{ "AmazonCounterfactualClassification": "Classify a given Amazon customer review text as either counterfactual or not-counterfactual", "AmazonPolarityClassification": "Classify Amazon reviews into positive or negative sentiment", "AmazonReviewsClassification": "Classify the given Amazon review into its appropriate rating category", "Banking77Classification": "Given a online banking query, find the corresponding intents", "EmotionClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise", "ImdbClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset", "MassiveIntentClassification": "Given a user utterance as query, find the user intents", "MassiveScenarioClassification": "Given a user utterance as query, find the user scenarios", "MTOPDomainClassification": "Classify the intent domain of the given utterance in task-oriented conversation", "MTOPIntentClassification": "Classify the intent of the given utterance in task-oriented conversation", "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic", "TweetSentimentExtractionClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral", "TNews": "Classify the fine-grained category of the given news title", "IFlyTek": "Given an App description text, find the appropriate fine-grained category", "MultilingualSentiment": "Classify sentiment of the customer review into positive, neutral, or negative", "JDReview": "Classify the customer review for iPhone on e-commerce platform into positive or negative", "OnlineShopping": "Classify the customer review for online shopping into positive or negative", "Waimai": "Classify the customer review from a food takeaway platform into positive or negative", "ArxivClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts", "ArxivClusteringS2S": "Identify the main and secondary category of Arxiv papers based on the titles", "BiorxivClusteringP2P": "Identify the main category of Biorxiv papers based on the titles and abstracts", "BiorxivClusteringP2P.v2": "Identify the main category of Biorxiv papers based on the titles and abstracts", "BiorxivClusteringS2S": "Identify the main category of Biorxiv papers based on the titles", "BiorxivClusteringS2S.v2": "Identify the main category of Biorxiv papers based on the titles", "MedrxivClusteringP2P": "Identify the main category of Medrxiv papers based on the titles and abstracts", "MedrxivClusteringP2P.v2": "Identify the main category of Medrxiv papers based on the titles and abstracts", "MedrxivClusteringS2S": "Identify the main category of Medrxiv papers based on the titles", "MedrxivClusteringS2S.v2": "Identify the main category of Medrxiv papers based on the titles", "RedditClustering": "Identify the topic or theme of Reddit posts based on the titles", "RedditClusteringP2P": "Identify the topic or theme of Reddit posts based on the titles and posts", "StackExchangeClustering": "Identify the topic or theme of StackExchange posts based on the titles", "StackExchangeClustering.v2": "Identify the topic or theme of StackExchange posts based on the titles", "StackExchangeClusteringP2P": "Identify the topic or theme of StackExchange posts based on the given paragraphs", "StackExchangeClusteringP2P.v2": "Identify the topic or theme of StackExchange posts based on the given paragraphs", "TwentyNewsgroupsClustering": "Identify the topic or theme of the given news articles", "CLSClusteringS2S": "Identify the main category of scholar papers based on the titles", "CLSClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts", "CLSClusteringP2P.v2": "Identify the main category of scholar papers based on the titles and abstracts", "ThuNewsClusteringS2S": "Identify the topic or theme of the given news articles based on the titles", "ThuNewsClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents", "AskUbuntuDupQuestions": "Retrieve duplicate questions from AskUbuntu forum", "MindSmallReranking": "Retrieve relevant news articles based on user browsing history", "SciDocsRR": "Given a title of a scientific paper, retrieve the titles of other relevant papers", "StackOverflowDupQuestions": "Retrieve duplicate questions from StackOverflow forum", "SprintDuplicateQuestions": "Retrieve semantically duplicate questions", "TwitterSemEval2015": "Retrieve tweets that are semantically similar to the given tweet", "TwitterURLCorpus": "Retrieve tweets that are semantically similar to the given tweet", "T2Reranking": "Given a Chinese search query, retrieve web passages that answer the question", "MMarcoReranking": "Given a Chinese search query, retrieve web passages that answer the question", "CMedQAv1-reranking": "Given a Chinese community medical question, retrieve replies that best answer the question", "CMedQAv2-reranking": "Given a Chinese community medical question, retrieve replies that best answer the question", "Ocnli": "Retrieve semantically similar text.", "Cmnli": "Retrieve semantically similar text.", "ArguAna": {"query": "Given a claim, find documents that refute the claim", "passage": "Given a claim, find documents that refute the claim"}, "ClimateFEVER": "Given a claim about climate change, retrieve documents that support or refute the claim", "ClimateFEVERHardNegatives": "Given a claim about climate change, retrieve documents that support or refute the claim", "DBPedia": "Given a query, retrieve relevant entity descriptions from DBPedia", "FEVER": "Given a claim, retrieve documents that support or refute the claim", "FEVERHardNegatives": "Given a claim, retrieve documents that support or refute the claim", "FiQA2018": "Given a financial question, retrieve user replies that best answer the question", "HotpotQA": "Given a multi-hop question, retrieve documents that can help answer the question", "HotpotQAHardNegatives": "Given a multi-hop question, retrieve documents that can help answer the question", "MSMARCO": "Given a web search query, retrieve relevant passages that answer the query", "NFCorpus": "Given a question, retrieve relevant documents that best answer the question", "NQ": "Given a question, retrieve Wikipedia passages that answer the question", "QuoraRetrieval": "Given a question, retrieve questions that are semantically equivalent to the given question", "SCIDOCS": "Given a title of a scientific paper, retrieve the titles of other relevant papers", "SciFact": "Given a scientific claim, retrieve documents that support or refute the claim", "Touche2020": "Given a question, retrieve detailed and persuasive arguments that answer the question", "Touche2020Retrieval.v3": "Given a question, retrieve detailed and persuasive arguments that answer the question", "TRECCOVID": "Given a medical query, retrieve documents that answer the query", "T2Retrieval": "Given a Chinese search query, retrieve web passages that answer the question", "MMarcoRetrieval": "Given a web search query, retrieve relevant passages that answer the query", "VoyageMMarcoReranking": "Given a Japanese search query, retrieve web passages that answer the question", "DuRetrieval": "Given a Chinese search query, retrieve web passages that answer the question", "CovidRetrieval": "Given a question on COVID-19, retrieve news articles that answer the question", "CmedqaRetrieval": "Given a Chinese community medical question, retrieve replies that best answer the question", "EcomRetrieval": "Given a user query from an e-commerce website, retrieve description sentences of relevant products", "MedicalRetrieval": "Given a medical question, retrieve user replies that best answer the question", "VideoRetrieval": "Given a video search query, retrieve the titles of relevant videos", "STSBenchmarkMultilingualSTS": "Retrieve semantically similar text", "SICKFr": "Retrieve semantically similar text", "SummEvalFr": "Given a news summary, retrieve other semantically similar summaries", "MasakhaNEWSClassification": "Classify the News in the given texts into one of the seven category: politics,sports,health,business,entertainment,technology,religion ", "OpusparcusPC":"Retrieve semantically similar text", "PAWSX":"Retrieve semantically similar text", "HALClusteringS2S": "Identify the main category of academic passage based on the titles and contents", "MasakhaNEWSClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents", "MasakhaNEWSClusteringS2S": "Identify the topic or theme of the given news articles based on the titles", "MLSUMClusteringP2P": "Identify the topic or theme of the given articles based on the titles and contents", "MLSUMClusteringS2S": "Identify the topic or theme of the given articles based on the titles", "SyntecReranking": "Given a question, retrieve passages that answer the question", "AlloprofReranking": "Given a question, retrieve passages that answer the question", "AlloprofRetrieval": "Given a question, retrieve passages that answer the question", "BSARDRetrieval": "Given a question, retrieve passages that answer the question", "SyntecRetrieval": "Given a question, retrieve passages that answer the question", "XPQARetrieval": "Given a question, retrieve passages that answer the question", "MintakaRetrieval": "Given a question, retrieve passages that answer the question", "CBD":"Classify the sentiment of polish tweet reviews", "PolEmo2.0-IN": "Classify the sentiment of medicine and hotels online reviews", "PolEmo2.0-OUT":"Classify the sentiment of products and school online reviews", "AllegroReviews": "Classify the sentiment of reviews from e-commerce marketplace Allegro", "PAC": "Classify Polish contract clauses into one of the following two types: \"Safe Contract Clauses\" and \"Unfair Contract Clauses\".", "SICK-E-PL": "Retrieve semantically similar text", "SICK-R-PL": "Retrieve semantically similar text", "STS22": "Retrieve semantically similar text", "AFQMC": "Retrieve semantically similar text", "AFQMC": "Retrieve semantically similar text", "BQ": "Retrieve semantically similar text", "LCQMC": "Retrieve semantically similar text", "PAWSX": "Retrieve semantically similar text", "QBQTC": "Retrieve semantically similar text", "STS12": "Retrieve semantically similar text", "PpcPC": "Retrieve semantically similar text", "CDSC-E": "Retrieve semantically similar text", "BornholmBitextMining": "Retrieve parallel sentences", "NorwegianCourtsBitextMining": "Retrieve parallel sentences", "PSC": "Retrieve semantically similar text", "EightTagsClustering": "Identify of headlines from social media posts in Polish into 8 categories: film, history, food, medicine, motorization, work, sport and technology", "ArguAna-PL": "Given a claim, find documents that refute the claim", "DBPedia-PL": "Given a query, retrieve relevant entity descriptions from DBPedia", "FiQA-PL": "Given a financial question, retrieve user replies that best answer the question", "HotpotQA-PL": "Given a multi-hop question, retrieve documents that can help answer the question", "MSMARCO-PL": "Given a web search query, retrieve relevant passages that answer the query", "NFCorpus-PL": "Given a question, retrieve relevant documents that best answer the question", "NQ-PL": "Given a question, retrieve Wikipedia passages that answer the question", "Quora-PL": "Given a question, retrieve questions that are semantically equivalent to the given question", "SCIDOCS-PL": "Given a title of a scientific paper, retrieve the titles of other relevant papers", "SciFact-PL": "Given a scientific claim, retrieve documents that support or refute the claim", "TRECCOVID-PL": "Given a medical query, retrieve documents that answer the query", "GeoreviewClassification": "Classify the organization rating based on the reviews", "HeadlineClassification": "Classify the topic or theme of the given news headline", "InappropriatenessClassification": "Classify the given message as either sensitive topic or not", "KinopoiskClassification": "Classify the sentiment expressed in the given movie review text", "RuReviewsClassification": "Classify product reviews into positive, negative or neutral sentiment", "RuSciBenchGRNTIClassification": "Classify the category of scientific papers based on the titles and abstracts", "RuSciBenchOECDClassification": "Classify the category of scientific papers based on the titles and abstracts", "GeoreviewClusteringP2P": "Identify the organization category based on the reviews", "RuSciBenchGRNTIClusteringP2P": "Identify the category of scientific papers based on the titles and abstracts", "RuSciBenchOECDClusteringP2P": "Identify the category of scientific papers based on the titles and abstracts", "TERRa": "Given a premise, retrieve a hypothesis that is entailed by the premise", "RuBQReranking": "Given a question, retrieve Wikipedia passages that answer the question", "RiaNewsRetrieval": "Given a headline, retrieval relevant articles", "RuBQRetrieval": "Given a question, retrieve Wikipedia passages that answer the question", "RUParaPhraserSTS": "Retrieve semantically similar text", "RuSTSBenchmarkSTS": "Retrieve semantically similar text", "AppsRetrieval": "Given a question about code problem, retrieval code that can solve user's problem", "COIRCodeSearchNetRetrieval": "Given a code snippet, retrieve the comment corresponding to that code.", "CodeEditSearchRetrieval": "Given a piece of code, retrieval code that in the ", "CodeFeedbackMT": "Given a question about coding, retrieval code or passage that can solve user's question", "CodeFeedbackST": "Given a question about coding, retrieval code or passage that can solve user's question", "CodeSearchNetCCRetrieval": "Given a code comment, retrieve the code snippet corresponding to that comment.", "CodeSearchNetRetrieval": "Given a code snippet, retrieve the comment corresponding to that code.", "CodeTransOceanContest": "Given a piece for code, retrieval semantically similar code", "CodeTransOceanDL": "Given a piece for code, retrieval semantically similar code", "CosQA": "Given a question about coding, retrieval code or passage that can solve user's question", "StackOverflowQA": "Given a question about coding, retrieval code or passage that can solve user's question", "SyntheticText2SQL": "Given a user's question, retrieve SQL queries that are appropriate responses to the question", "BibleNLPBitextMining": "Retrieve parallel sentences", "BUCC.v2": "Retrieve parallel sentences", "DiaBlaBitextMining": "Retrieve parallel sentences", "FloresBitextMining": "Retrieve parallel sentences", "IN22GenBitextMining": "Retrieve parallel sentences", "IndicGenBenchFloresBitextMining": "Retrieve parallel sentences", "NollySentiBitextMining": "Retrieve parallel sentences", "NTREXBitextMining": "Retrieve parallel sentences", "NusaTranslationBitextMining": "Retrieve parallel sentences", "NusaXBitextMining": "Retrieve parallel sentences", "Tatoeba": "Retrieve parallel sentences", "BulgarianStoreReviewSentimentClassfication": "Classify user reviews into positive, negative or mixed sentiment", "CzechProductReviewSentimentClassification": "Classify product reviews into positive, neutral, or negative sentiment", "GreekLegalCodeClassification": "Given a greek legal text, classify its topic", "DBpediaClassification": "Given a Wikipedia articles, categorized it into classes based on its DBpedia ontology", "FinancialPhrasebankClassification": "Given financial news, categorized by sentiment into positive, negative, or neutral", "PoemSentimentClassification": "Gvien a poem, categorized by sentiment into positive, no_impact, negative or mixed", "TweetTopicSingleClassification": "Gvien a twitter, classify its topic", "EstonianValenceClassification": "Given a news article, categorized by sentiment into negatiivne, positiivne, neutraalne or vastuolulin", "FilipinoShopeeReviewsClassification": "Given a shop review, classify its rating on a scale from 1 to 5", "GujaratiNewsClassification": "Given a Gujarati news articles, classify ist topic", "SentimentAnalysisHindi": "Given a hindi text, categorized by sentiment into positive, negative or neutral", "IndonesianIdClickbaitClassification": "Given an Indonesian news headlines, classify its into clickbait or non-clickbait", "ItaCaseholdClassification": "Given a judgments, classify its topic", "KorSarcasmClassification": "Given a twitter, categorized it into sarcasm or not_sarcasm", "KurdishSentimentClassification": "Given a text, categorized by sentiment into positive or negative", "MacedonianTweetSentimentClassification": "Given a Macedonian tweet, categorized by sentiment into positive, negative, or neutral", "AfriSentiClassification": "Given a text, categorized by sentiment into positive, negative, or neutral", "CataloniaTweetClassification": "Given a tweet, categorized by sentiment into AGAINST, FAVOR or NEUTRAL", "CyrillicTurkicLangClassification": "Given a text, classify its language", "IndicLangClassification": "Given a text, classify its language", "MultiHateClassification": "Given a text, categorized by sentiment into hate or non-hate", "NusaParagraphEmotionClassification": "Given a paragraph, classify its emotion", "NusaX-senti": "Given a text, categorized by sentiment into positive or negative", "SwissJudgementClassification": "Given a news article, categorized it into approval or dismissal", "NepaliNewsClassification": "Given a news article, categorized it into business, entertainment or sports", "OdiaNewsClassification": "Given a news article, categorized it into business, entertainment or sports", "PunjabiNewsClassification": "Given a news article, categorized it into two-classes", "SinhalaNewsClassification": "Given a news article, categorized it into political, business, technology, sports and Entertainment", "CSFDSKMovieReviewSentimentClassification": "Given a movie review, classify its rating on a scale from 0 to 5", "SiswatiNewsClassification": "Given a news article in Siswati, classify its topic", "SlovakMovieReviewSentimentClassification": "Given a movie review, categorized it into positive or negative", "SwahiliNewsClassification": "Given a news article, classify its domain", "TswanaNewsClassification": "Given a news article, classify its topic", "IsiZuluNewsClassification": "Given a news article, classify its topic", "WikiCitiesClustering": "Identify of Wikipedia articles of cities by country", "RomaniBibleClustering": "Identify verses from the Bible in Kalderash Romani by book.", "ArXivHierarchicalClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts", "ArXivHierarchicalClusteringS2S": "Identify the main and secondary category of Arxiv papers based on the titles", "BigPatentClustering.v2": "Identify the category of documents from the Big Patent dataset", "AlloProfClusteringS2S": "Identify the topic of document titles from Allo Prof dataset", "AlloProfClusteringS2S.v2": "Identify the topic of document titles from Allo Prof dataset", "AlloProfClusteringP2P": "Identify the topic of document titles and descriptions from Allo Prof dataset", "HALClusteringS2S.v2": "Identify the topic of titles from HAL", "SIB200ClusteringS2S": "Identify the category of documents", "WikiClusteringP2P.v2": "Identify the category of wiki passages", "PlscClusteringP2P.v2": "Identify the category of titles+abstracts from Library of Science", "KorHateSpeechMLClassification": "Given a Korean online news comments, classify its fine-grained hate speech classes", "MalteseNewsClassification": "Given a maltese new, classify its topic", "MultiEURLEXMultilabelClassification": "Given a text, classify its topic", "BrazilianToxicTweetsClassification": "Classify the toxic tweets in Brazilian Portuguese into one of the six categories: LGBTQ+phobia, Xenophobia, Obscene, Insult, Misogyny and Racism.", "CTKFactsNLI": "Retrieve semantically similar text", "indonli": "Retrieve semantically similar text", "ArmenianParaphrasePC": "Retrieve semantically similar text", "PawsXPairClassification": "Retrieve semantically similar text", "RTE3": "Retrieve semantically similar text", "XNLI": "Retrieve semantically similar text", "PpcPC": "Retrieve semantically similar text", "GermanSTSBenchmark": "Retrieve semantically similar text", "SICK-R": "Retrieve semantically similar text", "STS13": "Retrieve semantically similar text", "STS14": "Retrieve semantically similar text", "STSBenchmark": "Retrieve semantically similar text", "FaroeseSTS": "Retrieve semantically similar text", "FinParaSTS": "Retrieve semantically similar text", "JSICK": "Retrieve semantically similar text", "IndicCrosslingualSTS": "Retrieve parallel sentences", "SemRel24STS": "Retrieve semantically similar text", "STS17": "Retrieve semantically similar text", "STS22.v2": "Retrieve semantically similar text", "STSES": "Retrieve semantically similar text", "STSB": "Retrieve semantically similar text", "AILAStatutes": "Identifying the most relevant statutes for a given situation", "HagridRetrieval": "Given an information-seeking question, retrieve the best replies to answer the question", "LegalBenchCorporateLobbying": "Given a query, retrieve relevant legal bill summaries", "LEMBPasskeyRetrieval": "Retrieval the relevant passage for the given query", "BelebeleRetrieval": "Retrieval the relevant passage for the given query", "MLQARetrieval": "Retrieval the relevant passage for the given query", "StatcanDialogueDatasetRetrieval": "Retrieval the relevant passage for the given query", "WikipediaRetrievalMultilingual": "Retrieval the relevant passage for the given query", "Core17InstructionRetrieval": "Retrieval the relevant passage for the given query with conditions", "News21InstructionRetrieval": "Retrieval the relevant passage for the given query with conditions", "Robust04InstructionRetrieval": "Retrieval the relevant passage for the given query with conditions", "WebLINXCandidatesReranking": "Retrieval the relevant passage for the given query", "WikipediaRerankingMultilingual": "Retrieval the relevant passage for the given query", "STS15": "Retrieve semantically similar text", "MIRACLRetrievalHardNegatives": "Retrieval relevant passage for the given query", "BIOSSES": "Retrieve semantically similar text", "CQADupstackRetrieval": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "CQADupstackGamingRetrieval": {"query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question"}, "CQADupstackUnixRetrieval": {"query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question"}, "STS16": "Retrieve semantically similar text", "SummEval": "Retrieve semantically similar text", "ATEC": "Retrieve semantically similar text", "ScalaClassification": "Classify passages into correct or correct in Scandinavian Languages based on linguistic acceptability", "SpartQA": "Given the following spatial reasoning question, retrieve the right answer.", "CEDRClassification": "Given a comment as query, classify expressed emotions into joy, sadness, surprise, fear, and anger", "DalajClassification": "Classify texts based on linguistic acceptability in Swedish", "TempReasonL1": "Given the following question about time, retrieve the correct answer.", "WinoGrande": "Given the following sentence, retrieve an appropriate answer to fill in the missing underscored part.", "NordicLangClassification": "Classify texts based on language", "TwitterHjerneRetrieval": "Retrieve answers to questions asked in Danish tweets", "SwednClusteringP2P": "Identify news categories in Swedish passages" }