| { |
| "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.", |
| "LEMBPasskeyRetrieval": "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" |
| } |
