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14 categories" }, { "hf_id": "Yelp/yelp_review_full", "hf_subsample": null, "name": "yelp_review_full", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Yelp review 5-star rating classification" }, { "hf_id": "fancyzhx/amazon_polarity", "hf_subsample": null, "name": "amazon_polarity", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Amazon product review polarity; positive/negative" }, { "hf_id": "cornell-movie-review-data/rotten_tomatoes", "hf_subsample": null, "name": "rotten_tomatoes", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Rotten Tomatoes movie review sentiment; positive/negative" }, { "hf_id": "stanfordnlp/snli", "hf_subsample": null, "name": "snli", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Stanford NLI; premise/hypothesis pairs -> entailment/neutral/contradiction" }, { "hf_id": "nyu-mll/multi_nli", "hf_subsample": null, "name": "multi_nli", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Multi-Genre NLI; 10 diverse genres, 3-way NLI" }, { "hf_id": "google-research-datasets/paws", "hf_subsample": "labeled_final", "name": "paws", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Paraphrase Adversaries from Word Scrambling; challenging paraphrase detection" }, { "hf_id": "facebook/anli", "hf_subsample": null, "name": "anli", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Adversarial NLI; iteratively adversarial NLI benchmark" }, { "hf_id": "CogComp/trec", "hf_subsample": null, "name": "trec", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "TREC question type classification; 6 coarse classes" }, { "hf_id": "stanfordnlp/sst2", "hf_subsample": null, "name": "sst2_hf", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Stanford Sentiment Treebank 2-class (HF canonical version)" }, { "hf_id": "google/boolq", "hf_subsample": null, "name": "boolq_standalone", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "BoolQ standalone; naturally occurring yes/no questions with passage" }, { "hf_id": "facebook/belebele", "hf_subsample": "eng_Latn", "name": "belebele", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Belebele multilingual reading comprehension; multiple-choice" }, { "hf_id": "PKU-Alignment/BeaverTails", "hf_subsample": null, "name": "beavertails", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Safety classification of model responses; harmful vs safe" }, { "hf_id": "Rowan/hellaswag", "hf_subsample": null, "name": "hellaswag", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "HellaSwag; commonsense NLI, pick correct continuation from 4 choices" }, { "hf_id": "allenai/winogrande", "hf_subsample": "winogrande_xl", "name": "winogrande", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "WinoGrande; large-scale Winograd schema challenge" }, { "hf_id": "ybisk/piqa", "hf_subsample": null, "name": "piqa", "task": "text_classification", "input_features": [ "text", "text", "text" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Physical Intuition QA; goal + two solutions -> pick correct one" }, { "hf_id": "tau/commonsense_qa", "hf_subsample": null, "name": "commonsense_qa", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "CommonsenseQA; 5-choice commonsense reasoning questions" }, { "hf_id": "allenai/ai2_arc", "hf_subsample": "ARC-Easy", "name": "arc_easy", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "AI2 Reasoning Challenge Easy subset; multiple-choice science questions" }, { "hf_id": "allenai/ai2_arc", "hf_subsample": "ARC-Challenge", "name": "arc_challenge", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "AI2 Reasoning Challenge hard subset; grade-school science questions" }, { "hf_id": "allenai/openbookqa", "hf_subsample": "main", "name": "openbookqa", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "OpenBookQA; elementary science questions with open-book style" }, { "hf_id": "allenai/sciq", "hf_subsample": null, "name": "sciq", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "SciQ; crowdsourced science exam questions with 4 choices" }, { "hf_id": "rajpurkar/squad", "hf_subsample": null, "name": "squad", "task": "extractive_qa", "input_features": [ "text", "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "Stanford QA Dataset v1.1; extractive reading comprehension" }, { "hf_id": "rajpurkar/squad_v2", "hf_subsample": null, "name": "squad_v2", "task": "extractive_qa", "input_features": [ "text", "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "SQuAD v2; includes unanswerable questions" }, { "hf_id": "mandarjoshi/trivia_qa", "hf_subsample": "rc", "name": "trivia_qa", "task": "text_generation", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "TriviaQA; reading comprehension with trivia question-answer pairs" }, { "hf_id": "google-research-datasets/natural_questions", "hf_subsample": null, "name": "natural_questions", "task": "extractive_qa", "input_features": [ "text", "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "Google Natural Questions; real user queries from Google Search" }, { "hf_id": "hotpotqa/hotpot_qa", "hf_subsample": "distractor", "name": "hotpot_qa", "task": "extractive_qa", "input_features": [ "text", "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "HotpotQA; multi-hop reasoning QA with supporting facts" }, { "hf_id": "qiaojin/PubMedQA", "hf_subsample": "pqa_labeled", "name": "pubmed_qa", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "PubMedQA; biomedical research QA; yes/no/maybe" }, { "hf_id": "openlifescienceai/medmcqa", "hf_subsample": null, "name": "medmcqa", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Medical entrance exam QA; 4-choice medical questions" }, { "hf_id": "truthfulqa/truthful_qa", "hf_subsample": "multiple_choice", "name": "truthful_qa", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "TruthfulQA multiple choice; tests for truthful model answers" }, { "hf_id": "cais/mmlu", "hf_subsample": "all", "name": "mmlu", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "MMLU; 57-subject multiple choice; question -> A/B/C/D" }, { "hf_id": "TIGER-Lab/MMLU-Pro", "hf_subsample": null, "name": "mmlu_pro", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": true, "notes": "MMLU-Pro; harder 10-choice version of MMLU" }, { "hf_id": "eriktks/conll2003", "hf_subsample": null, "name": "conll2003", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "CoNLL-2003 NER; English newspaper NER with PER/ORG/LOC/MISC" }, { "hf_id": "unimelb-nlp/wikiann", "hf_subsample": "en", "name": "wikiann_en", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "WikiANN English NER; cross-lingual NER from Wikipedia" }, { "hf_id": "leondz/wnut_17", "hf_subsample": null, "name": "wnut17", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "WNUT-17 emerging entities NER; Twitter/social media domain" }, { "hf_id": "ncbi/ncbi_disease", "hf_subsample": null, "name": "ncbi_disease", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "NCBI Disease NER; biomedical disease entity recognition" }, { "hf_id": "Babelscape/wikineural", "hf_subsample": "en", "name": "wikineural", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "WikiNEuRal multilingual NER; automatically annotated Wikipedia" }, { "hf_id": "Babelscape/multinerd", "hf_subsample": null, "name": "multinerd", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "MultiNERD; 10 languages, 15 NE types including fine-grained" }, { "hf_id": "DFKI-SLT/few-nerd", "hf_subsample": "supervised", "name": "few_nerd", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "FewNERD; 66 fine-grained entity types NER dataset" }, { "hf_id": "masakhane/masakhaner2", "hf_subsample": "eng", "name": "masakhaner2", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "MasakhaNER 2.0; African languages NER" }, { "hf_id": "universal-dependencies/universal_dependencies", "hf_subsample": "en_ewt", "name": "universal_dependencies", "task": "sequence_labeling", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "Universal Dependencies POS tagging; English Web Treebank" }, { "hf_id": "ai4privacy/pii-masking-300k", "hf_subsample": null, "name": "pii_masking", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "PII Masking; 300k examples for personally identifiable information NER" }, { "hf_id": "abisee/cnn_dailymail", "hf_subsample": "3.0.0", "name": "cnn_dailymail", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "CNN/DailyMail news summarization; article -> highlights" }, { "hf_id": "EdinburghNLP/xsum", "hf_subsample": null, "name": "xsum", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "XSum; extreme summarization from BBC articles; single-sentence summaries" }, { "hf_id": "knkarthick/dialogsum", "hf_subsample": null, "name": "dialogsum", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "DialogSum; dialogue summarization dataset" }, { "hf_id": "knkarthick/samsum", "hf_subsample": null, "name": "samsum", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "SAMSum; messenger-style conversation summarization" }, { "hf_id": "FiscalNote/billsum", "hf_subsample": null, "name": "billsum", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "BillSum; US congressional and California bill summarization" }, { "hf_id": "ccdv/arxiv-summarization", "hf_subsample": null, "name": "arxiv_summarization", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "ArXiv paper abstract summarization; long scientific documents" }, { "hf_id": "ccdv/pubmed-summarization", "hf_subsample": null, "name": "pubmed_summarization", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "PubMed biomedical article summarization" }, { "hf_id": "alexfabbri/multi_news", "hf_subsample": null, "name": "multi_news", "task": "summarization", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "Multi-News; 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visual-linguistic compositionality; image-text matching" }, { "hf_id": "amirveyseh/acronym_identification", "hf_subsample": null, "name": "acronym_identification", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "Acronym Identification; scientific abbreviation detection and expansion" }, { "hf_id": "unimelb-nlp/wikiann", "hf_subsample": "de", "name": "wikiann_de", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "WikiANN German NER; cross-lingual NER" }, { "hf_id": "unimelb-nlp/wikiann", "hf_subsample": "zh", "name": "wikiann_zh", "task": "ner", "input_features": [ "sequence" ], "output_features": [ "sequence" ], "needs_custom_loader": false, "notes": "WikiANN Chinese NER; cross-lingual NER" }, { "hf_id": "datamol-io/polaris-admet-benchmark", "hf_subsample": null, "name": "admet_benchmark", "task": "regression", "input_features": [ "text" ], "output_features": [ "number" ], "needs_custom_loader": false, "notes": "ADMET molecular property regression benchmark from SMILES" }, { "hf_id": "mstz/obesity", "hf_subsample": null, "name": "obesity_prediction", "task": "tabular_classification", "input_features": [ "number", "category" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Obesity level prediction from lifestyle and biometric features" }, { "hf_id": "mstz/dry_bean", "hf_subsample": null, "name": "dry_bean", "task": "tabular_classification", "input_features": [ "number" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Dry Bean Dataset; 7-class legume variety classification from shape features" }, { "hf_id": "mstz/rice", "hf_subsample": null, "name": "rice_classification", "task": "tabular_classification", "input_features": [ "number" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Rice image classification (morphological features); 2 varieties" }, { "hf_id": "mstz/water_quality", "hf_subsample": null, "name": "water_quality", "task": "tabular_classification", "input_features": [ "number" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Water Quality; potability prediction from chemical properties" }, { "hf_id": "mstz/room_occupancy", "hf_subsample": null, "name": "room_occupancy", "task": "tabular_classification", "input_features": [ "number" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Room Occupancy; sensor data -> occupancy binary classification" }, { "hf_id": "mstz/online_shoppers", "hf_subsample": null, "name": "online_shoppers", "task": "tabular_classification", "input_features": [ "number", "category" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Online Shoppers Intention; predict purchase intent from clickstream" }, { "hf_id": "mstz/airline_passenger_satisfaction", "hf_subsample": null, "name": "airline_satisfaction", "task": "tabular_classification", "input_features": [ "number", "category" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Airline passenger satisfaction binary classification" }, { "hf_id": "mstz/spaceship_titanic", "hf_subsample": null, "name": "spaceship_titanic", "task": "tabular_classification", "input_features": [ "number", "category" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Spaceship Titanic; Kaggle competition tabular binary classification" }, { "hf_id": "mstz/stroke_prediction", "hf_subsample": null, "name": "stroke_prediction", "task": "tabular_classification", "input_features": [ "number", "category" ], "output_features": [ "binary" ], "needs_custom_loader": false, "notes": "Stroke prediction from health and demographic features" }, { "hf_id": "mstz/student_performance", "hf_subsample": null, "name": "student_performance", "task": "regression", "input_features": [ "number", "category" ], "output_features": [ "number" ], "needs_custom_loader": false, "notes": "Student performance regression; predict final grade from demographics" }, { "hf_id": "mstz/used_cars", "hf_subsample": null, "name": "used_cars_price", "task": "regression", "input_features": [ "number", "category" ], "output_features": [ "number" ], "needs_custom_loader": false, "notes": "Used car price regression from vehicle features" }, { "hf_id": "facebook/belebele", "hf_subsample": "fra_Latn", "name": "belebele_fr", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "Belebele French; multilingual reading comprehension" }, { "hf_id": "ibm/duorc", "hf_subsample": "SelfRC", "name": "duorc", "task": "extractive_qa", "input_features": [ "text", "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "DuoRC; reading comprehension from Wikipedia and IMDb movie plots" }, { "hf_id": "nq_open/nq_open", "hf_subsample": null, "name": "nq_open", "task": "text_generation", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "NQ-Open; open-domain version of Natural Questions" }, { "hf_id": "web_questions/web_questions", "hf_subsample": null, "name": "web_questions", "task": "text_generation", "input_features": [ "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "WebQuestions; Freebase-based open-domain QA" }, { "hf_id": "cardiffnlp/tweet_eval", "hf_subsample": "stance_abortion", "name": "tweeteval_stance", "task": "text_classification", "input_features": [ "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "TweetEval stance detection; against/in favor/neutral" }, { "hf_id": "ibm/claim_stance", "hf_subsample": null, "name": "claim_stance", "task": "text_classification", "input_features": [ "text", "text" ], "output_features": [ "category" ], "needs_custom_loader": false, "notes": "IBM Claim Stance Dataset; claim stance detection for debate topics" }, { "hf_id": "ibm-granite/ChartNet", "hf_subsample": null, "name": "chartnet", "task": "visual_question_answering", "input_features": [ "image", "text" ], "output_features": [ "text" ], "needs_custom_loader": false, "notes": "ChartNet; chart and figure visual QA" } ]