| { |
| "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.", |
| "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": "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 duplicate questions from Sprint forum.", |
| "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": "Given a Chinese community medical question, retrieve replies that best answer the question.", |
| "CMedQAv2": "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 scientific paper title, retrieve paper abstracts that are cited by the given paper.", |
| "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 query on COVID-19, 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.", |
| "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.", |
| "AlloProfClusteringP2P": "Identify the main category of Allo Prof document based on the titles and descriptions.", |
| "AlloProfClusteringS2S": "Identify the main category of Allo Prof document based on the titles.", |
| "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 in-domain (medicine and hotels) online reviews.", |
| "PolEmo2.0-OUT":"Classify the sentiment of out-of-domain (products and school) online reviews.", |
| "AllegroReviews": "Classify the sentiment of reviews from e-commerce marketplace Allegro.", |
| "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.", |
| "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 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 scientific paper title, retrieve paper abstracts that are cited by the given paper.", |
| "SciFact-PL": "Given a scientific claim, retrieve documents that support or refute the claim.", |
| "TRECCOVID-PL": "Given a query on COVID-19, 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 or negative sentiment.", |
| "CzechProductReviewSentimentClassification": "Classify product reviews into positive 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, 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.", |
| "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": "Given a tweet, classify its topic.", |
| "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": "Identifying the most relevant statutes for a given situation.", |
| "HagridRetrieval": "Retrieval the relevant passage for the given query.", |
| "LegalBenchCorporateLobbying": "Retrieval the relevant passage for the given query.", |
| "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.", |
| "News21InstructionRetrieval": "Retrieval the relevant passage for the given query.", |
| "Robust04InstructionRetrieval": "Retrieval the relevant passage for the given query.", |
| "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." |
| } |
