diff --git "a/huggingface_dataset/dataset_cards_with_paper_refs.csv" "b/huggingface_dataset/dataset_cards_with_paper_refs.csv"
new file mode 100644--- /dev/null
+++ "b/huggingface_dataset/dataset_cards_with_paper_refs.csv"
@@ -0,0 +1,2355 @@
+dataset_name,author,paper_refs
+super_glue,huggingface,[{'title': 'BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions'}]
+glue,huggingface,[{'title': 'Neural Network Acceptability Judgments'}]
+bigscience/P3,bigscience,['arxiv.org/abs/2110.08207']
+hendrycks_test,huggingface,"['arxiv.org/abs/2009.03300', {'title': 'Measuring Massive Multitask Language Understanding'}]"
+wikitext,huggingface,['arxiv.org/abs/1609.07843']
+blimp,huggingface,[{'title': 'BLiMP: A Benchmark of Linguistic Minimal Pairs for English'}]
+squad,huggingface,['bibtex']
+wino_bias,huggingface,"['arxiv.org/abs/1804.06876', 'bibtex']"
+red_caps,huggingface,['arxiv.org/abs/2111.11431']
+allenai/nllb,allenai,['arxiv.org/abs/2205.12654']
+trec,huggingface,['bibtex']
+adversarial_qa,huggingface,"['arxiv.org/abs/2002.00293', 'doi.org/10.1162/tacl_a_00338', 'bibtex']"
+anli,huggingface,['arxiv.org/abs/1910.14599']
+cnn_dailymail,huggingface,['bibtex']
+duorc,huggingface,"['arxiv.org/abs/1804.07927', 'bibtex']"
+Helsinki-NLP/tatoeba_mt,Helsinki-NLP,['bibtex']
+xsum,huggingface,"['arxiv.org/abs/1808.08745', {'title': ""Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization""}]"
+Anthropic/hh-rlhf,Anthropic,['arxiv.org/abs/2204.05862']
+sst2,huggingface,['bibtex']
+paws,huggingface,['arxiv.org/abs/1904.01130']
+race,huggingface,"['arxiv.org/abs/1704.04683', 'bibtex']"
+kilt_tasks,huggingface,"['arxiv.org/abs/2009.02252', 'bibtex']"
+ag_news,huggingface,[{'title': 'Character-level Convolutional Networks for Text Classification'}]
+cos_e,huggingface,"['arxiv.org/abs/1906.02361', 'bibtex']"
+ai2_arc,huggingface,['bibtex']
+cosmos_qa,huggingface,"['arxiv.org/abs/1909.00277', 'bibtex']"
+quail,huggingface,"['doi.org/10.1609/aaai.v34i05.6398', 'bibtex']"
+wikiann,huggingface,"['arxiv.org/abs/1902.00193', 'bibtex']"
+amazon_polarity,huggingface,['arxiv.org/abs/1509.01626']
+trivia_qa,huggingface,"['arxiv.org/abs/1705.03551', 'bibtex']"
+piqa,huggingface,"['arxiv.org/abs/1911.11641', 'bibtex']"
+yelp_review_full,huggingface,['arxiv.org/abs/1509.01626']
+ropes,huggingface,"['arxiv.org/abs/1908.05852', {'title': 'Reasoning Over Paragraph Effects in Situations'}]"
+wikisql,huggingface,"['arxiv.org/abs/1709.00103', 'bibtex']"
+wiki_qa,huggingface,['bibtex']
+wiki_hop,huggingface,['arxiv.org/abs/1710.06481']
+quoref,huggingface,['bibtex']
+common_gen,huggingface,"['arxiv.org/abs/1911.03705', 'bibtex']"
+squad_v2,huggingface,['bibtex']
+tweet_eval,huggingface,[{'title': '{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification'}]
+samsum,huggingface,"['arxiv.org/abs/1911.12237', 'bibtex']"
+gigaword,huggingface,"['arxiv.org/abs/1509.00685', 'doi.org/10.18653/v1/D15-1044},', {'title': 'English gigaword'}]"
+sciq,huggingface,[{'title': 'Crowdsourcing Multiple Choice Science Questions'}]
+hellaswag,huggingface,[{'title': 'HellaSwag: Can a Machine Really Finish Your Sentence?'}]
+conll2003,huggingface,['bibtex']
+qasc,huggingface,"['arxiv.org/abs/1910.11473', 'bibtex']"
+wiqa,huggingface,['bibtex']
+oscar,huggingface,"['arxiv.org/abs/2010.14571', 'bibtex']"
+facebook/flores,facebook,"['arxiv.org/abs/2207.04672', 'bibtex']"
+openai/webgpt_comparisons,openai,"['arxiv.org/abs/2112.09332', 'bibtex']"
+GEM/wiki_lingua,GEM,['bibtex']
+wiki_bio,huggingface,"['arxiv.org/abs/1603.07771', 'bibtex']"
+xtreme,huggingface,['bibtex']
+bigbench,huggingface,"['arxiv.org/abs/2206.04615', 'doi.org/10.48550/arxiv.2206.04615,']"
+mozilla-foundation/common_voice_11_0,mozilla-foundation,"['arxiv.org/abs/1912.06670', 'bibtex']"
+lex_glue,huggingface,"['arxiv.org/abs/2110.00976', {'title': 'LexGLUE: A Benchmark Dataset for Legal Language Understanding in English'}]"
+c4,huggingface,"['arxiv.org/abs/1910.10683', 'bibtex']"
+mc4,huggingface,"['arxiv.org/abs/1910.10683', 'bibtex']"
+openbookqa,huggingface,[{'title': 'Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering'}]
+csebuetnlp/xlsum,csebuetnlp,"['arxiv.org/abs/1607.01759', 'bibtex']"
+quarel,huggingface,[{'title': 'QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships'}]
+clips/mqa,clips,['bibtex']
+code_x_glue_ct_code_to_text,huggingface,[{'title': 'Codesearchnet challenge: Evaluating the state of semantic code search'}]
+bookcorpus,huggingface,['arxiv.org/abs/2105.05241']
+math_dataset,huggingface,['bibtex']
+story_cloze,huggingface,[{'title': 'Lsdsem 2017 shared task: The story cloze test'}]
+web_questions,huggingface,['bibtex']
+gsm8k,huggingface,"['arxiv.org/abs/2110.14168', {'title': 'Training Verifiers to Solve Math Word Problems'}]"
+openai_humaneval,huggingface,['arxiv.org/abs/2107.03374']
+amazon_reviews_multi,huggingface,"['arxiv.org/abs/2010.02573', {'title': 'The Multilingual Amazon Reviews Corpus'}]"
+common_voice,huggingface,['bibtex']
+ought/raft,ought,['arxiv.org/abs/2109.14076']
+klue,huggingface,['arxiv.org/abs/2105.09680']
+sick,huggingface,['bibtex']
+snli,huggingface,['bibtex']
+gsarti/flores_101,gsarti,"['arxiv.org/abs/2106.03193', {'title': 'The FLORES-101  Evaluation Benchmark for Low-Resource and Multilingual Machine Translation'}]"
+reddit,huggingface,['bibtex']
+the_pile,huggingface,['arxiv.org/abs/2101.00027']
+opus100,huggingface,['arxiv.org/abs/2004.11867']
+financial_phrasebank,huggingface,[{'title': 'Good debt or bad debt: Detecting semantic orientations in economic texts'}]
+textvqa,huggingface,"['arxiv.org/abs/1904.08920', {'title': 'Towards VQA Models That Can Read'}]"
+mnist,huggingface,[{'title': 'MNIST handwritten digit database'}]
+fashion_mnist,huggingface,"['arxiv.org/abs/1708.07747', 'bibtex']"
+newsgroup,huggingface,['doi.org/10.1016/B978-1-55860-377-6.50048-7)']
+mbpp,huggingface,"['arxiv.org/abs/2108.07732', {'title': 'Program Synthesis with Large Language Models'}]"
+stsb_multi_mt,huggingface,['arxiv.org/abs/1708.00055']
+eli5,huggingface,"['arxiv.org/abs/1907.09190', 'doi.org/10.18653/v1/p19-1346},', 'bibtex']"
+tydiqa,huggingface,[{'title': 'TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages'}]
+ptb_text_only,huggingface,['bibtex']
+ccdv/cnn_dailymail,ccdv,['bibtex']
+EleutherAI/lambada_openai,EleutherAI,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
+tau/scrolls,tau,['bibtex']
+squad_kor_v1,huggingface,"['arxiv.org/abs/1909.07005', {'title': 'Korquad1. 0: Korean qa dataset for machine reading comprehension'}]"
+tasksource/bigbench,tasksource,[{'title': 'Beyond the imitation game: Quantifying and extrapolating the capabilities of language models'}]
+scan,huggingface,['bibtex']
+gem,huggingface,"['arxiv.org/abs/2102.01672', 'bibtex']"
+guardian_authorship,huggingface,['bibtex']
+swag,huggingface,"['arxiv.org/abs/1808.05326', {'title': 'SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference'}]"
+poloclub/diffusiondb,poloclub,"['arxiv.org/abs/2210.14896', {'title': '{{DiffusionDB'}]"
+turkic_xwmt,huggingface,"['arxiv.org/abs/2109.04593', {'title': 'A Large-Scale Study of Machine Translation in Turkic Languages'}]"
+xquad,huggingface,['bibtex']
+OATML-Markslab/ProteinGym,OATML-Markslab,['arxiv.org/abs/2205.13760']
+librispeech_asr,huggingface,[{'title': 'Librispeech: an ASR corpus based on public domain audio books'}]
+subjqa,huggingface,"['arxiv.org/abs/2004.14283', 'bibtex']"
+multi_eurlex,huggingface,['arxiv.org/abs/2109.00904']
+boolq,huggingface,[{'title': 'BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions'}]
+yhavinga/ccmatrix,yhavinga,['arxiv.org/abs/1911.04944']
+mlqa,huggingface,[{'title': 'MLQA: Evaluating Cross-lingual Extractive Question Answering'}]
+wnut_17,huggingface,['bibtex']
+tapaco,huggingface,['doi.org/10.5281/zenodo.3707949}']
+senti_lex,huggingface,['bibtex']
+ted_talks_iwslt,huggingface,['bibtex']
+relbert/analogy_questions,relbert,[{'title': '{BERT'}]
+paws-x,huggingface,['arxiv.org/abs/1908.11828']
+commonsense_qa,huggingface,"['arxiv.org/abs/1811.00937', 'bibtex']"
+codeparrot/apps,codeparrot,"['arxiv.org/abs/2105.09938', {'title': 'Measuring Coding Challenge Competence With APPS'}]"
+gsarti/wmt_vat,gsarti,[{'title': 'Variance-Aware Machine Translation Test Sets'}]
+sst,huggingface,['bibtex']
+acronym_identification,huggingface,"['doi.org/10.18653/v1/2020.coling-main.292},', 'bibtex']"
+hans,huggingface,"['arxiv.org/abs/1902.01007', 'bibtex']"
+ethos,huggingface,['arxiv.org/abs/2006.08328']
+indic_glue,huggingface,['bibtex']
+joelito/mapa,joelito,['bibtex']
+scene_parse_150,huggingface,"['arxiv.org/abs/1608.05442', {'title': 'Scene Parsing through ADE20K Dataset'}]"
+poem_sentiment,huggingface,['arxiv.org/abs/2011.02686']
+xcopa,huggingface,[{'title': '{XCOPA: A'}]
+tasksource/mmlu,tasksource,[{'title': 'Measuring Massive Multitask Language Understanding'}]
+beyond/chinese_clean_passages_80m,beyond,"['doi.org/10.5281/zenodo.3402023}', {'title': 'GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation'}]"
+scientific_papers,huggingface,"['arxiv.org/abs/1804.05685', 'doi.org/10.18653/v1/n18-2097},', 'bibtex']"
+ade_corpus_v2,huggingface,"['doi.org/10.1016/j.jbi.2012.04.008"",', 'bibtex']"
+banking77,huggingface,"['arxiv.org/abs/2003.04807', 'bibtex']"
+silicone,huggingface,"['arxiv.org/abs/2009.11152', 'bibtex']"
+clarin-pl/polemo2-official,clarin-pl,['bibtex']
+go_emotions,huggingface,"['arxiv.org/abs/2005.00547', 'bibtex']"
+openslr,huggingface,"['doi.org/10.21437/Interspeech.2017-1139}', {'title': '{Rapid development of TTS corpora for four South African languages'}]"
+math_qa,huggingface,['bibtex']
+AmazonScience/massive,AmazonScience,"['arxiv.org/abs/2204.08582', 'bibtex']"
+allenai/metaicl-data,allenai,"['arxiv.org/abs/2005.00700', {'title': ' Meta{ICL'}]"
+GEM/xlsum,GEM,"['arxiv.org/abs/1607.01759', 'bibtex']"
+pubmed_qa,huggingface,['arxiv.org/abs/1909.06146']
+lmqg/qg_jaquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+mlsum,huggingface,[{'title': 'MLSUM: The Multilingual Summarization Corpus'}]
+pib,huggingface,"['arxiv.org/abs/2008.04860', 'doi.org/10.1145/3430984.3431026},', 'bibtex']"
+Babelscape/wikineural,Babelscape,"['arxiv.org/abs/1810.04805', 'bibtex']"
+clue,huggingface,['bibtex']
+cppe-5,huggingface,['arxiv.org/abs/2112.09569']
+dane,huggingface,['bibtex']
+hotpot_qa,huggingface,"['arxiv.org/abs/1809.09600', {'title': '{HotpotQA'}]"
+yelp_polarity,huggingface,['bibtex']
+hate_speech18,huggingface,['bibtex']
+stanfordnlp/SHP,stanfordnlp,['arxiv.org/abs/2001.08435']
+xquad_r,huggingface,[{'title': 'LAReQA: Language-agnostic answer retrieval from a multilingual pool'}]
+metashift,huggingface,['arxiv.org/abs/2202.06523']
+iwslt2017,huggingface,['bibtex']
+code_search_net,huggingface,"['arxiv.org/abs/1909.09436', {'title': '{CodeSearchNet'}]"
+ncbi_disease,huggingface,[{'title': 'NCBI disease corpus: a resource for disease name recognition and concept normalization'}]
+lmqg/qg_itquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+polyglot_ner,huggingface,['bibtex']
+openai/summarize_from_feedback,openai,"['arxiv.org/abs/2009.01325', 'bibtex']"
+mkb,huggingface,['arxiv.org/abs/2007.07691']
+food101,huggingface,[{'title': 'Food-101 -- Mining Discriminative Components with Random Forests'}]
+hate_speech_offensive,huggingface,"['arxiv.org/abs/1703.04009', {'title': 'Automated Hate Speech Detection and the Problem of Offensive Language'}]"
+vialibre/splittedspanish3bwc,vialibre,"['doi.org/10.5281/zenodo.3247731)', {'title': 'Spanish Pre-Trained BERT Model and Evaluation Data'}]"
+tner/wnut2017,tner,['bibtex']
+lama,huggingface,[{'title': 'Language Models as Knowledge Bases?'}]
+google/MusicCaps,google,['arxiv.org/abs/2301.11325']
+winograd_wsc,huggingface,[{'title': 'The winograd schema challenge'}]
+allenai/soda,allenai,"['arxiv.org/abs/2212.10465', {'title': 'SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization'}]"
+covost2,huggingface,['arxiv.org/abs/2007.10310']
+wiki_lingua,huggingface,"['arxiv.org/abs/2010.03093', {'title': 'WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization'}]"
+snips_built_in_intents,huggingface,['arxiv.org/abs/1805.10190']
+multi_x_science_sum,huggingface,"['arxiv.org/abs/2010.14235', {'title': 'Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles'}]"
+xcsr,huggingface,"['arxiv.org/abs/2106.06937', 'bibtex']"
+nuprl/MultiPL-E,nuprl,['arxiv.org/abs/2208.08227']
+germeval_14,huggingface,[{'title': 'NoSta-D Named Entity Annotation for German: Guidelines and Dataset'}]
+hyperpartisan_news_detection,huggingface,[{'title': 'Data for pan at semeval 2019 task 4: Hyperpartisan news detection'}]
+juletxara/xstory_cloze,juletxara,"['arxiv.org/abs/2112.10668', 'bibtex']"
+multi_woz_v22,huggingface,"['arxiv.org/abs/1810.00278', {'title': 'Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing'}]"
+timit_asr,huggingface,"['doi.org/10.35111/17gk-bn40},', {'title': 'TIMIT Acoustic-Phonetic Continuous Speech Corpus'}]"
+PolyAI/minds14,PolyAI,"['arxiv.org/abs/2104.08524', 'bibtex']"
+ecthr_cases,huggingface,['arxiv.org/abs/2103.13084']
+web_nlg,huggingface,"['doi.org/10.18653/v1/P17-1017},', 'bibtex']"
+competition_math,huggingface,[{'title': 'Measuring Mathematical Problem Solving With the MATH Dataset'}]
+pile-of-law/pile-of-law,pile-of-law,['arxiv.org/abs/2207.00220']
+fever,huggingface,['bibtex']
+lmqg/qg_squad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+jfrenz/legalglue,jfrenz,['arxiv.org/abs/2003.13016']
+xglue,huggingface,"['arxiv.org/abs/2004.01401', {'title': 'XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation'}]"
+allenai/prosocial-dialog,allenai,"['arxiv.org/abs/2205.12688', {'title': 'ProsocialDialog: A Prosocial Backbone for Conversational Agents'}]"
+svhn,huggingface,[{'title': 'Reading digits in natural images with unsupervised feature learning'}]
+exams,huggingface,"['arxiv.org/abs/2011.03080', {'title': 'EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering'}]"
+tner/conll2003,tner,['bibtex']
+tner/bc5cdr,tner,[{'title': 'Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task'}]
+asset,huggingface,['bibtex']
+americas_nli,huggingface,"['arxiv.org/abs/2104.08726', 'bibtex']"
+superb,huggingface,"['arxiv.org/abs/2105.01051', 'bibtex']"
+sms_spam,huggingface,[{'title': 'Contributions to the Study of SMS Spam Filtering: New Collection and Results'}]
+humicroedit,huggingface,"[{'title': '"" President Vows to Cut< Taxes> Hair"": Dataset and Analysis of Creative Text Editing for Humorous Headlines'}]"
+para_crawl,huggingface,['bibtex']
+climate_fever,huggingface,['arxiv.org/abs/2012.00614']
+circa,huggingface,['arxiv.org/abs/2010.03450']
+tab_fact,huggingface,"['arxiv.org/abs/1909.02164', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
+indonlp/indonlu,indonlp,"['arxiv.org/abs/1809.03391', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
+lmqg/qg_dequad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+conllpp,huggingface,[{'title': 'CrossWeigh: Training Named Entity Tagger from Imperfect Annotations'}]
+cc100,huggingface,['bibtex']
+squad_adversarial,huggingface,['bibtex']
+ms_marco,huggingface,"['arxiv.org/abs/1611.09268', 'bibtex']"
+evidence_infer_treatment,huggingface,"['arxiv.org/abs/2005.04177', {'title': 'Inferring Which Medical Treatments Work from Reports of Clinical Trials'}]"
+BeIR/msmarco,BeIR,[{'title': '{BEIR'}]
+e2e_nlg,huggingface,"['arxiv.org/abs/1706.09254', {'title': 'Evaluating the {{State'}]"
+rvl_cdip,huggingface,"['arxiv.org/abs/1502.07058', {'title': 'Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval'}]"
+common_language,huggingface,['doi.org/10.5281/zenodo.5036977}']
+metaeval/babi_nli,metaeval,[{'title': 'Towards ai-complete question answering: A set of prerequisite toy tasks'}]
+medical_questions_pairs,huggingface,['arxiv.org/abs/2008.13546']
+selqa,huggingface,['arxiv.org/abs/1606.00851']
+blended_skill_talk,huggingface,['arxiv.org/abs/2004.08449']
+discovery,huggingface,['bibtex']
+ehealth_kd,huggingface,['bibtex']
+clarin-pl/kpwr-ner,clarin-pl,['bibtex']
+un_multi,huggingface,['bibtex']
+cyberagent/crello,cyberagent,"['arxiv.org/abs/2108.01249', {'title': 'CanvasVAE: Learning to Generate Vector Graphic Documents'}]"
+health_fact,huggingface,"['arxiv.org/abs/2010.09926', 'bibtex']"
+conv_ai_2,huggingface,"['arxiv.org/abs/1902.00098', 'bibtex']"
+discofuse,huggingface,['arxiv.org/abs/1902.10526']
+mc_taco,huggingface,"['arxiv.org/abs/1909.03065', 'bibtex']"
+neulab/conala,neulab,[{'title': 'Learning to mine aligned code and natural language pairs from stack overflow'}]
+para_pat,huggingface,"['doi.org/10.6084/m9.figshare.12627632)', 'bibtex']"
+conll2012_ontonotesv5,huggingface,['bibtex']
+liar,huggingface,['arxiv.org/abs/1705.00648']
+wiki_asp,huggingface,['arxiv.org/abs/2011.07832']
+joelito/covid19_emergency_event,joelito,"['doi.org/10.18653/v1/2021.nllp-1.5', 'bibtex']"
+recipe_nlg,huggingface,['bibtex']
+blbooksgenre,huggingface,['doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)']
+big_patent,huggingface,"['arxiv.org/abs/1906.03741', 'bibtex']"
+ccdv/arxiv-summarization,ccdv,['bibtex']
+wikimedia/wit_base,wikimedia,"['arxiv.org/abs/2103.01913', {'title': 'WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning'}]"
+emo,huggingface,[{'title': 'SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text'}]
+wikitablequestions,huggingface,"['arxiv.org/abs/1508.00305', 'bibtex']"
+asapp/slue,asapp,[{'title': 'Slue: New benchmark tasks for spoken language understanding evaluation on natural speech'}]
+GEM/e2e_nlg,GEM,"['doi.org/10.1016/j.csl.2019.06.009),', 'bibtex']"
+BeIR/fiqa,BeIR,[{'title': '{BEIR'}]
+head_qa,huggingface,['bibtex']
+tner/wikiann,tner,['bibtex']
+truthful_qa,huggingface,['arxiv.org/abs/2109.07958']
+docred,huggingface,"['arxiv.org/abs/1906.06127', {'title': '{DocRED'}]"
+monash_tsf,huggingface,['doi.org/10.1016/j.ijforecast.2010.04.009)']
+aqua_rat,huggingface,[{'title': 'Program induction by rationale generation: Learning to solve and explain algebraic word problems'}]
+empathetic_dialogues,huggingface,"['arxiv.org/abs/1811.00207', 'bibtex']"
+un_pc,huggingface,['bibtex']
+knkarthick/dialogsum,knkarthick,['bibtex']
+clinc_oos,huggingface,['bibtex']
+ccdv/pubmed-summarization,ccdv,['bibtex']
+spider,huggingface,[{'title': 'Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task'}]
+art,huggingface,"['arxiv.org/abs/1908.05739', {'title': 'Abductive Commonsense Reasoning'}]"
+lince,huggingface,['bibtex']
+reuters21578,huggingface,['bibtex']
+cardiffnlp/tweet_sentiment_multilingual,cardiffnlp,['bibtex']
+aeslc,huggingface,"['arxiv.org/abs/1906.03497', 'bibtex']"
+bigbio/pubmed_qa,bigbio,[{'title': 'PubMedQA: A Dataset for Biomedical Research Question Answering'}]
+turk,huggingface,['bibtex']
+SirNeural/flan_v2,SirNeural,['arxiv.org/abs/2301.13688']
+enriched_web_nlg,huggingface,"['doi.org/10.18653/v1/P17-1017},', 'bibtex']"
+imppres,huggingface,['bibtex']
+craigslist_bargains,huggingface,['arxiv.org/abs/1808.09637']
+narrativeqa,huggingface,['bibtex']
+google_wellformed_query,huggingface,['arxiv.org/abs/1808.09419']
+conceptual_captions,huggingface,"[{'title': 'Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning'}]"
+edbeeching/decision_transformer_gym_replay,edbeeching,['arxiv.org/abs/2004.07219']
+ambig_qa,huggingface,[{'title': ' Natural questions: a benchmark for question answering research'}]
+un_ga,huggingface,['bibtex']
+conv_ai_3,huggingface,['arxiv.org/abs/2009.11352']
+ucberkeley-dlab/measuring-hate-speech,ucberkeley-dlab,"['arxiv.org/abs/2009.10277', {'title': 'Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application'}]"
+biosses,huggingface,[{'title': 'BIOSSES: a semantic sentence similarity estimation system for the biomedical domain'}]
+sem_eval_2010_task_8,huggingface,['bibtex']
+Muennighoff/flores200,Muennighoff,"['arxiv.org/abs/2207.04672', 'bibtex']"
+castorini/mr-tydi,castorini,[{'title': '{Mr. TyDi'}]
+md_gender_bias,huggingface,['bibtex']
+numer_sense,huggingface,"['arxiv.org/abs/2005.00683', {'title': 'Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models'}]"
+sem_eval_2018_task_1,huggingface,['doi.org/10.18653/v1/S18-1001']
+ted_hrlr,huggingface,['bibtex']
+code_x_glue_cc_code_refinement,huggingface,[{'title': 'CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence'}]
+coqa,huggingface,"['arxiv.org/abs/1808.07042', 'bibtex']"
+quac,huggingface,"['arxiv.org/abs/1808.07036', 'bibtex']"
+wiki_snippets,huggingface,[{'title': 'Wiki-40B: Multilingual Language Model Dataset'}]
+hlgd,huggingface,[{'title': 'News Headline Grouping as a Challenging NLU Task'}]
+freebase_qa,huggingface,['bibtex']
+mwsc,huggingface,"['arxiv.org/abs/1806.08730', {'title': 'The Natural Language Decathlon: Multitask Learning as Question Answering'}]"
+movie_rationales,huggingface,['bibtex']
+e2e_nlg_cleaned,huggingface,"['arxiv.org/abs/1706.09254', {'title': 'Evaluating the {{State'}]"
+cuad,huggingface,"['arxiv.org/abs/2103.06268', {'title': 'CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review'}]"
+code_x_glue_tc_text_to_code,huggingface,[{'title': 'Mapping language to code in programmatic context'}]
+covid_qa_castorini,huggingface,"['arxiv.org/abs/2004.11339', {'title': 'Rapidly Bootstrapping a Question Answering Dataset for COVID-19'}]"
+code_x_glue_cc_code_to_code_trans,huggingface,[{'title': 'CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence'}]
+gutenberg_time,huggingface,['arxiv.org/abs/2011.04124']
+qed,huggingface,['arxiv.org/abs/2009.06354']
+drop,huggingface,['bibtex']
+tmu_gfm_dataset,huggingface,['bibtex']
+nq_open,huggingface,"['doi.org/10.1162/tacl_a_00276', 'bibtex']"
+flue,huggingface,['arxiv.org/abs/1912.05372']
+ai4bharat/samanantar,ai4bharat,['arxiv.org/abs/2104.05596']
+castorini/mr-tydi-corpus,castorini,[{'title': '{Mr. TyDi'}]
+nlu_evaluation_data,huggingface,['arxiv.org/abs/1903.05566']
+allenai/real-toxicity-prompts,allenai,"['arxiv.org/abs/2009.11462', {'title': 'Realtoxicityprompts: Evaluating neural toxic degeneration in language models'}]"
+dair-ai/emotion,dair-ai,['bibtex']
+ashraq/esc50,ashraq,['doi.org/10.1145/2733373.2806390]']
+masakhaner,huggingface,"['arxiv.org/abs/2103.11811', {'title': 'MasakhaNER: Named Entity Recognition for African Languages'}]"
+tner/ontonotes5,tner,['bibtex']
+opus_paracrawl,huggingface,['bibtex']
+newspop,huggingface,"['arxiv.org/abs/1801.07055', {'title': 'Multi-Source Social Feedback of Online News Feeds'}]"
+facebook/multilingual_librispeech,facebook,"['arxiv.org/abs/2012.03411', {'title': 'MLS: A Large-Scale Multilingual Dataset for Speech Research'}]"
+nielsr/FUNSD_layoutlmv2,nielsr,"['arxiv.org/abs/1905.13538', 'bibtex']"
+allenai/multi_lexsum,allenai,"['arxiv.org/abs/2206.10883', 'doi.org/10.48550/arXiv.2206.10883},', 'bibtex']"
+skt/kobest_v1,skt,"['arxiv.org/abs/2204.04541', 'doi.org/10.48550/arxiv.2204.04541,']"
+bavard/personachat_truecased,bavard,"[{'title': 'Personalizing dialogue agents: I have a dog, do you have pets too?'}]"
+medal,huggingface,['bibtex']
+wiki_split,huggingface,"['arxiv.org/abs/1808.09468', 'bibtex']"
+assin2,huggingface,[{'title': 'The assin 2 shared task: a quick overview'}]
+Hellisotherpeople/DebateSum,Hellisotherpeople,['arxiv.org/abs/2011.07251']
+elenanereiss/german-ler,elenanereiss,"['arxiv.org/abs/2003.13016', 'doi.org/10.48550/arxiv.2003.13016,']"
+miam,huggingface,['bibtex']
+generated_reviews_enth,huggingface,"['arxiv.org/abs/1909.05858', {'title': 'scb-mt-en-th-2020: A Large English-Thai Parallel Corpus'}]"
+limit,huggingface,['bibtex']
+arabic_billion_words,huggingface,[{'title': '1.5 billion words arabic corpus'}]
+mocha,huggingface,['bibtex']
+speech_commands,huggingface,"['arxiv.org/abs/1804.03209', 'bibtex']"
+castorini/afriberta-corpus,castorini,['bibtex']
+qa_zre,huggingface,['bibtex']
+tomasg25/scientific_lay_summarisation,tomasg25,['arxiv.org/abs/2210.09932']
+reasoning-machines/gsm-hard,reasoning-machines,"['arxiv.org/abs/2211.10435', {'title': 'PAL: Program-aided Language Models'}]"
+kelm,huggingface,['arxiv.org/abs/2010.12688']
+zest,huggingface,"['arxiv.org/abs/2011.08115', 'bibtex']"
+Hello-SimpleAI/HC3-Chinese,Hello-SimpleAI,"['arxiv.org/abs/2301.07597', 'bibtex']"
+wikicorpus,huggingface,['bibtex']
+nchlt,huggingface,[{'title': 'Developing Text Resources for Ten South African Languages.'}]
+corypaik/prost,corypaik,"['arxiv.org/abs/2106.03634', 'bibtex']"
+rcds/swiss_judgment_prediction,rcds,['arxiv.org/abs/2110.00806']
+break_data,huggingface,[{'title': 'Break It Down: A Question Understanding Benchmark'}]
+blog_authorship_corpus,huggingface,[{'title': 'Effects of age and gender on blogging.'}]
+russian_super_glue,huggingface,[{'title': 'RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark'}]
+BeIR/fiqa-qrels,BeIR,[{'title': '{BEIR'}]
+igbo_monolingual,huggingface,['arxiv.org/abs/2004.00648']
+hatexplain,huggingface,"['arxiv.org/abs/2012.10289', {'title': 'HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection'}]"
+eurlex,huggingface,['bibtex']
+code_x_glue_cc_cloze_testing_maxmin,huggingface,[{'title': 'CodeXGLUE: An Open Challenge for Code Intelligence'}]
+natural_questions,huggingface,[{'title': 'Natural Questions: a Benchmark for Question Answering Research'}]
+Hello-SimpleAI/HC3,Hello-SimpleAI,"['arxiv.org/abs/2301.07597', 'bibtex']"
+asnq,huggingface,"['arxiv.org/abs/1911.04118', 'doi.org/10.1609/AAAI.V34I05.6282},', {'title': 'TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection'}]"
+stereoset,huggingface,[{'title': 'StereoSet: Measuring stereotypical bias in pretrained language models'}]
+deepset/germanquad,deepset,['arxiv.org/abs/2104.12741']
+andstor/the_pile_github,andstor,"['arxiv.org/abs/2101.00027', {'title': 'The {P'}]"
+GEM/totto,GEM,"['arxiv.org/abs/1603.07771', 'bibtex']"
+biomrc,huggingface,['bibtex']
+cfq,huggingface,"['arxiv.org/abs/1912.09713', 'bibtex']"
+civil_comments,huggingface,"['arxiv.org/abs/1903.04561', 'bibtex']"
+alt,huggingface,[{'title': 'Introduction of the asian language treebank'}]
+ccaligned_multilingual,huggingface,['bibtex']
+taskmaster2,huggingface,"['arxiv.org/abs/1909.05358', {'title': 'Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset'}]"
+code_x_glue_cc_cloze_testing_all,huggingface,[{'title': 'CodeXGLUE: An Open Challenge for Code Intelligence'}]
+nthngdy/oscar-mini,nthngdy,"['arxiv.org/abs/2010.14571', 'bibtex']"
+joelito/greek_legal_ner,joelito,['bibtex']
+deepmind/code_contests,deepmind,"['arxiv.org/abs/2203.07814', {'title': 'Competition-Level Code Generation with AlphaCode'}]"
+GEM/web_nlg,GEM,['bibtex']
+kd_conv,huggingface,['bibtex']
+greek_legal_code,huggingface,"['arxiv.org/abs/2109.15298', 'doi.org/10.5281/zenodo.5528002', 'bibtex']"
+bigbio/gad,bigbio,"['doi.org/10.1186/s12859-015-0472-9},', {'doi': '10.1186/s12859-015-0472-9'}]"
+gsarti/clean_mc4_it,gsarti,"['arxiv.org/abs/1910.10683', {'title': 'IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation'}]"
+conll2000,huggingface,['bibtex']
+miracl/miracl-corpus,miracl,['arxiv.org/abs/2210.09984']
+adv_glue,huggingface,[{'title': 'Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models'}]
+unicamp-dl/mmarco,unicamp-dl,['arxiv.org/abs/2108.13897']
+totto,huggingface,"['arxiv.org/abs/2004.14373', {'title': '{ToTTo'}]"
+wiki_auto,huggingface,"['arxiv.org/abs/2005.02324', 'bibtex']"
+facebook/voxpopuli,facebook,"['arxiv.org/abs/2101.00390', 'bibtex']"
+bookcorpusopen,huggingface,['arxiv.org/abs/2105.05241']
+codeparrot/xlcost-text-to-code,codeparrot,['arxiv.org/abs/2206.08474']
+conll2002,huggingface,['bibtex']
+harem,huggingface,[{'title': 'Harem: An advanced ner evaluation contest for portuguese'}]
+assin,huggingface,[{'title': 'ASSIN: Avaliacao de similaridade semantica e inferencia textual'}]
+debatelab/deepa2,debatelab,"['arxiv.org/abs/2110.01509', 'doi.org/),', {'title': 'DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models'}]"
+multilingual_librispeech,huggingface,"['arxiv.org/abs/2012.03411', {'title': 'MLS: A Large-Scale Multilingual Dataset for Speech Research'}]"
+lccc,huggingface,"['arxiv.org/abs/2008.03946', {'title': 'A Large-Scale Chinese Short-Text Conversation Dataset'}]"
+tne,huggingface,"['arxiv.org/abs/2109.12085', 'bibtex']"
+lc_quad,huggingface,[{'title': 'LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia'}]
+DDSC/angry-tweets,DDSC,[{'title': 'DaNLP: An open-source toolkit for Danish Natural Language Processing'}]
+autshumato,huggingface,[{'title': 'Processing parallel text corpora for three South African language pairs in the Autshumato project'}]
+batterydata/abbreviation_detection,batterydata,['arxiv.org/abs/2204.12061']
+germaner,huggingface,[{'title': 'GermaNER: Free Open German Named Entity Recognition Tool'}]
+Exr0n/wiki-entity-similarity,Exr0n,['arxiv.org/abs/2202.13581']
+ghomasHudson/muld,ghomasHudson,['arxiv.org/abs/2202.07362']
+nthngdy/oscar-small,nthngdy,"['arxiv.org/abs/2010.14571', 'bibtex']"
+anton-l/superb,anton-l,"['arxiv.org/abs/2105.01051', 'bibtex']"
+HUPD/hupd,HUPD,"['arxiv.org/abs/2207.04043', {'title': 'The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications'}]"
+neuralcatcher/hateful_memes,neuralcatcher,"['arxiv.org/abs/2005.04790', 'bibtex']"
+BeIR/scidocs,BeIR,[{'title': '{BEIR'}]
+coastalcph/fairlex,coastalcph,"['arxiv.org/abs/2103.13868', 'bibtex']"
+mrqa,huggingface,"['arxiv.org/abs/1910.09753', {'title': '{MRQA'}]"
+yizhongw/self_instruct,yizhongw,['arxiv.org/abs/2212.10560']
+kmfoda/booksum,kmfoda,"['arxiv.org/abs/2105.08209', {'title': 'BookSum: A Collection of Datasets for Long-form Narrative Summarization'}]"
+imagenet_sketch,huggingface,"['arxiv.org/abs/1905.13549', {'title': 'Learning Robust Global Representations by Penalizing Local Predictive Power'}]"
+newsqa,huggingface,[{'title': 'NewsQA: A Machine Comprehension Dataset'}]
+shibing624/nli_zh,shibing624,['arxiv.org/abs/1908.11828']
+cmrc2018,huggingface,['bibtex']
+cedr,huggingface,[{'title': 'Data-Driven Model for Emotion Detection in Russian Texts'}]
+llangnickel/long-covid-classification-data,llangnickel,"['doi.org/10.1093/database/baac048).', 'bibtex']"
+mt_eng_vietnamese,huggingface,['bibtex']
+woz_dialogue,huggingface,['arxiv.org/abs/1604.04562']
+bprec,huggingface,['bibtex']
+opus_wikipedia,huggingface,"['doi.org/10.1016/j.protcy.2014.11.024},', {'title': 'Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs'}]"
+sberquad,huggingface,"['arxiv.org/abs/1912.09723', 'bibtex']"
+stjiris/portuguese-legal-sentences-v0,stjiris,['bibtex']
+allenai/s2orc,allenai,"['doi.org/10.1093/llc/fqu052""']"
+reasoning_bg,huggingface,"['arxiv.org/abs/1908.01519', {'title': 'Beyond english-only reading comprehension: Experiments in zero-shot multilingual transfer for bulgarian'}]"
+EMBO/sd-nlp,EMBO,['doi.org/10.1038/nmeth.4471).']
+pietrolesci/gen_debiased_nli,pietrolesci,['bibtex']
+dynabench/dynasent,dynabench,"['arxiv.org/abs/2012.15349', {'title': '{DynaSent'}]"
+bsd_ja_en,huggingface,['bibtex']
+GEM/BiSECT,GEM,['bibtex']
+tweet_qa,huggingface,"['arxiv.org/abs/1907.06292', {'title': 'TweetQA: A Social Media Focused Question Answering Dataset'}]"
+squad_kor_v2,huggingface,['bibtex']
+sede,huggingface,['arxiv.org/abs/2106.05006']
+sentiment140,huggingface,[{'title': 'Twitter sentiment classification using distant supervision'}]
+DFKI-SLT/few-nerd,DFKI-SLT,['bibtex']
+few_rel,huggingface,['bibtex']
+patriziobellan/PET,patriziobellan,"['arxiv.org/abs/2203.04860', 'doi.org/10.48550/arXiv.2203.04860},', 'bibtex']"
+relbert/lexical_relation_classification,relbert,['bibtex']
+blbooks,huggingface,['doi.org/10.21250/db14']
+allenai/scitldr,allenai,"['arxiv.org/abs/2004.15011', {'title': '{TLDR'}]"
+wisesight_sentiment,huggingface,['doi.org/10.5281/zenodo.3457447}']
+lener_br,huggingface,['bibtex']
+IlyaGusev/gazeta,IlyaGusev,['arxiv.org/abs/2006.11063']
+iapp_wiki_qa_squad,huggingface,['doi.org/10.5281/zenodo.4539916}']
+google/xtreme_s,google,"['arxiv.org/abs/2203.10752', {'title': 'XTREME-S: Evaluating Cross-lingual Speech Representations'}]"
+mteb/amazon_counterfactual,mteb,['arxiv.org/abs/2104.06893']
+europa_ecdc_tm,huggingface,['doi.org/10.1007/s10579-014-9277-0}']
+IlyaGusev/headline_cause,IlyaGusev,['arxiv.org/abs/2108.12626']
+xed_en_fi,huggingface,"['arxiv.org/abs/2011.01612', {'title': 'XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection'}]"
+mkqa,huggingface,['arxiv.org/abs/2007.15207']
+SetFit/amazon_counterfactual,SetFit,['arxiv.org/abs/2104.06893']
+code_x_glue_tt_text_to_text,huggingface,[{'title': 'CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence'}]
+Muennighoff/flan,Muennighoff,['arxiv.org/abs/2109.01652']
+ekinakyurek/ftrace,ekinakyurek,"['arxiv.org/abs/2205.11482', 'doi.org/10.48550/arxiv.2205.11482,', {'title': 'Language Models as Knowledge Bases?'}]"
+ai4bharat/naamapadam,ai4bharat,['arxiv.org/abs/2212.10168']
+orange_sum,huggingface,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
+matinf,huggingface,['bibtex']
+qanta,huggingface,"['arxiv.org/abs/1904.04792', {'title': 'Quizbowl: The Case for Incremental Question Answering'}]"
+lewtun/github-issues,lewtun,"['arxiv.org/abs/2005.00614', 'doi.org/),', 'bibtex']"
+hope_edi,huggingface,['bibtex']
+the_pile_books3,huggingface,"['arxiv.org/abs/2101.00027', {'title': 'The {P'}]"
+sharc_modified,huggingface,['arxiv.org/abs/1909.03759']
+web_of_science,huggingface,[{'title': 'HDLTex: Hierarchical Deep Learning for Text Classification'}]
+chr_en,huggingface,"['arxiv.org/abs/2010.04791', {'title': 'ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization'}]"
+nell,huggingface,['bibtex']
+squad_es,huggingface,['bibtex']
+snow_simplified_japanese_corpus,huggingface,['bibtex']
+dynabench/qa,dynabench,"['arxiv.org/abs/2002.00293', 'doi.org/10.1162/tacl_a_00338', 'bibtex']"
+kinnews_kirnews,huggingface,"['arxiv.org/abs/2010.12174', {'title': 'KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi'}]"
+medical_dialog,huggingface,"['arxiv.org/abs/2004.03329', {'title': 'MedDialog: a large-scale medical dialogue dataset'}]"
+pec,huggingface,['bibtex']
+definite_pronoun_resolution,huggingface,[{'title': 'Resolving complex cases of definite pronouns: the winograd schema challenge'}]
+alisawuffles/WANLI,alisawuffles,['arxiv.org/abs/2201.05955']
+covid_qa_deepset,huggingface,[{'title': 'COVID-QA: A Question Answering Dataset for COVID-19'}]
+shunk031/wrime,shunk031,['bibtex']
+dennlinger/eur-lex-sum,dennlinger,"['arxiv.org/abs/2210.13448', 'bibtex']"
+schema_guided_dstc8,huggingface,"['arxiv.org/abs/1909.05855', 'bibtex']"
+joelito/legalnero,joelito,"['doi.org/10.18653/v1/2021.nllp-1.2', 'bibtex']"
+BeIR/msmarco-qrels,BeIR,[{'title': '{BEIR'}]
+adithya7/xlel_wd,adithya7,"['arxiv.org/abs/2204.06535', {'title': 'Multilingual Event Linking to Wikidata'}]"
+c3,huggingface,"['arxiv.org/abs/1904.09679', {'title': 'Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension'}]"
+pierreguillou/DocLayNet-small,pierreguillou,"['arxiv.org/abs/2206.01062', 'doi.org/10.1145/3534678.3539043', {'title': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation'}]"
+swda,huggingface,"['arxiv.org/abs/1711.05568', 'bibtex']"
+air_dialogue,huggingface,['bibtex']
+code_x_glue_cc_clone_detection_big_clone_bench,huggingface,[{'title': 'Towards a big data curated benchmark of inter-project code clones'}]
+allenai/mslr2022,allenai,[{'title': 'MSˆ2: Multi-Document Summarization of Medical Studies'}]
+scb_mt_enth_2020,huggingface,"['arxiv.org/abs/2007.03541', {'title': 'scb-mt-en-th-2020: A Large English-Thai Parallel Corpus'}]"
+multidoc2dial,huggingface,[{'title': 'MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents'}]
+machelreid/m2d2,machelreid,"['arxiv.org/abs/2210.07370', {'title': 'M2D2: A Massively Multi-domain Language Modeling Dataset'}]"
+asi/wikitext_fr,asi,"['arxiv.org/abs/1609.07843', 'bibtex']"
+code_x_glue_cc_code_completion_token,huggingface,[{'title': 'Probabilistic Model for Code with Decision Trees'}]
+lst20,huggingface,[{'title': 'The Annotation Guideline of LST20 Corpus'}]
+allegro/klej-cdsc-e,allegro,['bibtex']
+search_qa,huggingface,"['arxiv.org/abs/1704.05179', 'bibtex']"
+joelito/brazilian_court_decisions,joelito,"['arxiv.org/abs/1905.10348', 'doi.org/10.7717/peerj-cs.904', 'bibtex']"
+nlpaueb/finer-139,nlpaueb,"['arxiv.org/abs/2203.06482', {'title': 'FiNER: Financial Numeric Entity Recognition for XBRL Tagging'}]"
+jnlpba,huggingface,['bibtex']
+nli_tr,huggingface,['bibtex']
+dutch_social,huggingface,['doi.org/10.5072/FK2/MTPTL7']
+europa_eac_tm,huggingface,['doi.org/10.1007/s10579-014-9277-0}']
+scielo,huggingface,"['arxiv.org/abs/1905.01852', {'title': 'A Large Parallel Corpus of Full-Text Scientific Articles'}]"
+HuggingFaceH4/stack-exchange-preferences,HuggingFaceH4,['arxiv.org/abs/2112.00861']
+arxiv_dataset,huggingface,['arxiv.org/abs/1905.00075']
+pn_summary,huggingface,"['arxiv.org/abs/2012.11204', {'title': 'Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization'}]"
+doc2dial,huggingface,['bibtex']
+wider_face,huggingface,"['arxiv.org/abs/1511.06523', 'bibtex']"
+persian_ner,huggingface,['bibtex']
+tweets_ar_en_parallel,huggingface,[{'title': 'Constructing a Bilingual Corpus of Parallel Tweets'}]
+allenai/scifact,allenai,['bibtex']
+bertin-project/mc4-es-sampled,bertin-project,"['arxiv.org/abs/1910.10683', 'bibtex']"
+kor_nli,huggingface,[{'title': 'KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding'}]
+pg19,huggingface,"['arxiv.org/abs/1911.05507', 'bibtex']"
+cakiki/args_me,cakiki,['doi.org/10.5281/zenodo.4139439}']
+swedish_medical_ner,huggingface,[{'title': 'Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs'}]
+tau/sled,tau,[{'title': 'Efficient Long-Text Understanding with Short-Text Models'}]
+bigbio/biored,bigbio,"['doi.org/10.48550/arXiv.2204.04263},', 'bibtex']"
+GEM/wiki_cat_sum,GEM,"['arxiv.org/abs/1906.04687', 'bibtex']"
+offenseval_dravidian,huggingface,['bibtex']
+tuple_ie,huggingface,[{'title': 'Answering Complex Questions Using Open Information Extraction'}]
+society-ethics/lila_camera_traps,society-ethics,"['doi.org/10.1007/978-3-030-01270-0\\_28},', 'bibtex']"
+edinburghcstr/ami,edinburghcstr,['arxiv.org/abs/1906.11047']
+indonli,huggingface,['bibtex']
+classla/setimes_sr,classla,['bibtex']
+GEM/turku_paraphrase_corpus,GEM,[{'title': 'Finnish Paraphrase Corpus'}]
+kilt_wikipedia,huggingface,['bibtex']
+BeIR/beir,BeIR,[{'title': '{BEIR'}]
+GEM/mlb_data_to_text,GEM,['bibtex']
+fake_news_english,huggingface,"['doi.org/10.1145/3201064.3201100', 'bibtex']"
+katanaml/cord,katanaml,[{'title': 'CORD: A Consolidated Receipt Dataset for Post-OCR Parsing'}]
+id_liputan6,huggingface,"['arxiv.org/abs/2011.00679', {'title': 'Liputan6: A Large-scale Indonesian Dataset for Text Summarization'}]"
+juletxara/xstory_cloze_mt_nllb-3B,juletxara,"['arxiv.org/abs/2112.10668', 'bibtex']"
+the_pile_openwebtext2,huggingface,"['arxiv.org/abs/2101.00027', {'title': 'The {P'}]"
+code_x_glue_cc_defect_detection,huggingface,[{'title': 'Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks'}]
+hybrid_qa,huggingface,"['arxiv.org/abs/1909.05358', {'title': 'HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data'}]"
+offenseval2020_tr,huggingface,['bibtex']
+kor_nlu,huggingface,['arxiv.org/abs/2004.03289']
+thegoodfellas/brwac_tiny,thegoodfellas,[{'title': 'The brwac corpus: A new open resource for brazilian portuguese'}]
+ronec,huggingface,"['arxiv.org/abs/1909.01247', {'title': 'Introducing RONEC--the Romanian Named Entity Corpus'}]"
+taskmaster1,huggingface,"['arxiv.org/abs/1909.05358', {'title': 'Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset'}]"
+ubuntu_dialogs_corpus,huggingface,"['arxiv.org/abs/1506.08909', 'bibtex']"
+allenai/cord19,allenai,"['arxiv.org/abs/2004.07180', {'title': 'CORD-19: The Covid-19 Open Research Dataset'}]"
+McGill-NLP/FaithDial,McGill-NLP,"['arxiv.org/abs/2204.10757', {'title': 'FaithDial: A Faithful Benchmark for Information-Seeking Dialogue'}]"
+NbAiLab/norne,NbAiLab,['arxiv.org/abs/1911.12146']
+language-and-voice-lab/samromur_asr,language-and-voice-lab,['bibtex']
+cfilt/iitb-english-hindi,cfilt,['bibtex']
+MLCommons/ml_spoken_words,MLCommons,[{'title': 'Multilingual Spoken Words Corpus'}]
+vivos,huggingface,"['doi.org/10.5281/zenodo.7068130', 'bibtex']"
+allenai/csabstruct,allenai,"['arxiv.org/abs/1909.04054', {'title': 'Pretrained Language Models for Sequential Sentence Classification'}]"
+arcd,huggingface,['bibtex']
+casino,huggingface,[{'title': 'CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems'}]
+Divyanshu/indicxnli,Divyanshu,"['arxiv.org/abs/2204.08776', 'doi.org/10.48550/arxiv.2204.08776,']"
+deal_or_no_dialog,huggingface,"['arxiv.org/abs/1706.05125', {'title': 'Deal or no deal? end-to-end learning for negotiation dialogues'}]"
+covid_qa_ucsd,huggingface,"['arxiv.org/abs/2005.05442', {'title': 'CovidDialog: Medical Dialogue Datasets about COVID-19'}]"
+indonlp/NusaX-senti,indonlp,['arxiv.org/abs/2205.15960']
+masakhane/masakhaner2,masakhane,"['arxiv.org/abs/2103.11811', {'title': 'MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition'}]"
+wmt20_mlqe_task2,huggingface,['arxiv.org/abs/1902.08646']
+tner/multinerd,tner,['bibtex']
+joelito/online_terms_of_service,joelito,"['doi.org/10.18653/v1/2021.nllp-1.1', 'bibtex']"
+newsroom,huggingface,['bibtex']
+hate_speech_pl,huggingface,"[{'title': ""Czy komputer rozpozna hejtera? Wykorzystanie uczenia maszynowego (ML) w jako{\\'s""}]"
+code_x_glue_cc_code_completion_line,huggingface,[{'title': 'Probabilistic Model for Code with Decision Trees'}]
+csebuetnlp/xnli_bn,csebuetnlp,['arxiv.org/abs/2101.00204']
+silver/personal_dialog,silver,"['arxiv.org/abs/1901.09672', {'title': 'Personalized dialogue generation with diversified traits'}]"
+lmqg/qag_squad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+projecte-aina/parlament_parla,projecte-aina,"['doi.org/10.5281/zenodo.5541827}', {'title': 'ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions'}]"
+squad_v1_pt,huggingface,['bibtex']
+GEM/mlsum,GEM,['bibtex']
+GEM/schema_guided_dialog,GEM,"['arxiv.org/abs/1909.05855', {'title': 'Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset'}]"
+LIUM/tedlium,LIUM,[{'title': 'TED-LIUM: an Automatic Speech Recognition dedicated corpus'}]
+fquad,huggingface,['arxiv.org/abs/2002.06071']
+electricity_load_diagrams,huggingface,"['doi.org/10.1145/3209978.3210006},', 'bibtex']"
+id_clickbait,huggingface,"['doi.org/10.1016/j.dib.2020.106231"",', 'bibtex']"
+ascent_kb,huggingface,['arxiv.org/abs/2011.00905']
+indonlp/NusaX-MT,indonlp,['arxiv.org/abs/2205.15960']
+aslg_pc12,huggingface,[{'title': 'English-asl gloss parallel corpus 2012: Aslg-pc12'}]
+EMBO/sd-nlp-non-tokenized,EMBO,['doi.org/10.1038/nmeth.4471).']
+allenai/qasper,allenai,"['arxiv.org/abs/2105.03011', {'title': 'A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers'}]"
+cdsc,huggingface,[{'title': 'Polish evaluation dataset for compositional distributional semantics models'}]
+eu_regulatory_ir,huggingface,"['arxiv.org/abs/2101.10726', 'bibtex']"
+hebrew_sentiment,huggingface,['bibtex']
+GEM/OrangeSum,GEM,['bibtex']
+labr,huggingface,[{'title': 'Labr: A large scale arabic book reviews dataset'}]
+told-br,huggingface,"['arxiv.org/abs/2010.04543', 'bibtex']"
+xor_tydi_qa,huggingface,"['arxiv.org/abs/2010.11856', {'title': 'XOR QA: Cross-lingual Open-Retrieval Question Answering'}]"
+compguesswhat,huggingface,[{'title': 'CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning'}]
+wi_locness,huggingface,['bibtex']
+DDSC/partial-danish-gigaword-no-twitter,DDSC,[{'title': '{The Danish Gigaword Corpus'}]
+metaeval/scinli,metaeval,['bibtex']
+py_ast,huggingface,"['doi.org/10.1145/2983990.2984041},', 'bibtex']"
+ds4sd/DocLayNet,ds4sd,"['doi.org/10.1145/3534678.3539043', {'title': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation'}]"
+GEM/conversational_weather,GEM,['bibtex']
+dbrd,huggingface,"['arxiv.org/abs/1910.00896', 'bibtex']"
+multi_booked,huggingface,"['arxiv.org/abs/1803.08614', 'bibtex']"
+numeric_fused_head,huggingface,['bibtex']
+GEM/turku_hockey_data2text,GEM,['bibtex']
+med_hop,huggingface,['arxiv.org/abs/1710.06481']
+gsarti/itacola,gsarti,"['arxiv.org/abs/2109.12053', 'bibtex']"
+eraser_multi_rc,huggingface,['bibtex']
+khalidalt/tydiqa-primary,khalidalt,[{'title': 'TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages'}]
+BritishLibraryLabs/EThOS-PhD-metadata,BritishLibraryLabs,['doi.org/10.23636/ybpt-nh33']
+igbo_ner,huggingface,['arxiv.org/abs/2004.00648']
+gsarti/change_it,gsarti,['bibtex']
+style_change_detection,huggingface,[{'title': 'Shared Tasks on Authorship Analysis at PAN 2020'}]
+has_part,huggingface,['arxiv.org/abs/2006.07510']
+irc_disentangle,huggingface,"['arxiv.org/abs/1810.11118', 'bibtex']"
+renumics/dcase23-task2-enriched,renumics,"['arxiv.org/abs/2205.13879', 'doi.org/10.5281/zenodo.7687464}']"
+classla/FRENK-hate-hr,classla,['arxiv.org/abs/1906.02045']
+classla/FRENK-hate-sl,classla,['arxiv.org/abs/1906.02045']
+classla/FRENK-hate-en,classla,['arxiv.org/abs/1906.02045']
+ollie,huggingface,['bibtex']
+khalidalt/tydiqa-goldp,khalidalt,[{'title': 'TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages'}]
+deepset/germandpr,deepset,['arxiv.org/abs/2104.12741']
+vctk,huggingface,"['doi.org/10.7488/ds/2645)', {'title': 'CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit'}]"
+GEM/dart,GEM,"['doi.org/10.5204/mcj.315)).', 'bibtex']"
+sileod/probability_words_nli,sileod,[{'title': 'Probing neural language models for understanding of words of estimative probability'}]
+BeIR/beir-corpus,BeIR,[{'title': '{BEIR'}]
+dennlinger/klexikon,dennlinger,"['arxiv.org/abs/2201.07198', 'bibtex']"
+PlanTL-GOB-ES/SQAC,PlanTL-GOB-ES,"['arxiv.org/abs/1606.05250', 'bibtex']"
+bigbio/chemprot,bigbio,['bibtex']
+ro_sent,huggingface,[{'title': 'The birth of Romanian BERT'}]
+demelin/moral_stories,demelin,"[{'title': 'Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences'}]"
+onestop_qa,huggingface,"['arxiv.org/abs/2004.14797', 'bibtex']"
+cail2018,huggingface,['arxiv.org/abs/1807.02478']
+ctu-aic/ctkfacts_nli,ctu-aic,['arxiv.org/abs/2201.11115']
+narrativeqa_manual,huggingface,['bibtex']
+arsentd_lev,huggingface,"['arxiv.org/abs/1906.01830', {'title': 'Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets'}]"
+crawl_domain,huggingface,[{'title': 'Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities'}]
+curiosity_dialogs,huggingface,[{'title': 'Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity'}]
+AmazonScience/mintaka,AmazonScience,['bibtex']
+bigbio/bc7_litcovid,bigbio,['bibtex']
+dart,huggingface,"['arxiv.org/abs/2007.02871', {'title': 'DART: Open-Domain Structured Data Record to Text Generation'}]"
+GEM/sportsett_basketball,GEM,['bibtex']
+gfissore/arxiv-abstracts-2021,gfissore,['arxiv.org/abs/1905.00075']
+eli5_category,huggingface,['bibtex']
+caner,huggingface,['bibtex']
+linnaeus,huggingface,"['doi.org/10.1186/1471-2105-11-85},', 'bibtex']"
+bertin-project/mc4-sampling,bertin-project,['bibtex']
+swahili_news,huggingface,['doi.org/10.5281/zenodo.4300293)']
+eugenesiow/Set5,eugenesiow,[{'title': 'Low-complexity single-image super-resolution based on nonnegative neighbor embedding'}]
+social_bias_frames,huggingface,['bibtex']
+Paul/hatecheck,Paul,"['arxiv.org/abs/2012.15606', 'bibtex']"
+msr_sqa,huggingface,['bibtex']
+allegro_reviews,huggingface,['bibtex']
+GEM/wiki_auto_asset_turk,GEM,"['arxiv.org/abs/1910.02677', 'bibtex']"
+derek-thomas/ScienceQA,derek-thomas,"['arxiv.org/abs/2209.09513', {'title': 'Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering'}]"
+per_sent,huggingface,"['arxiv.org/abs/2011.06128', {'title': ""Author's Sentiment Prediction""}]"
+turkish_ner,huggingface,"['arxiv.org/abs/1702.02363', 'bibtex']"
+lmqg/qa_squadshifts_synthetic,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+DFKI-SLT/mobie,DFKI-SLT,['bibtex']
+eth_py150_open,huggingface,[{'title': 'Learning and Evaluating Contextual Embedding of Source Code'}]
+allenai/peer_read,allenai,"['arxiv.org/abs/1804.09635', {'title': 'A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications'}]"
+hate_offensive,huggingface,"['arxiv.org/abs/1905.12516', {'title': 'Automated Hate Speech Detection and the Problem of Offensive Language'}]"
+eugenesiow/Set14,eugenesiow,[{'title': 'On single image scale-up using sparse-representations'}]
+hkcancor,huggingface,['bibtex']
+midas/openkp,midas,[{'title': 'Open Domain Web Keyphrase Extraction Beyond Language Modeling'}]
+code_x_glue_tc_nl_code_search_adv,huggingface,[{'title': 'Codesearchnet challenge: Evaluating the state of semantic code search'}]
+ar_sarcasm,huggingface,['bibtex']
+bigbio/nlm_gene,bigbio,['bibtex']
+BeIR/trec-covid,BeIR,[{'title': '{BEIR'}]
+Babelscape/rebel-dataset,Babelscape,"['arxiv.org/abs/2005.00614', 'bibtex']"
+code_x_glue_cc_clone_detection_poj104,huggingface,[{'title': 'Convolutional neural networks over tree structures for programming language processing'}]
+BeIR/fever,BeIR,[{'title': '{BEIR'}]
+thai_toxicity_tweet,huggingface,[{'title': 'Annotation and Classification of Toxicity for Thai Twitter'}]
+qanastek/EMEA-V3,qanastek,['bibtex']
+best2009,huggingface,[{'title': 'BEST 2009: Thai word segmentation software contest'}]
+eugenesiow/BSD100,eugenesiow,[{'title': 'A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics'}]
+GEM/squad_v2,GEM,"['arxiv.org/abs/1806.03822', {'title': 'Know What You Don’t Know: Unanswerable Questions for SQuAD'}]"
+ajgt_twitter_ar,huggingface,[{'title': 'Arabic tweets sentimental analysis using machine learning'}]
+nlphuji/flickr30k,nlphuji,[{'title': 'From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions'}]
+afrikaans_ner_corpus,huggingface,['bibtex']
+wili_2018,huggingface,['doi.org/10.5281/zenodo.841984}']
+ttc4900,huggingface,"['doi.org/10.5505/pajes.2018.15931},', 'bibtex']"
+qanastek/MASSIVE,qanastek,"['arxiv.org/abs/2204.08582', 'bibtex']"
+DFKI-SLT/tacred,DFKI-SLT,"['arxiv.org/abs/2104.08398', 'bibtex']"
+cmu_hinglish_dog,huggingface,[{'title': 'A Dataset for Document Grounded Conversations'}]
+cardiffnlp/tweet_topic_single,cardiffnlp,"['arxiv.org/abs/2209.09824', 'bibtex']"
+brwac,huggingface,[{'title': 'The brwac corpus: A new open resource for brazilian portuguese'}]
+bigbio/bioid,bigbio,[{'title': 'Bio-ID track overview'}]
+McGill-NLP/mlquestions,McGill-NLP,['bibtex']
+x_stance,huggingface,"['arxiv.org/abs/2003.08385', 'bibtex']"
+dialog_re,huggingface,"['arxiv.org/abs/2004.08056', {'title': 'Dialogue-Based Relation Extraction'}]"
+diplomacy_detection,huggingface,['bibtex']
+gooaq,huggingface,"['arxiv.org/abs/2104.08727', {'title': 'GooAQ: Open Question Answering with Diverse Answer Types'}]"
+conv_questions,huggingface,['arxiv.org/abs/1910.03262']
+bbaw_egyptian,huggingface,[{'title': 'Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs'}]
+liveqa,huggingface,['bibtex']
+ar_cov19,huggingface,"['arxiv.org/abs/2004.05861', {'title': 'ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks'}]"
+bbc_hindi_nli,huggingface,['bibtex']
+flax-sentence-embeddings/Gender_Bias_Evaluation_Set,flax-sentence-embeddings,['arxiv.org/abs/1906.00591']
+crd3,huggingface,[{'title': 'Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset'}]
+taskmaster3,huggingface,"['arxiv.org/abs/1909.05358', {'title': 'Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset'}]"
+bn_hate_speech,huggingface,"['arxiv.org/abs/2004.07807', {'title': 'Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network'}]"
+glucose,huggingface,"['arxiv.org/abs/2009.07758', {'title': 'GLUCOSE: GeneraLized and COntextualized Story Explanations'}]"
+joelito/german_argument_mining,joelito,['doi.org/10.5220/0010187305150521']
+the_pile_stack_exchange,huggingface,"['arxiv.org/abs/2101.00027', {'title': 'The {P'}]"
+bigbio/med_qa,bigbio,[{'title': 'What disease does this patient have? a large-scale open domain question answering dataset from medical exams'}]
+kor_sae,huggingface,"['arxiv.org/abs/1912.00342', {'title': 'Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives'}]"
+PlanTL-GOB-ES/cantemist-ner,PlanTL-GOB-ES,"[{'title': 'Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.'}]"
+com_qa,huggingface,['bibtex']
+PlanTL-GOB-ES/pharmaconer,PlanTL-GOB-ES,['bibtex']
+grail_qa,huggingface,['arxiv.org/abs/2011.07743']
+sem_eval_2020_task_11,huggingface,['arxiv.org/abs/2009.02696']
+MoritzLaurer/multilingual-NLI-26lang-2mil7,MoritzLaurer,[{'title': 'Less {Annotating'}]
+bhavnicksm/sentihood,bhavnicksm,['arxiv.org/abs/1610.03771']
+gap,huggingface,"['arxiv.org/abs/1810.05201', 'bibtex']"
+atomic,huggingface,[{'title': 'ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning'}]
+capes,huggingface,[{'title': 'A Parallel Corpus of Theses and Dissertations Abstracts'}]
+amttl,huggingface,[{'title': 'Adaptive multi-task transfer learning for Chinese word segmentation in medical text'}]
+re_dial,huggingface,[{'title': 'Towards Deep Conversational Recommendations'}]
+coached_conv_pref,huggingface,[{'title': 'Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences'}]
+hover,huggingface,['arxiv.org/abs/2011.03088']
+opinosis,huggingface,[{'title': 'Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions'}]
+event2Mind,huggingface,"['arxiv.org/abs/1805.06939', 'bibtex']"
+coarse_discourse,huggingface,[{'title': 'Characterizing Online Discussion Using Coarse Discourse Sequences'}]
+disfl_qa,huggingface,['bibtex']
+fake_news_filipino,huggingface,[{'title': 'Localization of Fake News Detection via Multitask Transfer Learning'}]
+counter,huggingface,"['doi.org/10.1007/s10579-016-9367-2""']"
+hate_speech_filipino,huggingface,[{'title': 'Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.'}]
+cawac,huggingface,['bibtex']
+hebrew_projectbenyehuda,huggingface,['bibtex']
+tals/vitaminc,tals,['bibtex']
+Llamacha/monolingual-quechua-iic,Llamacha,[{'title': 'Introducing QuBERT: A Large Monolingual Corpus and BERT Model for Southern Quechua'}]
+GEM/RotoWire_English-German,GEM,['bibtex']
+msr_text_compression,huggingface,[{'title': 'A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs'}]
+cs_restaurants,huggingface,"['arxiv.org/abs/1910.05298', 'bibtex']"
+finer,huggingface,['arxiv.org/abs/1908.04212']
+hausa_voa_ner,huggingface,['bibtex']
+laroseda,huggingface,"['arxiv.org/abs/1901.06543', {'title': 'Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set'}]"
+hda_nli_hindi,huggingface,['bibtex']
+GEM/dstc10_track2_task2,GEM,"[{'title': '"" How Robust ru?"": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations'}]"
+id_panl_bppt,huggingface,['bibtex']
+mac_morpho,huggingface,[{'title': 'Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese'}]
+kor_3i4k,huggingface,"['arxiv.org/abs/1811.04231', {'title': 'Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency'}]"
+m_lama,huggingface,"['arxiv.org/abs/2102.00894', 'bibtex']"
+tashkeela,huggingface,"[{'title': 'Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems'}]"
+kor_hate,huggingface,['arxiv.org/abs/2005.12503']
+NbAiLab/NCC_small_100,NbAiLab,"['arxiv.org/abs/2104.09617', {'title': 'Operationalizing a National Digital Library: The Case for a {N'}]"
+hindi_discourse,huggingface,['bibtex']
+metrec,huggingface,[{'title': 'MetRec: A dataset for meter classification of arabic poetry'}]
+sofc_materials_articles,huggingface,['arxiv.org/abs/2006.03039']
+hippocorpus,huggingface,['bibtex']
+time_dial,huggingface,"['arxiv.org/abs/2106.04571', 'bibtex']"
+librispeech_lm,huggingface,[{'title': 'Librispeech: an ASR corpus based on public domain audio books'}]
+GEM/viggo,GEM,['bibtex']
+metooma,huggingface,[{'title': '#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement'}]
+mutual_friends,huggingface,"['arxiv.org/abs/1704.07130', 'bibtex']"
+parsinlu_reading_comprehension,huggingface,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+GroNLP/divemt,GroNLP,"['arxiv.org/abs/2205.12215', 'bibtex']"
+msr_genomics_kbcomp,huggingface,['bibtex']
+menyo20k_mt,huggingface,"['arxiv.org/abs/2103.08647', 'bibtex']"
+PiC/phrase_similarity,PiC,[{'title': 'PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search'}]
+moroco,huggingface,"['arxiv.org/abs/1901.06543', 'bibtex']"
+ilist,huggingface,['bibtex']
+psc,huggingface,[{'title': 'The {P'}]
+GEM/ART,GEM,"['arxiv.org/abs/1908.05739', {'title': 'Abductive Commonsense Reasoning'}]"
+yoruba_text_c3,huggingface,['bibtex']
+wikitext_tl39,huggingface,['arxiv.org/abs/1907.00409']
+abdusah/masc_dev,abdusah,['doi.org/10.21227/e1qb-jv46']
+GEM/SciDuet,GEM,['bibtex']
+NYTK/HuWNLI,NYTK,[{'title': 'HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából'}]
+NYTK/HuCOLA,NYTK,[{'title': 'HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából'}]
+NYTK/HuSST,NYTK,[{'title': 'HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából'}]
+tamilmixsentiment,huggingface,['bibtex']
+tunizi,huggingface,['arxiv.org/abs/2004.14303']
+NYTK/HuRC,NYTK,[{'title': 'HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából'}]
+gigant/romanian_speech_synthesis_0_8_1,gigant,['bibtex']
+relbert/semeval2012_relational_similarity,relbert,['bibtex']
+HHousen/ParaSCI,HHousen,['arxiv.org/abs/2101.08382']
+twi_wordsim353,huggingface,['bibtex']
+newsph,huggingface,['arxiv.org/abs/2010.11574']
+swahili,huggingface,['doi.org/10.5281/zenodo.3553423']
+newsph_nli,huggingface,[{'title': 'Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation'}]
+twi_text_c3,huggingface,['bibtex']
+NYTK/HuCoPA,NYTK,[{'title': 'HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából'}]
+sharc,huggingface,['arxiv.org/abs/1809.01494']
+GEM/common_gen,GEM,"['arxiv.org/abs/1910.13461', 'bibtex']"
+ro_sts,huggingface,[{'title': 'Liro: Benchmark and leaderboard for romanian language tasks'}]
+GEM/RiSAWOZ,GEM,['bibtex']
+isixhosa_ner_corpus,huggingface,['bibtex']
+yoruba_gv_ner,huggingface,['bibtex']
+kan_hope,huggingface,['arxiv.org/abs/2108.04616']
+albertvillanova/carbon_24,albertvillanova,"['arxiv.org/abs/2110.06197', {'title': 'Crystal Diffusion Variational Autoencoder for Periodic Material Generation'}]"
+isizulu_ner_corpus,huggingface,['bibtex']
+GEM/Taskmaster,GEM,"['arxiv.org/abs/2012.12458', {'title': 'TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems'}]"
+CyranoB/polarity,CyranoB,['arxiv.org/abs/1509.01626']
+times_of_india_news_headlines,huggingface,['doi.org/10.7910/DVN/DPQMQH}']
+stsb_mt_sv,huggingface,"['arxiv.org/abs/2009.03116', {'title': 'Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity'}]"
+GEM/surface_realisation_st_2020,GEM,['bibtex']
+GEM/cs_restaurants,GEM,['bibtex']
+refresd,huggingface,"['arxiv.org/abs/1907.05791', 'bibtex']"
+opus_dogc,huggingface,['bibtex']
+GEM/cochrane-simplification,GEM,['bibtex']
+Aisha/BAAD16,Aisha,"['arxiv.org/abs/2001.05316', {'title': 'Authorship Attribution in Bangla literature using Character-level CNN'}]"
+setswana_ner_corpus,huggingface,['bibtex']
+sepedi_ner,huggingface,['bibtex']
+ctu-aic/csfever,ctu-aic,['arxiv.org/abs/1803.05355']
+siswati_ner_corpus,huggingface,['bibtex']
+ro_sts_parallel,huggingface,[{'title': 'Liro: Benchmark and leaderboard for romanian language tasks'}]
+SoLID/shellcode_i_a32,SoLID,"['arxiv.org/abs/2104.13100', 'bibtex']"
+GEM/CrossWOZ,GEM,['bibtex']
+corypaik/coda,corypaik,['arxiv.org/abs/2110.08182']
+sesotho_ner_corpus,huggingface,['bibtex']
+pass,huggingface,['arxiv.org/abs/2109.13228']
+BSC-LT/SQAC,BSC-LT,"['arxiv.org/abs/2107.07253', 'bibtex']"
+NbAiLab/bokmaal_admin,NbAiLab,"['arxiv.org/abs/2104.09617', {'title': 'Operationalizing a National Digital Library: The Case for a {N'}]"
+debatelab/aaac,debatelab,['arxiv.org/abs/2110.01509']
+castorini/msmarco_v2_passage_doc2query-t5_expansions,castorini,[{'title': 'From doc2query to {docTTTTTquery'}]
+allegro/klej-polemo2-out,allegro,['bibtex']
+DanL/scientific-challenges-and-directions-dataset,DanL,['arxiv.org/abs/2108.13751']
+wisesight1000,huggingface,"['doi.org/10.5281/zenodo.3457447}', {'title': 'TLex: Thai lexeme analyser based on the conditional random fields'}]"
+Intel/WEC-Eng,Intel,['bibtex']
+classla/copa_hr,classla,"['arxiv.org/abs/2005.00333', 'bibtex']"
+allegro/klej-polemo2-in,allegro,['bibtex']
+igbo_english_machine_translation,huggingface,['arxiv.org/abs/2004.00648']
+classla/reldi_sr,classla,"[{'title': 'Tviterasi, tviteraši or twitteraši? Producing and analysing a normalised dataset of Croatian and Serbian tweets'}]"
+classla/reldi_hr,classla,"[{'title': 'Tviterasi, tviteraši or twitteraši? Producing and analysing a normalised dataset of Croatian and Serbian tweets'}]"
+Firoj/HumAID,Firoj,['bibtex']
+castorini/msmarco_v1_passage_doc2query-t5_expansions,castorini,[{'title': 'From doc2query to {docTTTTTquery'}]
+allegro/klej-psc,allegro,[{'title': 'The {P'}]
+polsum,huggingface,['bibtex']
+BSC-LT/tecla,BSC-LT,"['doi.org/10.5281/zenodo.4627198', 'bibtex']"
+castorini/msmarco_v2_doc_doc2query-t5_expansions,castorini,[{'title': 'From doc2query to {docTTTTTquery'}]
+Mansooreh/sharif-emotional-speech-dataset,Mansooreh,['doi.org/10.1007/s10579-018-9427-x}']
+castorini/msmarco_v1_doc_segmented_doc2query-t5_expansions,castorini,[{'title': 'From doc2query to {docTTTTTquery'}]
+castorini/msmarco_v1_doc_doc2query-t5_expansions,castorini,[{'title': 'From doc2query to {docTTTTTquery'}]
+collectivat/tv3_parla,collectivat,['bibtex']
+castorini/msmarco_v2_doc_segmented_doc2query-t5_expansions,castorini,[{'title': 'From doc2query to {docTTTTTquery'}]
+imvladikon/knesset_meetings_corpus,imvladikon,['doi.org/10.5281/zenodo.2707356']
+BSC-LT/ancora-ca-ner,BSC-LT,"['doi.org/10.5281/zenodo.4529299', 'bibtex']"
+midas/inspec,midas,[{'title': 'Improved automatic keyword extraction given more linguistic knowledge'}]
+anton-l/common_language,anton-l,['doi.org/10.5281/zenodo.5036977}']
+BSC-LT/xquad-ca,BSC-LT,"['arxiv.org/abs/1910.11856', 'doi.org/10.5281/zenodo.4526224', 'bibtex']"
+BSC-LT/viquiquad,BSC-LT,"['arxiv.org/abs/1606.05250', 'doi.org/10.5281/zenodo.4562345', 'bibtex']"
+BSC-LT/sts-ca,BSC-LT,"['doi.org/10.5281/zenodo.4529184', 'bibtex']"
+holylovenia/recam,holylovenia,['bibtex']
+cylee/github-issues,cylee,"['arxiv.org/abs/2005.00614', 'doi.org/),', 'bibtex']"
+fractalego/QA_to_statements,fractalego,['arxiv.org/abs/1809.02922']
+BeIR/scifact,BeIR,[{'title': '{BEIR'}]
+visual_genome,huggingface,"['arxiv.org/abs/1602.07332', {'title': 'Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations'}]"
+eugenesiow/PIRM,eugenesiow,['arxiv.org/abs/1809.07517']
+bigbio/bc5cdr,bigbio,"['doi.org/10.1093/database/baw068},', 'bibtex']"
+jimregan/clarinpl_studio,jimregan,"['arxiv.org/abs/1706.00245', {'title': 'Polish read speech corpus for speech tools and services'}]"
+german-nlp-group/german_common_crawl,german-nlp-group,[{'title': 'CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data'}]
+bigbio/bionlp_st_2013_cg,bigbio,['bibtex']
+mariosasko/glue,mariosasko,[{'title': 'Neural Network Acceptability Judgments'}]
+allenai/scicite,allenai,"['arxiv.org/abs/1904.01608', 'bibtex']"
+shunk031/JGLUE,shunk031,['bibtex']
+SpeedOfMagic/ontonotes_english,SpeedOfMagic,['bibtex']
+lucasmccabe/logiqa,lucasmccabe,[{'title': 'Logiqa: A challenge dataset for machine reading comprehension with logical reasoning'}]
+projecte-aina/catalanqa,projecte-aina,['arxiv.org/abs/1606.05250']
+GEM/xwikis,GEM,['arxiv.org/abs/2202.09583']
+GBaker/MedQA-USMLE-4-options,GBaker,[{'title': 'What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams'}]
+philschmid/flanv2,philschmid,['arxiv.org/abs/2301.13688']
+tner/tweetner7,tner,"['arxiv.org/abs/2210.03797', 'bibtex']"
+bigbio/bioasq_task_b,bigbio,['bibtex']
+BeIR/arguana,BeIR,[{'title': '{BEIR'}]
+bigbio/tmvar_v3,bigbio,"['arxiv.org/abs/2204.03637', 'doi.org/10.48550/arxiv.2204.03637,']"
+bigbio/ncbi_disease,bigbio,[{'title': 'NCBI disease corpus: A resource for disease name recognition and concept normalization'}]
+akariasai/PopQA,akariasai,[{'title': 'When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories '}]
+masakhane/mafand,masakhane,['bibtex']
+wdc/products-2017,wdc,[{'title': 'The WDC training dataset and gold standard for large-scale product matching'}]
+adithya7/xlel_wd_dictionary,adithya7,"['arxiv.org/abs/2204.06535', {'title': 'Multilingual Event Linking to Wikidata'}]"
+graphs-datasets/ZINC,graphs-datasets,"['doi.org/10.1021/acs.jcim.5b00559', 'bibtex']"
+NeelNanda/counterfact-tracing,NeelNanda,['arxiv.org/abs/2211.00593']
+metaeval/tomi-nli,metaeval,"['arxiv.org/abs/2301.05948', {'title': 'tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation'}]"
+GEM/FairytaleQA,GEM,"['arxiv.org/abs/2203.13947', 'bibtex']"
+midas/kptimes,midas,[{'title': 'KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents'}]
+qwedsacf/grade-school-math-instructions,qwedsacf,[{'title': 'Training Verifiers to Solve Math Word Problems'}]
+sayakpaul/nyu_depth_v2,sayakpaul,"['arxiv.org/abs/1903.03273', 'bibtex']"
+midas/semeval2017,midas,"['arxiv.org/abs/1704.02853', 'bibtex']"
+csebuetnlp/squad_bn,csebuetnlp,['arxiv.org/abs/2101.00204']
+jordiae/exebench,jordiae,"['doi.org/10.1145/3520312.3534867},', 'bibtex']"
+lmqg/qg_squadshifts,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+ruanchaves/b2w-reviews01,ruanchaves,[{'title': 'B2W-reviews01: an open product reviews corpus'}]
+BeIR/nq,BeIR,[{'title': '{BEIR'}]
+relbert/t_rex_relational_similarity,relbert,[{'title': 'T-rex: A large scale alignment of natural language with knowledge base triples'}]
+metaeval/sts-companion,metaeval,['bibtex']
+juny116/few_glue,juny116,['arxiv.org/abs/2012.15723']
+lmqg/qag_jaquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+StonyBrookNLP/tellmewhy,StonyBrookNLP,['bibtex']
+tner/wikineural,tner,['bibtex']
+bigbio/ebm_pico,bigbio,['bibtex']
+CodedotAI/code_clippy,CodedotAI,['arxiv.org/abs/2107.03374']
+knkarthick/samsum,knkarthick,"['arxiv.org/abs/1911.12237', 'bibtex']"
+bigbio/biosses,bigbio,[{'title': 'BIOSSES: a semantic sentence similarity estimation system for the biomedical domain'}]
+wanyu/IteraTeR_full_sent,wanyu,['arxiv.org/abs/2203.03802']
+ccdv/mediasum,ccdv,[{'title': 'MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization'}]
+norne,huggingface,"['arxiv.org/abs/1911.12146', {'title': 'NorNE: Annotating Named Entities for Norwegian'}]"
+armanc/pubmed-rct20k,armanc,[{'title': 'Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts'}]
+BeIR/fever-qrels,BeIR,[{'title': '{BEIR'}]
+bigbio/drugprot,bigbio,['bibtex']
+climatebert/environmental_claims,climatebert,['arxiv.org/abs/2209.00507']
+PolyAI/banking77,PolyAI,"['arxiv.org/abs/2003.04807', 'bibtex']"
+allenai/scico,allenai,[{'title': 'SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts'}]
+GEM/SIMPITIKI,GEM,[{'title': 'SIMPITIKI: a Simplification corpus for Italian'}]
+chrishuber/kaggle_mnli,chrishuber,['arxiv.org/abs/1704.05426']
+nlphuji/winogavil,nlphuji,"['arxiv.org/abs/2207.12576', {'title': 'WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models'}]"
+DFKI-SLT/wikitext_linked,DFKI-SLT,[{'title': 'Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing'}]
+abdusah/masc,abdusah,['doi.org/10.21227/e1qb-jv46']
+florianbussmann/FUNSD-vu2020revising,florianbussmann,"['arxiv.org/abs/2010.05322', {'title': 'Revising FUNSD dataset for key-value detection in document images'}]"
+Pratik/Gujarati_OpenSLR,Pratik,"[{'title': '{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems'}]"
+biglam/nls_chapbook_illustrations,biglam,"['doi.org/10.1145/3476887.3476893},', 'bibtex']"
+NbAiLab/NCC_small_divided,NbAiLab,"['arxiv.org/abs/2104.09617', {'title': 'Operationalizing a National Digital Library: The Case for a {N'}]"
+ehcalabres/ravdess_speech,ehcalabres,['doi.org/10.1371/journal.pone.0196391']
+bigbio/biomrc,bigbio,['bibtex']
+HiTZ/euscrawl,HiTZ,['arxiv.org/abs/2203.08111']
+osyvokon/pavlick-formality-scores,osyvokon,['bibtex']
+guoqiang/cuge,guoqiang,['bibtex']
+bigscience/xP3megds,bigscience,['arxiv.org/abs/2211.01786']
+nlphuji/mscoco_2014_5k_test_image_text_retrieval,nlphuji,[{'title': 'Microsoft coco: Common objects in context'}]
+din0s/asqa,din0s,['arxiv.org/abs/2204.06092']
+shahules786/prosocial_augmented,shahules786,[{'title': 'ProsocialDialog: A Prosocial Backbone for Conversational Agents'}]
+BeIR/nfcorpus,BeIR,[{'title': '{BEIR'}]
+pierreguillou/DocLayNet-base,pierreguillou,"['arxiv.org/abs/2206.01062', 'doi.org/10.1145/3534678.3539043', {'title': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation'}]"
+bigbio/n2c2_2018_track2,bigbio,"['doi.org/10.1093/jamia/ocz166},', 'bibtex']"
+BeIR/scifact-qrels,BeIR,[{'title': '{BEIR'}]
+graphs-datasets/IMDB-BINARY,graphs-datasets,"['doi.org/10.1145/2783258.2783417},', 'bibtex']"
+qwant/squad_fr,qwant,['bibtex']
+bigbio/cellfinder,bigbio,[{'title': 'Annotating and evaluating text for stem cell research'}]
+qanastek/ECDC,qanastek,"['doi.org/10.1007/s10579-014-9277-0},', 'bibtex']"
+Anthropic/model-written-evals,Anthropic,['arxiv.org/abs/1804.09301']
+wanyu/IteraTeR_human_sent,wanyu,['arxiv.org/abs/2203.03802']
+jpwahle/autoencoder-paraphrase-dataset,jpwahle,[{'title': 'Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection'}]
+BeIR/scidocs-qrels,BeIR,[{'title': '{BEIR'}]
+shanya/crd3,shanya,[{'title': 'Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset'}]
+DFKI-SLT/kbp37,DFKI-SLT,"['arxiv.org/abs/1508.01006', 'bibtex']"
+copenlu/answerable_tydiqa,copenlu,[{'title': 'TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages'}]
+nightingal3/fig-qa,nightingal3,"['arxiv.org/abs/2204.12632', 'doi.org/10.48550/arxiv.2204.12632,']"
+WillHeld/JamPatoisNLI,WillHeld,"['doi.org/10.48550/arXiv.2212.03419},', 'bibtex']"
+pkavumba/balanced-copa,pkavumba,['bibtex']
+dali-does/clevr-math,dali-does,"['arxiv.org/abs/2208.05358', 'doi.org/10.48550/arxiv.2208.05358,']"
+pietrolesci/ag_news,pietrolesci,[{'title': 'Character-level Convolutional Networks for Text Classification'}]
+lmqg/qa_harvesting_from_wikipedia,lmqg,['bibtex']
+drAbreu/bc4chemd_ner,drAbreu,[{'title': 'The CHEMDNER corpus of chemicals and drugs and its annotation principles'}]
+pierreguillou/DocLayNet-large,pierreguillou,"['arxiv.org/abs/2206.01062', 'doi.org/10.1145/3534678.3539043', {'title': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation'}]"
+bigbio/gnormplus,bigbio,['doi.org/10.1155/2015/918710}']
+strombergnlp/rumoureval_2019,strombergnlp,"['arxiv.org/abs/1809.06683', 'bibtex']"
+koutch/dublin_programming_data,koutch,[{'title': 'user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code'}]
+ai4bharat/IndicQuestionGeneration,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
+Jzuluaga/uwb_atcc,Jzuluaga,"['arxiv.org/abs/2203.16822', {'title': 'How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications'}]"
+launch/open_question_type,launch,['bibtex']
+copenlu/citeworth,copenlu,[{'title': '{CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding'}]
+csebuetnlp/BanglaParaphrase,csebuetnlp,"['arxiv.org/abs/2210.05109', {'title': 'BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset'}]"
+launch/gov_report,launch,['bibtex']
+persiannlp/parsinlu_translation_en_fa,persiannlp,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+merionum/ru_paraphraser,merionum,[{'title': 'ParaPhraser: Russian paraphrase corpus and shared task'}]
+shunk031/cocostuff,shunk031,['arxiv.org/abs/1612.03716']
+lasha-nlp/CONDAQA,lasha-nlp,"['arxiv.org/abs/2211.00295', {'title': 'CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation'}]"
+jpwahle/autoregressive-paraphrase-dataset,jpwahle,['arxiv.org/abs/2210.03568']
+eastwind/self-instruct-base,eastwind,['arxiv.org/abs/2212.10560']
+copenlu/scientific-exaggeration-detection,copenlu,[{'title': '{Semi-Supervised Exaggeration Detection of Health Science Press Releases'}]
+COLD-team/COLD,COLD-team,[{'title': 'COLD: Annotation scheme and evaluation data set for complex offensive language in English'}]
+bigbio/codiesp,bigbio,"[{'title': 'Overview of Automatic Clinical Coding: Annotations, Guidelines, and Solutions for non-English Clinical Cases at CodiEsp Track of CLEF eHealth 2020.'}]"
+bond005/sberdevices_golos_100h_farfield,bond005,['arxiv.org/abs/2106.10161']
+JanosAudran/financial-reports-sec,JanosAudran,['doi.org/10.5281/zenodo.5589195']
+MilaNLProc/honest,MilaNLProc,"[{'title': '""{HONEST'}]"
+TurkuNLP/xlsum-fi,TurkuNLP,['bibtex']
+BeIR/arguana-qrels,BeIR,[{'title': '{BEIR'}]
+bond005/sberdevices_golos_10h_crowd,bond005,['arxiv.org/abs/2106.10161']
+tner/fin,tner,['bibtex']
+mteb/bucc-bitext-mining,mteb,['arxiv.org/abs/2104.06893']
+bigbio/chebi_nactem,bigbio,['bibtex']
+ai4bharat/IndicHeadlineGeneration,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
+cardiffnlp/tweet_topic_multi,cardiffnlp,"['arxiv.org/abs/2209.09824', 'bibtex']"
+bigbio/hallmarks_of_cancer,bigbio,"['doi.org/10.1093/bioinformatics/btv585},', 'bibtex']"
+qanastek/ELRC-Medical-V2,qanastek,['bibtex']
+EMBO/BLURB,EMBO,"['doi.org/10.1093/bioinformatics/btv585},', 'bibtex']"
+relbert/t_rex,relbert,[{'title': 'T-rex: A large scale alignment of natural language with knowledge base triples'}]
+Muennighoff/mbpp,Muennighoff,"['arxiv.org/abs/2108.07732', {'title': 'Program Synthesis with Large Language Models'}]"
+kasnerz/hitab,kasnerz,[{'title': 'HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation'}]
+sbu_captions,huggingface,['bibtex']
+mozilla-foundation/common_voice_1_0,mozilla-foundation,"['arxiv.org/abs/1912.06670', 'bibtex']"
+juletxara/xquad_xtreme,juletxara,['bibtex']
+projecte-aina/xquad-ca,projecte-aina,"['arxiv.org/abs/2107.07903', 'doi.org/10.5281/zenodo.4526223)', 'bibtex']"
+OGB/ogbg-molhiv,OGB,['bibtex']
+gtfintechlab/finer-ord,gtfintechlab,[{'title': 'FiNER: Financial Named Entity Recognition Dataset and Weak-supervision Model'}]
+alexfabbri/answersumm,alexfabbri,['arxiv.org/abs/2111.06474']
+AhmedSSoliman/DJANGO,AhmedSSoliman,"['doi.org/10.1109/ASE.2015.36},', 'bibtex']"
+cjvt/sentinews,cjvt,"['doi.org/10.1007/s10579-018-9413-3', {'title': 'Annotated news corpora and a lexicon for sentiment analysis in Slovene'}]"
+bigbio/chia,bigbio,"[{'title': 'Chia, a large annotated corpus of clinical trial eligibility criteria'}]"
+MLRS/korpus_malti,MLRS,['bibtex']
+bigbio/cpi,bigbio,[{'title': 'Automated recognition of functional compound-protein relationships in literature'}]
+GBaker/MedQA-USMLE-4-options-hf,GBaker,[{'title': 'What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams'}]
+eloukas/edgar-corpus,eloukas,"['arxiv.org/abs/2109.14394', 'doi.org/10.18653/v1/2022.acl-long.303', 'bibtex']"
+detection-datasets/fashionpedia,detection-datasets,"['arxiv.org/abs/2004.12276', {'title': 'Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset'}]"
+laugustyniak/political-advertising-pl,laugustyniak,['bibtex']
+bigbio/an_em,bigbio,['bibtex']
+lara-martin/Scifi_TV_Shows,lara-martin,[{'title': 'Story Realization: Expanding Plot Events into Sentences'}]
+nlp-thedeep/humset,nlp-thedeep,['bibtex']
+bigbio/scielo,bigbio,[{'title': 'A Large Parallel Corpus of Full-Text Scientific Articles'}]
+bigbio/umnsrs,bigbio,[{'title': 'Semantic similarity and relatedness between clinical terms: an experimental study'}]
+jonathanli/echr,jonathanli,['arxiv.org/abs/1906.02059']
+HuggingFaceH4/hhh_alignment,HuggingFaceH4,"['arxiv.org/abs/2112.00861', 'bibtex']"
+jordanparker6/publaynet,jordanparker6,"['arxiv.org/abs/1908.07836', {'title': ' PubLayNet: largest dataset ever for document layout analysis '}]"
+qanastek/HoC,qanastek,[{'title': 'Automatic semantic classification of scientific literature according to the hallmarks of cancer'}]
+KocLab-Bilkent/turkish-constitutional-court,KocLab-Bilkent,"['doi.org/10.1016/j.ipm.2021.102684', {'title': '{Natural language processing in law: Prediction of outcomes in the higher courts of Turkey'}]"
+Jzuluaga/atcosim_corpus,Jzuluaga,"['arxiv.org/abs/2203.16822', {'title': 'How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications'}]"
+tomekkorbak/pile-detoxify,tomekkorbak,['arxiv.org/abs/1907.11692']
+domenicrosati/QA2D,domenicrosati,"['arxiv.org/abs/1809.02922', 'bibtex']"
+bigbio/muchmore,bigbio,"[{'title': 'A multi-layered, xml-based approach to the integration of linguistic and semantic annotations'}]"
+quickdraw,huggingface,"['arxiv.org/abs/1704.03477', 'bibtex']"
+copenlu/spiced,copenlu,[{'title': '{Modeling Information Change in Science Communication with Semantically Matched Paraphrases'}]
+SALT-NLP/ImplicitHate,SALT-NLP,['bibtex']
+bigbio/scitail,bigbio,['bibtex']
+kakaobrain/coyo-700m,kakaobrain,['arxiv.org/abs/2102.05918']
+DFKI-SLT/cross_ner,DFKI-SLT,"['arxiv.org/abs/2012.04373', {'title': 'CrossNER: Evaluating Cross-Domain Named Entity Recognition'}]"
+RussianNLP/tape,RussianNLP,"['arxiv.org/abs/2210.12813', {'title': 'TAPE: Assessing Few-shot Russian Language Understanding'}]"
+codeparrot/codecomplex,codeparrot,['bibtex']
+bigbio/bionlp_st_2019_bb,bigbio,['bibtex']
+bigbio/bionlp_st_2011_epi,bigbio,['bibtex']
+s-nlp/paradetox,s-nlp,['bibtex']
+zoheb/sketch-scene,zoheb,[{'title': 'FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.'}]
+lmqg/qag_itquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+iluvvatar/RuNNE,iluvvatar,"['arxiv.org/abs/2108.13112', {'title': '{RuNNE-2022 Shared Task: Recognizing Nested Named Entities'}]"
+bigbio/distemist,bigbio,['bibtex']
+albertvillanova/visual-spatial-reasoning,albertvillanova,"['arxiv.org/abs/2205.00363', {'title': 'Visual Spatial Reasoning'}]"
+bigbio/bionlp_st_2011_id,bigbio,['bibtex']
+neulab/docprompting-conala,neulab,[{'title': 'DocCoder: Generating Code by Retrieving and Reading Docs'}]
+bigbio/bionlp_st_2013_pc,bigbio,['bibtex']
+google/wit,google,"['arxiv.org/abs/2103.01913', {'title': 'WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning'}]"
+graphs-datasets/CIFAR10,graphs-datasets,"['arxiv.org/abs/2003.00982', 'bibtex']"
+lhoestq/custom_squad,lhoestq,['bibtex']
+ai4bharat/IndicSentenceSummarization,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
+BeIR/nfcorpus-qrels,BeIR,[{'title': '{BEIR'}]
+thepurpleowl/codequeries,thepurpleowl,"['arxiv.org/abs/2209.08372', 'doi.org/10.48550/arxiv.2209.08372,']"
+bigbio/pdr,bigbio,[{'title': 'A corpus of plant--disease relations in the biomedical domain'}]
+ciempiess/ciempiess_test,ciempiess,"['doi.org/10.35111/xdx5-n815},']"
+bigbio/ask_a_patient,bigbio,['bibtex']
+HuggingFaceM4/charades,HuggingFaceM4,"['arxiv.org/abs/1604.01753', 'bibtex']"
+Jiangjie/ekar_chinese,Jiangjie,['bibtex']
+LLukas22/NLQuAD,LLukas22,['bibtex']
+bigbio/ddi_corpus,bigbio,"['doi.org/10.1016/j.jbi.2013.07.011},', 'bibtex']"
+Short-Answer-Feedback/saf_communication_networks_english,Short-Answer-Feedback,['bibtex']
+feradauto/MoralExceptQA,feradauto,"['arxiv.org/abs/2210.01478', 'doi.org/10.48550/arxiv.2210.01478,']"
+copenlu/fever_gold_evidence,copenlu,['bibtex']
+BeIR/trec-news-generated-queries,BeIR,[{'title': '{BEIR'}]
+persiannlp/parsinlu_entailment,persiannlp,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+IDEA-CCNL/AFQMC,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
+larrylawl/douban-dushu,larrylawl,[{'title': 'LSICC: A Large Scale Informal Chinese Corpus'}]
+qanastek/WMT-16-PubMed,qanastek,['bibtex']
+relbert/nell_relational_similarity,relbert,['bibtex']
+bigbio/ctebmsp,bigbio,"['doi.org/10.1186/s12911-021-01395-z},', 'bibtex']"
+voidful/NMSQA,voidful,['arxiv.org/abs/2203.04911']
+HuggingFaceH4/self-instruct-seed,HuggingFaceH4,['arxiv.org/abs/2212.10560']
+jonatli/the_pile_mystic,jonatli,['arxiv.org/abs/2101.00027']
+blinoff/kinopoisk,blinoff,[{'title': 'Research of lexical approach and machine learning methods for sentiment analysis'}]
+philschmid/emotion,philschmid,['bibtex']
+pszemraj/SQuALITY-v1.3,pszemraj,['arxiv.org/abs/2205.11465']
+language-and-voice-lab/althingi_asr,language-and-voice-lab,"['doi.org/10.35111/695b-6697},']"
+huggan/summer2winter_yosemite,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+bigbio/spl_adr_200db,bigbio,['bibtex']
+bigbio/anat_em,bigbio,[{'title': 'Anatomical entity mention recognition at literature scale'}]
+imvladikon/nemo_corpus,imvladikon,"['doi.org/10.1162/tacl\\_a\\_00404},', 'bibtex']"
+bigbio/bioinfer,bigbio,[{'title': 'BioInfer: a corpus for information extraction in the biomedical domain'}]
+EleutherAI/wikitext_document_level,EleutherAI,['arxiv.org/abs/1609.07843']
+bigbio/scai_disease,bigbio,['bibtex']
+bigbio/mednli,bigbio,"['doi.org/10.13026/c2rs98,']"
+lavis-nlp/german_legal_sentences,lavis-nlp,['arxiv.org/abs/2005.13342']
+bigbio/bioscope,bigbio,"[{'title': 'The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes'}]"
+bigbio/twadrl,bigbio,['bibtex']
+cfilt/HiNER-original,cfilt,"['arxiv.org/abs/2204.13743', 'doi.org/10.48550/arxiv.2204.13743,']"
+demelin/wino_x,demelin,[{'title': 'Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution'}]
+relbert/relation_mapping,relbert,[{'title': 'The latent relation mapping engine: Algorithm and experiments'}]
+anab/copa-sse,anab,['arxiv.org/abs/2201.06777']
+rbawden/DiaBLa,rbawden,['bibtex']
+graphs-datasets/MUTAG,graphs-datasets,"['doi.org/10.1021/jm00106a046', 'bibtex']"
+bigbio/bionlp_shared_task_2009,bigbio,['bibtex']
+bigbio/n2c2_2010,bigbio,"['doi.org/10.1136/amiajnl-2011-000203},', 'bibtex']"
+bigbio/bionlp_st_2011_ge,bigbio,['bibtex']
+knkarthick/xsum,knkarthick,['arxiv.org/abs/1808.08745']
+bigbio/bionlp_st_2013_ge,bigbio,['bibtex']
+bigbio/genetag,bigbio,"['doi.org/10.1186/1471-2105-6-S1-S3},', 'bibtex']"
+PlanTL-GOB-ES/sts-es,PlanTL-GOB-ES,"[{'title': 'Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability'}]"
+bigbio/jnlpba,bigbio,['bibtex']
+bigbio/sciq,bigbio,['bibtex']
+ai4bharat/IndicParaphrase,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
+bigbio/medal,bigbio,[{'title': 'MeDAL\\: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining'}]
+bigbio/swedish_medical_ner,bigbio,['bibtex']
+BennoKrojer/ImageCoDe,BennoKrojer,['arxiv.org/abs/2203.15867']
+ett,huggingface,"['arxiv.org/abs/2012.07436', 'bibtex']"
+bigbio/meddialog,bigbio,"['arxiv.org/abs/2004.03329', 'bibtex']"
+CLUTRR/v1,CLUTRR,"['arxiv.org/abs/1908.06177', 'bibtex']"
+BeIR/hotpotqa,BeIR,[{'title': '{BEIR'}]
+bigbio/mqp,bigbio,['bibtex']
+bigbio/seth_corpus,bigbio,['doi.org/10.1093/bioinformatics/btw234']
+bigbio/scai_chemical,bigbio,['bibtex']
+cjvt/cosimlex,cjvt,['bibtex']
+thesofakillers/SemCor,thesofakillers,['bibtex']
+SLPL/naab,SLPL,"['arxiv.org/abs/2208.13486', 'doi.org/10.48550/arXiv.2208.13486](https://doi.org/10.48550/arXiv.2208.13486)', {'title': 'naab: A ready-to-use plug-and-play corpus for Farsi'}]"
+bigbio/meddocan,bigbio,"[{'title': 'Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.'}]"
+persiannlp/parsinlu_translation_fa_en,persiannlp,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+bigbio/n2c2_2014_deid,bigbio,"['doi.org/10.1016/j.jbi.2015.06.007},', 'bibtex']"
+martinjosifoski/SynthIE,martinjosifoski,"['arxiv.org/abs/2303.04132', {'title': 'Exploiting Asymmetry for Synthetic Training Data Generation: {S'}]"
+bigbio/biorelex,bigbio,['bibtex']
+bigbio/pubtator_central,bigbio,"['doi.org/10.1093/nar/gkz389},', {'title': '{PubTator central: automated concept annotation for biomedical full text articles'}]"
+bigbio/bionlp_st_2013_gro,bigbio,['bibtex']
+bigbio/ehr_rel,bigbio,[{'title': 'Biomedical Concept Relatedness {--'}]
+evaluate/glue-ci,evaluate,[{'title': 'Neural Network Acceptability Judgments'}]
+maveriq/DocBank,maveriq,['arxiv.org/abs/2006.01038']
+neulab/tldr,neulab,[{'title': 'DocCoder: Generating Code by Retrieving and Reading Docs'}]
+malteos/germeval2017,malteos,[{'title': '{GermEval 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback'}]
+bigbio/scicite,bigbio,['bibtex']
+bigbio/tmvar_v1,bigbio,[{'title': 'tmVar: a text mining approach for extracting sequence variants in biomedical literature'}]
+Paul/hatecheck-spanish,Paul,['arxiv.org/abs/2206.09917']
+koutch/refactory_programming_data,koutch,[{'title': 'Re-factoring based Program Repair applied to Programming Assignments'}]
+biglam/loc_beyond_words,biglam,"['arxiv.org/abs/2005.01583', 'doi.org/10.1145/3340531.3412767},', 'bibtex']"
+Shirali/ISSAI_KSC_335RS_v_1_1,Shirali,['bibtex']
+bigbio/citation_gia_test_collection,bigbio,"['doi.org/10.1155/2015/918710}', 'bibtex']"
+RCC-MSU/collection3,RCC-MSU,['bibtex']
+IDEA-CCNL/PretrainCorpusDemo,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
+readerbench/ro-fb-offense,readerbench,[{'title': 'FB-RO-Offense – A Romanian Dataset and Baseline Models for detecting Offensive Language in Facebook Comments'}]
+ai4bharat/IndicWikiBio,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
+midas/ldkp3k,midas,[{'title': 'LDKP - A Dataset for Identifying Keyphrases from Long Scientific Documents'}]
+Team-PIXEL/rendered-bookcorpus,Team-PIXEL,"['arxiv.org/abs/1506.06724', {'title': 'Language Modelling with Pixels'}]"
+stanford-crfm/DSIR-filtered-pile-50M,stanford-crfm,"['arxiv.org/abs/2302.03169', 'bibtex']"
+bigbio/mediqa_qa,bigbio,['bibtex']
+bigbio/progene,bigbio,['bibtex']
+bigbio/biology_how_why_corpus,bigbio,['bibtex']
+bigbio/cantemist,bigbio,"[{'title': 'Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.'}]"
+bigbio/bionlp_st_2011_rel,bigbio,['bibtex']
+bigbio/chemdner,bigbio,"['doi.org/10.1186/1758-2946-7-S1-S2},', {'title': 'The CHEMDNER corpus of chemicals and drugs and its annotation principles'}]"
+bigbio/bio_sim_verb,bigbio,['bibtex']
+ariesutiono/entailment-bank-v3,ariesutiono,[{'title': 'Explaining Answers with Entailment Trees'}]
+bigbio/verspoor_2013,bigbio,[{'title': 'Annotating the biomedical literature for the human variome'}]
+bigbio/bio_simlex,bigbio,['bibtex']
+breakend/nllb-multi-domain,breakend,"['arxiv.org/abs/2207.04672', 'bibtex']"
+bigbio/genia_ptm_event_corpus,bigbio,['bibtex']
+midas/krapivin,midas,[{'title': 'Large Dataset for Keyphrases Extraction'}]
+declare-lab/cicero,declare-lab,"['arxiv.org/abs/2203.13926', {'title': 'CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues'}]"
+pinecone/core-2020-05-10-deduplication,pinecone,[{'title': 'Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings'}]
+imvladikon/paranames,imvladikon,"['arxiv.org/abs/2202.14035', {'title': 'ParaNames: A Massively Multilingual Entity Name Corpus'}]"
+kanishka/comps,kanishka,"['arxiv.org/abs/2210.01963', {'title': 'COMPS: Conceptual Minimal Pair Sentences for testing Property Knowledge and Inheritance in Pre-trained Language Models'}]"
+bigbio/hprd50,bigbio,[{'title': 'RelEx—Relation extraction using dependency parse trees'}]
+phucdev/noisyner,phucdev,"['arxiv.org/abs/2101.09763', 'doi.org/10.15155/1-00-0000-0000-0000-00073L),', 'bibtex']"
+cfilt/iwn_wordlists,cfilt,[{'title': 'IndoWordNet'}]
+bigbio/multi_xscience,bigbio,"['arxiv.org/abs/2010.14235', 'doi.org/10.48550/arxiv.2010.14235,']"
+lmqg/qag_ruquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+projecte-aina/gencata,projecte-aina,['bibtex']
+graphs-datasets/MNIST,graphs-datasets,"['arxiv.org/abs/2003.00982', 'bibtex']"
+tner/bionlp2004,tner,['bibtex']
+strombergnlp/twitter_pos,strombergnlp,[{'title': 'Named entity recognition in tweets: an experimental study'}]
+batubayk/TR-News,batubayk,['bibtex']
+BeIR/fiqa-generated-queries,BeIR,[{'title': '{BEIR'}]
+bigbio/lll,bigbio,['bibtex']
+lmqg/qa_harvesting_from_wikipedia_pseudo,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+strombergnlp/x-stance,strombergnlp,"['arxiv.org/abs/2003.08385', {'title': 'X-stance: A multilingual multi-target dataset for stance detection'}]"
+bigbio/psytar,bigbio,"['doi.org/10.1016/j.jbi.2018.12.005},', 'bibtex']"
+danwakeem/wikitablequestions-wtq,danwakeem,"['arxiv.org/abs/1508.00305', 'bibtex']"
+conceptual_12m,huggingface,"['arxiv.org/abs/2102.08981', {'title': '{Conceptual 12M'}]"
+mrm8488/unnatural-instructions-full,mrm8488,['arxiv.org/abs/2212.09689']
+shunk031/jsnli,shunk031,[{'title': '機械翻訳を用いた自然言語推論データセットの多言語化'}]
+fabraz/writingPromptAug,fabraz,['arxiv.org/abs/1805.04833']
+yuningm/citesum,yuningm,['arxiv.org/abs/2205.06207']
+orieg/elsevier-oa-cc-by,orieg,"['arxiv.org/abs/2008.00774', 'doi.org/10.48550/arXiv.2008.00774)', {'title': 'Elsevier OA CC-By Corpus'}]"
+bigbio/genia_term_corpus,bigbio,['bibtex']
+graphs-datasets/PROTEINS,graphs-datasets,"['doi.org/10.1093/bioinformatics/bti1007},', 'bibtex']"
+lmqg/qg_subjqa,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+bigbio/blurb,bigbio,['bibtex']
+djaym7/wiki_dialog,djaym7,[{'title': 'Dialog Inpainting: Turning Documents to Dialogs'}]
+gsarti/mt_geneval,gsarti,"['arxiv.org/abs/2211.01355', 'doi.org/10.18653/v1/N18-2003).', 'bibtex']"
+bigbio/osiris,bigbio,['doi.org/10.1186/1471-2105-9-84}']
+bigbio/meqsum,bigbio,['bibtex']
+bigbio/n2c2_2009,bigbio,"['doi.org/10.1136/jamia.2010.003947},', 'bibtex']"
+silver/mmchat,silver,"['arxiv.org/abs/2108.07154', 'bibtex']"
+bigbio/mayosrs,bigbio,[{'title': 'Measures of semantic similarity and relatedness in the biomedical domain'}]
+bigbio/minimayosrs,bigbio,[{'title': 'Measures of semantic similarity and relatedness in the biomedical domain'}]
+UCL-DARK/ludwig,UCL-DARK,"['doi.org/10.1016/j.procs.2020.04.251)', 'bibtex']"
+jmhessel/newyorker_caption_contest,jmhessel,"['arxiv.org/abs/2209.06293', {'title': 'Do Androids Laugh at Electric Sheep? Humor ""Understanding"" Benchmarks from The New Yorker Caption Contest'}]"
+GEM/indonlg,GEM,['bibtex']
+huggan/apple2orange,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+bigbio/paramed,bigbio,['bibtex']
+israel/Amharic-News-Text-classification-Dataset,israel,"['arxiv.org/abs/2103.05639', 'doi.org/10.48550/arxiv.2103.05639,']"
+BeIR/quora,BeIR,[{'title': '{BEIR'}]
+Non-Residual-Prompting/C2Gen,Non-Residual-Prompting,['arxiv.org/abs/1911.03705']
+bigbio/scifact,bigbio,['bibtex']
+jpwahle/machine-paraphrase-dataset,jpwahle,[{'title': 'Identifying Machine-Paraphrased Plagiarism'}]
+cjvt/slownet,cjvt,"[{'title': 'sloWNet 3.0: development, extension and cleaning'}]"
+tner/tweebank_ner,tner,"['arxiv.org/abs/2201.07281', 'bibtex']"
+bigbio/medhop,bigbio,['bibtex']
+BeIR/nq-qrels,BeIR,[{'title': '{BEIR'}]
+loubnabnl/humaneval_infilling,loubnabnl,[{'title': 'Efficient Training of Language Models to Fill in the Middle'}]
+mxeval/multi-humaneval,mxeval,"['arxiv.org/abs/2210.14868', {'title': 'Multi-lingual Evaluation of Code Generation Models'}]"
+EleutherAI/arithmetic,EleutherAI,[{'title': 'Language Models are Few-Shot Learners'}]
+AigizK/bashkir-russian-parallel-corpora,AigizK,[{'title': 'Bashkir-Russian parallel corpora'}]
+bigbio/tmvar_v2,bigbio,[{'title': 'tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine'}]
+pain/AASL,pain,"['arxiv.org/abs/2301.11932', 'doi.org/10.48550/arxiv.2301.11932,']"
+dl4phys/top_tagging,dl4phys,"['arxiv.org/abs/1902.09914', 'doi.org/10.5281/zenodo.2603256}']"
+ds4sd/icdar2023-doclaynet,ds4sd,"['doi.org/10.1145/3534678.3539043},', {'title': 'DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation'}]"
+HIT-TMG/Hansel,HIT-TMG,"['arxiv.org/abs/2207.13005', 'doi.org/10.1145/3539597.3570418},', 'bibtex']"
+lcampillos/ctebmsp,lcampillos,[{'title': 'A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine'}]
+declare-lab/HyperRED,declare-lab,"['arxiv.org/abs/2211.10018', {'title': 'A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach'}]"
+shmuhammad/AfriSenti-twitter-sentiment,shmuhammad,[{'title': 'AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages'}]
+webis/args_me,webis,['doi.org/10.5281/zenodo.4139439}']
+UKPLab/TexPrax,UKPLab,"['arxiv.org/abs/2208.07846', {'title': 'TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation'}]"
+ccdv/WCEP-10,ccdv,"['arxiv.org/abs/2005.10070', 'bibtex']"
+bigbio/pico_extraction,bigbio,['bibtex']
+Paul/hatecheck-italian,Paul,['arxiv.org/abs/2206.09917']
+metaeval/implicatures,metaeval,"['arxiv.org/abs/2301.05948', {'title': 'Conversational implicatures in English dialogue: Annotated dataset'}]"
+bigbio/mediqa_rqe,bigbio,['bibtex']
+DFKI-SLT/gids,DFKI-SLT,"['arxiv.org/abs/1804.06987', 'bibtex']"
+akash418/germeval_2017,akash418,[{'title': '{GermEval 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback'}]
+rongzhangibm/NaturalQuestionsV2,rongzhangibm,[{'title': 'Natural Questions: a Benchmark for Question Answering Research'}]
+BeIR/webis-touche2020,BeIR,[{'title': '{BEIR'}]
+PM-AI/germandpr-beir,PM-AI,['arxiv.org/abs/2104.08663']
+juletxara/visual-spatial-reasoning,juletxara,"['arxiv.org/abs/2205.00363', {'title': 'Visual Spatial Reasoning'}]"
+bigbio/euadr,bigbio,"['doi.org/10.1016/j.jbi.2012.04.004},', {'title': 'The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships'}]"
+bigbio/evidence_inference,bigbio,['bibtex']
+jpwahle/dblp-discovery-dataset,jpwahle,[{'title': 'D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research'}]
+zpn/zinc20,zpn,"['doi.org/10.1021/acs.jcim.0c00675},', {'doi': '10.1021/acs.jcim.0c00675'}]"
+deutsche-telekom/ger-backtrans-paraphrase,deutsche-telekom,['arxiv.org/abs/1907.05791']
+lmqg/qa_squadshifts,lmqg,"['arxiv.org/abs/2004.14444', {'title': 'The effect of natural distribution shift on question answering models'}]"
+jonathanli/eurlex,jonathanli,['bibtex']
+Goud/Goud-sum,Goud,[{'title': 'Goud.ma: a News Article Dataset for Summarization in Moroccan Darija'}]
+alex-apostolo/filtered-cuad,alex-apostolo,"['arxiv.org/abs/2103.06268', {'title': 'CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review'}]"
+McGill-NLP/TopiOCQA,McGill-NLP,"['arxiv.org/abs/2110.00768', 'doi.org/10.1162/tacl\\_a\\_00471},', {'title': 'Topi{OCQA'}]"
+Paul/hatecheck-mandarin,Paul,['arxiv.org/abs/2206.09917']
+indonesian-nlp/mc4-id,indonesian-nlp,"['arxiv.org/abs/1910.10683', 'bibtex']"
+arka0821/multi_document_summarization,arka0821,"['arxiv.org/abs/2010.14235', {'title': 'Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles'}]"
+BeIR/trec-covid-qrels,BeIR,[{'title': '{BEIR'}]
+silver/lccc,silver,"['arxiv.org/abs/2008.03946', {'title': 'A Large-Scale Chinese Short-Text Conversation Dataset'}]"
+persiannlp/parsinlu_sentiment,persiannlp,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+DFKI-SLT/knowledge_net,DFKI-SLT,['bibtex']
+cfilt/HiNER-collapsed,cfilt,"['arxiv.org/abs/2204.13743', 'doi.org/10.48550/arxiv.2204.13743,']"
+bigbio/cas,bigbio,[{'title': '{CAS'}]
+lapix/CCAgT,lapix,"['doi.org/10.2139/ssrn.4126881)', {'doi': '10.2139/ssrn.4126881'}]"
+narad/ravdess,narad,['doi.org/10.1371/journal.pone.0196391.']
+BeIR/hotpotqa-qrels,BeIR,[{'title': '{BEIR'}]
+bigbio/pubhealth,bigbio,[{'title': 'Explainable automated fact-checking for public health claims'}]
+intfloat/query2doc_msmarco,intfloat,[{'title': 'Query2doc: Query Expansion with Large Language Models'}]
+svakulenk0/qrecc,svakulenk0,[{'title': 'Open-Domain Question Answering Goes Conversational via Question Rewriting'}]
+DFKI-SLT/fabner,DFKI-SLT,"['doi.org/10.1007/s10845-021-01807-x},', 'bibtex']"
+nedjmaou/MLMA_hate_speech,nedjmaou,['bibtex']
+Leyo/ActivityNet_Captions,Leyo,"['arxiv.org/abs/1705.00754', {'title': 'Dense-Captioning Events in Videos'}]"
+navanchauhan/decimer-data-mini,navanchauhan,['doi.org/10.26434/chemrxiv.14479287.v1']
+SLPL/naab-raw,SLPL,"['arxiv.org/abs/2208.13486', 'doi.org/10.48550/arXiv.2208.13486](https://doi.org/10.48550/arXiv.2208.13486).', {'title': 'naab: A ready-to-use plug-and-play corpus for Farsi'}]"
+DDSC/dagw_reddit_filtered_v1.0.0,DDSC,['arxiv.org/abs/2112.11446']
+mweiss/fashion_mnist_corrupted,mweiss,"['arxiv.org/abs/1906.02337', {'title': 'Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning'}]"
+Serverless/dev_mode-wtq,Serverless,"['arxiv.org/abs/1508.00305', 'bibtex']"
+shahules786/prosocial-confessions,shahules786,[{'title': 'ProsocialDialog: A Prosocial Backbone for Conversational Agents'}]
+PiC/phrase_retrieval,PiC,[{'title': 'PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search'}]
+lcampillos/CLARA-MeD,lcampillos,['doi.org/10.20350/digitalCSIC/14644](https://doi.org/10.20350/digitalCSIC/14644)']
+juletxara/tydiqa_xtreme,juletxara,"['arxiv.org/abs/2003.11080', {'title': 'TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages'}]"
+neulab/mconala,neulab,[{'title': 'MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages'}]
+pile-of-law/eoir_privacy,pile-of-law,['arxiv.org/abs/2207.00220']
+threite/Bundestag-v2,threite,['doi.org/10.7910/DVN/L4OAKN']
+antoniomenezes/go_emotions_ptbr,antoniomenezes,"['arxiv.org/abs/2005.00547', 'bibtex']"
+biglam/old_bailey_proceedings,biglam,['bibtex']
+LHF/escorpius,LHF,"['arxiv.org/abs/2206.15147', 'bibtex']"
+bigbio/mlee,bigbio,[{'title': 'Event extraction across multiple levels of biological organization'}]
+strombergnlp/broad_twitter_corpus,strombergnlp,[{'title': 'Broad twitter corpus: A diverse named entity recognition resource'}]
+liyucheng/chinese_metaphor_dataset,liyucheng,['bibtex']
+cgarciae/cartoonset,cgarciae,"['arxiv.org/abs/1711.05139', 'bibtex']"
+jason9693/APEACH,jason9693,"['arxiv.org/abs/2202.12459', {'title': 'APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets'}]"
+bigbio/nlm_wsd,bigbio,['bibtex']
+bigbio/pharmaconer,bigbio,['bibtex']
+tner/btc,tner,['bibtex']
+tushar117/xalign,tushar117,"['arxiv.org/abs/2202.00291', {'title': 'XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages'}]"
+conglu/vd4rl,conglu,['arxiv.org/abs/2206.04779']
+copenlu/sufficient_facts,copenlu,"['doi.org/10.1162/tacl\\_a\\_00486},', 'bibtex']"
+bigbio/genia_relation_corpus,bigbio,['bibtex']
+projecte-aina/tecla,projecte-aina,['doi.org/10.5281/zenodo.7334110])']
+Roh/ryanspeech,Roh,"['arxiv.org/abs/2106.08468', {'title': 'RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis'}]"
+matejklemen/vuamc,matejklemen,['bibtex']
+irds/trec-robust04,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+BeIR/climate-fever,BeIR,[{'title': '{BEIR'}]
+zjunlp/KGEditor,zjunlp,"['arxiv.org/abs/2301.10405', 'doi.org/10.48550/arXiv.2301.10405},', 'bibtex']"
+GEM/squality,GEM,"['arxiv.org/abs/2205.11465', {'title': 'S{Q'}]"
+midas/semeval2010,midas,['bibtex']
+midas/duc2001,midas,['bibtex']
+DFKI-SLT/cross_re,DFKI-SLT,"['arxiv.org/abs/2210.09345', 'bibtex']"
+din0s/msmarco-nlgen,din0s,"['arxiv.org/abs/1611.09268', {'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]"
+iluvvatar/NEREL,iluvvatar,"['doi.org/10.48550/arXiv.2108.13112)', {'title': 'NEREL: A Russian Dataset with Nested Named Entities, Relations and Events'}]"
+severo/glue,severo,[{'title': 'Neural Network Acceptability Judgments'}]
+lmqg/qag_esquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+DFKI-SLT/SemEval2018_Task7,DFKI-SLT,['bibtex']
+abdouaziiz/fquad,abdouaziiz,['arxiv.org/abs/2002.06071']
+gigant/horse2zebra,gigant,"['arxiv.org/abs/1703.10593', {'title': 'Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks'}]"
+PiC/phrase_sense_disambiguation,PiC,[{'title': 'PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search'}]
+bigbio/n2c2_2018_track1,bigbio,"['doi.org/10.1093/jamia/ocz163},', 'bibtex']"
+kevinjesse/ManyTypes4TypeScript,kevinjesse,['doi.org/10.5281/zenodo.6387001](https://doi.org/10.5281/zenodo.6387001)']
+IIC/spanish_biomedical_crawled_corpus,IIC,['arxiv.org/abs/2109.07765']
+BeIR/bioasq-generated-queries,BeIR,[{'title': '{BEIR'}]
+wardenga/lsoie,wardenga,['arxiv.org/abs/2101.11177']
+readerbench/news-ro-offense,readerbench,['doi.org/10.37789/rochi.2022.1.1.12}']
+poojaruhal/Code-comment-classification,poojaruhal,"['doi.org/10.1016/j.jss.2021.111047', {'title': 'How to identify class comment types? A multi-language approach for class comment classification'}]"
+rahular/itihasa,rahular,['bibtex']
+lmqg/qag_koquad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+ConvLab/sgd1,ConvLab,[{'title': 'SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems'}]
+language-and-voice-lab/malromur_asr,language-and-voice-lab,[{'title': 'Málrómur: A manually verified corpus of recorded Icelandic speech'}]
+giulio98/xlcost-single-prompt,giulio98,['arxiv.org/abs/2206.08474']
+DFKI-SLT/brat,DFKI-SLT,[{'title': 'An argument-annotated corpus of scientific publications'}]
+BeIR/dbpedia-entity,BeIR,[{'title': '{BEIR'}]
+lmqg/qag_dequad,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+PlanTL-GOB-ES/MLDoc,PlanTL-GOB-ES,['bibtex']
+IDEA-CCNL/laion2B-multi-chinese-subset,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
+stjiris/IRIS_sts,stjiris,['bibtex']
+sagteam/author_profiling,sagteam,[{'title': 'СРАВНЕНИЕ ТОЧНОСТЕЙ МЕТОДОВ НА ОСНОВЕ ЯЗЫКОВЫХ И ГРАФОВЫХ НЕЙРОСЕТЕВЫХ МОДЕЛЕЙ ДЛЯ ОПРЕДЕЛЕНИЯ ПРИЗНАКОВ АВТОРСКОГО ПРОФИЛЯ ПО ТЕКСТАМ НА РУССКОМ ЯЗЫКЕ'}]
+bigbio/n2c2_2006_deid,bigbio,"['doi.org/10.1197/jamia.M2444},', 'bibtex']"
+bigbio/msh_wsd,bigbio,[{'title': 'Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation'}]
+BeIR/nq-generated-queries,BeIR,[{'title': '{BEIR'}]
+kakaobrain/coyo-labeled-300m,kakaobrain,['arxiv.org/abs/2010.11929']
+sustcsenlp/bn_emotion_speech_corpus,sustcsenlp,['doi.org/10.1371/journal.pone.0250173)']
+bigbio/bioasq_task_c_2017,bigbio,[{'title': 'Results of the fifth edition of the {B'}]
+AnanthZeke/naamapadam,AnanthZeke,['arxiv.org/abs/2212.10168']
+irds/mmarco_v2_it_dev,irds,[{'title': '{mMARCO'}]
+BeIR/scifact-generated-queries,BeIR,[{'title': '{BEIR'}]
+polinaeterna/lila_camera_traps,polinaeterna,"['doi.org/10.1007/978-3-030-01270-0\\_28},', 'bibtex']"
+GEM/TaTA,GEM,['arxiv.org/abs/2211.00142']
+Basvoju/SemEval2018Task7,Basvoju,['bibtex']
+bigbio/n2c2_2006_smokers,bigbio,"['doi.org/10.1197/jamia.M2408},', 'bibtex']"
+cahya/fleurs,cahya,"['arxiv.org/abs/2205.12446', {'title': 'FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech'}]"
+biglam/illustrated_ads,biglam,['doi.org/10.5281/zenodo.5838410](https://doi.org/10.5281/zenodo.5838410)']
+language-and-voice-lab/samromur_children,language-and-voice-lab,"['doi.org/10.35111/frrj-qd60},']"
+mediabiasgroup/mbib-base,mediabiasgroup,[{'title': 'Introducing MBIB - the first Media Bias Identification Benchmark Task and Dataset Collection'}]
+MultiCoNER/multiconer_v2,MultiCoNER,[{'title': '{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)'}]
+BeIR/scidocs-generated-queries,BeIR,[{'title': '{BEIR'}]
+severo/mnist,severo,[{'title': 'MNIST handwritten digit database'}]
+webimmunization/COVID-19-vaccine-attitude-tweets,webimmunization,"['doi.org/10.1145/3472720.3483619)', {'title': 'Be Careful Who You Follow: The Impact of the Initial Set of Friends on COVID-19 Vaccine Tweets'}]"
+tner/ttc_dummy,tner,['bibtex']
+BeIR/webis-touche2020-qrels,BeIR,[{'title': '{BEIR'}]
+biglam/unsilence_voc,biglam,"['arxiv.org/abs/2210.02194', 'doi.org/10.5281/zenodo.6958524']"
+toloka/CrowdSpeech,toloka,['bibtex']
+relbert/scientific_and_creative_analogy,relbert,"['arxiv.org/abs/2211.15268', {'title': 'Scientific and Creative Analogies in Pretrained Language Models'}]"
+neulab/odex,neulab,[{'title': 'Execution-Based Evaluation for Open-Domain Code Generation'}]
+HuggingFaceM4/something_something_v2,HuggingFaceM4,"['arxiv.org/abs/1706.04261', {'title': 'The"" something something"" video database for learning and evaluating visual common sense'}]"
+haritzpuerto/MetaQA_Agents_Predictions,haritzpuerto,"['arxiv.org/abs/2112.01922', {'title': 'MetaQA: Combining Expert Agents for Multi-Skill Question Answering'}]"
+bigbio/n2c2_2011,bigbio,"['doi.org/10.1136/amiajnl-2011-000784},', 'bibtex']"
+bigbio/n2c2_2008,bigbio,"['doi.org/10.1197/jamia.M3115},', 'bibtex']"
+bigbio/mediqa_nli,bigbio,"['doi.org/10.13026/gtv4-g455,']"
+nlphuji/utk_faces,nlphuji,"['arxiv.org/abs/1702.08423', {'title': 'Age Progression/Regression by Conditional Adversarial Autoencoder'}]"
+armanc/ScienceQA,armanc,['bibtex']
+gabrielrstan/CORAA-v1.1,gabrielrstan,['arxiv.org/abs/2110.15731']
+khaclinh/pp4av,khaclinh,[{'title': 'PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving'}]
+vishal-burman/c4-faqs,vishal-burman,"['arxiv.org/abs/1910.10683', 'bibtex']"
+khaclinh/testdata,khaclinh,[{'title': 'PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving'}]
+Paul/hatecheck-portuguese,Paul,['arxiv.org/abs/2206.09917']
+icelab/ntrs_meta,icelab,"['doi.org/10.1109/BigData52589.2021.9671853"",...},...]']"
+djstrong/oscar-small,djstrong,"['arxiv.org/abs/2010.14571', 'bibtex']"
+midas/ldkp10k,midas,[{'title': 'LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents'}]
+albertvillanova/tmp-mention,albertvillanova,['arxiv.org/abs/2012.03411']
+yhavinga/cnn_dailymail_dutch,yhavinga,['bibtex']
+domenicrosati/TruthfulQA,domenicrosati,['arxiv.org/abs/2109.07958']
+huggan/monet2photo,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+graphs-datasets/AIDS,graphs-datasets,[{'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]
+ENM/dataset-prueba,ENM,"['doi.org/10.18653/v1/n18-2097},', 'bibtex']"
+bigbio/ntcir_13_medweb,bigbio,['bibtex']
+webis/conclugen,webis,"['doi.org/10.18653/v1/2021.findings-acl.306},', 'bibtex']"
+lmqg/qg_frquad_dummy,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+embedding-data/WikiAnswers,embedding-data,"['doi.org/10.1145/2623330.2623677)', 'bibtex']"
+taln-ls2n/kpbiomed,taln-ls2n,['arxiv.org/abs/2211.12124']
+ruslan/bioleaflets-biomedical-ner,ruslan,['bibtex']
+strombergnlp/nordic_langid,strombergnlp,['bibtex']
+sjyhne/mapai_dataset,sjyhne,['bibtex']
+collectivat/una-fraza-al-diya,collectivat,['arxiv.org/abs/2205.15599']
+bigbio/pcr,bigbio,[{'title': 'A corpus for plant-chemical relationships in the biomedical domain'}]
+cjvt/slo_thesaurus,cjvt,[{'title': 'From translation equivalents to synonyms: creation of a Slovene thesaurus using word co-occurrence network analysis'}]
+roman_urdu_hate_speech,huggingface,[{'title': 'Hate-speech and offensive language detection in roman Urdu'}]
+pensieves/mimicause,pensieves,"['arxiv.org/abs/2110.07090', {'title': 'MIMICause: Representation and automatic extraction of causal relation types from clinical notes'}]"
+projecte-aina/catalan_general_crawling,projecte-aina,"['arxiv.org/abs/2107.07903', 'bibtex']"
+sayakpaul/instructpix2pix-demo,sayakpaul,['arxiv.org/abs/2211.09800']
+emozilla/soda_synthetic_dialogue,emozilla,['bibtex']
+Fhrozen/AudioSet2K22,Fhrozen,[{'title': 'Audio Set: An ontology and human-labeled dataset for audio events'}]
+rifkiaputri/idk-mrc,rifkiaputri,['arxiv.org/abs/2210.13778']
+launch/gov_report_qs,launch,['bibtex']
+VietAI/vi_pubmed,VietAI,['arxiv.org/abs/2210.05610']
+lmvasque/hablacultura,lmvasque,['bibtex']
+colbertv2/lotte,colbertv2,['arxiv.org/abs/2112.01488']
+nikitam/ACES,nikitam,"['arxiv.org/abs/2210.15615', 'bibtex']"
+biglam/gallica_literary_fictions,biglam,['doi.org/10.5281/zenodo.4660197']
+microsoft/CLUES,microsoft,[{'title': 'Few-Shot Learning Evaluation in Natural Language Understanding'}]
+ruanchaves/hatebr,ruanchaves,[{'title': 'HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection'}]
+eliolio/docvqa,eliolio,['arxiv.org/abs/2007.00398']
+drt/kqa_pro,drt,"['arxiv.org/abs/2007.03875', {'title': '{KQA P'}]"
+zpn/bace_regression,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+cbasu/Med-EASi,cbasu,[{'title': 'Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts'}]
+jeanlee/kmhas_korean_hate_speech,jeanlee,"['arxiv.org/abs/2208.10684', 'bibtex']"
+biwi_kinect_head_pose,huggingface,['bibtex']
+sartajekram/BanglaRQA,sartajekram,['bibtex']
+persiannlp/parsinlu_query_paraphrasing,persiannlp,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+Filippo/osdg_cd,Filippo,['doi.org/10.5281/zenodo.6393942)']
+amphora/korfin-asc,amphora,"['arxiv.org/abs/2301.03136', {'title': 'Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance'}]"
+research-backup/semeval2012_relational_similarity_v2,research-backup,['bibtex']
+jet-universe/jetclass,jet-universe,"['arxiv.org/abs/2202.03772', 'bibtex']"
+strombergnlp/bornholmsk,strombergnlp,['bibtex']
+MicPie/unpredictable_en-wikipedia-org,MicPie,['arxiv.org/abs/2208.01009']
+biglam/cultural_heritage_metadata_accuracy,biglam,"['doi.org/10.6084/m9.figshare.13359104.v1](https://doi.org/10.6084/m9.figshare.13359104.v1)', 'bibtex']"
+irds/disks45_nocr_trec-robust-2004,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+midas/citeulike180,midas,['bibtex']
+kudo-research/mustc-en-es-text-only,kudo-research,"['doi.org/10.1016/j.csl.2020.101155},', {'title': 'MuST-C: A multilingual corpus for end-to-end speech translation'}]"
+strombergnlp/dkstance,strombergnlp,['bibtex']
+gsarti/magpie,gsarti,['bibtex']
+uoe-nlp/multi3-nlu,uoe-nlp,['arxiv.org/abs/2212.10455']
+huggan/horse2zebra,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+Ruth-Ann/jampatoisnli,Ruth-Ann,"['arxiv.org/abs/2212.03419', 'doi.org/10.48550/arxiv.2212.03419,']"
+colbertv2/lotte_passages,colbertv2,['arxiv.org/abs/2112.01488']
+sagnikrayc/quasar,sagnikrayc,['arxiv.org/abs/1707.03904']
+strombergnlp/bornholmsk_parallel,strombergnlp,['bibtex']
+Paul/hatecheck-german,Paul,['arxiv.org/abs/2206.09917']
+albertvillanova/mtet,albertvillanova,['bibtex']
+acul3/KoPI-CC,acul3,['bibtex']
+Jiangjie/ekar_english,Jiangjie,['bibtex']
+midas/www,midas,['bibtex']
+strombergnlp/named_timexes,strombergnlp,['bibtex']
+yhavinga/xsum_dutch,yhavinga,"[{'title': ""Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization""}]"
+mxeval/mathqa-x,mxeval,"['arxiv.org/abs/2210.14868', {'title': 'Multi-lingual Evaluation of Code Generation Models'}]"
+strombergnlp/ara-stance,strombergnlp,"['arxiv.org/abs/2104.13559', 'bibtex']"
+osyvokon/wiki-edits-uk,osyvokon,['bibtex']
+sxu/CANLI,sxu,['bibtex']
+Fhrozen/tau_srir_db,Fhrozen,['doi.org/10.5281/zenodo.5476980)']
+biglam/european_art,biglam,"['arxiv.org/abs/2211.01226', 'doi.org/10.5281/zenodo.6984525']"
+mbartolo/synQA,mbartolo,"['arxiv.org/abs/1606.05250', 'bibtex']"
+dennlinger/wiki-paragraphs,dennlinger,"['arxiv.org/abs/2012.03619', 'doi.org/10.1145/3462757.3466085},', 'bibtex']"
+KGraph/FB15k-237,KGraph,[{'title': 'Modeling relational data with graph convolutional networks'}]
+midas/kdd,midas,['bibtex']
+biu-nlp/alsqa,biu-nlp,"['arxiv.org/abs/2210.12673', 'doi.org/10.48550/arxiv.2210.12673,']"
+demelin/understanding_fables,demelin,"['arxiv.org/abs/2206.04615', {'title': 'Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models'}]"
+usc-isi/WikiConvert,usc-isi,['bibtex']
+midas/cstr,midas,"['doi.org/10.1145/313238.313437},', 'bibtex']"
+lm4pt/bpsad,lm4pt,['bibtex']
+michelecafagna26/hl,michelecafagna26,[{'title': 'HL Dataset: Grounding High-Level Linguistic Concepts in Vision'}]
+irds/mmarco_v2_it,irds,[{'title': '{mMARCO'}]
+lmqg/qa_squad,lmqg,['bibtex']
+lmqg/qg_tweetqa,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+dominguesm/brwac,dominguesm,[{'title': 'The brwac corpus: A new open resource for brazilian portuguese'}]
+iluvvatar/RuREBus,iluvvatar,['bibtex']
+ConvLab/sgd,ConvLab,[{'title': 'Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset'}]
+ConvLab/tm3,ConvLab,['bibtex']
+Paul/hatecheck-arabic,Paul,['arxiv.org/abs/2206.09917']
+mvarma/medwiki,mvarma,"['arxiv.org/abs/2110.08228', 'bibtex']"
+surrey-nlp/PLOD-filtered,surrey-nlp,['arxiv.org/abs/2204.12061']
+pszemraj/multi_fc,pszemraj,"['arxiv.org/abs/1909.03242', 'bibtex']"
+abhishek/hagrid,abhishek,['arxiv.org/abs/2206.08219']
+nguyenvulebinh/libris_clean_100,nguyenvulebinh,[{'title': 'Librispeech: an ASR corpus based on public domain audio books'}]
+VIMA/VIMA-Data,VIMA,"['arxiv.org/abs/2210.03094', {'title': 'VIMA: General Robot Manipulation with Multimodal Prompts'}]"
+projecte-aina/teca,projecte-aina,"['arxiv.org/abs/2107.07903', 'doi.org/10.5281/zenodo.4519349)', 'bibtex']"
+ulysses-camara/ulysses-ner-br,ulysses-camara,"['doi.org/10.1007/978-3-030-98305-5_1}', {'title': 'UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition'}]"
+patrickvonplaten/librispeech_asr_self_contained,patrickvonplaten,[{'title': 'Librispeech: an ASR corpus based on public domain audio books'}]
+GBaker/MedQA-USMLE-4-options-hf-cosine-similarity,GBaker,[{'title': 'What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams'}]
+wise-east/spolin,wise-east,"['arxiv.org/abs/2004.09544', {'title': 'Grounding Conversations with Improvised Dialogues'}]"
+lmvasque/kwiziq,lmvasque,['bibtex']
+slone/myv_ru_2022,slone,['arxiv.org/abs/2209.09368']
+BeIR/climate-fever-qrels,BeIR,[{'title': '{BEIR'}]
+ai4bharat/Aksharantar,ai4bharat,['arxiv.org/abs/2205.03018']
+rajeshradhakrishnan/malayalam_wiki,rajeshradhakrishnan,[{'title': 'Common Crawl - Malayalam'}]
+collectivat/salom-ladino-articles,collectivat,['arxiv.org/abs/2205.15599']
+juancopi81/mutopia_guitar_dataset,juancopi81,['arxiv.org/abs/2008.06048']
+ConvLab/multiwoz21,ConvLab,['bibtex']
+McGill-NLP/feedbackQA,McGill-NLP,['arxiv.org/abs/2204.03025']
+ruanchaves/bt11,ruanchaves,[{'title': 'Improving the tokenisation of identifier names'}]
+keshan/wit-dataset,keshan,[{'title': 'WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning'}]
+ruanchaves/test_stanford,ruanchaves,['arxiv.org/abs/1501.03210']
+facebook/panda,facebook,['arxiv.org/abs/2205.12586']
+MicPie/unpredictable_studystack-com,MicPie,['arxiv.org/abs/2208.01009']
+darrow-ai/USClassActionOutcomes_ExpertsAnnotations,darrow-ai,['arxiv.org/abs/2211.00582']
+strombergnlp/ipm_nel,strombergnlp,[{'title': 'Analysis of named entity recognition and linking for tweets'}]
+yhavinga/squad_v2_dutch,yhavinga,['bibtex']
+sdadas/sick_pl,sdadas,['bibtex']
+jpwahle/etpc,jpwahle,['bibtex']
+projecte-aina/vilaquad,projecte-aina,"['arxiv.org/abs/2107.07903', 'doi.org/10.5281/zenodo.4562337', 'bibtex']"
+tharindu/SOLID,tharindu,['bibtex']
+biglam/atypical_animacy,biglam,"['arxiv.org/abs/2005.11140', 'bibtex']"
+HuggingFaceM4/howto100m,HuggingFaceM4,[{'title': 'How{T'}]
+MicPie/unpredictable_wkdu-org,MicPie,['arxiv.org/abs/2208.01009']
+launch/reddit_qg,launch,['bibtex']
+ruanchaves/dev_stanford,ruanchaves,[{'title': 'Segmenting hashtags and analyzing their grammatical structure'}]
+ConvLab/camrest,ConvLab,['bibtex']
+venelin/inferes,venelin,['arxiv.org/abs/2210.03068']
+strombergnlp/ans-stance,strombergnlp,"['arxiv.org/abs/2005.10410', 'bibtex']"
+strombergnlp/twitter_pos_vcb,strombergnlp,[{'title': 'Twitter part-of-speech tagging for all: Overcoming sparse and noisy data'}]
+graphs-datasets/twitch_egos,graphs-datasets,[{'title': '{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs'}]
+tarteel-ai/quranqa,tarteel-ai,"['doi.org/10.1145/3400396},', 'bibtex']"
+cjvt/sloie,cjvt,"['doi.org/10.1016/j.knosys.2021.107606},', {'title': 'MICE: Mining Idioms with Contextual Embeddings'}]"
+projecte-aina/catalan_government_crawling,projecte-aina,"['arxiv.org/abs/2107.07903', 'bibtex']"
+LeandraFichtel/KAMEL,LeandraFichtel,[{'title': 'KAMEL: Knowledge Analysis with Multitoken Entities in Language Models'}]
+strombergnlp/polstance,strombergnlp,[{'title': 'Political Stance in Danish'}]
+ConvLab/tm1,ConvLab,[{'title': 'Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset'}]
+ipipan/nkjp1m,ipipan,"['doi.org/10.31286/JP.101.2.5"",', 'bibtex']"
+nateraw/espeni-2,nateraw,['doi.org/10.5281/zenodo.6606485}']
+sagot/lefff_morpho,sagot,['bibtex']
+ruanchaves/stan_large,ruanchaves,['bibtex']
+ConvLab/woz,ConvLab,['bibtex']
+khalidalt/SANAD,khalidalt,[{'title': 'Sanad: Single-label arabic news articles dataset for automatic text categorization'}]
+ruanchaves/lynx,ruanchaves,[{'title': 'Recognizing words from source code identifiers using speech recognition techniques'}]
+darrow-ai/USClassActions,darrow-ai,['arxiv.org/abs/2211.00582']
+chenghao/cuad_qa,chenghao,"['arxiv.org/abs/2103.06268', {'title': 'CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review'}]"
+projecte-aina/sts-ca,projecte-aina,"['arxiv.org/abs/2107.07903', 'doi.org/10.5281/zenodo.4529183)', 'bibtex']"
+Gabriel/citesum_swe,Gabriel,['arxiv.org/abs/2205.06207']
+irds/mr-tydi_ru,irds,[{'title': '{Mr. TyDi'}]
+ruanchaves/jhotdraw,ruanchaves,[{'title': 'Recognizing words from source code identifiers using speech recognition techniques'}]
+ruanchaves/loyola,ruanchaves,[{'title': 'An empirical study of identifier splitting techniques'}]
+MariaIsabel/FR_NFR_Spanish_requirements_classification,MariaIsabel,['doi.org/10.5281/zenodo.6556541']
+ruanchaves/hashset_distant_sampled,ruanchaves,"['arxiv.org/abs/2201.06741', {'title': 'HashSet--A Dataset For Hashtag Segmentation'}]"
+mrm8488/unnatural-instructions-core,mrm8488,['arxiv.org/abs/2212.09689']
+tomekkorbak/pile-pii-scrubadub,tomekkorbak,['arxiv.org/abs/2101.00027']
+totuta/youtube_subs_howto100M,totuta,"['arxiv.org/abs/1906.03327', {'title': 'How{T'}]"
+qwedsacf/competition_math,qwedsacf,[{'title': 'Measuring Mathematical Problem Solving With the MATH Dataset'}]
+ConvLab/tm2,ConvLab,[{'title': 'Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset'}]
+sinhala-nlp/SemiSOLD,sinhala-nlp,[{'title': 'SOLD: Sinhala Offensive Language Dataset'}]
+ruanchaves/snap,ruanchaves,[{'title': 'Segmenting hashtags using automatically created training data'}]
+civility-lab/incivility-arizona-daily-star-comments,civility-lab,"['doi.org/10.1111/jcom.12104).', 'bibtex']"
+ruanchaves/binkley,ruanchaves,['bibtex']
+sdadas/ppc,sdadas,['bibtex']
+projecte-aina/catalan_textual_corpus,projecte-aina,"['arxiv.org/abs/2107.07903', 'bibtex']"
+yoshitomo-matsubara/srsd-feynman_hard,yoshitomo-matsubara,"['arxiv.org/abs/2206.10540', {'title': 'Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery'}]"
+strombergnlp/rustance,strombergnlp,"['arxiv.org/abs/1809.01574', {'title': 'Stance prediction for russian: data and analysis'}]"
+ruanchaves/hashset_distant,ruanchaves,"['arxiv.org/abs/2201.06741', {'title': 'HashSet--A Dataset For Hashtag Segmentation'}]"
+strombergnlp/danfever,strombergnlp,['bibtex']
+elkarhizketak,huggingface,['bibtex']
+lmqg/qg_annotation,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+lmvasque/coh-metrix-esp,lmvasque,['bibtex']
+yoshitomo-matsubara/srsd-feynman_easy,yoshitomo-matsubara,"['arxiv.org/abs/2206.10540', {'title': 'Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery'}]"
+TUMLegalTech/echr_rational,TUMLegalTech,[{'title': 'Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts'}]
+strombergnlp/zulu_stance,strombergnlp,"['arxiv.org/abs/2205.03153', {'title': 'Bridging the Domain Gap for Stance Detection for the Zulu language'}]"
+w11wo/imdb-javanese,w11wo,[{'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]
+janck/bigscience-lama,janck,[{'title': 'Language Models as Knowledge Bases?'}]
+sdadas/8tags,sdadas,['bibtex']
+PolyAI/evi,PolyAI,"['arxiv.org/abs/2204.13496', 'bibtex']"
+tartuNLP/EstCOPA,tartuNLP,"['doi.org/10.22364/bjmc.2022.10.3.19},', {'title': 'Estonian Language Understanding: a Case Study on the COPA Task'}]"
+KTH/hungarian-single-speaker-tts,KTH,"['arxiv.org/abs/1903.11269', {'title': 'CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages'}]"
+research-backup/semeval2012_relational_similarity_v6,research-backup,['bibtex']
+biglam/berlin_state_library_ocr,biglam,['doi.org/10.5281/zenodo.2702544)']
+vadis/sv-ident,vadis,['bibtex']
+BeIR/trec-covid-generated-queries,BeIR,[{'title': '{BEIR'}]
+morteza/cogtext,morteza,['arxiv.org/abs/2203.11016']
+strombergnlp/itu_faroese_danish,strombergnlp,['arxiv.org/abs/2206.08727']
+cjvt/slo_collocations,cjvt,[{'title': 'Collocations dictionary of modern Slovene'}]
+ruanchaves/boun,ruanchaves,[{'title': 'Segmenting hashtags and analyzing their grammatical structure'}]
+codesue/kelly,codesue,"['doi.org/10.1007/s10579-013-9251-2},', {'doi': '10.1007/s10579-013-9251-2'}]"
+ruanchaves/nru_hse,ruanchaves,"['arxiv.org/abs/1911.03270', {'title': 'Char-RNN and Active Learning for Hashtag Segmentation'}]"
+pauli31/czech-subjectivity-dataset,pauli31,"['arxiv.org/abs/2204.13915', {'title': 'Czech Dataset for Cross-lingual Subjectivity Classification'}]"
+DFKI-SLT/science_ie,DFKI-SLT,"['arxiv.org/abs/1704.02853', 'bibtex']"
+statworx/swiss-dialects,statworx,['doi.org/10.13092/lo.98.5947']
+launch/ampere,launch,['bibtex']
+embedding-data/PAQ_pairs,embedding-data,[{'title': 'PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them'}]
+GrainsPolito/BBBicycles,GrainsPolito,[{'title': 'Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification'}]
+Paul/hatecheck-dutch,Paul,['arxiv.org/abs/2206.09917']
+income/nfcorpus-top-20-gen-queries,income,[{'title': '{BEIR'}]
+LRGB/peptides-functional,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+wanyu/IteraTeR_full_doc,wanyu,['arxiv.org/abs/2203.03802']
+Axel578/mydt,Axel578,"['arxiv.org/abs/1911.12237', 'bibtex']"
+batterydata/paper-abstracts,batterydata,[{'title': 'BatteryBERT: A Pretrained Language Model for Battery Database Enhancement'}]
+MFreidank/glenda,MFreidank,['bibtex']
+persiannlp/parsinlu_reading_comprehension,persiannlp,"['arxiv.org/abs/2012.06154', {'title': 'ParsiNLU: A Suite of Language Understanding Challenges for Persian'}]"
+mxeval/mbxp,mxeval,"['arxiv.org/abs/2210.14868', {'title': 'Multi-lingual Evaluation of Code Generation Models'}]"
+MicPie/unpredictable_phonearena-com,MicPie,['arxiv.org/abs/2208.01009']
+surrey-nlp/PLOD-unfiltered,surrey-nlp,['arxiv.org/abs/2204.12061']
+MicPie/unpredictable_cluster05,MicPie,['arxiv.org/abs/2208.01009']
+n-iv/sq,n-iv,"['doi.org/10.57967/hf/0324,']"
+income/scidocs-top-20-gen-queries,income,[{'title': '{BEIR'}]
+neuclir/hc4,neuclir,"['arxiv.org/abs/2201.09992', 'bibtex']"
+MicPie/unpredictable_rated-low,MicPie,['arxiv.org/abs/2208.01009']
+sasha/australian_sea_slugs,sasha,['doi.org/10.15468/gtoiks']
+LRGB/voc_superpixels_edge_wt_coord_feat_30,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+income/cqadupstack-physics-top-20-gen-queries,income,[{'title': '{BEIR'}]
+biu-nlp/CoreSearch,biu-nlp,"['arxiv.org/abs/2210.12654', 'bibtex']"
+relbert/nell,relbert,['bibtex']
+Livingwithmachines/MapReader_Data_SIGSPATIAL_2022,Livingwithmachines,"['doi.org/10.5281/zenodo.7147906', 'bibtex']"
+Twitter/SignedGraphs,Twitter,"['arxiv.org/abs/2201.11675', {'title': 'Learning Stance Embeddings from Signed Social Graphs'}]"
+HuggingFaceM4/ActivitiyNet_Captions,HuggingFaceM4,"['arxiv.org/abs/1705.00754', {'title': 'Dense-Captioning Events in Videos'}]"
+csebuetnlp/CrossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
+wanyu/IteraTeR_human_doc,wanyu,['arxiv.org/abs/2203.03802']
+RGBD-SOD/rgbdsod_datasets,RGBD-SOD,[{'title': 'RGB-D saliency detection via cascaded mutual information minimization'}]
+svnfs/depth-of-field,svnfs,[{'title': 'DoF: An image dataset for depth of field classification'}]
+zpn/clintox,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+biglam/yalta_ai_segmonto_manuscript_dataset,biglam,"['arxiv.org/abs/2207.11230', 'doi.org/10.5281/zenodo.6814770](https://doi.org/10.5281/zenodo.6814770)']"
+mounikaiiith/Telugu_Emotion,mounikaiiith,"[{'title': 'Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP tasks in Telugu Language'}]"
+sinhala-nlp/SOLD,sinhala-nlp,[{'title': 'SOLD: Sinhala Offensive Language Dataset'}]
+income/scifact-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MMG/SpanishBFF,MMG,"['arxiv.org/abs/2302.12746', 'doi.org/10.48550/arxiv.2302.12746,']"
+wanyu/IteraTeR_v2,wanyu,['arxiv.org/abs/2204.03685']
+mxeval/mxeval,mxeval,"['arxiv.org/abs/2210.14868', {'title': 'Multi-lingual Evaluation of Code Generation Models'}]"
+nbroad/mediasum,nbroad,"['arxiv.org/abs/2103.06410', {'title': 'MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization'}]"
+BeIR/dbpedia-entity-generated-queries,BeIR,[{'title': '{BEIR'}]
+batterydata/battery-device-data-qa,batterydata,[{'title': 'BatteryBERT: A Pretrained Language Model for Battery Database Enhancement'}]
+MicPie/unpredictable_w3-org,MicPie,['arxiv.org/abs/2208.01009']
+anjalyjayakrishnan/test,anjalyjayakrishnan,"['arxiv.org/abs/2206.01205', {'title': 'Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages'}]"
+lmvasque/caes,lmvasque,"['doi.org/10.1080/23247797.2015.1084685"",', 'bibtex']"
+barkermrl/imagenet-a,barkermrl,[{'title': 'Natural Adversarial Examples'}]
+pysentimiento/spanish-tweets,pysentimiento,['bibtex']
+OGB/ogbg-molpcba,OGB,['bibtex']
+Team-PIXEL/rendered-wikipedia-english,Team-PIXEL,"['arxiv.org/abs/2207.06991', {'title': 'Language Modelling with Pixels'}]"
+rubentito/mp-docvqa,rubentito,"['arxiv.org/abs/2212.05935', {'title': 'Hierarchical multimodal transformers for Multi-Page DocVQA'}]"
+DFKI-SLT/scidtb,DFKI-SLT,['bibtex']
+jonathanli/law-stack-exchange,jonathanli,['bibtex']
+irds/msmarco-document-v2,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+pritamdeka/cord-19-fulltext,pritamdeka,[{'title': 'CORD-19: The Covid-19 Open Research Dataset'}]
+Rexhaif/ru-med-ner,Rexhaif,['arxiv.org/abs/2201.06499']
+krandiash/sc09,krandiash,"[{'title': ""It's Raw! Audio Generation with State-Space Models""}]"
+Paul/hatecheck-french,Paul,['arxiv.org/abs/2206.09917']
+billray110/corpus-of-diverse-styles,billray110,"['arxiv.org/abs/2010.05700', 'bibtex']"
+chcaa/dagw-word-frequencies-normalized-by-domain,chcaa,['arxiv.org/abs/2005.03521']
+Paul/hatecheck-polish,Paul,['arxiv.org/abs/2206.09917']
+muhammadravi251001/augmented-indo-nli,muhammadravi251001,['bibtex']
+NazaGara/wikiner-es,NazaGara,['doi.org/10.1016/j.artint.2012.03.006)']
+Silvia/WITS,Silvia,['bibtex']
+ConvLab/kvret,ConvLab,['bibtex']
+pritamdeka/cord-19-abstract,pritamdeka,[{'title': 'CORD-19: The Covid-19 Open Research Dataset'}]
+qanastek/ANTILLES,qanastek,['bibtex']
+graphs-datasets/reddit_threads,graphs-datasets,[{'title': '{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs'}]
+csebuetnlp/BanglaNMT,csebuetnlp,['bibtex']
+hackathon-pln-es/biomed_squad_es_v2,hackathon-pln-es,['bibtex']
+fangyuan/lfqa_discourse,fangyuan,"['arxiv.org/abs/2203.11048', {'title': 'How Do We Answer Complex Questions: Discourse Structure of Long-form Answers'}]"
+ConvLab/metalwoz,ConvLab,['bibtex']
+UKPLab/hate_speech_offensive,UKPLab,['arxiv.org/abs/1703.04009']
+mounikaiiith/Telugu_Sentiment,mounikaiiith,"[{'title': 'Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP tasks in Telugu Language'}]"
+Genius1237/TyDiP,Genius1237,['bibtex']
+fever/feverous,fever,"['arxiv.org/abs/2106.05707', 'bibtex']"
+zpn/delaney,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+muhammadravi251001/translated-indo-nli,muhammadravi251001,['bibtex']
+keshan/multispeaker-tts-sinhala,keshan,"['doi.org/10.21437/SLTU.2018-14}', 'bibtex']"
+irds/wikiclir_ru,irds,['bibtex']
+slnader/fcc-comments,slnader,"['doi.org/10.1002/poi3.327', {'title': 'Do fake online comments pose a threat to regulatory policymaking? Evidence from Internet regulation in the United States'}]"
+gusevski/factrueval2016,gusevski,['arxiv.org/abs/2005.00614']
+AhmedSSoliman/CodeXGLUE-CONCODE,AhmedSSoliman,[{'title': 'Mapping language to code in programmatic context'}]
+AhmedSSabir/Textual-Image-Caption-Dataset,AhmedSSabir,"['arxiv.org/abs/2301.08784', {'title': 'Visual Semantic Relatedness Dataset for Image Captioning'}]"
+vector/structuretest,vector,[{'title': 'Neural Network Acceptability Judgments'}]
+tiagoblima/nilc-school-books,tiagoblima,[{'title': 'Predição da Complexidade Textual de Recursos Educacionais Abertos em Português'}]
+MicPie/unpredictable_5k,MicPie,['arxiv.org/abs/2208.01009']
+BeIR/hotpotqa-generated-queries,BeIR,[{'title': '{BEIR'}]
+SerdarHelli/SegmentationOfTeethPanoramicXRayImages,SerdarHelli,[{'title': 'Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing'}]
+BeIR/arguana-generated-queries,BeIR,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster12,MicPie,['arxiv.org/abs/2208.01009']
+KnutJaegersberg/Interpretable_word_embeddings_large_cskg,KnutJaegersberg,['arxiv.org/abs/1806.05521']
+MicPie/unpredictable_full,MicPie,['arxiv.org/abs/2208.01009']
+income/bioasq-top-20-gen-queries,income,[{'title': '{BEIR'}]
+ruanchaves/hashset_manual,ruanchaves,"['arxiv.org/abs/2201.06741', {'title': 'HashSet--A Dataset For Hashtag Segmentation'}]"
+zpn/tox21_srp53,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+brema76/political_personalization_it,brema76,[{'title': 'Combining NLP techniques and statistical modeling to analyze gender gaps in the mediated personalization of politics'}]
+sanchit-gandhi/librispeech_asr_dummy,sanchit-gandhi,[{'title': 'Librispeech: an ASR corpus based on public domain audio books'}]
+income/nq-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster19,MicPie,['arxiv.org/abs/2208.01009']
+filwsyl/ascend,filwsyl,['arxiv.org/abs/2112.06223']
+projecte-aina/viquiquad,projecte-aina,"['arxiv.org/abs/2107.07903', 'doi.org/10.5281/zenodo.4562344)', 'bibtex']"
+MicPie/unpredictable_cluster11,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_baseball-fantasysports-yahoo-com,MicPie,['arxiv.org/abs/2208.01009']
+BeIR/cqadupstack-qrels,BeIR,[{'title': '{BEIR'}]
+toloka/WSDMCup2023,toloka,"['doi.org/10.5281/zenodo.7057740', 'bibtex']"
+cooleel/xfund_de,cooleel,['bibtex']
+income/cqadupstack-webmasters-top-20-gen-queries,income,[{'title': '{BEIR'}]
+BeIR/climate-fever-generated-queries,BeIR,[{'title': '{BEIR'}]
+NbAiLab/nb_bert_debiased,NbAiLab,"['arxiv.org/abs/2104.09617', {'title': 'Operationalizing a National Digital Library: The Case for a {N'}]"
+MicPie/unpredictable_support-google-com,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_rated-high,MicPie,['arxiv.org/abs/2208.01009']
+mounikaiiith/Telugu-Sarcasm,mounikaiiith,"[{'title': 'Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP tasks in Telugu Language'}]"
+pain/ArASL_Database_Grayscale,pain,"['doi.org/10.1016/j.dib.2019.103777},', {'title': 'ArASL: Arabic Alphabets Sign Language Dataset'}]"
+MicPie/unpredictable_cluster02,MicPie,['arxiv.org/abs/2208.01009']
+projecte-aina/ancora-ca-ner,projecte-aina,"['arxiv.org/abs/2107.07903', 'doi.org/10.5281/zenodo.4529299)', 'bibtex']"
+irds/mmarco_zh_train,irds,[{'title': '{mMARCO'}]
+MicPie/unpredictable_cluster21,MicPie,['arxiv.org/abs/2208.01009']
+quincyqiang/test,quincyqiang,[{'title': 'Neural Network Acceptability Judgments'}]
+MicPie/unpredictable_cluster23,MicPie,['arxiv.org/abs/2208.01009']
+income/cqadupstack-android-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_dummies-com,MicPie,['arxiv.org/abs/2208.01009']
+income/signal1m-top-20-gen-queries,income,[{'title': '{BEIR'}]
+research-backup/semeval2012_relational_similarity_v4,research-backup,['bibtex']
+yoshitomo-matsubara/srsd-feynman_medium,yoshitomo-matsubara,"['arxiv.org/abs/2206.10540', {'title': 'Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery'}]"
+MicPie/unpredictable_cluster13,MicPie,['arxiv.org/abs/2208.01009']
+zpn/pcba_686978,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+yarongef/human_proteome_doublets,yarongef,"['doi.org/10.1093/bioinformatics/btac474},']"
+income/fiqa-top-20-gen-queries,income,[{'title': '{BEIR'}]
+graphs-datasets/AQSOL,graphs-datasets,"['arxiv.org/abs/2003.00982', 'bibtex']"
+MicPie/unpredictable_mmo-champion-com,MicPie,['arxiv.org/abs/2208.01009']
+nateraw/us-accidents,nateraw,['arxiv.org/abs/1906.05409']
+MicPie/unpredictable_mgoblog-com,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_sporcle-com,MicPie,['arxiv.org/abs/2208.01009']
+SimulaMet-HOST/VISEM-Tracking,SimulaMet-HOST,"['arxiv.org/abs/2212.02842', {'title': 'VISEM-Tracking: Human Spermatozoa Tracking Dataset'}]"
+OGB/ogbg-ppa,OGB,['bibtex']
+income/cqadupstack-mathematica-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_gamefaqs-com,MicPie,['arxiv.org/abs/2208.01009']
+GuiGel/meddocan,GuiGel,"[{'title': 'Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results'}]"
+income/cqadupstack-wordpress-top-20-gen-queries,income,[{'title': '{BEIR'}]
+biglam/v4design_europeana_style_dataset,biglam,['doi.org/10.5281/zenodo.4896487}']
+Amir13/conll2003-persian,Amir13,"['arxiv.org/abs/2302.09611', 'doi.org/10.48550/arxiv.2302.09611,']"
+Twitter/HashtagPrediction,Twitter,"['arxiv.org/abs/2209.07562', {'title': 'TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations'}]"
+filwsyl/video_tags,filwsyl,[{'title': 'MNIST handwritten digit database'}]
+Amir13/ncbi-persian,Amir13,"['arxiv.org/abs/2302.09611', 'doi.org/10.48550/arxiv.2302.09611,']"
+income/climate-fever-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_rated-medium,MicPie,['arxiv.org/abs/2208.01009']
+taqwa92/cm.trial,taqwa92,"['arxiv.org/abs/1912.06670', 'bibtex']"
+MicPie/unpredictable_dividend-com,MicPie,['arxiv.org/abs/2208.01009']
+income/cqadupstack-stats-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster14,MicPie,['arxiv.org/abs/2208.01009']
+lishuyang/recipepairs,lishuyang,['bibtex']
+OGB/ogbg-code2,OGB,['bibtex']
+MicPie/unpredictable_bulbapedia-bulbagarden-net,MicPie,['arxiv.org/abs/2208.01009']
+irds/beir_fiqa_train,irds,"['arxiv.org/abs/2104.08663', {'title': ""WWW'18 Open Challenge: Financial Opinion Mining and Question Answering""}]"
+ConvLab/sgd2,ConvLab,[{'title': 'SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems'}]
+GEM/xmediasum,GEM,['bibtex']
+yarongef/human_proteome_singlets,yarongef,"['doi.org/10.1093/bioinformatics/btac474},']"
+yarongef/human_proteome_triplets,yarongef,"['doi.org/10.1093/bioinformatics/btac474},']"
+BeIR/dbpedia-entity-qrels,BeIR,[{'title': '{BEIR'}]
+Sidd2899/MyspeechASR,Sidd2899,[{'title': 'Myspeech: an ASR corpus based on public domain audio books'}]
+income/trec-news-top-20-gen-queries,income,[{'title': '{BEIR'}]
+mounikaiiith/Telugu_Clickbait,mounikaiiith,[{'title': 'Clickbait Detection in Telugu: Overcoming NLP Challenges in Resource-Poor Languages using Benchmarked Techniques'}]
+income/cqadupstack-gis-top-20-gen-queries,income,[{'title': '{BEIR'}]
+Krystalan/xmediasum,Krystalan,['bibtex']
+MicPie/unpredictable_cluster29,MicPie,['arxiv.org/abs/2208.01009']
+batterydata/cner,batterydata,['arxiv.org/abs/2006.03039']
+MicPie/unpredictable_unique,MicPie,['arxiv.org/abs/2208.01009']
+research-backup/semeval2012_relational_similarity_v7,research-backup,['bibtex']
+jonathanli/legal-advice-reddit,jonathanli,['bibtex']
+MicPie/unpredictable_cluster24,MicPie,['arxiv.org/abs/2208.01009']
+BeIR/quora-generated-queries,BeIR,[{'title': '{BEIR'}]
+income/robust04-top-20-gen-queries,income,[{'title': '{BEIR'}]
+income/cqadupstack-unix-top-20-gen-queries,income,[{'title': '{BEIR'}]
+income/cqadupstack-programmers-top-20-gen-queries,income,[{'title': '{BEIR'}]
+BeIR/cqadupstack-generated-queries,BeIR,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster28,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_cram-com,MicPie,['arxiv.org/abs/2208.01009']
+BeIR/quora-qrels,BeIR,[{'title': '{BEIR'}]
+sileod/discourse_marker_qa,sileod,['bibtex']
+MicPie/unpredictable_msdn-microsoft-com,MicPie,['arxiv.org/abs/2208.01009']
+sebastian-hofstaetter/tripclick-training,sebastian-hofstaetter,['arxiv.org/abs/2201.00365']
+zpn/bace_classification,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+MicPie/unpredictable_ensembl-org,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_cluster01,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_cluster20,MicPie,['arxiv.org/abs/2208.01009']
+ConvLab/sgd3,ConvLab,[{'title': 'SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems'}]
+MicPie/unpredictable_cluster06,MicPie,['arxiv.org/abs/2208.01009']
+income/cqadupstack-tex-top-20-gen-queries,income,[{'title': '{BEIR'}]
+income/trec-covid-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster09,MicPie,['arxiv.org/abs/2208.01009']
+income/cqadupstack-gaming-top-20-gen-queries,income,[{'title': '{BEIR'}]
+BigBang/galaxyzoo-decals,BigBang,['doi.org/10.5281/zenodo.4573248}']
+niurl/eraser_esnli,niurl,['arxiv.org/abs/1911.03429']
+graphs-datasets/CSL,graphs-datasets,"['arxiv.org/abs/2003.00982', 'bibtex']"
+BeIR/webis-touche2020-generated-queries,BeIR,[{'title': '{BEIR'}]
+vkpriya/str-2022,vkpriya,"['arxiv.org/abs/2110.04845', {'title': 'What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study'}]"
+nazneen/self-instruct-seed,nazneen,['arxiv.org/abs/2212.10560']
+BeIR/nfcorpus-generated-queries,BeIR,[{'title': '{BEIR'}]
+ctu-aic/ctkfacts,ctu-aic,['arxiv.org/abs/2201.11115']
+wkrl/cord,wkrl,[{'title': 'CORD: A Consolidated Receipt Dataset for Post-OCR Parsing'}]
+thsant/wgisd,thsant,"['arxiv.org/abs/1803.09010', 'doi.org/10.1007/978-3-030-65414-6_19)).']"
+conceptofmind/pile_cc,conceptofmind,['arxiv.org/abs/2101.00027']
+research-backup/semeval2012_relational_similarity_v5,research-backup,['bibtex']
+MicPie/unpredictable_cluster03,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_cluster00,MicPie,['arxiv.org/abs/2208.01009']
+sberbank-ai/Peter,sberbank-ai,['arxiv.org/abs/2103.09354']
+rony/climate-change-MRC,rony,[{'title': 'Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering.'}]
+julien-c/reactiongif,julien-c,['arxiv.org/abs/2105.09967']
+nateraw/rice-image-dataset,nateraw,['doi.org/10.1016/j.compag.2021.106285']
+MicPie/unpredictable_wiki-openmoko-org,MicPie,['arxiv.org/abs/2208.01009']
+zpn/lipo,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+acul3/Laion_Indo,acul3,"['arxiv.org/abs/2111.02114', 'bibtex']"
+irds/trec-arabic_ar2002,irds,[{'title': 'The TREC-2002 Arabic/English CLIR Track'}]
+MicPie/unpredictable_cluster10,MicPie,['arxiv.org/abs/2208.01009']
+vpetukhov/bible_tts_hausa,vpetukhov,['arxiv.org/abs/2207.03546']
+ConvLab/sgd4,ConvLab,[{'title': 'SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems'}]
+thennal/IMaSC,thennal,['arxiv.org/abs/2211.12796']
+tonytan48/Re-DocRED,tonytan48,"['arxiv.org/abs/2205.12696', {'title': 'Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction'}]"
+MicPie/unpredictable_cluster08,MicPie,['arxiv.org/abs/2208.01009']
+BeIR/signal1m-generated-queries,BeIR,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster-noise,MicPie,['arxiv.org/abs/2208.01009']
+nateraw/espeni-3,nateraw,['doi.org/10.5281/zenodo.6606485}']
+MicPie/unpredictable_cluster26,MicPie,['arxiv.org/abs/2208.01009']
+mounikaiiith/Telugu-Hatespeech,mounikaiiith,"[{'title': 'Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP tasks in Telugu Language'}]"
+nlphuji/vasr,nlphuji,['arxiv.org/abs/2212.04542']
+graphs-datasets/deezer_ego_nets,graphs-datasets,[{'title': '{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs'}]
+sakharamg/AviationQA,sakharamg,['arxiv.org/abs/2301.04013']
+MicPie/unpredictable_cluster17,MicPie,['arxiv.org/abs/2208.01009']
+zpn/bbbp,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+income/arguana-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster07,MicPie,['arxiv.org/abs/2208.01009']
+graphs-datasets/MD17-naphthalene,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+zpn/clearance,zpn,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+Poulpidot/FrenchHateSpeechSuperset,Poulpidot,"['doi.org/10.3390/info13070318', {'title': 'An Annotated Corpus for Sexism Detection in French Tweets'}]"
+income/dbpedia-entity-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_cappex-com,MicPie,['arxiv.org/abs/2208.01009']
+Lo/clip-bert-data,Lo,['arxiv.org/abs/2109.11321']
+BeIR/fever-generated-queries,BeIR,[{'title': '{BEIR'}]
+parambharat/mile_dataset,parambharat,['arxiv.org/abs/2207.13331']
+income/quora-top-20-gen-queries,income,[{'title': '{BEIR'}]
+Paul/hatecheck-hindi,Paul,['arxiv.org/abs/2206.09917']
+deutsche-telekom/NLU-Evaluation-Data-en-de,deutsche-telekom,['arxiv.org/abs/1903.05566']
+research-backup/semeval2012_relational_similarity_v3,research-backup,['bibtex']
+toloka/VoxDIY-RusNews,toloka,['bibtex']
+ConvLab/sgd5,ConvLab,[{'title': 'SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems'}]
+hendrycks/ethics,hendrycks,[{'title': 'Aligning AI With Shared Human Values'}]
+Xieyiyiyi/ceshi0119,Xieyiyiyi,[{'title': 'BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions'}]
+MicPie/unpredictable_cluster22,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_cluster25,MicPie,['arxiv.org/abs/2208.01009']
+MicPie/unpredictable_cluster04,MicPie,['arxiv.org/abs/2208.01009']
+irds/codesearchnet,irds,[{'title': 'CodeSearchNet Challenge: Evaluating the State of Semantic Code Search'}]
+cryptexcode/MPST,cryptexcode,['bibtex']
+lmqg/qag_tweetqa,lmqg,"['arxiv.org/abs/2210.03992', 'bibtex']"
+batubayk/HU-News,batubayk,['bibtex']
+BeIR/robust04-generated-queries,BeIR,[{'title': '{BEIR'}]
+barkermrl/mnist-c,barkermrl,['arxiv.org/abs/1906.02337']
+MicPie/unpredictable_sittercity-com,MicPie,['arxiv.org/abs/2208.01009']
+marksverdhei/clickbait_title_classification,marksverdhei,"['arxiv.org/abs/1610.09786', {'title': 'Stop Clickbait: Detecting and preventing clickbaits in online news media'}]"
+income/hotpotqa-top-20-gen-queries,income,[{'title': '{BEIR'}]
+UKPLab/amazon_counterfactual_en,UKPLab,['arxiv.org/abs/2104.06893']
+pysentimiento/spanish-tweets-small,pysentimiento,['bibtex']
+nlphuji/open_images_dataset_v7,nlphuji,"['arxiv.org/abs/2105.02317', {'title': 'A Step Toward More Inclusive People Annotations for Fairness'}]"
+copenlu/tydiqa_copenlu,copenlu,[{'title': 'TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages'}]
+recmeapp/AARSynth,recmeapp,['doi.org/10.1109/BigData50022.2020.9377983.']
+mqddb/test-dataset,mqddb,[{'title': 'MNIST handwritten digit database'}]
+MicPie/unpredictable_cluster18,MicPie,['arxiv.org/abs/2208.01009']
+Twitter/TwitterFollowGraph,Twitter,[{'title': 'kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval'}]
+Adapting/chinese_biomedical_NER_dataset,Adapting,[{'title': 'Conceptualized Representation Learning for Chinese Biomedical Text Mining'}]
+income/cqadupstack-english-top-20-gen-queries,income,[{'title': '{BEIR'}]
+lorenzoscottb/PLANE-ood,lorenzoscottb,['bibtex']
+income/webis-touche2020-top-20-gen-queries,income,[{'title': '{BEIR'}]
+biu-nlp/WEC-Eng,biu-nlp,['bibtex']
+MicPie/unpredictable_cluster15,MicPie,['arxiv.org/abs/2208.01009']
+methodidacte/penguins,methodidacte,['doi.org/10.1371/journal.pone.0090081']
+MicPie/unpredictable_cluster27,MicPie,['arxiv.org/abs/2208.01009']
+irds/codesearchnet_train,irds,[{'title': 'CodeSearchNet Challenge: Evaluating the State of Semantic Code Search'}]
+Twitter/TwitterFaveGraph,Twitter,"['arxiv.org/abs/2210.16271', {'title': 'MiCRO: Multi-interest Candidate Retrieval Online'}]"
+rogerdehe/xfund,rogerdehe,['bibtex']
+income/fever-top-20-gen-queries,income,[{'title': '{BEIR'}]
+MicPie/unpredictable_cluster16,MicPie,['arxiv.org/abs/2208.01009']
+Amir13/ontonotes5-persian,Amir13,"['arxiv.org/abs/2302.09611', 'doi.org/10.48550/arxiv.2302.09611,']"
+Matrix430/CONDA,Matrix430,"['arxiv.org/abs/2106.06213', 'bibtex']"
+holylovenia/TITML-IDN,holylovenia,[{'title': 'A large vocabulary continuous speech recognition system for Indonesian language'}]
+huggan/vangogh2photo,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+planhanasan/github-issues,planhanasan,"['arxiv.org/abs/2005.00614', 'doi.org/),', 'bibtex']"
+djghosh/wds_cars_test,djghosh,[{'title': '3D Object Representations for Fine-Grained Categorization'}]
+Peihao/test-dateset,Peihao,"['arxiv.org/abs/1910.10683', 'bibtex']"
+nateraw/food101,nateraw,[{'title': 'Food-101 -- Mining Discriminative Components with Random Forests'}]
+LRGB/voc_superpixels_edge_wt_coord_feat_10,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+nlphuji/fairface_val_padding_125,nlphuji,"[{'title': 'FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation'}]"
+conceptofmind/pile_wikipedia_en,conceptofmind,['arxiv.org/abs/2101.00027']
+LRGB/peptides-structural,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+kietzmannlab/ecoset,kietzmannlab,"['doi.org/10.1073/pnas.2011417118)', {'title': 'An ecologically motivated image dataset for deep learning yields better models of human vision'}]"
+ZIZOU/Arabic_Squad,ZIZOU,['bibtex']
+irds/mmarco_v2_zh,irds,[{'title': '{mMARCO'}]
+nlphuji/fairface_val_padding_025,nlphuji,"[{'title': 'FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation'}]"
+bstds/indo_law,bstds,[{'title': 'Predicting the Category and the Length of Punishment in Indonesian Courts Based on Previous Court Decision Documents'}]
+semeru/Text-Code-concode-Java,semeru,[{'title': 'Mapping language to code in programmatic context'}]
+google/cvss,google,"['arxiv.org/abs/2201.03713', {'title': '{CVSS'}]"
+Jzuluaga/atco2_corpus_1h,Jzuluaga,"['arxiv.org/abs/2211.04054', {'title': 'How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications'}]"
+LanceaKing/asvspoof2019,LanceaKing,['arxiv.org/abs/1911.01601']
+huggan/cityscapes,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+madrylab/imagenet-star-tokens,madrylab,['arxiv.org/abs/2302.07865']
+sasha/birdsnap,sasha,[{'title': 'Birdsnap: Large-scale fine-grained visual categorization of birds'}]
+bloomberg/entsum,bloomberg,"['doi.org/10.35111/77ba-9x74,', 'bibtex']"
+WillHeld/blimp,WillHeld,"['doi.org/10.1162/tacl_a_00321},', 'bibtex']"
+irds/nyt_wksup,irds,['bibtex']
+irds/mr-tydi_te_train,irds,[{'title': '{Mr. TyDi'}]
+irds/lotte_pooled_test_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_hotpotqa,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/car_v1.5_trec-y1_manual,irds,[{'title': 'TREC Complex Answer Retrieval Overview.'}]
+irds/lotte_science_dev_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+larrylawl/multilexnorm,larrylawl,['bibtex']
+becurrio/advABSA,becurrio,['bibtex']
+irds/beir_hotpotqa_test,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+timbrooks/instructpix2pix-clip-filtered,timbrooks,['arxiv.org/abs/2211.09800']
+irds/argsme_2020-04-01_touche-2020-task-1_uncorrected,irds,['bibtex']
+conceptofmind/pile_open_subtitles,conceptofmind,['arxiv.org/abs/2101.00027']
+irds/car_v2.0,irds,[{'title': '{TREC CAR'}]
+irds/mmarco_v2_dt_train,irds,[{'title': '{mMARCO'}]
+irds/mr-tydi_ja_train,irds,[{'title': '{Mr. TyDi'}]
+severo/winogavil,severo,"['arxiv.org/abs/2207.12576', {'title': 'WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models'}]"
+irds/mr-tydi_ja_dev,irds,[{'title': '{Mr. TyDi'}]
+nateraw/hyperbard,nateraw,['doi.org/10.5281/zenodo.6627159}']
+irds/msmarco-passage_eval,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/mr-tydi_th_train,irds,[{'title': '{Mr. TyDi'}]
+irds/mr-tydi_ar,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_zh_dev_v1.1,irds,[{'title': '{mMARCO'}]
+irds/lotte_recreation_dev_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+GateNLP/broad_twitter_corpus,GateNLP,[{'title': 'Broad twitter corpus: A diverse named entity recognition resource'}]
+irds/tripclick_train_tail,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+actdan2016/sample1,actdan2016,['arxiv.org/abs/2111.11431']
+irds/nyt_trec-core-2017,irds,['bibtex']
+irds/mmarco_v2_ru_dev,irds,[{'title': '{mMARCO'}]
+irds/wikiclir_ro,irds,['bibtex']
+irds/beir_dbpedia-entity_test,irds,"['arxiv.org/abs/2104.08663', {'title': 'DBpedia-Entity v2: A Test Collection for Entity Search'}]"
+irds/mmarco_pt_dev_v1.1,irds,[{'title': '{mMARCO'}]
+irds/wikir_fr14k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+irds/gov_trec-web-2002_named-page,irds,[{'title': 'Overview of the TREC-2002 Web Track'}]
+biglam/early_printed_books_font_detection,biglam,['doi.org/10.5281/zenodo.3366686']
+irds/beir_quora_test,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+DavidVivancos/MindBigData2022,DavidVivancos,['arxiv.org/abs/2212.14746']
+graphs-datasets/MD17-uracil,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+NoraAlt/Mawqif_Stance-Detection,NoraAlt,['bibtex']
+breadlicker45/1m-YA-dataset,breadlicker45,['doi.org/10.5281/zenodo.5259952']
+rlasseri/test-OrangeSum-small,rlasseri,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
+nlphuji/dollar_street_test,nlphuji,[{'title': 'The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World'}]
+irds/lotte_pooled_test_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mmarco_v2_id_dev,irds,[{'title': '{mMARCO'}]
+irds/mmarco_v2_de_dev,irds,[{'title': '{mMARCO'}]
+LRGB/voc_superpixels_edge_wt_region_boundary_30,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+orhunc/Bias-Evaluation-Turkish,orhunc,['arxiv.org/abs/1903.10561']
+irds/beir_nfcorpus_test,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/mr-tydi_ar_test,irds,[{'title': '{Mr. TyDi'}]
+LRGB/coco_superpixels_edge_wt_only_coord_30,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+irds/mmarco_id,irds,[{'title': '{mMARCO'}]
+irds/mmarco_zh_dev,irds,[{'title': '{mMARCO'}]
+arch-raven/music-fingerprint-dataset,arch-raven,['arxiv.org/abs/2010.11910']
+irds/mmarco_es,irds,[{'title': '{mMARCO'}]
+ConvLab/crosswoz,ConvLab,['bibtex']
+irds/mr-tydi_ar_dev,irds,[{'title': '{Mr. TyDi'}]
+nlphuji/flickr_1k_test_image_text_retrieval,nlphuji,[{'title': 'From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions'}]
+irds/argsme_2020-04-01_touche-2021-task-1,irds,['bibtex']
+irds/mr-tydi_fi_dev,irds,[{'title': '{Mr. TyDi'}]
+Loie/VGGSound,Loie,['arxiv.org/abs/2004.14368']
+irds/mmarco_v2_vi_dev,irds,[{'title': '{mMARCO'}]
+huggan/ae_photos,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+djghosh/wds_imagenet-o_test,djghosh,"['arxiv.org/abs/1907.07174', {'title': 'Natural Adversarial Examples'}]"
+lsb/million-english-numbers,lsb,['arxiv.org/abs/1803.09010']
+irds/mr-tydi_te_test,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_zh_dev_small,irds,[{'title': '{mMARCO'}]
+irds/msmarco-passage,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/msmarco-passage_train_triples-small,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/trec-arabic_ar2001,irds,"[{'title': 'The TREC-2001 Cross-Language Information Retrieval Track: Searching Arabic using English, French or Arabic Queries'}]"
+irds/wapo_v2_trec-news-2019,irds,[{'title': 'TREC 2019 News Track Overview'}]
+irds/msmarco-document-v2_trec-dl-2020,irds,[{'title': 'Overview of the TREC 2020 deep learning track'}]
+irds/aquaint_trec-robust-2005,irds,[{'title': 'Overview of the TREC 2005 Robust Retrieval Track'}]
+irds/mr-tydi_id_test,irds,[{'title': '{Mr. TyDi'}]
+djghosh/wds_country211_test,djghosh,"['arxiv.org/abs/2103.00020', 'bibtex']"
+irds/wikiclir_pt,irds,['bibtex']
+irds/clueweb12_b13_ntcir-www-2,irds,[{'title': 'Overview of the NTCIR-14 We Want Web Task'}]
+djghosh/wds_vtab-resisc45_test,djghosh,"['arxiv.org/abs/1703.00121', 'bibtex']"
+irds/mmarco_v2_id_train,irds,[{'title': '{mMARCO'}]
+irds/disks45_nocr_trec7,irds,[{'title': 'Overview of the Seventh Text Retrieval Conference (TREC-7)'}]
+irds/mmarco_v2_pt_dev,irds,[{'title': '{mMARCO'}]
+irds/trec-robust04_fold3,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+society-ethics/papers,society-ethics,['arxiv.org/abs/1906.02569']
+irds/wikiclir_ja,irds,['bibtex']
+irds/hc4_ru,irds,"['arxiv.org/abs/2201.09992', 'bibtex']"
+irds/codec_economics,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+egm517/hupd_augmented,egm517,"['arxiv.org/abs/2207.04043', {'title': 'The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications'}]"
+prerona/new_dataset,prerona,['bibtex']
+irds/clueweb12_b13_trec-misinfo-2019,irds,[{'title': 'Overview of the TREC 2019 Decision Track'}]
+irds/argsme_1.0_touche-2020-task-1_uncorrected,irds,['bibtex']
+irds/car_v1.5,irds,[{'title': '{TREC CAR'}]
+Short-Answer-Feedback/saf_micro_job_german,Short-Answer-Feedback,['bibtex']
+irds/lotte_lifestyle_dev_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/gov_trec-web-2002,irds,[{'title': 'Overview of the TREC-2002 Web Track'}]
+djghosh/wds_imagenet-a_test,djghosh,"['arxiv.org/abs/1907.07174', {'title': 'Natural Adversarial Examples'}]"
+irds/trec-robust04_fold5,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+irds/lotte_pooled_dev,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+NathanGavenski/How-Resilient-are-Imitation-Learning-Methods-to-Sub-Optimal-Experts,NathanGavenski,[{'title': 'How Resilient are Imitation Learning Methods to Sub-Optimal Experts?'}]
+DFKI-SLT/sciarg,DFKI-SLT,[{'title': 'An argument-annotated corpus of scientific publications'}]
+irds/msmarco-passage_trec-dl-hard_fold4,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/wikiclir_uk,irds,['bibtex']
+irds/cord19_trec-covid,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+chcaa/dagw-word-frequencies,chcaa,['arxiv.org/abs/2005.03521']
+irds/msmarco-passage_trec-dl-hard_fold3,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+huggan/iphone2dslr_flower,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+irds/mmarco_v2_ja,irds,[{'title': '{mMARCO'}]
+irds/beir_fever_test,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/mmarco_v2_de_train,irds,[{'title': '{mMARCO'}]
+irds/beir_fever_train,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/nfcorpus_train_video,irds,['bibtex']
+irds/trec-spanish_trec3,irds,[{'title': 'Overview of the Third Text REtrieval Conference (TREC-3)'}]
+irds/tripclick_val_head_dctr,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+irds/medline_2017_trec-pm-2018,irds,[{'title': 'Overview of the TREC 2018 Precision Medicine Track'}]
+ncats/EpiSet4BinaryClassification,ncats,[{'title': 'Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed'}]
+nehruperumalla/forms,nehruperumalla,[{'title': 'CORD: A Consolidated Receipt Dataset for Post-OCR Parsing'}]
+irds/mmarco_v2_hi,irds,[{'title': '{mMARCO'}]
+irds/mmarco_de_dev,irds,[{'title': '{mMARCO'}]
+ZhaofengWu/transparency-data,ZhaofengWu,"['arxiv.org/abs/2210.07468', 'bibtex']"
+irds/nfcorpus_train,irds,['bibtex']
+djghosh/wds_imagenet1k_test,djghosh,['bibtex']
+LRGB/voc_superpixels_edge_wt_only_coord_10,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+Intel/CoreSearch,Intel,['bibtex']
+irds/disks45_nocr_trec-robust-2004_fold2,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+hugginglearners/malayalam_news,hugginglearners,[{'title': 'AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages'}]
+irds/mr-tydi_bn_train,irds,[{'title': '{Mr. TyDi'}]
+irds/gov2_trec-tb-2006_efficiency,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+nlphuji/beyond_web_scraping,nlphuji,"['arxiv.org/abs/2301.02560', 'bibtex']"
+irds/mmarco_v2_fr,irds,[{'title': '{mMARCO'}]
+jeasinema/SQA3D,jeasinema,['arxiv.org/abs/2210.07474']
+irds/istella22,irds,[{'title': 'The Istella22 Dataset: Bridging Traditional and Neural Learning to Rank Evaluation'}]
+irds/mr-tydi_en,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_v2_hi_train,irds,[{'title': '{mMARCO'}]
+kmfoda/gov_report,kmfoda,[{'title': 'BookSum: A Collection of Datasets for Long-form Narrative Summarization'}]
+irds/beir_msmarco,irds,"['arxiv.org/abs/2104.08663', {'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]"
+irds/natural-questions,irds,[{'title': 'Natural Questions: a Benchmark for Question Answering Research'}]
+irds/tripclick,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+irds/mmarco_it_dev,irds,[{'title': '{mMARCO'}]
+irds/msmarco-document-v2_trec-dl-2019_judged,irds,[{'title': 'Overview of the TREC 2019 deep learning track'}]
+sled-umich/Conversation-Entailment,sled-umich,['bibtex']
+irds/msmarco-document-v2_trec-dl-2020_judged,irds,[{'title': 'Overview of the TREC 2020 deep learning track'}]
+irds/clinicaltrials_2017_trec-pm-2017,irds,[{'title': 'Overview of the TREC 2017 Precision Medicine Track'}]
+irds/nfcorpus_dev,irds,['bibtex']
+nateraw/sync_food101,nateraw,[{'title': 'Food-101 -- Mining Discriminative Components with Random Forests'}]
+irds/trec-spanish_trec4,irds,[{'title': 'Overview of the Fourth Text REtrieval Conference (TREC-4)'}]
+dominguesm/positive-reframing-ptbr-dataset,dominguesm,"['arxiv.org/abs/2204.02952', 'bibtex']"
+irds/trec-mandarin_trec5,irds,[{'title': 'Spanish and Chinese Document Retrieval in TREC-5'}]
+irds/beir_scifact_test,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+ThierryZhou/test,ThierryZhou,['arxiv.org/abs/2111.11431']
+irds/tweets2013-ia_trec-mb-2014,irds,[{'title': 'Overview of the TREC-2014 Microblog Track'}]
+irds/wikiclir_tl,irds,['bibtex']
+irds/msmarco-document_trec-dl-hard_fold1,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/lotte_lifestyle_test,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+deepsynthbody/deepfake_ecg,deepsynthbody,[{'title': 'DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine'}]
+sharejing/BiPaR,sharejing,['arxiv.org/abs/1910.05040']
+mbateman/github-issues,mbateman,"['arxiv.org/abs/2005.00614', 'doi.org/),', 'bibtex']"
+irds/msmarco-qna,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/beir_trec-covid,irds,"['arxiv.org/abs/2104.08663', {'title': 'CORD-19: The Covid-19 Open Research Dataset'}]"
+research-backup/semeval2012_relational_similarity,research-backup,['bibtex']
+irds/gov2_trec-tb-2005_efficiency,irds,[{'title': 'The TREC 2005 Terabyte Track'}]
+irds/gov2_trec-tb-2005_named-page,irds,[{'title': 'The TREC 2005 Terabyte Track'}]
+irds/msmarco-passage_train_triples-v2,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/tripclick_train_torso,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+conceptofmind/pile_open_web_text_2,conceptofmind,['arxiv.org/abs/2101.00027']
+irds/mmarco_de,irds,[{'title': '{mMARCO'}]
+irds/mmarco_v2_pt,irds,[{'title': '{mMARCO'}]
+irds/lotte_lifestyle_test_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+graphs-datasets/alchemy,graphs-datasets,"['arxiv.org/abs/1906.09427', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+irds/clueweb12_b13_clef-ehealth_sv,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+ai4bharat/kathbath,ai4bharat,"['arxiv.org/abs/2208.11761', 'doi.org/10.48550/arxiv.2208.11761,']"
+irds/mmarco_it,irds,[{'title': '{mMARCO'}]
+irds/trec-cast_v1,irds,[{'title': 'CAsT 2019: The Conversational Assistance Track Overview'}]
+chcaa/dagw-word-frequencies-by-domain,chcaa,['arxiv.org/abs/2005.03521']
+irds/kilt_codec_economics,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+irds/mr-tydi_te,irds,[{'title': '{Mr. TyDi'}]
+irds/mr-tydi_sw_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/wapo_v2_trec-news-2018,irds,[{'title': 'TREC 2018 News Track Overview'}]
+biu-nlp/Controlled-Text-Reduction-dataset,biu-nlp,"['arxiv.org/abs/2210.13449', 'doi.org/10.48550/arxiv.2210.13449,']"
+irds/medline_2004_trec-genomics-2004,irds,[{'title': 'TREC 2004 Genomics Track Overview'}]
+irds/mmarco_it_train,irds,[{'title': '{mMARCO'}]
+bs-la/xP3ru,bs-la,[{'title': 'BLOOM+ 1: Adding Language Support to BLOOM for Zero-Shot Prompting'}]
+irds/hc4_fa,irds,"['arxiv.org/abs/2201.09992', 'bibtex']"
+irds/mr-tydi_bn_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/lotte_lifestyle_dev,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mmarco_v2_de,irds,[{'title': '{mMARCO'}]
+irds/wikiclir_fi,irds,['bibtex']
+irds/wikiclir_de,irds,['bibtex']
+irds/mr-tydi_ja,irds,[{'title': '{Mr. TyDi'}]
+irds/lotte_pooled_dev_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_scifact,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+djghosh/wds_imagenet-r_test,djghosh,"['arxiv.org/abs/2006.16241', {'title': 'The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization'}]"
+irds/argsme_2020-04-01_touche-2020-task-1,irds,['bibtex']
+irds/beir_nfcorpus,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+argosopentech/xlent-de_en,argosopentech,['bibtex']
+irds/tripclick_train_head_dctr,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+irds/cord19_trec-covid_round5,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+irds/wikiclir_sv,irds,['bibtex']
+irds/beir_fever_dev,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+r-three/fib,r-three,[{'title': 'Evaluating the Factual Consistency of Large Language Models Through Summarization'}]
+irds/nfcorpus,irds,['bibtex']
+irds/nfcorpus_test,irds,['bibtex']
+irds/mr-tydi_en_train,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_v2_ru_train,irds,[{'title': '{mMARCO'}]
+irds/disks45_nocr_trec-robust-2004_fold5,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+graphs-datasets/MD17-benzene,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+spacemanidol/msmarco_passage_ranking,spacemanidol,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/antique_test,irds,[{'title': 'ANTIQUE: A Non-Factoid Question Answering Benchmark'}]
+imvladikon/parashoot,imvladikon,"['arxiv.org/abs/1606.05250', {'title': 'ParaShoot: A Hebrew Question Answering Dataset'}]"
+irds/nyt,irds,[{'title': 'The new york times annotated corpus'}]
+benlipkin/braincode-neurips2022,benlipkin,['bibtex']
+azad-wolf-se/MH-FED,azad-wolf-se,"['arxiv.org/abs/2203.05975', 'bibtex']"
+ICML2022/ProteinGym,ICML2022,['arxiv.org/abs/2205.13760']
+irds/wikiclir_ar,irds,['bibtex']
+lfoppiano/SuperMat,lfoppiano,"['doi.org/10.1080/27660400.2021.1918396).', 'bibtex']"
+irds/beir_fiqa_dev,irds,"['arxiv.org/abs/2104.08663', {'title': ""WWW'18 Open Challenge: Financial Opinion Mining and Question Answering""}]"
+irds/clueweb12_b13_clef-ehealth_hu,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+irds/mr-tydi_te_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/beir_dbpedia-entity_dev,irds,"['arxiv.org/abs/2104.08663', {'title': 'DBpedia-Entity v2: A Test Collection for Entity Search'}]"
+irds/mr-tydi_fi_train,irds,[{'title': '{Mr. TyDi'}]
+research-backup/conceptnet,research-backup,[{'title': 'Conceptnet 5.5: An open multilingual graph of general knowledge'}]
+irds/mmarco_v2_dt_dev,irds,[{'title': '{mMARCO'}]
+irds/codec_history,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+irds/mmarco_fr_train,irds,[{'title': '{mMARCO'}]
+irds/wikir_it16k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+midas/pubmed,midas,[{'title': 'Keyphrase Extraction from Single Documents in the Open Domain Exploiting Linguistic and Statistical Methods'}]
+irds/wikiclir_nn,irds,['bibtex']
+irds/codesearchnet_valid,irds,[{'title': 'CodeSearchNet Challenge: Evaluating the State of Semantic Code Search'}]
+irds/lotte_lifestyle_dev_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mr-tydi_sw,irds,[{'title': '{Mr. TyDi'}]
+irds/clueweb12_b13_clef-ehealth_cs,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+irds/lotte_writing_test_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mmarco_id_train,irds,[{'title': '{mMARCO'}]
+irds/msmarco-passage_trec-dl-hard_fold1,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+parvezmrobin/MCMD,parvezmrobin,['doi.org/10.5281/zenodo.5025758).']
+LHF/escorpius-m,LHF,"['arxiv.org/abs/2206.15147', 'bibtex']"
+irds/clueweb12_b13_clef-ehealth,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+physionet/mimic-iv-demo,physionet,['doi.org/10.13026/07hj-2a80']
+irds/cord19_trec-covid_round1,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+pariajm/sharif_emotional_speech_dataset,pariajm,['doi.org/10.1007/s10579-018-9427-x}']
+LRGB/voc_superpixels_edge_wt_only_coord_30,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+yjernite/EthicsSocietyBlogBias,yjernite,"['arxiv.org/abs/2203.07785', 'doi.org/10.57967/hf/0208},', 'bibtex']"
+HuggingFaceM4/TGIF,HuggingFaceM4,['arxiv.org/abs/1604.02748']
+irds/mr-tydi_id_train,irds,[{'title': '{Mr. TyDi'}]
+irds/msmarco-document_trec-dl-hard,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/mr-tydi_id_dev,irds,[{'title': '{Mr. TyDi'}]
+djghosh/wds_renderedsst2_test,djghosh,"['arxiv.org/abs/1910.04867', {'title': 'The Visual Task Adaptation Benchmark'}]"
+djghosh/wds_imagenet_sketch_test,djghosh,"['arxiv.org/abs/1905.13549', {'title': 'Learning Robust Global Representations by Penalizing Local Predictive Power'}]"
+huggan/grumpifycat,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+irds/mmarco_v2_it_train,irds,[{'title': '{mMARCO'}]
+saibo/bookcorpus_deduplicated,saibo,"['arxiv.org/abs/2105.05241', 'doi.org/10.48550/arxiv.2105.05241,']"
+irds/cord19_trec-covid_round2,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+biglam/yalta_ai_tabular_dataset,biglam,"['arxiv.org/abs/2207.11230', 'doi.org/10.5281/zenodo.6827706](https://doi.org/10.5281/zenodo.6827706)']"
+irds/mr-tydi_th_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/mr-tydi_sw_test,irds,[{'title': '{Mr. TyDi'}]
+irds/wikiclir_ko,irds,['bibtex']
+irds/wikiclir_zh,irds,['bibtex']
+irds/gov2_trec-tb-2006_efficiency_stream3,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+irds/medline_2017_trec-pm-2017,irds,[{'title': 'Overview of the TREC 2017 Precision Medicine Track'}]
+irds/mr-tydi_ru_train,irds,[{'title': '{Mr. TyDi'}]
+irds/kilt_codec,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+lhoestq/nllb,lhoestq,['arxiv.org/abs/2205.12654']
+Fatima-Gh/GLARE,Fatima-Gh,['doi.org/10.5281/zenodo.6457824)']
+irds/msmarco-document-v2_trec-dl-2019,irds,[{'title': 'Overview of the TREC 2019 deep learning track'}]
+irds/touche-image_2022-06-13_touche-2022-task-3,irds,['bibtex']
+irds/antique_train_split200-valid,irds,[{'title': 'ANTIQUE: A Non-Factoid Question Answering Benchmark'}]
+irds/wikiclir_es,irds,['bibtex']
+vector/test_demo,vector,['bibtex']
+irds/lotte_recreation_dev_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+ivelin/ui_refexp,ivelin,['arxiv.org/abs/2107.13731']
+umanlp/xscitldr,umanlp,"['doi.org/10.1145/3529372.3530938},', 'bibtex']"
+irds/wikiclir_en-simple,irds,['bibtex']
+saadob12/Autochart,saadob12,['arxiv.org/abs/2108.06897']
+elmurod1202/uzbek-sentiment-analysis,elmurod1202,['bibtex']
+irds/trec-cast_v1_2020,irds,[{'title': 'CAsT 2020: The Conversational Assistance Track Overview'}]
+irds/pmc_v1_trec-cds-2014,irds,[{'title': 'Overview of the TREC 2014 Clinical Decision Support Track'}]
+ganchengguang/resume-5label-classification,ganchengguang,[{'title': '英文履歴書データ抽出システムへの BERT 適用性の検討'}]
+irds/mr-tydi_sw_train,irds,[{'title': '{Mr. TyDi'}]
+irds/clueweb12_touche-2021-task-2,irds,['bibtex']
+jonathanli/hyperpartisan-longformer-split,jonathanli,['arxiv.org/abs/2004.05150']
+imranraad/github-emotion-surprise,imranraad,['arxiv.org/abs/2208.05573']
+irds/msmarco-passage_dev_judged,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+irds/codec,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+irds/mmarco_ru_train,irds,[{'title': '{mMARCO'}]
+graphs-datasets/MD17-malonaldehyde,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+djghosh/wds_imagenetv2_test,djghosh,"['arxiv.org/abs/1902.10811', 'bibtex']"
+saadob12/chart-to-text,saadob12,['arxiv.org/abs/2203.06486']
+irds/msmarco-document_trec-dl-hard_fold4,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/kilt_codec_history,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+irds/car_v1.5_trec-y1_auto,irds,[{'title': 'TREC Complex Answer Retrieval Overview.'}]
+irds/mr-tydi_id,irds,[{'title': '{Mr. TyDi'}]
+semeru/Text-Code-CodeSearchNet-Python,semeru,[{'title': 'Codesearchnet challenge: Evaluating the state of semantic code search'}]
+irds/mmarco_v2_hi_dev,irds,[{'title': '{mMARCO'}]
+irds/tripclick_train_hofstaetter-triples,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+irds/disks45_nocr_trec8,irds,[{'title': 'Overview of the Eight Text Retrieval Conference (TREC-8)'}]
+irds/beir_quora,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+Ramos-Ramos/smallnorb,Ramos-Ramos,"['doi.org/10.1109/CVPR.2004.1315150)', {'title': 'Learning methods for generic object recognition with invariance to pose and lighting'}]"
+LRGB/coco_superpixels_edge_wt_region_boundary_10,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+graphs-datasets/MD17-toluene,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+projecte-aina/cat_manynames,projecte-aina,['bibtex']
+LRGB/voc_superpixels_edge_wt_region_boundary_10,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+irds/lotte_science_test_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/lotte_science_dev,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mmarco_ru_dev,irds,[{'title': '{mMARCO'}]
+irds/mr-tydi_en_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/mr-tydi_fi_test,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_v2_dt,irds,[{'title': '{mMARCO'}]
+irds/lotte_technology_dev_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+toloka/TolokerGraph,toloka,['doi.org/10.5281/zenodo.7620795).']
+irds/lotte_technology_test_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/nyt_wksup_valid,irds,['bibtex']
+irds/lotte_technology_dev_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mmarco_zh,irds,[{'title': '{mMARCO'}]
+irds/clueweb12_b13_clef-ehealth_pl,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+irds/nfcorpus_test_nontopic,irds,['bibtex']
+irds/wikir_en59k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+irds/mmarco_v2_ru,irds,[{'title': '{mMARCO'}]
+irds/msmarco-passage_trec-dl-hard,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/lotte_technology_dev,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/antique,irds,[{'title': 'ANTIQUE: A Non-Factoid Question Answering Benchmark'}]
+Martha-987/vivos,Martha-987,"['doi.org/10.5281/zenodo.7068130', 'bibtex']"
+gimmaru/github-issues,gimmaru,"['arxiv.org/abs/2005.00614', 'doi.org/&lt;/span&gt;),', 'bibtex']"
+irds/wikiclir_tr,irds,['bibtex']
+irds/cord19,irds,[{'title': 'CORD-19: The Covid-19 Open Research Dataset'}]
+irds/beir_quora_dev,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/mmarco_es_dev,irds,[{'title': '{mMARCO'}]
+irds/clueweb12_touche-2020-task-2,irds,['bibtex']
+irds/clueweb12_b13_clef-ehealth_fr,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+irds/mmarco_v2_es,irds,[{'title': '{mMARCO'}]
+irds/lotte_technology_test_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/disks45_nocr_trec-robust-2004_fold4,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+chcaa/dagw-word-frequencies-by-domain-with-pos-tags,chcaa,['arxiv.org/abs/2005.03521']
+irds/mr-tydi_th_test,irds,[{'title': '{Mr. TyDi'}]
+irds/gov2_trec-tb-2006,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+irds/mr-tydi_fi,irds,[{'title': '{Mr. TyDi'}]
+nealmgkr/tminer_hs,nealmgkr,[{'title': 'T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification'}]
+omar-sharif/BAD-Bengali-Aggressive-Text-Dataset,omar-sharif,"['doi.org/10.1016/j.neucom.2021.12.022},', {'title': 'Tackling Cyber-Aggression: Identification and Fine-Grained Categorization of Aggressive Texts on Social Media using Weighted Ensemble of Transformers'}]"
+irds/lotte_science_dev_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+djghosh/wds_vtab-pcam_test,djghosh,['arxiv.org/abs/1806.03962']
+irds/disks45_nocr_trec-robust-2004_fold1,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+WillHeld/demszky_pairs,WillHeld,[{'title': 'Learning to Recognize Dialect Features'}]
+laion/filtered-wit,laion,['arxiv.org/abs/2103.00020']
+sled-umich/Action-Effect,sled-umich,['bibtex']
+unicamp-dl/mrobust,unicamp-dl,"['arxiv.org/abs/2108.13897', 'doi.org/10.48550/arxiv.2209.13738,']"
+irds/mmarco_v2_es_train,irds,[{'title': '{mMARCO'}]
+irds/nyt_wksup_train,irds,['bibtex']
+conceptofmind/pile_hacker_news,conceptofmind,['arxiv.org/abs/2101.00027']
+irds/beir_climate-fever,irds,"['arxiv.org/abs/2104.08663', {'title': 'CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims'}]"
+irds/mmarco_pt_train_v1.1,irds,[{'title': '{mMARCO'}]
+irds/clueweb12_b13_ntcir-www-1,irds,[{'title': 'Overview of the NTCIR-13 We Want Web Task'}]
+irds/lotte_writing_dev,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_msmarco_train,irds,"['arxiv.org/abs/2104.08663', {'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]"
+djghosh/wds_objectnet_test,djghosh,['bibtex']
+irds/medline_2004_trec-genomics-2005,irds,[{'title': 'TREC 2005 Genomics Track Overview'}]
+irds/beir_fiqa_test,irds,"['arxiv.org/abs/2104.08663', {'title': ""WWW'18 Open Challenge: Financial Opinion Mining and Question Answering""}]"
+piuba-bigdata/contextualized_hate_speech,piuba-bigdata,"['arxiv.org/abs/2210.00465', {'title': 'Assessing the impact of contextual information in hate speech detection'}]"
+irds/mr-tydi_bn,irds,[{'title': '{Mr. TyDi'}]
+irds/lotte_recreation_test_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+conceptofmind/pile_uspto_backgrounds,conceptofmind,['arxiv.org/abs/2101.00027']
+irds/lotte_writing_test,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/nfcorpus_train_nontopic,irds,['bibtex']
+irds/mmarco_v2_es_dev,irds,[{'title': '{mMARCO'}]
+irds/disks45_nocr_trec-robust-2004_fold3,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+webnlg/challenge-2023,webnlg,"['doi.org/10.18653/v1/P17-1017},', 'bibtex']"
+irds/gov_trec-web-2004,irds,[{'title': 'Overview of the TREC-2004 Web Track'}]
+irds/gov2_trec-tb-2006_efficiency_stream1,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+irds/beir_arguana,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/lotte_lifestyle_test_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/gov2_trec-tb-2006_efficiency_stream2,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+irds/msmarco-document_trec-dl-hard_fold3,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/wikiclir_pl,irds,['bibtex']
+djghosh/wds_vtab-eurosat_test,djghosh,"['arxiv.org/abs/1709.00029', {'title': 'Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification'}]"
+qanastek/Biosses-BLUE,qanastek,"['doi.org/10.1093/bioinformatics/btx238},', 'bibtex']"
+irds/mmarco_v2_ar_train,irds,[{'title': '{mMARCO'}]
+mrm8488/goemotions,mrm8488,"['arxiv.org/abs/2005.00547', 'bibtex']"
+irds/msmarco-passage_dev,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+society-ethics/BlogPostBias,society-ethics,"['arxiv.org/abs/2203.07785', 'doi.org/10.57967/hf/0214},', 'bibtex']"
+irds/nfcorpus_test_video,irds,['bibtex']
+irds/mr-tydi_ru_test,irds,[{'title': '{Mr. TyDi'}]
+irds/highwire_trec-genomics-2006,irds,[{'title': 'TREC 2006 Genomics Track Overview'}]
+irds/beir_webis-touche2020_v2,irds,"['arxiv.org/abs/2104.08663', {'title': ""Overview of Touch{\\'e""}]"
+huggan/facades,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+irds/wikiclir_fr,irds,['bibtex']
+irds/kilt_codec_politics,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+irds/lotte_writing_dev_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+LRGB/coco_superpixels_edge_wt_only_coord_10,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+drAbreu/sd-nlp-2,drAbreu,['doi.org/10.1038/nmeth.4471).']
+winfried/gnn_bvp_solver,winfried,['arxiv.org/abs/2206.14092']
+irds/wikir_ens78k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+huggan/ukiyoe2photo,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+irds/lotte_writing_dev_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_msmarco_dev,irds,"['arxiv.org/abs/2104.08663', {'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]"
+irds/tweets2013-ia,irds,"[{'title': 'Finally, a Downloadable Test Collection of Tweets'}]"
+SauravMaheshkar/tox21_SRp53,SauravMaheshkar,"['arxiv.org/abs/1703.00564', 'doi.org/10.48550/arxiv.1703.00564,']"
+irds/gov_trec-web-2003,irds,[{'title': 'Overview of the TREC 2003 Web Track'}]
+irds/codesearchnet_test,irds,[{'title': 'CodeSearchNet Challenge: Evaluating the State of Semantic Code Search'}]
+LRGB/coco_superpixels_edge_wt_coord_feat_10,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+dianalogan/Marketing-Budget-and-Actual-Sales-Dataset,dianalogan,[{'title': '{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification'}]
+irds/msmarco-document,irds,[{'title': 'MS MARCO: A Human Generated MAchine Reading COmprehension Dataset'}]
+Fhrozen/dcase22_task3,Fhrozen,"['doi.org/10.5281/zenodo.4064792),']"
+irds/wikir_es13k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+irds/mmarco_es_train,irds,[{'title': '{mMARCO'}]
+irds/pmc_v1_trec-cds-2015,irds,[{'title': 'Overview of the TREC 2015 Clinical Decision Support Track'}]
+irds/wikiclir_it,irds,['bibtex']
+irds/pmc_v2_trec-cds-2016,irds,[{'title': 'Overview of the TREC 2016 Clinical Decision Support Track'}]
+irds/mr-tydi_bn_test,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_de_train,irds,[{'title': '{mMARCO'}]
+irds/mr-tydi_en_test,irds,[{'title': '{Mr. TyDi'}]
+LRGB/coco_superpixels_edge_wt_only_feat_30,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+djghosh/wds_vtab-dmlab_test,djghosh,"['arxiv.org/abs/1910.04867', {'title': 'The Visual Task Adaptation Benchmark'}]"
+irds/wikiclir_vi,irds,['bibtex']
+irds/mmarco_v2_fr_train,irds,[{'title': '{mMARCO'}]
+irds/clinicaltrials_2019_trec-pm-2019,irds,[{'title': 'Overview of the TREC 2019 Precision Medicine Track'}]
+irds/codesearchnet_challenge,irds,[{'title': 'CodeSearchNet Challenge: Evaluating the State of Semantic Code Search'}]
+irds/trec-robust04_fold4,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+irds/mmarco_pt_train,irds,[{'title': '{mMARCO'}]
+ICML2022/EfficientDatasetCondensation,ICML2022,[{'title': 'Dataset Condensation via Efficient Synthetic-Data Parameterization'}]
+irds/trec-cast_v0,irds,[{'title': 'CAsT 2019: The Conversational Assistance Track Overview'}]
+irds/gov_trec-web-2003_named-page,irds,[{'title': 'Overview of the TREC 2003 Web Track'}]
+irds/mmarco_v2_ar_dev,irds,[{'title': '{mMARCO'}]
+irds/mmarco_v2_id,irds,[{'title': '{mMARCO'}]
+irds/beir_nq,irds,"['arxiv.org/abs/2104.08663', {'title': 'Natural Questions: a Benchmark for Question Answering Research'}]"
+taesiri/GamePhysics,taesiri,['arxiv.org/abs/2203.11096']
+irds/trec-mandarin_trec6,irds,[{'title': 'Chinese Document Retrieval at TREC-6'}]
+graphs-datasets/MD17-ethanol,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+irds/mmarco_v2_ar,irds,[{'title': '{mMARCO'}]
+djghosh/wds_vtab-clevr_closest_object_distance_test,djghosh,"['arxiv.org/abs/1612.06890', 'bibtex']"
+irds/trec-cast_v1_2020_judged,irds,[{'title': 'CAsT 2020: The Conversational Assistance Track Overview'}]
+EMBO/sd-character-level-ner,EMBO,['doi.org/10.1038/nmeth.4471).']
+djghosh/wds_gtsrb_test,djghosh,"['doi.org/10.1016/j.neunet.2012.02.016)', 'bibtex']"
+irds/mmarco_v2_zh_dev,irds,[{'title': '{mMARCO'}]
+rajeshradhakrishnan/malayalam_news,rajeshradhakrishnan,[{'title': 'AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages'}]
+irds/hc4_zh,irds,"['arxiv.org/abs/2201.09992', 'bibtex']"
+irds/wikiclir_ca,irds,['bibtex']
+irds/mmarco_v2_zh_train,irds,[{'title': '{mMARCO'}]
+djghosh/wds_vtab-svhn_test,djghosh,[{'title': 'Reading Digits in Natural Images with Unsupervised Feature Learning'}]
+irds/mr-tydi_ko_train,irds,[{'title': '{Mr. TyDi'}]
+irds/wikir_en1k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+irds/gov2_trec-tb-2006_efficiency_stream4,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+conceptofmind/pile_dm_mathematics,conceptofmind,['arxiv.org/abs/2101.00027']
+irds/msmarco-document_trec-dl-hard_fold5,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/antique_train_split200-train,irds,[{'title': 'ANTIQUE: A Non-Factoid Question Answering Benchmark'}]
+irds/mmarco_pt_dev_small,irds,[{'title': '{mMARCO'}]
+irds/tripclick_train_head,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+irds/mmarco_pt_dev,irds,[{'title': '{mMARCO'}]
+cemachelen/LIFD_Magnetic_Field_Data,cemachelen,['doi.org/10.1098/rsta.2000.0569](https://royalsocietypublishing.org/doi/10.1098/rsta.2000.0569)']
+irds/wikiclir_cs,irds,['bibtex']
+irds/clinicaltrials_2017_trec-pm-2018,irds,[{'title': 'Overview of the TREC 2018 Precision Medicine Track'}]
+ryanxingql/MFQEv2,ryanxingql,"['arxiv.org/abs/1902.09707', 'doi.org/10.1109%2Ftpami.2019.2944806},', {'doi': '10.1109/tpami.2019.2944806'}]"
+irds/gov2_trec-tb-2006_named-page,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+xuyeliu/notebookCDG,xuyeliu,['arxiv.org/abs/2104.01002']
+irds/beir_msmarco_test,irds,"['arxiv.org/abs/2104.08663', {'title': 'Overview of the TREC 2019 deep learning track'}]"
+irds/mmarco_fr_dev,irds,[{'title': '{mMARCO'}]
+irds/wikiclir_no,irds,['bibtex']
+irds/lotte_pooled_dev_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+graphs-datasets/MD17-aspirin,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+irds/lotte_science_test,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_hotpotqa_train,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+graphs-datasets/MD17-salicylic_acid,graphs-datasets,"['doi.org/10.1126%2Fsciadv.1603015},', {'title': 'TUDataset: A collection of benchmark datasets for learning with graphs'}]"
+irds/trec-robust04_fold2,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+irds/lotte_technology_test,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+LRGB/coco_superpixels_edge_wt_region_boundary_30,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+irds/beir_nfcorpus_dev,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/clueweb12_touche-2022-task-2_expanded-doc-t5-query,irds,['bibtex']
+irds/msmarco-passage_trec-dl-hard_fold5,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/beir_hotpotqa_dev,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/mr-tydi_th,irds,[{'title': '{Mr. TyDi'}]
+uva-irlab/canard_quretec,uva-irlab,['arxiv.org/abs/2005.11723']
+LRGB/PCQM-Contact,LRGB,[{'title': 'Long Range Graph Benchmark'}]
+irds/nfcorpus_dev_nontopic,irds,['bibtex']
+irds/antique_train,irds,[{'title': 'ANTIQUE: A Non-Factoid Question Answering Benchmark'}]
+simeneide/recsys_slates_dataset,simeneide,"['arxiv.org/abs/2104.15046', 'doi.org/10.1007/s10618-022-00849-w']"
+irds/highwire_trec-genomics-2007,irds,[{'title': 'TREC 2007 Genomics Track Overview'}]
+sshleifer/pseudo_bart_xsum,sshleifer,"[{'title': ""Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization""}]"
+irds/codec_politics,irds,[{'title': 'CODEC: Complex Document and Entity Collection'}]
+SberDevices/Golos,SberDevices,['arxiv.org/abs/2106.10161']
+amitdanin/s3_spyder,amitdanin,[{'title': 'Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task'}]
+irds/mr-tydi_ko_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/gov2_trec-tb-2004,irds,[{'title': 'Overview of the TREC 2004 Terabyte Track'}]
+irds/beir_fever,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/gov2_trec-tb-2005,irds,[{'title': 'The TREC 2005 Terabyte Track'}]
+irds/mr-tydi_ar_train,irds,[{'title': '{Mr. TyDi'}]
+irds/antique_test_non-offensive,irds,[{'title': 'ANTIQUE: A Non-Factoid Question Answering Benchmark'}]
+irds/mr-tydi_ja_test,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_v2_vi,irds,[{'title': '{mMARCO'}]
+antoinelb7/alloprof,antoinelb7,['arxiv.org/abs/2302.07738']
+irds/mmarco_v2_pt_train,irds,[{'title': '{mMARCO'}]
+irds/tweets2013-ia_trec-mb-2013,irds,[{'title': 'Overview of the TREC-2013 Microblog Track'}]
+irds/lotte_recreation_test_forum,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mr-tydi_ko,irds,[{'title': '{Mr. TyDi'}]
+irds/trec-robust04_fold1,irds,[{'title': 'Overview of the TREC 2004 Robust Retrieval Track'}]
+djghosh/wds_vtab-clevr_count_all_test,djghosh,"['arxiv.org/abs/1612.06890', 'bibtex']"
+irds/beir_fiqa,irds,"['arxiv.org/abs/2104.08663', {'title': ""WWW'18 Open Challenge: Financial Opinion Mining and Question Answering""}]"
+irds/mmarco_v2_ja_train,irds,[{'title': '{mMARCO'}]
+irds/lotte_pooled_test,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/mmarco_v2_fr_dev,irds,[{'title': '{mMARCO'}]
+irds/gov2_trec-tb-2006_efficiency_10k,irds,[{'title': 'The TREC 2006 Terabyte Track'}]
+irds/wikiclir_sw,irds,['bibtex']
+irds/mmarco_id_dev,irds,[{'title': '{mMARCO'}]
+irds/mmarco_ru,irds,[{'title': '{mMARCO'}]
+irds/nfcorpus_dev_video,irds,['bibtex']
+irds/mmarco_v2_ja_dev,irds,[{'title': '{mMARCO'}]
+irds/msmarco-document_trec-dl-hard_fold2,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/msmarco-passage_trec-dl-hard_fold2,irds,[{'title': 'How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset'}]
+irds/argsme_2020-04-01_processed_touche-2022-task-1,irds,['bibtex']
+irds/lotte_science_test_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_webis-touche2020,irds,"['arxiv.org/abs/2104.08663', {'title': ""Overview of Touch{\\'e""}]"
+Leyo/TGIF,Leyo,['arxiv.org/abs/1604.02748']
+irds/mr-tydi_ru_dev,irds,[{'title': '{Mr. TyDi'}]
+irds/tripclick_train,irds,[{'title': 'TripClick: The Log Files of a Large Health Web Search Engine'}]
+djaym7/wiki_dialog_mlm,djaym7,['arxiv.org/abs/2205.09073']
+irds/cord19_trec-covid_round3,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+irds/clueweb12_b13_clef-ehealth_de,irds,[{'title': 'The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval'}]
+irds/cord19_trec-covid_round4,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+irds/beir_nfcorpus_train,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/lotte_recreation_test,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+huggan/cezanne2photo,huggan,"['arxiv.org/abs/1703.10593', 'bibtex']"
+irds/beir_scifact_train,irds,"['arxiv.org/abs/2104.08663', 'bibtex']"
+irds/lotte_recreation_dev,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+irds/beir_dbpedia-entity,irds,"['arxiv.org/abs/2104.08663', {'title': 'DBpedia-Entity v2: A Test Collection for Entity Search'}]"
+irds/mmarco_pt,irds,[{'title': '{mMARCO'}]
+sustcsenlp/bn_news_summarization,sustcsenlp,['doi.org/10.1007/978-981-33-4673-4_4)']
+irds/mmarco_fr,irds,[{'title': '{mMARCO'}]
+irds/wikiclir_nl,irds,['bibtex']
+djghosh/wds_food101_test,djghosh,[{'title': 'Food-101 -- Mining Discriminative Components with Random Forests'}]
+irds/cord19_fulltext_trec-covid,irds,[{'title': 'TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection'}]
+s-nlp/ru_paradetox,s-nlp,[{'title': 'RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora'}]
+irds/wikir_en78k,irds,[{'title': 'WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset'}]
+irds/lotte_writing_test_search,irds,"['arxiv.org/abs/2112.01488', 'bibtex']"
+dmargutierrez/Babelscape-wikineural-joined,dmargutierrez,['bibtex']
+irds/mr-tydi_ko_test,irds,[{'title': '{Mr. TyDi'}]
+irds/mmarco_v2_vi_train,irds,[{'title': '{mMARCO'}]