{ "BornholmBitextMining": "Retrieve parallel sentences between Danish and Bornholmsk dialect", "CEDRClassification": "Classify the emotion expressed in the given text into one of five categories: joy, sadness, surprise, fear, or anger", "DalajClassification": "Classify the linguistic acceptability of the given Swedish sentence", "NorwegianCourtsBitextMining": "Retrieve parallel sentences between Norwegian Bokmål and Nynorsk", "ScalaClassification": "Classify the linguistic acceptability of the given Scandinavian sentence", "SpartQA": "Given a spatial reasoning question, retrieve the passage that answers the question", "SwednClusteringP2P": "Identify the topic or theme of the given Swedish news articles", "TempReasonL1": "Given a temporal reasoning question, retrieve the passage that answers the question", "TwitterHjerneRetrieval": "Given a Danish question, retrieve the corresponding answer", "WinoGrande": "Given a commonsense reasoning question, retrieve the passage that answers the question", "AmazonCounterfactualClassification": "Given an Amazon review, judge whether it is counterfactual.", "AmazonPolarityClassification": "Classifying Amazon reviews into positive or negative sentiment", "AmazonReviewsClassification": "Classifying the given Amazon review into its appropriate rating category", "Banking77Classification": "Given an 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": "Classifying 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": "Classifying the intent domain of the given utterance in task-oriented conversation", "MTOPIntentClassification": "Classifying the intent of the given utterance in task-oriented conversation", "ToxicConversationsClassification": "Classifying the given comments as either toxic or not toxic", "TweetSentimentExtractionClassification": "Classifying the sentiment of a given tweet as either positive, negative, or neutral", "TNews": "Categorizing the given news title", "IFlyTek": "Given an App description text, find the appropriate fine-grained category", "MultilingualSentiment": "Classifying sentiment of the customer review into positive, neutral, or negative", "JDReview": "Classifying sentiment of the customer review for iPhoneinto positive or negative", "OnlineShopping": "Classifying sentiment of the customer reviewinto 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", "BiorxivClusteringS2S": "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", "MedrxivClusteringS2S": "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", "StackExchangeClusteringP2P": "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", "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": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "MindSmallReranking": "Given a query, retrieve documents that answer the query.", "SciDocsRR": "Given a query, retrieve documents that answer the query.", "StackOverflowDupQuestions": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "SprintDuplicateQuestions": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question", "TwitterSemEval2015": "Retrieve semantically similar text.", "TwitterURLCorpus": "Retrieve semantically similar text.", "T2Reranking": "Given a query, retrieve documents that answer the query.", "MmarcoReranking": "Given a query, retrieve documents that answer the query.", "CMedQAv1": "Given a query, retrieve documents that answer the query.", "CMedQAv2": "Given a query, retrieve documents that answer the query.", "Ocnli": "Retrieve semantically similar text.", "Cmnli": "Retrieve semantically similar text.", "ArguAna": { "query": "Given a claim, retrieve documents that support or refute the claim", "passage": "Given a claim, retrieve documents that support or refute the claim" }, "ClimateFEVER": "Given a claim, retrieve documents that support or refute the claim", "ClimateFEVERHardNegatives": "Given a claim, retrieve documents that support or refute the claim", "DBPedia": "Given a query, retrieve documents that answer the query.", "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 query, retrieve documents that answer the query.", "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 query, retrieve documents that answer the query.", "SCIDOCS": "Given a query, retrieve documents that answer the query.", "SciFact": "Given a scientific claim, retrieve documents that support or refute the claim", "Touche2020": "Given a query, retrieve documents that answer the query.", "Touche2020Retrieval.v3": "Given a query, retrieve documents that answer the query.", "TRECCOVID": "Given a query, retrieve documents that answer the query.", "T2Retrieval": "Given a question, retrieve passages that answer the question", "MMarcoRetrieval": "Given a web search query, retrieve relevant passages that answer the query", "DuRetrieval": "Given a question, retrieve passages that answer the question", "CovidRetrieval": "Given a query on COVID-19, retrieve documents that answer the query", "CmedqaRetrieval": "Given a query, retrieve documents that answer the query.", "EcomRetrieval": "Given a query, retrieve documents that answer the query.", "MedicalRetrieval": "Given a query, retrieve documents that answer the query.", "VideoRetrieval": "Given a query, retrieve documents that answer the query.", "STSBenchmarkMultilingualSTS": "Retrieve semantically similar text", "SICKFr": "Retrieve semantically similar text", "SummEvalFr": "Retrieve semantically similar text.", "MasakhaNEWSClassification": "Categorizing the given news title", "OpusparcusPC": "Retrieve semantically similar text", "PawsX": "Retrieve semantically similar text", "AlloProfClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts", "AlloProfClusteringS2S": "Identify the main category of scholar papers based on the titles", "HALClusteringS2S": "Identify the main category of scholar papers based on the titles", "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 Reddit posts based on the titles and posts", "MLSUMClusteringS2S": "Identify the topic or theme of Reddit posts 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": "Classifying the sentiment of a given tweet as either positive, negative, or neutral", "PolEmo2.0-IN": "Classifying sentiment of the customer review into positive, neutral, or negative", "PolEmo2.0-OUT": "Classifying sentiment of the customer review into positive, neutral, or negative", "AllegroReviews": "Classifying sentiment of the customer review into positive, neutral, or negative", "PAC": "Classify the sentence into one of the two types: 'BEZPIECZNE_POSTANOWIENIE_UMOWNE' and 'KLAUZULA_ABUZYWNA'", "SICK-E-PL": "Retrieve semantically similar text", "SICK-R-PL": "Retrieve semantically similar text", "STS22": "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", "PPC": "Retrieve semantically similar text", "CDSC-E": "Retrieve semantically similar text", "PSC": "Retrieve semantically similar text", "8TagsClustering": "Identify the topic or theme of the given news articles", "ArguAna-PL": "Given a claim, retrieve documents that support or refute the claim", "DBPedia-PL": "Given a query, retrieve documents that answer the query.", "FiQA-PL": "Given a query, retrieve documents that answer the query.", "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 query, retrieve documents that answer the query.", "SCIDOCS-PL": "Given a query, retrieve documents that answer the query.", "SciFact-PL": "Given a scientific claim, retrieve documents that support or refute the claim", "TRECCOVID-PL": "Given a query, retrieve documents that answer the query.", "GeoreviewClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "HeadlineClassification": "Categorizing the given news title", "InappropriatenessClassification": "Classifying the given comments as either toxic or not toxic", "KinopoiskClassification": "Classifying the sentiment expressed in the given movie review text from the IMDB dataset", "RuReviewsClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "RuSciBenchGRNTIClassification": "Categorizing the given news title", "RuSciBenchOECDClassification": "Categorizing the given news title", "GeoreviewClusteringP2P": "Identify the topic or theme of Reddit posts based on the titles and posts", "RuSciBenchGRNTIClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts", "RuSciBenchOECDClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts", "TERRa": "Retrieve semantically similar text.", "RuBQReranking": "Given a question, retrieve Wikipedia passages that answer the question", "RiaNewsRetrieval": "Given a query, retrieve documents that answer the query.", "RuBQRetrieval": "Given a question, retrieve Wikipedia passages that answer the question", "RUParaPhraserSTS": "Retrieve semantically similar text", "RuSTSBenchmarkSTS": "Retrieve semantically similar text", "AppsRetrieval": "Given a query, retrieve documents that answer the query.", "COIRCodeSearchNetRetrieval": "Given a query, retrieve documents that answer the query.", "CodeEditSearchRetrieval": "Given a query, retrieve documents that answer the query.", "CodeFeedbackMT": "Given a query, retrieve documents that answer the query.", "CodeFeedbackST": "Given a query, retrieve documents that answer the query.", "CodeSearchNetCCRetrieval": "Given a query, retrieve documents that answer the query.", "CodeSearchNetRetrieval": "Given a query, retrieve documents that answer the query.", "CodeTransOceanContest": "Given a query, retrieve documents that answer the query.", "CodeTransOceanDL": "Given a query, retrieve documents that answer the query.", "CosQA": "Given a query, retrieve documents that answer the query.", "StackOverflowQA": "Given a query, retrieve documents that answer the query.", "SyntheticText2SQL": "Given a query, retrieve documents that answer the query.", "BibleNLPBitextMining": "Retrieve semantically similar text.", "BUCC.v2": "Retrieve semantically similar text.", "DiaBlaBitextMining": "Retrieve semantically similar text.", "FloresBitextMining": "Retrieve semantically similar text.", "IN22GenBitextMining": "Retrieve semantically similar text.", "IndicGenBenchFloresBitextMining": "Retrieve semantically similar text.", "NollySentiBitextMining": "Retrieve semantically similar text.", "NTREXBitextMining": "Retrieve semantically similar text.", "NusaTranslationBitextMining": "Retrieve semantically similar text.", "NusaXBitextMining": "Retrieve semantically similar text.", "Tatoeba": "Retrieve semantically similar text.", "BulgarianStoreReviewSentimentClassfication": "Classifying sentiment of the customer review into positive, neutral, or negative", "CzechProductReviewSentimentClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "GreekLegalCodeClassification": "Categorizing the given news title", "DBpediaClassification": "Given an App description text, find the appropriate fine-grained category", "FinancialPhrasebankClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "PoemSentimentClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "TweetTopicSingleClassification": "Categorizing the given news title", "EstonianValenceClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "FilipinoShopeeReviewsClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "GujaratiNewsClassification": "Categorizing the given news title", "SentimentAnalysisHindi": "Classifying sentiment of the customer review into positive, neutral, or negative", "IndonesianIdClickbaitClassification": "Categorizing the given news title", "ItaCaseholdClassification": "Categorizing the given news title", "KorSarcasmClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "KurdishSentimentClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "MacedonianTweetSentimentClassification": "Classifying the sentiment of a given tweet as either positive, negative, or neutral", "AfriSentiClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "CataloniaTweetClassification": "Classifying the sentiment of a given tweet as either positive, negative, or neutral", "CyrillicTurkicLangClassification": "Given a text, classify its language", "IndicLangClassification": "Given a text, classify its language", "MultiHateClassification": "Classifying the given comments as either toxic or not toxic", "NusaParagraphEmotionClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise", "NusaX-senti": "Classifying sentiment of the customer review into positive, neutral, or negative", "SwissJudgementClassification": "Classifying sentiment of the customer review into positive, neutral, or negative", "NepaliNewsClassification": "Categorizing the given news title", "OdiaNewsClassification": "Categorizing the given news title", "PunjabiNewsClassification": "Categorizing the given news title", "SinhalaNewsClassification": "Categorizing the given news title", "CSFDSKMovieReviewSentimentClassification": "Classifying the sentiment expressed in the given movie review text from the IMDB dataset", "SiswatiNewsClassification": "Categorizing the given news title", "SlovakMovieReviewSentimentClassification": "Classifying the sentiment expressed in the given movie review text from the IMDB dataset", "SwahiliNewsClassification": "Categorizing the given news title", "TswanaNewsClassification": "Categorizing the given news title", "IsiZuluNewsClassification": "Categorizing the given news title", "WikiCitiesClustering": "Identify the topic or theme of the given news articles", "RomaniBibleClustering": "Identify the topic or theme of the given news articles", "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 main category of scholar papers based on the titles and abstracts", "AlloProfClusteringS2S.v2": "Identify the main category of scholar papers based on the titles", "HALClusteringS2S.v2": "Identify the main category of scholar papers based on the titles", "SIB200ClusteringS2S": "Identify the topic or theme of the given news articles", "WikiClusteringP2P.v2": "Identify the topic or theme of the given news articles", "PlscClusteringP2P.v2": "Identify the main category of scholar papers based on the titles and abstracts", "KorHateSpeechMLClassification": "Classifying the given comments as either toxic or not toxic", "MalteseNewsClassification": "Categorizing the given news title", "MultiEURLEXMultilabelClassification": "Categorizing the given news title", "BrazilianToxicTweetsClassification": "Classifying the given comments as either toxic or not toxic", "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 semantically similar text", "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": "Given a query, retrieve documents that answer the query.", "HagridRetrieval": "Given a query, retrieve documents that answer the query.", "LegalBenchCorporateLobbying": "Given a query, retrieve documents that answer the query.", "LEMBNarrativeQARetrieval": "Given a query, retrieve documents that answer the query.", "LEMBNeedleRetrieval": "Given a query, retrieve documents that answer the query.", "LEMBPasskeyRetrieval": "Given a query, retrieve documents that answer the query.", "LEMBQMSumRetrieval": "Given a query, retrieve documents that answer the query.", "LEMBSummScreenFDRetrieval": "Given a query, retrieve documents that answer the query.", "LEMBWikimQARetrieval": "Given a query, retrieve documents that answer the query.", "BelebeleRetrieval": "Given a query, retrieve documents that answer the query.", "MLQARetrieval": "Given a query, retrieve documents that answer the query.", "StatcanDialogueDatasetRetrieval": "Given a query, retrieve documents that answer the query.", "WikipediaRetrievalMultilingual": "Given a query, retrieve documents that answer the query.", "Core17InstructionRetrieval": "Given a query, retrieve documents that answer the query.", "News21InstructionRetrieval": "Given a query, retrieve documents that answer the query.", "Robust04InstructionRetrieval": "Given a query, retrieve documents that answer the query.", "WebLINXCandidatesReranking": "Given a query, retrieve documents that answer the query.", "WikipediaRerankingMultilingual": "Given a query, retrieve documents that answer the query.", "STS15": "Retrieve semantically similar text", "MIRACLRetrievalHardNegatives": "Given a question, retrieve passages that answer the question", "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" }