621ffdd236468d709f181d58

amirveyseh/acronym_identification

amirveyseh

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": [], "paperswithcode_id": "acronym-identification", "pretty_name": "Acronym Identification Dataset", "tags": ["acronym-identification"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-long", "1": "B-short", "2": "I-long", "3": "I-short", "4": "O"}}}}], "splits": [{"name": "train", "num_bytes": 7792771, "num_examples": 14006}, {"name": "validation", "num_bytes": 952689, "num_examples": 1717}, {"name": "test", "num_bytes": 987712, "num_examples": 1750}], "download_size": 2071007, "dataset_size": 9733172}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "train-eval-index": [{"config": "default", "task": "token-classification", "task_id": "entity_extraction", "splits": {"eval_split": "test"}, "col_mapping": {"tokens": "tokens", "labels": "tags"}}]}

False

2024-01-09T11:39:57.000Z

15ef643450d589d5883e289ffadeb03563e80a9e

Dataset Card for Acronym Identification Dataset Dataset Summary This dataset contains the training, validation, and test data for the Shared Task 1: Acronym Identification of the AAAI-21 Workshop on Scientific Document Understanding. Supported Tasks and Leaderboards The dataset supports an acronym-identification task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared… See the full description on the dataset page: https://huggingface.co/datasets/amirveyseh/acronym_identification.

acronym-identification

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d59

ade-benchmark-corpus/ade_corpus_v2

ade-benchmark-corpus

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification", "token-classification"], "task_ids": ["coreference-resolution", "fact-checking"], "pretty_name": "Adverse Drug Reaction Data v2", "config_names": ["Ade_corpus_v2_classification", "Ade_corpus_v2_drug_ade_relation", "Ade_corpus_v2_drug_dosage_relation"], "dataset_info": [{"config_name": "Ade_corpus_v2_classification", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Not-Related", "1": "Related"}}}}], "splits": [{"name": "train", "num_bytes": 3403699, "num_examples": 23516}], "download_size": 1706476, "dataset_size": 3403699}, {"config_name": "Ade_corpus_v2_drug_ade_relation", "features": [{"name": "text", "dtype": "string"}, {"name": "drug", "dtype": "string"}, {"name": "effect", "dtype": "string"}, {"name": "indexes", "struct": [{"name": "drug", "sequence": [{"name": "start_char", "dtype": "int32"}, {"name": "end_char", "dtype": "int32"}]}, {"name": "effect", "sequence": [{"name": "start_char", "dtype": "int32"}, {"name": "end_char", "dtype": "int32"}]}]}], "splits": [{"name": "train", "num_bytes": 1545993, "num_examples": 6821}], "download_size": 491362, "dataset_size": 1545993}, {"config_name": "Ade_corpus_v2_drug_dosage_relation", "features": [{"name": "text", "dtype": "string"}, {"name": "drug", "dtype": "string"}, {"name": "dosage", "dtype": "string"}, {"name": "indexes", "struct": [{"name": "drug", "sequence": [{"name": "start_char", "dtype": "int32"}, {"name": "end_char", "dtype": "int32"}]}, {"name": "dosage", "sequence": [{"name": "start_char", "dtype": "int32"}, {"name": "end_char", "dtype": "int32"}]}]}], "splits": [{"name": "train", "num_bytes": 64697, "num_examples": 279}], "download_size": 33004, "dataset_size": 64697}], "configs": [{"config_name": "Ade_corpus_v2_classification", "data_files": [{"split": "train", "path": "Ade_corpus_v2_classification/train-*"}]}, {"config_name": "Ade_corpus_v2_drug_ade_relation", "data_files": [{"split": "train", "path": "Ade_corpus_v2_drug_ade_relation/train-*"}]}, {"config_name": "Ade_corpus_v2_drug_dosage_relation", "data_files": [{"split": "train", "path": "Ade_corpus_v2_drug_dosage_relation/train-*"}]}], "train-eval-index": [{"config": "Ade_corpus_v2_classification", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}

False

2024-01-09T11:42:58.000Z

4ba01c71687dd7c996597042449448ea312126cf

Dataset Card for Adverse Drug Reaction Data v2 Dataset Summary ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug. DRUG-AE.rel provides relations between drugs and adverse effects. DRUG-DOSE.rel provides relations between drugs and dosages. ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain… See the full description on the dataset page: https://huggingface.co/datasets/ade-benchmark-corpus/ade_corpus_v2.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d5a

UCLNLP/adversarial_qa

UCLNLP

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "open-domain-qa"], "paperswithcode_id": "adversarialqa", "pretty_name": "adversarialQA", "dataset_info": [{"config_name": "adversarialQA", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "metadata", "struct": [{"name": "split", "dtype": "string"}, {"name": "model_in_the_loop", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 27858686, "num_examples": 30000}, {"name": "validation", "num_bytes": 2757092, "num_examples": 3000}, {"name": "test", "num_bytes": 2919479, "num_examples": 3000}], "download_size": 5301049, "dataset_size": 33535257}, {"config_name": "dbert", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "metadata", "struct": [{"name": "split", "dtype": "string"}, {"name": "model_in_the_loop", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9345521, "num_examples": 10000}, {"name": "validation", "num_bytes": 918156, "num_examples": 1000}, {"name": "test", "num_bytes": 971290, "num_examples": 1000}], "download_size": 2689032, "dataset_size": 11234967}, {"config_name": "dbidaf", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "metadata", "struct": [{"name": "split", "dtype": "string"}, {"name": "model_in_the_loop", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9282482, "num_examples": 10000}, {"name": "validation", "num_bytes": 917907, "num_examples": 1000}, {"name": "test", "num_bytes": 946947, "num_examples": 1000}], "download_size": 2721341, "dataset_size": 11147336}, {"config_name": "droberta", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "metadata", "struct": [{"name": "split", "dtype": "string"}, {"name": "model_in_the_loop", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9270683, "num_examples": 10000}, {"name": "validation", "num_bytes": 925029, "num_examples": 1000}, {"name": "test", "num_bytes": 1005242, "num_examples": 1000}], "download_size": 2815452, "dataset_size": 11200954}], "configs": [{"config_name": "adversarialQA", "data_files": [{"split": "train", "path": "adversarialQA/train-*"}, {"split": "validation", "path": "adversarialQA/validation-*"}, {"split": "test", "path": "adversarialQA/test-*"}]}, {"config_name": "dbert", "data_files": [{"split": "train", "path": "dbert/train-*"}, {"split": "validation", "path": "dbert/validation-*"}, {"split": "test", "path": "dbert/test-*"}]}, {"config_name": "dbidaf", "data_files": [{"split": "train", "path": "dbidaf/train-*"}, {"split": "validation", "path": "dbidaf/validation-*"}, {"split": "test", "path": "dbidaf/test-*"}]}, {"config_name": "droberta", "data_files": [{"split": "train", "path": "droberta/train-*"}, {"split": "validation", "path": "droberta/validation-*"}, {"split": "test", "path": "droberta/test-*"}]}], "train-eval-index": [{"config": "adversarialQA", "task": "question-answering", "task_id": "extractive_question_answering", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "question", "context": "context", "answers": {"text": "text", "answer_start": "answer_start"}}, "metrics": [{"type": "squad", "name": "SQuAD"}]}]}

False

2023-12-21T14:20:00.000Z

c2d5f738db1ad21a4126a144dfbb00cb51e0a4a9

Dataset Card for adversarialQA Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial… See the full description on the dataset page: https://huggingface.co/datasets/UCLNLP/adversarial_qa.

adversarialqa

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d5b

Yale-LILY/aeslc

Yale-LILY

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "aeslc", "pretty_name": "AESLC: Annotated Enron Subject Line Corpus", "tags": ["aspect-based-summarization", "conversations-summarization", "multi-document-summarization", "email-headline-generation"], "dataset_info": {"features": [{"name": "email_body", "dtype": "string"}, {"name": "subject_line", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11897245, "num_examples": 14436}, {"name": "validation", "num_bytes": 1659987, "num_examples": 1960}, {"name": "test", "num_bytes": 1383452, "num_examples": 1906}], "download_size": 7948020, "dataset_size": 14940684}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-01-09T11:49:13.000Z

2305f2e63b68056f9b9037a3805c8c196e0d5581

Dataset Card for "aeslc" Dataset Summary A collection of email messages of employees in the Enron Corporation. There are two features: email_body: email body text. subject_line: email subject text. Supported Tasks and Leaderboards More Information Needed Languages Monolingual English (mainly en-US) with some exceptions. Dataset Structure Data Instances default Size of downloaded dataset… See the full description on the dataset page: https://huggingface.co/datasets/Yale-LILY/aeslc.

aeslc

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d5c

nwu-ctext/afrikaans_ner_corpus

nwu-ctext

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["af"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Afrikaans Ner Corpus", "license_details": "Creative Commons Attribution 2.5 South Africa License", "dataset_info": {"config_name": "afrikaans_ner_corpus", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "OUT", "1": "B-PERS", "2": "I-PERS", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-MISC", "8": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 4025651, "num_examples": 8962}], "download_size": 944804, "dataset_size": 4025651}, "configs": [{"config_name": "afrikaans_ner_corpus", "data_files": [{"split": "train", "path": "afrikaans_ner_corpus/train-*"}], "default": true}]}

False

2024-01-09T11:51:47.000Z

445834a997dce8b40e1d108638064381de80c497

Dataset Card for Afrikaans Ner Corpus Dataset Summary The Afrikaans Ner Corpus is an Afrikaans dataset developed by The Centre for Text Technology (CTexT), North-West University, South Africa. The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards. Supported Tasks and Leaderboards [More… See the full description on the dataset page: https://huggingface.co/datasets/nwu-ctext/afrikaans_ner_corpus.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d5d

fancyzhx/ag_news

fancyzhx

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "paperswithcode_id": "ag-news", "pretty_name": "AG\u2019s News Corpus", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "World", "1": "Sports", "2": "Business", "3": "Sci/Tech"}}}}], "splits": [{"name": "train", "num_bytes": 29817303, "num_examples": 120000}, {"name": "test", "num_bytes": 1879474, "num_examples": 7600}], "download_size": 19820267, "dataset_size": 31696777}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}

False

2024-03-07T12:02:37.000Z

eb185aade064a813bc0b7f42de02595523103ca4

Dataset Card for "ag_news" Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc)… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/ag_news.

ag-news

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d5e

allenai/ai2_arc

allenai

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "multiple-choice-qa"], "pretty_name": "Ai2Arc", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "ARC-Challenge", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 349760, "num_examples": 1119}, {"name": "test", "num_bytes": 375511, "num_examples": 1172}, {"name": "validation", "num_bytes": 96660, "num_examples": 299}], "download_size": 449460, "dataset_size": 821931}, {"config_name": "ARC-Easy", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 619000, "num_examples": 2251}, {"name": "test", "num_bytes": 657514, "num_examples": 2376}, {"name": "validation", "num_bytes": 157394, "num_examples": 570}], "download_size": 762935, "dataset_size": 1433908}], "configs": [{"config_name": "ARC-Challenge", "data_files": [{"split": "train", "path": "ARC-Challenge/train-*"}, {"split": "test", "path": "ARC-Challenge/test-*"}, {"split": "validation", "path": "ARC-Challenge/validation-*"}]}, {"config_name": "ARC-Easy", "data_files": [{"split": "train", "path": "ARC-Easy/train-*"}, {"split": "test", "path": "ARC-Easy/test-*"}, {"split": "validation", "path": "ARC-Easy/validation-*"}]}]}

False

2023-12-21T15:09:48.000Z

210d026faf9955653af8916fad021475a3f00453

Dataset Card for "ai2_arc" Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences… See the full description on the dataset page: https://huggingface.co/datasets/allenai/ai2_arc.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d5f

google/air_dialogue

google

{"annotations_creators": ["crowdsourced"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["conversational", "dialogue-generation", "dialogue-modeling", "language-modeling", "masked-language-modeling"], "pretty_name": "AirDialogue", "dataset_info": [{"config_name": "air_dialogue_data", "features": [{"name": "action", "struct": [{"name": "status", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "flight", "sequence": "int32"}]}, {"name": "intent", "struct": [{"name": "return_month", "dtype": "string"}, {"name": "return_day", "dtype": "string"}, {"name": "max_price", "dtype": "int32"}, {"name": "departure_airport", "dtype": "string"}, {"name": "max_connections", "dtype": "int32"}, {"name": "departure_day", "dtype": "string"}, {"name": "goal", "dtype": "string"}, {"name": "departure_month", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "return_airport", "dtype": "string"}]}, {"name": "timestamps", "sequence": "int64"}, {"name": "dialogue", "sequence": "string"}, {"name": "expected_action", "struct": [{"name": "status", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "flight", "sequence": "int32"}]}, {"name": "search_info", "list": [{"name": "button_name", "dtype": "string"}, {"name": "field_name", "dtype": "string"}, {"name": "field_value", "dtype": "string"}, {"name": "timestmamp", "dtype": "int64"}]}, {"name": "correct_sample", "dtype": "bool_"}], "splits": [{"name": "train", "num_bytes": 353718365, "num_examples": 321459}, {"name": "validation", "num_bytes": 44441818, "num_examples": 40363}], "download_size": 141766743, "dataset_size": 398160183}, {"config_name": "air_dialogue_kb", "features": [{"name": "kb", "list": [{"name": "airline", "dtype": "string"}, {"name": "class", "dtype": "string"}, {"name": "departure_airport", "dtype": "string"}, {"name": "departure_day", "dtype": "string"}, {"name": "departure_month", "dtype": "string"}, {"name": "departure_time_num", "dtype": "int32"}, {"name": "flight_number", "dtype": "int32"}, {"name": "num_connections", "dtype": "int32"}, {"name": "price", "dtype": "int32"}, {"name": "return_airport", "dtype": "string"}, {"name": "return_day", "dtype": "string"}, {"name": "return_month", "dtype": "string"}, {"name": "return_time_num", "dtype": "int32"}]}, {"name": "reservation", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 782590970, "num_examples": 321459}, {"name": "validation", "num_bytes": 98269609, "num_examples": 40363}], "download_size": 57883938, "dataset_size": 880860579}], "configs": [{"config_name": "air_dialogue_data", "data_files": [{"split": "train", "path": "air_dialogue_data/train-*"}, {"split": "validation", "path": "air_dialogue_data/validation-*"}], "default": true}, {"config_name": "air_dialogue_kb", "data_files": [{"split": "train", "path": "air_dialogue_kb/train-*"}, {"split": "validation", "path": "air_dialogue_kb/validation-*"}]}]}

False

2024-03-07T15:22:15.000Z

dbdbe7bcef8d344bc3c68a05600f3d95917d6898

Dataset Card for air_dialogue Dataset Summary AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. News in v1.3: We have included the test split of the AirDialogue dataset. We… See the full description on the dataset page: https://huggingface.co/datasets/google/air_dialogue.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d60

komari6/ajgt_twitter_ar

komari6

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "Arabic Jordanian General Tweets", "dataset_info": {"config_name": "plain_text", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Positive"}}}}], "splits": [{"name": "train", "num_bytes": 175420, "num_examples": 1800}], "download_size": 91857, "dataset_size": 175420}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train", "path": "plain_text/train-*"}], "default": true}]}

False

2024-01-09T11:58:01.000Z

af3f2fa5462ac461b696cb300d66e07ad366057f

Dataset Card for Arabic Jordanian General Tweets Dataset Summary Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. Supported Tasks and Leaderboards The dataset was published on this paper. Languages The dataset is based on Arabic. Dataset Structure Data Instances A binary datset with with negative… See the full description on the dataset page: https://huggingface.co/datasets/komari6/ajgt_twitter_ar.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d61

legacy-datasets/allegro_reviews

legacy-datasets

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["pl"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-scoring", "text-scoring"], "paperswithcode_id": "allegro-reviews", "pretty_name": "Allegro Reviews", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "rating", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 4899535, "num_examples": 9577}, {"name": "test", "num_bytes": 514523, "num_examples": 1006}, {"name": "validation", "num_bytes": 515781, "num_examples": 1002}], "download_size": 3923657, "dataset_size": 5929839}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}

False

2024-01-09T11:59:39.000Z

71593d1379934286885c53d147bc863ffe830745

Dataset Card for [Dataset Name] Dataset Summary Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review). We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your… See the full description on the dataset page: https://huggingface.co/datasets/legacy-datasets/allegro_reviews.

allegro-reviews

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d62

tblard/allocine

tblard

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["fr"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "allocine", "pretty_name": "Allocin\u00e9", "dataset_info": {"config_name": "allocine", "features": [{"name": "review", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "splits": [{"name": "train", "num_bytes": 91330632, "num_examples": 160000}, {"name": "validation", "num_bytes": 11546242, "num_examples": 20000}, {"name": "test", "num_bytes": 11547689, "num_examples": 20000}], "download_size": 75125954, "dataset_size": 114424563}, "configs": [{"config_name": "allocine", "data_files": [{"split": "train", "path": "allocine/train-*"}, {"split": "validation", "path": "allocine/validation-*"}, {"split": "test", "path": "allocine/test-*"}], "default": true}], "train-eval-index": [{"config": "allocine", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"review": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}

False

2024-01-09T12:02:24.000Z

a4654f4896408912913a62ace89614879a549287

Dataset Card for Allociné Dataset Summary The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the Allociné.fr community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k). Supported Tasks and Leaderboards text-classification, sentiment-classification: The dataset can be used… See the full description on the dataset page: https://huggingface.co/datasets/tblard/allocine.

allocine

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d63

mutiyama/alt

mutiyama

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["bn", "en", "fil", "hi", "id", "ja", "km", "lo", "ms", "my", "th", "vi", "zh"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation", "token-classification"], "task_ids": ["parsing"], "paperswithcode_id": "alt", "pretty_name": "Asian Language Treebank", "config_names": ["alt-en", "alt-jp", "alt-km", "alt-my", "alt-my-transliteration", "alt-my-west-transliteration", "alt-parallel"], "dataset_info": [{"config_name": "alt-en", "features": [{"name": "SNT.URLID", "dtype": "string"}, {"name": "SNT.URLID.SNTID", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "value", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10075569, "num_examples": 17889}, {"name": "validation", "num_bytes": 544719, "num_examples": 988}, {"name": "test", "num_bytes": 567272, "num_examples": 1017}], "download_size": 3781814, "dataset_size": 11187560}, {"config_name": "alt-jp", "features": [{"name": "SNT.URLID", "dtype": "string"}, {"name": "SNT.URLID.SNTID", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "word_alignment", "dtype": "string"}, {"name": "jp_tokenized", "dtype": "string"}, {"name": "en_tokenized", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21888277, "num_examples": 17202}, {"name": "validation", "num_bytes": 1181555, "num_examples": 953}, {"name": "test", "num_bytes": 1175592, "num_examples": 931}], "download_size": 10355366, "dataset_size": 24245424}, {"config_name": "alt-km", "features": [{"name": "SNT.URLID", "dtype": "string"}, {"name": "SNT.URLID.SNTID", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "km_pos_tag", "dtype": "string"}, {"name": "km_tokenized", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12015371, "num_examples": 18088}, {"name": "validation", "num_bytes": 655212, "num_examples": 1000}, {"name": "test", "num_bytes": 673733, "num_examples": 1018}], "download_size": 4344096, "dataset_size": 13344316}, {"config_name": "alt-my", "features": [{"name": "SNT.URLID", "dtype": "string"}, {"name": "SNT.URLID.SNTID", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "value", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20433243, "num_examples": 18088}, {"name": "validation", "num_bytes": 1111394, "num_examples": 1000}, {"name": "test", "num_bytes": 1135193, "num_examples": 1018}], "download_size": 6569025, "dataset_size": 22679830}, {"config_name": "alt-my-transliteration", "features": [{"name": "en", "dtype": "string"}, {"name": "my", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4249316, "num_examples": 84022}], "download_size": 2163951, "dataset_size": 4249316}, {"config_name": "alt-my-west-transliteration", "features": [{"name": "en", "dtype": "string"}, {"name": "my", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7411911, "num_examples": 107121}], "download_size": 2857511, "dataset_size": 7411911}, {"config_name": "alt-parallel", "features": [{"name": "SNT.URLID", "dtype": "string"}, {"name": "SNT.URLID.SNTID", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["bg", "en", "en_tok", "fil", "hi", "id", "ja", "khm", "lo", "ms", "my", "th", "vi", "zh"]}}}], "splits": [{"name": "train", "num_bytes": 68445916, "num_examples": 18088}, {"name": "validation", "num_bytes": 3710979, "num_examples": 1000}, {"name": "test", "num_bytes": 3814431, "num_examples": 1019}], "download_size": 34707907, "dataset_size": 75971326}], "configs": [{"config_name": "alt-en", "data_files": [{"split": "train", "path": "alt-en/train-*"}, {"split": "validation", "path": "alt-en/validation-*"}, {"split": "test", "path": "alt-en/test-*"}]}, {"config_name": "alt-jp", "data_files": [{"split": "train", "path": "alt-jp/train-*"}, {"split": "validation", "path": "alt-jp/validation-*"}, {"split": "test", "path": "alt-jp/test-*"}]}, {"config_name": "alt-km", "data_files": [{"split": "train", "path": "alt-km/train-*"}, {"split": "validation", "path": "alt-km/validation-*"}, {"split": "test", "path": "alt-km/test-*"}]}, {"config_name": "alt-my", "data_files": [{"split": "train", "path": "alt-my/train-*"}, {"split": "validation", "path": "alt-my/validation-*"}, {"split": "test", "path": "alt-my/test-*"}]}, {"config_name": "alt-my-transliteration", "data_files": [{"split": "train", "path": "alt-my-transliteration/train-*"}]}, {"config_name": "alt-my-west-transliteration", "data_files": [{"split": "train", "path": "alt-my-west-transliteration/train-*"}]}, {"config_name": "alt-parallel", "data_files": [{"split": "train", "path": "alt-parallel/train-*"}, {"split": "validation", "path": "alt-parallel/validation-*"}, {"split": "test", "path": "alt-parallel/test-*"}], "default": true}]}

False

2024-01-09T12:07:24.000Z

afbd92e198bbcf17f660e03076fd2938f5a4bbb2

Dataset Card for Asian Language Treebank (ALT) Dataset Summary The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as described in this Web page. The process of building ALT began… See the full description on the dataset page: https://huggingface.co/datasets/mutiyama/alt.

alt

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d64

fancyzhx/amazon_polarity

fancyzhx

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "Amazon Review Polarity", "dataset_info": {"config_name": "amazon_polarity", "features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1604364432, "num_examples": 3600000}, {"name": "test", "num_bytes": 178176193, "num_examples": 400000}], "download_size": 1145430497, "dataset_size": 1782540625}, "configs": [{"config_name": "amazon_polarity", "data_files": [{"split": "train", "path": "amazon_polarity/train-*"}, {"split": "test", "path": "amazon_polarity/test-*"}], "default": true}], "train-eval-index": [{"config": "amazon_polarity", "task": "text-classification", "task_id": "binary_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"content": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}

False

2024-01-09T12:23:33.000Z

9d9c45c18f8c3cf1b23a3c27917b60cbf28f3289

Dataset Card for Amazon Review Polarity Dataset Summary The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. Supported Tasks and Leaderboards text-classification, sentiment-classification: The dataset is mainly used for text classification: given the content and the title, predict… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/amazon_polarity.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d65

defunct-datasets/amazon_reviews_multi

defunct-datasets

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["de", "en", "es", "fr", "ja", "zh"], "license": ["other"], "multilinguality": ["monolingual", "multilingual"], "size_categories": ["100K<n<1M", "1M<n<10M"], "source_datasets": ["original"], "task_categories": ["summarization", "text-generation", "fill-mask", "text-classification"], "task_ids": ["text-scoring", "language-modeling", "masked-language-modeling", "sentiment-classification", "sentiment-scoring", "topic-classification"], "paperswithcode_id": null, "pretty_name": "The Multilingual Amazon Reviews Corpus", "dataset_info": [{"config_name": "all_languages", "features": [{"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "reviewer_id", "dtype": "string"}, {"name": "stars", "dtype": "int32"}, {"name": "review_body", "dtype": "string"}, {"name": "review_title", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "product_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 364405048, "num_examples": 1200000}, {"name": "validation", "num_bytes": 9047533, "num_examples": 30000}, {"name": "test", "num_bytes": 9099141, "num_examples": 30000}], "download_size": 640320386, "dataset_size": 382551722}, {"config_name": "de", "features": [{"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "reviewer_id", "dtype": "string"}, {"name": "stars", "dtype": "int32"}, {"name": "review_body", "dtype": "string"}, {"name": "review_title", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "product_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64485678, "num_examples": 200000}, {"name": "validation", "num_bytes": 1605727, "num_examples": 5000}, {"name": "test", "num_bytes": 1611044, "num_examples": 5000}], "download_size": 94802490, "dataset_size": 67702449}, {"config_name": "en", "features": [{"name": "review_id", "dtype": "string"}, 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"num_examples": 200000}, {"name": "validation", "num_bytes": 1303958, "num_examples": 5000}, {"name": "test", "num_bytes": 1312347, "num_examples": 5000}], "download_size": 81345461, "dataset_size": 54991963}, {"config_name": "fr", "features": [{"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "reviewer_id", "dtype": "string"}, {"name": "stars", "dtype": "int32"}, {"name": "review_body", "dtype": "string"}, {"name": "review_title", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "product_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54593565, "num_examples": 200000}, {"name": "validation", "num_bytes": 1340763, "num_examples": 5000}, {"name": "test", "num_bytes": 1364510, "num_examples": 5000}], "download_size": 85917293, "dataset_size": 57298838}, {"config_name": "ja", "features": [{"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "reviewer_id", "dtype": "string"}, {"name": "stars", "dtype": "int32"}, {"name": "review_body", "dtype": "string"}, {"name": "review_title", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "product_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82401390, "num_examples": 200000}, {"name": "validation", "num_bytes": 2035391, "num_examples": 5000}, {"name": "test", "num_bytes": 2048048, "num_examples": 5000}], "download_size": 177773783, "dataset_size": 86484829}, {"config_name": "zh", "features": [{"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "reviewer_id", "dtype": "string"}, {"name": "stars", "dtype": "int32"}, {"name": "review_body", "dtype": "string"}, {"name": "review_title", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "product_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51947668, "num_examples": 200000}, {"name": "validation", "num_bytes": 1287106, "num_examples": 5000}, {"name": "test", "num_bytes": 1302711, "num_examples": 5000}], "download_size": 114387247, "dataset_size": 54537485}], "config_names": ["all_languages", "de", "en", "es", "fr", "ja", "zh"], "viewer": false}

False

2023-11-02T14:52:21.000Z

b6115b04af1d02b3c30849bdd4c55899bff0ae63

We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long. Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.

2022-03-02T23:29:22.000Z

@inproceedings{marc_reviews, title={The Multilingual Amazon Reviews Corpus}, author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing}, year={2020} }

621ffdd236468d709f181d66

defunct-datasets/amazon_us_reviews

defunct-datasets

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"marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7197687124, "num_examples": 10319090}], "download_size": 2740337188, "dataset_size": 7197687124}, {"config_name": "Digital_Ebook_Purchase_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7302303804, "num_examples": 12520722}], "download_size": 2689739299, "dataset_size": 7302303804}]}

False

2023-11-02T14:57:03.000Z

e1bfd57e2da5dc7dc4c748eb4a4a112c71e85162

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews. Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters). Each Dataset contains the following columns: - marketplace: 2 letter country code of the marketplace where the review was written. - customer_id: Random identifier that can be used to aggregate reviews written by a single author. - review_id: The unique ID of the review. - product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id. - product_parent: Random identifier that can be used to aggregate reviews for the same product. - product_title: Title of the product. - product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts). - star_rating: The 1-5 star rating of the review. - helpful_votes: Number of helpful votes. - total_votes: Number of total votes the review received. - vine: Review was written as part of the Vine program. - verified_purchase: The review is on a verified purchase. - review_headline: The title of the review. - review_body: The review text. - review_date: The date the review was written.

2022-03-02T23:29:22.000Z

\

621ffdd236468d709f181d67

sewon/ambig_qa

sewon

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False

2024-01-09T12:27:07.000Z

e969d0132f4dd28c2939d55be34f1788c00ccfe7

Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions Dataset Summary AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions… See the full description on the dataset page: https://huggingface.co/datasets/sewon/ambig_qa.

ambigqa

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d68

nala-cub/americas_nli

nala-cub

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False

2024-01-23T09:18:27.000Z

1f3f4fa57acb59b2f352031de45ba08227d972c0

Dataset Card for AmericasNLI Dataset Summary AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a… See the full description on the dataset page: https://huggingface.co/datasets/nala-cub/americas_nli.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d69

legacy-datasets/ami

legacy-datasets

{"pretty_name": "AMI Corpus", "annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "dataset_info": [{"config_name": "microphone-single", "features": [{"name": "word_ids", "sequence": "string"}, {"name": "word_start_times", "sequence": "float32"}, {"name": "word_end_times", "sequence": "float32"}, {"name": "word_speakers", "sequence": "string"}, {"name": "segment_ids", "sequence": "string"}, {"name": "segment_start_times", "sequence": "float32"}, {"name": "segment_end_times", "sequence": "float32"}, {"name": "segment_speakers", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "channels", "sequence": "string"}, {"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 42013753, "num_examples": 134}, {"name": "validation", "num_bytes": 5110497, "num_examples": 18}, {"name": "test", "num_bytes": 4821283, "num_examples": 16}], "download_size": 11387715153, "dataset_size": 51945533}, {"config_name": "microphone-multi", "features": [{"name": "word_ids", "sequence": "string"}, {"name": "word_start_times", "sequence": "float32"}, {"name": "word_end_times", "sequence": "float32"}, {"name": "word_speakers", "sequence": "string"}, {"name": "segment_ids", "sequence": "string"}, {"name": "segment_start_times", "sequence": "float32"}, {"name": "segment_end_times", "sequence": "float32"}, {"name": "segment_speakers", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "channels", "sequence": "string"}, {"name": "file-1-1", "dtype": "string"}, {"name": "file-1-2", "dtype": "string"}, {"name": "file-1-3", "dtype": "string"}, {"name": "file-1-4", "dtype": "string"}, {"name": "file-1-5", "dtype": "string"}, {"name": "file-1-6", "dtype": "string"}, {"name": "file-1-7", "dtype": "string"}, {"name": "file-1-8", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 42126341, "num_examples": 134}, {"name": "validation", "num_bytes": 5125645, "num_examples": 18}, {"name": "test", "num_bytes": 4834751, "num_examples": 16}], "download_size": 90941506169, "dataset_size": 52086737}, {"config_name": "headset-single", "features": [{"name": "word_ids", "sequence": "string"}, {"name": "word_start_times", "sequence": "float32"}, {"name": "word_end_times", "sequence": "float32"}, {"name": "word_speakers", "sequence": "string"}, {"name": "segment_ids", "sequence": "string"}, {"name": "segment_start_times", "sequence": "float32"}, {"name": "segment_end_times", "sequence": "float32"}, {"name": "segment_speakers", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "channels", "sequence": "string"}, {"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 42491091, "num_examples": 136}, {"name": "validation", "num_bytes": 5110497, "num_examples": 18}, {"name": "test", "num_bytes": 4821283, "num_examples": 16}], "download_size": 11505070978, "dataset_size": 52422871}, {"config_name": "headset-multi", "features": [{"name": "word_ids", "sequence": "string"}, {"name": "word_start_times", "sequence": "float32"}, {"name": "word_end_times", "sequence": "float32"}, {"name": "word_speakers", "sequence": "string"}, {"name": "segment_ids", "sequence": "string"}, {"name": "segment_start_times", "sequence": "float32"}, {"name": "segment_end_times", "sequence": "float32"}, {"name": "segment_speakers", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "channels", "sequence": "string"}, {"name": "file-0", "dtype": "string"}, {"name": "file-1", "dtype": "string"}, {"name": "file-2", "dtype": "string"}, {"name": "file-3", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 42540063, "num_examples": 136}, {"name": "validation", "num_bytes": 5116989, "num_examples": 18}, {"name": "test", "num_bytes": 4827055, "num_examples": 16}], "download_size": 45951596391, "dataset_size": 52484107}]}

False

2024-01-18T11:01:45.000Z

81c6507a5cead40db13e77610fdcdf5c0f6261e4

The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers. \n

2022-03-02T23:29:22.000Z

@inproceedings{10.1007/11677482_3, author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre}, title = {The AMI Meeting Corpus: A Pre-Announcement}, year = {2005}, isbn = {3540325492}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, url = {https://doi.org/10.1007/11677482_3}, doi = {10.1007/11677482_3}, abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated.}, booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction}, pages = {28–39}, numpages = {12}, location = {Edinburgh, UK}, series = {MLMI'05} }

621ffdd236468d709f181d6a

gavinxing/amttl

gavinxing

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["zh"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["parsing"], "pretty_name": "AMTTL", "dataset_info": {"config_name": "amttl", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "B", "1": "I", "2": "E", "3": "S"}}}}], "splits": [{"name": "train", "num_bytes": 1132196, "num_examples": 3063}, {"name": "validation", "num_bytes": 324358, "num_examples": 822}, {"name": "test", "num_bytes": 328509, "num_examples": 908}], "download_size": 274351, "dataset_size": 1785063}, "configs": [{"config_name": "amttl", "data_files": [{"split": "train", "path": "amttl/train-*"}, {"split": "validation", "path": "amttl/validation-*"}, {"split": "test", "path": "amttl/test-*"}], "default": true}]}

False

2024-01-09T12:28:18.000Z

271a5aa99e75e936e334b3c52ec178f08bced629

Dataset Card for AMTTL Dataset Summary [More Information Needed] Supported Tasks and Leaderboards [More Information Needed] Languages [More Information Needed] Dataset Structure Data Instances [More Information Needed] Data Fields [More Information Needed] Data Splits [More Information Needed] Dataset Creation Curation Rationale [More Information… See the full description on the dataset page: https://huggingface.co/datasets/gavinxing/amttl.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d6b

facebook/anli

facebook

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False

2023-12-21T15:34:02.000Z

8e4813d81f46d313dac7892e1c28076917cfcdf9

Dataset Card for "anli" Dataset Summary The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits. Supported Tasks and Leaderboards More Information Needed Languages… See the full description on the dataset page: https://huggingface.co/datasets/facebook/anli.

anli

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d6c

sealuzh/app_reviews

sealuzh

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False

2024-01-09T12:30:17.000Z

9eaa95f66364367e8752b0f34c00f67aafa95d15

Dataset Card for [Dataset Name] Dataset Summary It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches) Supported… See the full description on the dataset page: https://huggingface.co/datasets/sealuzh/app_reviews.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d6d

deepmind/aqua_rat

deepmind

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "aqua-rat", "pretty_name": "Algebra Question Answering with Rationales", "dataset_info": [{"config_name": "raw", "features": [{"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "correct", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 42333059, "num_examples": 97467}, {"name": "test", "num_bytes": 116759, "num_examples": 254}, {"name": "validation", "num_bytes": 118616, "num_examples": 254}], "download_size": 25568676, "dataset_size": 42568434}, {"config_name": "tokenized", "features": [{"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "correct", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46493643, "num_examples": 97467}, {"name": "test", "num_bytes": 126263, "num_examples": 254}, {"name": "validation", "num_bytes": 128853, "num_examples": 254}], "download_size": 26429873, "dataset_size": 46748759}], "configs": [{"config_name": "raw", "data_files": [{"split": "train", "path": "raw/train-*"}, {"split": "test", "path": "raw/test-*"}, {"split": "validation", "path": "raw/validation-*"}], "default": true}, {"config_name": "tokenized", "data_files": [{"split": "train", "path": "tokenized/train-*"}, {"split": "test", "path": "tokenized/test-*"}, {"split": "validation", "path": "tokenized/validation-*"}]}]}

False

2024-01-09T12:33:06.000Z

33301c6a050c96af81f63cad5562cb5363e88971

Dataset Card for AQUA-RAT Dataset Summary A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question. Supported Tasks and Leaderboards Languages en Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/aqua_rat.

aqua-rat

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d6e

google-research-datasets/aquamuse

google-research-datasets

{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|natural_questions", "extended|other-Common-Crawl", "original"], "task_categories": ["other", "question-answering", "text2text-generation"], "task_ids": ["abstractive-qa", "extractive-qa"], "paperswithcode_id": "aquamuse", "pretty_name": "AQuaMuSe", "tags": ["query-based-multi-document-summarization"], "dataset_info": [{"config_name": "abstractive", "features": [{"name": "query", "dtype": "string"}, {"name": "input_urls", "sequence": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6434893, "num_examples": 6253}, {"name": "test", "num_bytes": 843165, "num_examples": 811}, {"name": "validation", "num_bytes": 689093, "num_examples": 661}], "download_size": 5167854, "dataset_size": 7967151}, {"config_name": "extractive", "features": [{"name": "query", "dtype": "string"}, {"name": "input_urls", "sequence": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6434893, "num_examples": 6253}, {"name": "test", "num_bytes": 843165, "num_examples": 811}, {"name": "validation", "num_bytes": 689093, "num_examples": 661}], "download_size": 5162151, "dataset_size": 7967151}], "configs": [{"config_name": "abstractive", "data_files": [{"split": "train", "path": "abstractive/train-*"}, {"split": "test", "path": "abstractive/test-*"}, {"split": "validation", "path": "abstractive/validation-*"}]}, {"config_name": "extractive", "data_files": [{"split": "train", "path": "extractive/train-*"}, {"split": "test", "path": "extractive/test-*"}, {"split": "validation", "path": "extractive/validation-*"}]}]}

False

2024-01-09T12:36:37.000Z

84df3ebd8bfe31e2875d242300161ea64ac2b06b

Dataset Card for AQuaMuSe Dataset Summary AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl) This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in AQuaMuSe… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/aquamuse.

aquamuse

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d6f

bigIR/ar_cov19

bigIR

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["ar"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "paperswithcode_id": "arcov-19", "pretty_name": "ArCOV19", "tags": ["data-mining"], "dataset_info": {"config_name": "ar_cov19", "features": [{"name": "tweetID", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72223634, "num_examples": 3140158}], "download_size": 23678407, "dataset_size": 72223634}}

False

2023-09-19T06:52:17.000Z

447b2a5a20c9e8ffaee0f14b31697be7b0dec403

ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others

arcov-19

2022-03-02T23:29:22.000Z

@article{haouari2020arcov19, title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks}, author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed}, journal={arXiv preprint arXiv:2004.05861}, year={2020}

621ffdd236468d709f181d70

hadyelsahar/ar_res_reviews

hadyelsahar

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "ArRestReviews", "dataset_info": {"features": [{"name": "polarity", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}, {"name": "text", "dtype": "string"}, {"name": "restaurant_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3617085, "num_examples": 8364}], "download_size": 1887029, "dataset_size": 3617085}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-01-09T12:38:13.000Z

d51bf2435d030e0041344f576c5e8d7154828977

Dataset Card for ArRestReviews Dataset Summary Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis Supported Tasks and Leaderboards [More Information Needed] Languages The dataset is based on Arabic. Dataset Structure Data Instances A typical data point comprises of the following: "polarity": which is a string value of either 0 or 1 indicating the sentiment around the review… See the full description on the dataset page: https://huggingface.co/datasets/hadyelsahar/ar_res_reviews.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d71

iabufarha/ar_sarcasm

iabufarha

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["ar"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-semeval_2017", "extended|other-astd"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "ArSarcasm", "tags": ["sarcasm-detection"], "dataset_info": {"features": [{"name": "dialect", "dtype": {"class_label": {"names": {"0": "egypt", "1": "gulf", "2": "levant", "3": "magreb", "4": "msa"}}}}, {"name": "sarcasm", "dtype": {"class_label": {"names": {"0": "non-sarcastic", "1": "sarcastic"}}}}, {"name": "sentiment", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}, {"name": "original_sentiment", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}, {"name": "tweet", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1829159, "num_examples": 8437}, {"name": "test", "num_bytes": 458210, "num_examples": 2110}], "download_size": 1180619, "dataset_size": 2287369}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-01-09T12:42:05.000Z

557bf94ac6177cc442f42d0b09b6e4b76e8f47c9

Dataset Card for ArSarcasm Dataset Summary ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. For more details, please check the paper From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/iabufarha/ar_sarcasm.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d72

abuelkhair-corpus/arabic_billion_words

abuelkhair-corpus

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": null, "pretty_name": "Arabic Billion Words", "dataset_info": [{"config_name": "Alittihad", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1601790302, "num_examples": 349342}], "download_size": 348259999, "dataset_size": 1601790302}, {"config_name": "Almasryalyoum", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1056197870, "num_examples": 291723}], "download_size": 242604438, "dataset_size": 1056197870}, {"config_name": "Almustaqbal", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1545659336, "num_examples": 446873}], "download_size": 350826797, "dataset_size": 1545659336}, {"config_name": "Alqabas", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2631729746, "num_examples": 817274}], "download_size": 595274646, "dataset_size": 2631729746}, {"config_name": "Echoroukonline", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 464386206, "num_examples": 139732}], "download_size": 108184378, "dataset_size": 464386206}, {"config_name": "Ryiadh", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3101294859, "num_examples": 858188}], "download_size": 691264971, "dataset_size": 3101294859}, {"config_name": "Sabanews", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 198019614, "num_examples": 92149}], "download_size": 38214558, "dataset_size": 198019614}, {"config_name": "SaudiYoum", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2723291416, "num_examples": 888068}], "download_size": 605537923, "dataset_size": 2723291416}, {"config_name": "Techreen", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1103458209, "num_examples": 314597}], "download_size": 252976781, "dataset_size": 1103458209}, {"config_name": "Youm7", "features": [{"name": "url", "dtype": "string"}, {"name": "head_line", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3004689464, "num_examples": 1172136}], "download_size": 617708074, "dataset_size": 3004689464}], "config_names": ["Alittihad", "Almasryalyoum", "Almustaqbal", "Alqabas", "Echoroukonline", "Ryiadh", "Sabanews", "SaudiYoum", "Techreen", "Youm7"]}

False

2024-01-18T11:01:47.000Z

c948146dc6e63d56b3469be209ea7e35a4ed5579

Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there are about three million unique words. The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML.

2022-03-02T23:29:22.000Z

@article{el20161, title={1.5 billion words arabic corpus}, author={El-Khair, Ibrahim Abu}, journal={arXiv preprint arXiv:1611.04033}, year={2016} }

621ffdd236468d709f181d73

QCRI/arabic_pos_dialect

QCRI

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ar"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": ["n<1K"], "source_datasets": ["extended"], "task_categories": ["token-classification"], "task_ids": ["part-of-speech"], "pretty_name": "Arabic POS Dialect", "dataset_info": [{"config_name": "egy", "features": [{"name": "fold", "dtype": "int32"}, {"name": "subfold", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "segments", "sequence": "string"}, {"name": "pos_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 269629, "num_examples": 350}], "download_size": 89684, "dataset_size": 269629}, {"config_name": "glf", "features": [{"name": "fold", "dtype": "int32"}, {"name": "subfold", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "segments", "sequence": "string"}, {"name": "pos_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 239883, "num_examples": 350}], "download_size": 89178, "dataset_size": 239883}, {"config_name": "lev", "features": [{"name": "fold", "dtype": "int32"}, {"name": "subfold", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "segments", "sequence": "string"}, {"name": "pos_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 263102, "num_examples": 350}], "download_size": 97055, "dataset_size": 263102}, {"config_name": "mgr", "features": [{"name": "fold", "dtype": "int32"}, {"name": "subfold", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "segments", "sequence": "string"}, {"name": "pos_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 245717, "num_examples": 350}], "download_size": 90503, "dataset_size": 245717}], "configs": [{"config_name": "egy", "data_files": [{"split": "train", "path": "egy/train-*"}]}, {"config_name": "glf", "data_files": [{"split": "train", "path": "glf/train-*"}]}, {"config_name": "lev", "data_files": [{"split": "train", "path": "lev/train-*"}]}, {"config_name": "mgr", "data_files": [{"split": "train", "path": "mgr/train-*"}]}]}

False

2024-01-09T12:43:34.000Z

897e2cecae33a242f5003922d3f1564f0c55c3dd

Dataset Card for Arabic POS Dialect Dataset Summary This dataset was created to support part of speech (POS) tagging in dialects of Arabic. It contains sets of 350 manually segmented and POS tagged tweets for each of four dialects: Egyptian, Levantine, Gulf, and Maghrebi. Supported Tasks and Leaderboards The dataset can be used to train a model for Arabic token segmentation and part of speech tagging in Arabic dialects. Success on this task is… See the full description on the dataset page: https://huggingface.co/datasets/QCRI/arabic_pos_dialect.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d74

halabi2016/arabic_speech_corpus

halabi2016

{"pretty_name": "Arabic Speech Corpus", "annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["ar"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "paperswithcode_id": "arabic-speech-corpus", "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "train-eval-index": [{"config": "clean", "task": "automatic-speech-recognition", "task_id": "speech_recognition", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"file": "path", "text": "text"}, "metrics": [{"type": "wer", "name": "WER"}, {"type": "cer", "name": "CER"}]}], "dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "phonetic", "dtype": "string"}, {"name": "orthographic", "dtype": "string"}], "config_name": "clean", "splits": [{"name": "train", "num_bytes": 1002365, "num_examples": 1813}, {"name": "test", "num_bytes": 65784, "num_examples": 100}], "download_size": 1192302846, "dataset_size": 1068149}}

False

2024-08-14T14:21:32.000Z

a66b1d6ba1c5cc79570bffcd4d83b9ce566db2b4

This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

arabic-speech-corpus

2022-03-02T23:29:22.000Z

@phdthesis{halabi2016modern, title={Modern standard Arabic phonetics for speech synthesis}, author={Halabi, Nawar}, year={2016}, school={University of Southampton} }

621ffdd236468d709f181d75

hsseinmz/arcd

hsseinmz

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ar"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "arcd", "pretty_name": "ARCD", "language_bcp47": ["ar-SA"], "dataset_info": {"config_name": "plain_text", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 811036, "num_examples": 693}, {"name": "validation", "num_bytes": 885620, "num_examples": 702}], "download_size": 365858, "dataset_size": 1696656}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train", "path": "plain_text/train-*"}, {"split": "validation", "path": "plain_text/validation-*"}], "default": true}]}

False

2024-01-09T12:44:24.000Z

cc6906b6eda547e4ffc63b8d88ccca7e0515187a

Dataset Card for "arcd" Dataset Summary Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances plain_text Size of downloaded dataset files: 1.94 MB Size of the generated dataset: 1.70 MB Total amount… See the full description on the dataset page: https://huggingface.co/datasets/hsseinmz/arcd.

arcd

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d76

ramybaly/arsentd_lev

ramybaly

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["apc", "ajp"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification", "topic-classification"], "paperswithcode_id": "arsentd-lev", "pretty_name": "ArSenTD-LEV", "dataset_info": {"features": [{"name": "Tweet", "dtype": "string"}, {"name": "Country", "dtype": {"class_label": {"names": {"0": "jordan", "1": "lebanon", "2": "syria", "3": "palestine"}}}}, {"name": "Topic", "dtype": "string"}, {"name": "Sentiment", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive", "3": "very_negative", "4": "very_positive"}}}}, {"name": "Sentiment_Expression", "dtype": {"class_label": {"names": {"0": "explicit", "1": "implicit", "2": "none"}}}}, {"name": "Sentiment_Target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1233980, "num_examples": 4000}], "download_size": 392666, "dataset_size": 1233980}}

False

2024-01-18T11:01:50.000Z

ce4d032917566e486a90330392bc7853280e7249

The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria.

arsentd-lev

2022-03-02T23:29:22.000Z

@article{ArSenTDLev2018, title={ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets}, author={Baly, Ramy, and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Bashir Shaban, Khaled}, journal={OSACT3}, pages={}, year={2018}}

621ffdd236468d709f181d77

allenai/art

allenai

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "text-classification"], "task_ids": ["natural-language-inference"], "paperswithcode_id": "art-dataset", "pretty_name": "Abductive Reasoning in narrative Text", "tags": ["abductive-natural-language-inference"], "dataset_info": {"config_name": "anli", "features": [{"name": "observation_1", "dtype": "string"}, {"name": "observation_2", "dtype": "string"}, {"name": "hypothesis_1", "dtype": "string"}, {"name": "hypothesis_2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2"}}}}], "splits": [{"name": "validation", "num_bytes": 311146, "num_examples": 1532}, {"name": "train", "num_bytes": 33918790, "num_examples": 169654}], "download_size": 9191805, "dataset_size": 34229936}, "configs": [{"config_name": "anli", "data_files": [{"split": "validation", "path": "anli/validation-*"}, {"split": "train", "path": "anli/train-*"}], "default": true}]}

False

2024-01-09T12:45:10.000Z

df6c96ba77462a86dc1cf530c12a69da47ea42e7

Dataset Card for "art" Dataset Summary ART consists of over 20k commonsense narrative contexts and 200k explanations. The Abductive Natural Language Inference Dataset from AI2. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances anli Size of downloaded dataset files: 5.12 MB Size of the generated dataset: 34.36 MB… See the full description on the dataset page: https://huggingface.co/datasets/allenai/art.

art-dataset

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d78

arxiv-community/arxiv_dataset

arxiv-community

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation", "summarization", "text-retrieval"], "task_ids": ["document-retrieval", "entity-linking-retrieval", "explanation-generation", "fact-checking-retrieval", "text-simplification"], "paperswithcode_id": null, "pretty_name": "arXiv Dataset", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "submitter", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "journal-ref", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "report-no", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "update_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3056873071, "num_examples": 2349354}], "download_size": 0, "dataset_size": 3056873071}}

False

2024-01-18T11:01:52.000Z

c70944cb158dcdab8a5403b1fa20f28119f701a6

A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.

2022-03-02T23:29:22.000Z

@misc{clement2019arxiv, title={On the Use of ArXiv as a Dataset}, author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi}, year={2019}, eprint={1905.00075}, archivePrefix={arXiv}, primaryClass={cs.IR} }

621ffdd236468d709f181d79

tuanphong/ascent_kb

tuanphong

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "paperswithcode_id": "ascentkb", "pretty_name": "Ascent KB", "tags": ["knowledge-base"], "dataset_info": [{"config_name": "canonical", "features": [{"name": "arg1", "dtype": "string"}, {"name": "rel", "dtype": "string"}, {"name": "arg2", "dtype": "string"}, {"name": "support", "dtype": "int64"}, {"name": "facets", "list": [{"name": "value", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "support", "dtype": "int64"}]}, {"name": "source_sentences", "list": [{"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2976665740, "num_examples": 8904060}], "download_size": 898478552, "dataset_size": 2976665740}, {"config_name": "open", "features": [{"name": "subject", "dtype": "string"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "string"}, {"name": "support", "dtype": "int64"}, {"name": "facets", "list": [{"name": "value", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "support", "dtype": "int64"}]}, {"name": "source_sentences", "list": [{"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2882646222, "num_examples": 8904060}], "download_size": 900156754, "dataset_size": 2882646222}], "configs": [{"config_name": "canonical", "data_files": [{"split": "train", "path": "canonical/train-*"}], "default": true}, {"config_name": "open", "data_files": [{"split": "train", "path": "open/train-*"}]}]}

False

2024-01-09T14:44:26.000Z

9157196d77890cf20b57075353813b34dba3426e

Dataset Card for Ascent KB Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the Max Planck Institute for Informatics. The focus of this dataset is on everyday concepts such as elephant, car, laptop, etc. The current version of Ascent KB (v1.0.0) is approximately 19 times larger than ConceptNet (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is… See the full description on the dataset page: https://huggingface.co/datasets/tuanphong/ascent_kb.

ascentkb

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d7a

achrafothman/aslg_pc12

achrafothman

{"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["ase", "en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "aslg-pc12", "pretty_name": "English-ASL Gloss Parallel Corpus 2012", "dataset_info": {"features": [{"name": "gloss", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13475111, "num_examples": 87710}], "download_size": 7583458, "dataset_size": 13475111}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-01-09T12:45:54.000Z

cb7cd272db8fcd4004ee04ddf50e194c15ea24d6

Dataset Card for "aslg_pc12" Dataset Summary Synthetic English-ASL Gloss Parallel Corpus 2012 Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances default Size of downloaded dataset files: 12.77 MB Size of the generated dataset: 13.50 MB Total amount of disk used: 26.27 MB An example of 'train' looks as follows. {… See the full description on the dataset page: https://huggingface.co/datasets/achrafothman/aslg_pc12.

aslg-pc12

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d7b

AmazonScience/asnq

AmazonScience

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|natural_questions"], "task_categories": ["multiple-choice"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "asnq", "pretty_name": "Answer Sentence Natural Questions (ASNQ)", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "sentence_in_long_answer", "dtype": "bool"}, {"name": "short_answer_in_sentence", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3656865072, "num_examples": 20377568}, {"name": "validation", "num_bytes": 168004403, "num_examples": 930062}], "download_size": 2496835395, "dataset_size": 3824869475}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]}

False

2024-01-09T15:33:53.000Z

32291fc9663b9ee88abb97114e52501bdd58a129

Dataset Card for "asnq" Dataset Summary ASNQ is a dataset for answer sentence selection derived from Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). Each example contains a question, candidate sentence, label indicating whether or not the sentence answers the question, and two additional features -- sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the candidate sentence is contained in the long_answer and if the… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/asnq.

asnq

2022-03-02T23:29:22.000Z

621ffdd236468d709f181d7c

facebook/asset

facebook

{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original", "extended|other-turkcorpus"], "task_categories": ["text-classification", "text2text-generation"], "task_ids": ["text-simplification"], "paperswithcode_id": "asset", "pretty_name": "ASSET", "config_names": ["ratings", "simplification"], "tags": ["simplification-evaluation"], "dataset_info": [{"config_name": "ratings", "features": [{"name": "original", "dtype": "string"}, {"name": "simplification", "dtype": "string"}, {"name": "original_sentence_id", "dtype": "int32"}, {"name": "aspect", "dtype": {"class_label": {"names": {"0": "meaning", "1": "fluency", "2": "simplicity"}}}}, {"name": "worker_id", "dtype": "int32"}, {"name": "rating", "dtype": "int32"}], "splits": [{"name": "full", "num_bytes": 1036845, "num_examples": 4500}], "download_size": 44642, "dataset_size": 1036845}, {"config_name": "simplification", "features": [{"name": "original", "dtype": "string"}, {"name": "simplifications", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2303484, "num_examples": 2000}, {"name": "test", "num_bytes": 411019, "num_examples": 359}], "download_size": 1055163, "dataset_size": 2714503}], "configs": [{"config_name": "ratings", "data_files": [{"split": "full", "path": "ratings/full-*"}]}, {"config_name": "simplification", "data_files": [{"split": "validation", "path": "simplification/validation-*"}, {"split": "test", "path": "simplification/test-*"}], "default": true}]}

False

2023-12-21T15:41:23.000Z

c7f2fa4bae55ae656091805d4416c1374582bb4e

Dataset Card for ASSET Dataset Summary ASSET (Alva-Manchego et al., 2020) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from TurkCorpus (Xu et al., 2016) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence splitting in HSplit), the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/asset.

asset

2022-03-02T23:29:22.000Z