingLoss(nn.Module):
    method __init__ (line 173) | def __init__(self, label_smoothing, tgt_vocab_size, ignore_index=-100):
    method forward (line 184) | def forward(self, output, target):
  class NMTLossCompute (line 196) | class NMTLossCompute(LossComputeBase):
    method __init__ (line 201) | def __init__(self, generator, symbols, vocab_size, label_smoothing=0.0):
    method _make_shard_state (line 211) | def _make_shard_state(self, target, tgt_num_tokens, output):
    method _compute_loss (line 218) | def _compute_loss(self, output, target, **kwargs):
  function filter_shard_state (line 230) | def filter_shard_state(state, shard_size=None):
  function shards (line 246) | def shards(state, shard_size, eval_only=False):

FILE: utils_nlp/models/transformers/bertsum/model_builder.py
  function load_optimizer_checkpoint (line 23) | def load_optimizer_checkpoint(optimizer, checkpoint):
  function build_optim (line 34) | def build_optim(
  function build_optim_bert (line 60) | def build_optim_bert(
  function build_optim_dec (line 90) | def build_optim_dec(
  function get_generator (line 119) | def get_generator(vocab_size, dec_hidden_size):
  class Transformer (line 126) | class Transformer(nn.Module):
    method __init__ (line 127) | def __init__(self, temp_dir, model_class, pretrained_model_name, pretr...
    method forward (line 136) | def forward(self, x, segs, mask):
  class BertSumExt (line 147) | class BertSumExt(nn.Module):
    method __init__ (line 148) | def __init__(self, encoder, args, model_class, pretrained_model_name, ...
    method load_cp (line 184) | def load_cp(self, pt):
    method forward (line 187) | def forward(self, x, segs, clss, mask, mask_cls, labels=None, sentence...
  class Bert (line 205) | class Bert(nn.Module):
    method __init__ (line 206) | def __init__(self, large, temp_dir, finetune=False):
    method forward (line 219) | def forward(self, x, segs, mask):
  class AbsSummarizer (line 230) | class AbsSummarizer(nn.Module):
    method __init__ (line 231) | def __init__(
    method load_checkpoint (line 353) | def load_checkpoint(self, checkpoint):
    method forward (line 372) | def forward(

FILE: utils_nlp/models/transformers/bertsum/neural.py
  function aeq (line 11) | def aeq(*args):
  function sequence_mask (line 22) | def sequence_mask(lengths, max_len=None):
  function gelu (line 36) | def gelu(x):
  class GlobalAttention (line 50) | class GlobalAttention(nn.Module):
    method __init__ (line 105) | def __init__(self, dim, attn_type="dot"):
    method score (line 126) | def score(self, h_t, h_s):
    method forward (line 166) | def forward(self, source, memory_bank, memory_lengths=None, memory_mas...
  class PositionwiseFeedForward (line 228) | class PositionwiseFeedForward(nn.Module):
    method __init__ (line 238) | def __init__(self, d_model, d_ff, dropout=0.1):
    method forward (line 247) | def forward(self, x):
  class MultiHeadedAttention (line 253) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 295) | def __init__(self, head_count, model_dim, dropout=0.1, use_final_linea...
    method forward (line 312) | def forward(
  class DecoderState (line 468) | class DecoderState(object):
    method detach (line 477) | def detach(self):
    method beam_update (line 482) | def beam_update(self, idx, positions, beam_size):
    method map_batch_fn (line 498) | def map_batch_fn(self, fn):

FILE: utils_nlp/models/transformers/bertsum/optimizers.py
  function use_gpu (line 16) | def use_gpu(opt):
  function build_optim (line 25) | def build_optim(model, opt, checkpoint):
  class MultipleOptimizer (line 72) | class MultipleOptimizer(object):
    method __init__ (line 75) | def __init__(self, op):
    method zero_grad (line 79) | def zero_grad(self):
    method step (line 84) | def step(self):
    method state (line 90) | def state(self):
    method state_dict (line 94) | def state_dict(self):
    method load_state_dict (line 98) | def load_state_dict(self, state_dicts):
  class Optimizer (line 105) | class Optimizer(object):
    method __init__ (line 136) | def __init__(
    method set_parameters (line 167) | def set_parameters(self, params):
    method _set_rate (line 198) | def _set_rate(self, learning_rate):
    method step (line 206) | def step(self):
    method add_param_group (line 237) | def add_param_group(self, param_group):
    method load_state_dict (line 287) | def load_state_dict(self, state_dict):
    method state_dict (line 290) | def state_dict(self):
    method zero_grad (line 294) | def zero_grad(self):

FILE: utils_nlp/models/transformers/bertsum/penalties.py
  class PenaltyBuilder (line 10) | class PenaltyBuilder(object):
    method __init__ (line 19) | def __init__(self, length_pen):
    method length_penalty (line 22) | def length_penalty(self):
    method length_wu (line 34) | def length_wu(self, beam, logprobs, alpha=0.0):
    method length_average (line 43) | def length_average(self, beam, logprobs, alpha=0.0):
    method length_none (line 49) | def length_none(self, beam, logprobs, alpha=0.0, beta=0.0):

FILE: utils_nlp/models/transformers/bertsum/predictor.py
  function build_predictor (line 19) | def build_predictor(
  function tile (line 44) | def tile(x, count, dim=0):
  class Translator (line 68) | class Translator(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 141) | def forward(self, src, segs, mask_src):
    method _fast_translate_batch (line 161) | def _fast_translate_batch(self, src, segs, mask_src, max_length, min_l...

FILE: utils_nlp/models/transformers/common.py
  class Transformer (line 30) | class Transformer:
    method __init__ (line 31) | def __init__(self, model_name, model, cache_dir):
    method model_name (line 38) | def model_name(self):
    method model_type (line 42) | def model_type(self):
    method set_seed (line 46) | def set_seed(seed, cuda=True):
    method get_default_optimizer (line 54) | def get_default_optimizer(model, weight_decay, learning_rate, adam_eps...
    method get_default_scheduler (line 80) | def get_default_scheduler(optimizer, warmup_steps, num_training_steps):
    method prepare_model_and_optimizer (line 88) | def prepare_model_and_optimizer(
    method fine_tune (line 151) | def fine_tune(
    method predict (line 294) | def predict(self, eval_dataloader, get_inputs, num_gpus, gpu_ids, verb...
    method save_model (line 319) | def save_model(self, file_name=None):
    method load_model (line 350) | def load_model(self, file_name):

FILE: utils_nlp/models/transformers/datasets.py
  class SCDataSet (line 14) | class SCDataSet(Dataset):
    method __init__ (line 17) | def __init__(self, df, text_col, label_col, transform, **transform_args):
    method __getitem__ (line 39) | def __getitem__(self, idx):
    method __len__ (line 61) | def __len__(self):
  class SPCDataSet (line 65) | class SPCDataSet(Dataset):
    method __init__ (line 68) | def __init__(
    method __getitem__ (line 99) | def __getitem__(self, idx):
    method __len__ (line 125) | def __len__(self):
  class QADataset (line 154) | class QADataset(Dataset):
    method __init__ (line 155) | def __init__(
    method __getitem__ (line 210) | def __getitem__(self, idx):
    method __len__ (line 233) | def __len__(self):
  function _line_iter (line 237) | def _line_iter(file_path):
  function _preprocess (line 243) | def _preprocess(sentences, preprocess_pipeline, word_tokenize=None):
  function _create_data_from_iterator (line 266) | def _create_data_from_iterator(iterator, preprocessing, word_tokenize):
  class IterableSummarizationDataset (line 275) | class IterableSummarizationDataset(IterableDataset):
    method __init__ (line 276) | def __init__(
    method __iter__ (line 329) | def __iter__(self):
    method get_source (line 333) | def get_source(self):
    method get_target (line 336) | def get_target(self):
  class SummarizationDataset (line 340) | class SummarizationDataset(Dataset):
    method __init__ (line 341) | def __init__(
    method shorten (line 439) | def shorten(self, top_n=None):
    method __getitem__ (line 453) | def __getitem__(self, idx):
    method __len__ (line 465) | def __len__(self):
    method get_source (line 468) | def get_source(self):
    method get_source_txt (line 471) | def get_source_txt(self):
    method get_target_txt (line 474) | def get_target_txt(self):
    method get_target (line 477) | def get_target(self):
    method save_to_jsonl (line 480) | def save_to_jsonl(self, output_file):
  function parallel_preprocess (line 490) | def parallel_preprocess(

FILE: utils_nlp/models/transformers/extractive_summarization.py
  class Bunch (line 51) | class Bunch(object):
    method __init__ (line 54) | def __init__(self, adict):
  function get_dataloader (line 58) | def get_dataloader(
  function get_pred (line 88) | def get_pred(
  class ExtSumProcessedData (line 168) | class ExtSumProcessedData:
    method save_data (line 173) | def save_data(data_iter, is_test=False, save_path="./", chunk_size=None):
    method _get_files (line 209) | def _get_files(self, root):
    method splits (line 225) | def splits(self, root, train_iterable=False):
  function preprocess_single_add_oracleids (line 248) | def preprocess_single_add_oracleids(input_data, oracle_mode="greedy", se...
  function parallel_preprocess (line 277) | def parallel_preprocess(input_data, preprocess, num_pool=-1):
  class ExtSumProcessor (line 309) | class ExtSumProcessor:
    method __init__ (line 312) | def __init__(
    method list_supported_models (line 362) | def list_supported_models():
    method model_name (line 366) | def model_name(self):
    method model_name (line 370) | def model_name(self, value):
    method get_inputs (line 381) | def get_inputs(batch, device, model_name, train_mode=True):
    method preprocess (line 435) | def preprocess(self, input_data_list, oracle_mode="greedy", selections...
    method collate (line 455) | def collate(self, data, block_size, device, train_mode=True):
    method encode_single (line 487) | def encode_single(self, d, block_size, train_mode=True):
  class ExtractiveSummarizer (line 557) | class ExtractiveSummarizer(Transformer):
    method __init__ (line 560) | def __init__(
    method list_supported_models (line 620) | def list_supported_models():
    method fit (line 623) | def fit(
    method predict (line 780) | def predict(
    method predict_scores (line 881) | def predict_scores(self, test_dataloader, num_gpus=1, gpu_ids=None, ve...
    method save_model (line 912) | def save_model(self, full_name=None):

FILE: utils_nlp/models/transformers/named_entity_recognition.py
  class TokenClassificationProcessor (line 27) | class TokenClassificationProcessor:
    method __init__ (line 40) | def __init__(self, model_name="bert-base-cased", to_lower=False, cache...
    method get_inputs (line 52) | def get_inputs(batch, device, model_name, train_mode=True):
    method create_label_map (line 88) | def create_label_map(label_lists, trailing_piece_tag="X"):
    method preprocess (line 110) | def preprocess(
  class TokenClassifier (line 272) | class TokenClassifier(Transformer):
    method __init__ (line 285) | def __init__(self, model_name="bert-base-cased", num_labels=2, cache_d...
    method list_supported_models (line 295) | def list_supported_models():
    method fit (line 298) | def fit(
    method predict (line 410) | def predict(self, test_dataloader, num_gpus=None, gpu_ids=None, verbos...
    method get_predicted_token_labels (line 442) | def get_predicted_token_labels(self, predictions, label_map, dataset):
    method get_true_test_labels (line 492) | def get_true_test_labels(self, label_map, dataset):

FILE: utils_nlp/models/transformers/question_answering.py
  function _list_supported_models (line 83) | def _list_supported_models():
  class QAProcessor (line 87) | class QAProcessor:
    method __init__ (line 109) | def __init__(
    method model_name (line 127) | def model_name(self):
    method model_name (line 131) | def model_name(self, value):
    method model_type (line 143) | def model_type(self):
    method get_inputs (line 147) | def get_inputs(batch, device, model_name, train_mode=True):
    method list_supported_models (line 183) | def list_supported_models():
    method preprocess (line 186) | def preprocess(
    method postprocess (line 331) | def postprocess(
  class QAResult (line 450) | class QAResult(QAResult_):
  class QAResultExtended (line 483) | class QAResultExtended(QAResultExtended_):
  class AnswerExtractor (line 509) | class AnswerExtractor(Transformer):
    method __init__ (line 528) | def __init__(
    method list_supported_models (line 539) | def list_supported_models():
    method fit (line 542) | def fit(
    method predict (line 663) | def predict(self, test_dataloader, num_gpus=None, gpu_ids=None, verbos...
  function postprocess_bert_answer (line 733) | def postprocess_bert_answer(
  function postprocess_xlnet_answer (line 1029) | def postprocess_xlnet_answer(
  function _is_iterable_but_not_string (line 1280) | def _is_iterable_but_not_string(obj):
  function _create_qa_example (line 1285) | def _create_qa_example(qa_input, is_training):
  function _create_qa_features (line 1392) | def _create_qa_features(
  function _get_final_text (line 1786) | def _get_final_text(pred_text, orig_text, do_lower_case, verbose_logging...
  function _get_best_indexes (line 1884) | def _get_best_indexes(logits, n_best_size):
  function _compute_softmax (line 1896) | def _compute_softmax(scores):

FILE: utils_nlp/models/transformers/sequence_classification.py
  class Processor (line 23) | class Processor:
    method __init__ (line 38) | def __init__(self, model_name="bert-base-cased", to_lower=False, cache...
    method get_inputs (line 50) | def get_inputs(batch, device, model_name, train_mode=True):
    method text_transform (line 86) | def text_transform(text, tokenizer, max_len=MAX_SEQ_LEN):
    method text_pair_transform (line 118) | def text_pair_transform(text_1, text_2, tokenizer, max_len=MAX_SEQ_LEN):
    method dataset_from_dataframe (line 186) | def dataset_from_dataframe(
  class SequenceClassifier (line 210) | class SequenceClassifier(Transformer):
    method __init__ (line 211) | def __init__(self, model_name="bert-base-cased", num_labels=2, cache_d...
    method list_supported_models (line 221) | def list_supported_models():
    method fit (line 224) | def fit(
    method predict (line 336) | def predict(self, test_dataloader, num_gpus=None, gpu_ids=None, verbos...

FILE: utils_nlp/models/xlnet/common.py
  class Language (line 12) | class Language(Enum):
  class Tokenizer (line 20) | class Tokenizer:
    method __init__ (line 21) | def __init__(
    method preprocess_classification_tokens (line 33) | def preprocess_classification_tokens(self, examples, max_seq_length):
  function log_xlnet_params (line 114) | def log_xlnet_params(local_dict):

FILE: utils_nlp/models/xlnet/sequence_classification.py
  class XLNetSequenceClassifier (line 21) | class XLNetSequenceClassifier:
    method __init__ (line 24) | def __init__(
    method fit (line 82) | def fit(
    method predict (line 274) | def predict(