| { |
| "AILAStatutes-query": "Identifying the most relevant statutes for a given situation", |
| "AfriSentiClassification": "Given a text, categorized by sentiment into positive, negative, or neutral", |
| "AlloProfClusteringS2S.v2": "Identify the topic of document titles from Allo Prof dataset", |
| "AlloprofReranking-query": "Given a question, retrieve passages that answer the question", |
| "AmazonCounterfactualClassification": "Given an Amazon review, judge whether it is counterfactual.", |
| "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", |
| "ArguAna-query": "Given a claim, find documents that refute the claim", |
| "ArmenianParaphrasePC": "Retrieve semantically similar text", |
| "BUCC.v2": "Retrieve parallel sentences", |
| "BelebeleRetrieval-query": "Retrieval the relevant passage for the given query", |
| "BibleNLPBitextMining": "Retrieve parallel sentences", |
| "BigPatentClustering.v2": "Identify the category of documents from the Big Patent dataset", |
| "BiorxivClusteringP2P.v2": "Identify the main category of Biorxiv papers based on the titles and abstracts", |
| "BornholmBitextMining": "Retrieve parallel sentences", |
| "BrazilianToxicTweetsClassification": "Classify the toxic tweets in Brazilian Portuguese into one of the six categories: LGBTQ+phobia, Xenophobia, Obscene, Insult, Misogyny and Racism.", |
| "BulgarianStoreReviewSentimentClassfication": "Classify user reviews into positive, negative or mixed sentiment", |
| "CEDRClassification": "Given a comment as query, classify expressed emotions into joy, sadness, surprise, fear, and anger", |
| "CLSClusteringP2P.v2": "Identify the main category of scholar papers based on the titles and abstracts", |
| "CSFDSKMovieReviewSentimentClassification": "Given a movie review, classify its rating on a scale from 0 to 5", |
| "CTKFactsNLI": "Retrieve semantically similar text", |
| "CataloniaTweetClassification": "Given a tweet, classify its sentiment into AGAINST, FAVOR or NEUTRAL towards Catalonia's independence.", |
| "Core17InstructionRetrieval-query": "Retrieve relevant passages for the given query with conditions", |
| "CovidRetrieval-query": "Given a question on COVID-19, retrieve news articles that answer the question", |
| "CyrillicTurkicLangClassification": "Given a text, classify its language", |
| "CzechProductReviewSentimentClassification": "Classify product reviews into positive, neutral, or negative sentiment", |
| "DBpediaClassification": "Given the following text, retrieve the appropriate DBpedia category including Company, EducationalInstitution, Artist, Athlete, OfficeHolder, MeanOfTransportation, Building, NaturalPlace, Village, Animal, Plant, Album, Film, WrittenWork.", |
| "DalajClassification": "Classify texts based on linguistic acceptability in Swedish", |
| "DiaBlaBitextMining": "Retrieve parallel sentences", |
| "EstonianValenceClassification": "Given a news article, categorized by sentiment into negatiivne, positiivne, neutraalne or vastuolulin", |
| "FaroeseSTS": "Retrieve semantically similar text", |
| "FilipinoShopeeReviewsClassification": "Given a shop review, classify its rating on a scale from 1 to 5", |
| "FinParaSTS": "Retrieve semantically similar text", |
| "FinancialPhrasebankClassification": "Given financial news, categorized by sentiment into positive, negative, or neutral", |
| "FloresBitextMining": "Retrieve parallel sentences", |
| "GermanSTSBenchmark": "Retrieve semantically similar text", |
| "GreekLegalCodeClassification": "Given a greek legal text, classify its topic", |
| "GujaratiNewsClassification": "Given a Gujarati news articles, classify ist topic", |
| "HALClusteringS2S.v2": "Identify the topic of titles from HAL", |
| "HagridRetrieval-query": "Given a question, retrieve relevant responses", |
| "IN22GenBitextMining": "Retrieve parallel sentences", |
| "IndicCrosslingualSTS": "Retrieve semantically similar text", |
| "IndicGenBenchFloresBitextMining": "Retrieve parallel sentences", |
| "IndicLangClassification": "Given a text, classify its language", |
| "IndonesianIdClickbaitClassification": "Given an Indonesian news headlines, classify its into clickbait or non-clickbait", |
| "IsiZuluNewsClassification": "Given a news article, classify its topic", |
| "ItaCaseholdClassification": "Given a judgments, classify its topic", |
| "JSICK": "Retrieve semantically similar text", |
| "KorHateSpeechMLClassification": "Given a Korean online news comments, classify its fine-grained hate speech classes", |
| "KorSarcasmClassification": "Given a twitter, categorized it into sarcasm or not_sarcasm", |
| "KurdishSentimentClassification": "Given a text, categorized by sentiment into positive or negative", |
| "LEMBPasskeyRetrieval-query": "Retrieval the relevant passage for the given query", |
| "LegalBenchCorporateLobbying-query": "Given a query, retrieve relevant legal bill summaries", |
| "MIRACLRetrievalHardNegatives-query": "Retrieve Wikipedia passages that answer the question", |
| "MLQARetrieval-query": "Retrieval the relevant passage for the given query", |
| "MacedonianTweetSentimentClassification": "Given a Macedonian tweet, categorized by sentiment into positive, negative, or neutral", |
| "MalteseNewsClassification": "Given a maltese new, classify its topic", |
| "MasakhaNEWSClassification": "Classify the News in the given texts into one of the seven category: politics,sports,health,business,entertainment,technology,religion ", |
| "MasakhaNEWSClusteringS2S": "Identify the topic or theme of the given news articles based on the titles", |
| "MassiveIntentClassification": "Given a user utterance as query, find the user intents", |
| "MedrxivClusteringP2P.v2": "Identify the main category of Medrxiv papers based on the titles and abstracts", |
| "MultiEURLEXMultilabelClassification": "Given a text, classify its topic", |
| "MultiHateClassification": "Given a text, categorized by sentiment into hate or non-hate", |
| "NTREXBitextMining": "Retrieve parallel sentences", |
| "NepaliNewsClassification": "Given a news article, categorized it into business, entertainment or sports", |
| "News21InstructionRetrieval-query": "Retrieve relevant passages for the given query with conditions", |
| "NollySentiBitextMining": "Retrieve parallel sentences", |
| "NordicLangClassification": "Given a text in a Nordic language, classify the language into one of the following categories: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokmål), Faroese, Icelandic.", |
| "NorwegianCourtsBitextMining": "Retrieve parallel sentences", |
| "NusaParagraphEmotionClassification": "Classify the emotion into one of the following categories: fear, sadness, anger, happy, love, surprise, shame.", |
| "NusaTranslationBitextMining": "Retrieve parallel sentences", |
| "NusaX-senti": "Given a text, categorized by sentiment into positive or negative", |
| "NusaXBitextMining": "Retrieve parallel sentences", |
| "OdiaNewsClassification": "Given a news article, categorized it into business, entertainment or sports", |
| "OpusparcusPC": "Retrieve semantically similar text", |
| "PAC": "Classify Polish contract clauses into one of the following two types: \"Safe Contract Clauses\" and \"Unfair Contract Clauses\".", |
| "PawsXPairClassification": "Retrieve semantically similar text", |
| "PlscClusteringP2P.v2": "Identify the category of titles+abstracts from Library of Science", |
| "PoemSentimentClassification": "Given the following verse from a poem, classify its sentiment as negative, neutral, positive, or mixed.", |
| "PolEmo2.0-OUT": "Classify the sentiment of products and school online reviews", |
| "PpcPC": "Retrieve semantically similar text", |
| "PunjabiNewsClassification": "Given a news article, categorized it into two-classes", |
| "RTE3": "Retrieve semantically similar text", |
| "Robust04InstructionRetrieval-query": "Retrieve relevant passages for the given query with conditions", |
| "RomaniBibleClustering": "Identify verses from the Bible in Kalderash Romani by book.", |
| "RuBQReranking-query": "Given a question, retrieve Wikipedia passages that answer the question", |
| "SCIDOCS-query": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper", |
| "SIB200ClusteringS2S": "Identify the category of documents", |
| "SICK-R": "Retrieve semantically similar text", |
| "STS12": "Retrieve semantically related sentences", |
| "STS13": "Retrieve semantically similar text", |
| "STS14": "Retrieve semantically similar text", |
| "STS15": "Retrieve semantically similar text", |
| "STS17": "Retrieve semantically similar text", |
| "STS22.v2": "Given a document, retrieve semantically related documents", |
| "STSB": "Retrieve semantically similar text", |
| "STSBenchmark": "Retrieve semantically similar text", |
| "STSES": "Given a Spanish sentence, retrieve semantically related Spanish sentences", |
| "ScalaClassification": "Classify passages into correct or correct in Scandinavian Languages based on linguistic acceptability", |
| "SemRel24STS": "Retrieve semantically similar text", |
| "SentimentAnalysisHindi": "Given a hindi text, categorized by sentiment into positive, negative or neutral", |
| "SinhalaNewsClassification": "Given a news article, categorized it into political, business, technology, sports and Entertainment", |
| "SiswatiNewsClassification": "Identify fine-grained news categories in Siswati language.", |
| "SlovakMovieReviewSentimentClassification": "Given a movie review, categorized it into positive or negative", |
| "SpartQA-query": "Given the following spatial reasoning question, retrieve the right answer.", |
| "SprintDuplicateQuestions": "Find questions that have the same meaning as the input question", |
| "StackExchangeClustering.v2": "Identify the topic or theme of StackExchange posts based on the titles", |
| "StackOverflowQA-query": "Given a question about coding, retrieval code or passage that can solve user's question", |
| "StatcanDialogueDatasetRetrieval-query": "Retrieval the relevant passage for the given query", |
| "SwahiliNewsClassification": "Given a news article, classify its domain", |
| "SwednClusteringP2P": "Identify news categories in Swedish passages", |
| "SwissJudgementClassification": "Given a news article, categorized it into approval or dismissal", |
| "T2Reranking-query": "Given a Chinese search query, retrieve web passages that answer the question", |
| "TERRa": "Given a premise, retrieve a hypothesis that is entailed by the premise", |
| "TRECCOVID-query": "Given a medical query, retrieve documents that answer the query", |
| "Tatoeba": "Retrieve parallel sentences", |
| "TempReasonL1-query": "Given the following question about time, retrieve the correct answer.", |
| "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic", |
| "TswanaNewsClassification": "Given a news article, classify its topic", |
| "TweetTopicSingleClassification": "Gvien a twitter, classify its topic", |
| "TwitterHjerneRetrieval-query": "Retrieve answers to questions asked in Danish tweets", |
| "TwitterURLCorpus": "Find tweets that have the same meaning as the input tweet", |
| "VoyageMMarcoReranking-query": "Given a Japanese search query, retrieve web passages that answer the question", |
| "WebLINXCandidatesReranking-query": "Retrieval the relevant passage for the given query", |
| "WikiCitiesClustering": "Identify of Wikipedia articles of cities by country", |
| "WikiClusteringP2P.v2": "Identify the category of wiki passages", |
| "WikipediaRerankingMultilingual-query": "Retrieval the relevant passage for the given query", |
| "WikipediaRetrievalMultilingual-query": "Retrieval the relevant passage for the given query", |
| "WinoGrande-query": "Given the following sentence, retrieve an appropriate answer to fill in the missing underscored part.", |
| "XNLI": "Retrieve semantically similar text", |
| "indonli": "Retrieve semantically similar text" |
| } |
