config_sentence_transformers.json

{
  "prompts": {
    "STS": "Retrieve semantically similar text.",
    "BitextMining": "Retrieve translations of the following text.",
    "Classification": "Classify the topic of the given text.",
    "MultilabelClassification": "Classify the topic of the given text.",
    "Clustering": "Classify the topic of the given text.",
    "PairClassification": "Retrieve semantically entailed text.",
    "query": "Given a question, retrieve passages that can help answer the question.",
    "document": "",
    "BulgarianStoreReviewSentimentClassfication": "Classify the sentiment of the given text.",
    "CzechProductReviewSentimentClassification": "Classify the sentiment of the given text.",
    "FinancialPhrasebankClassification": "Classify the sentiment of the given text.",
    "PoemSentimentClassification": "Classify the sentiment of the given text.",
    "EstonianValenceClassification": "Classify the sentiment of the given text.",
    "FilipinoShopeeReviewsClassification": "Classify the sentiment of the given text.",
    "SentimentAnalysisHindi": "Classify the sentiment of the given text.",
    "KurdishSentimentClassification": "Classify the sentiment of the given text.",
    "MacedonianTweetSentimentClassification": "Classify the sentiment of the given text.",
    "AfriSentiClassification": "Classify the sentiment of the given text.",
    "CataloniaTweetClassification": "Classify the sentiment of the given text.",
    "MultiHateClassification": "Classify the sentiment of the given text.",
    "NusaParagraphEmotionClassification": "Classify the sentiment of the given text.",
    "NusaX-senti": "Classify the sentiment of the given text.",
    "SwissJudgementClassification": "Classify the sentiment of the given text.",
    "PolEmo2.0-OUT": "Classify the sentiment of the given text.",
    "CSFDSKMovieReviewSentimentClassification": "Classify the sentiment of the given text.",
    "SlovakMovieReviewSentimentClassification": "Classify the sentiment of the given text.",
    "CyrillicTurkicLangClassification": "Classify the text into its language.",
    "IndicLangClassification": "Classify the text into its language.",
    "NordicLangClassification": "Classify the text into its language.",
    "ScalaClassification": "Classify the given sentence as linguistically acceptable or not acceptable.",
    "DalajClassification": "Classify the given sentence as linguistically acceptable or not acceptable.",
    "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic.",
    "IndonesianIdClickbaitClassification": "Classify the given text as either clickbait or not clickbait.",
    "KorSarcasmClassification": "Classify the given text as either sarcasm or not sarcasm.",
    "AmazonCounterfactualClassification": "Classify a given Amazon customer review text as either counterfactual or not counterfactual.",
    "MassiveIntentClassification": "Given a user utterance as query, find the user intents.",
    "PAC": "Classify the given clause as either abusive or not abusive.",
    "Banking77Classification": "Given an online banking query, find the corresponding intents.",
    "ImdbClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset.",
    "MTOPDomainClassification": "Classify the intent domain of the given utterance in task-oriented conversation.",
    "MassiveScenarioClassification": "Given a user utterance as query, find the user scenarios.",
    "TweetSentimentExtractionClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral",
    "WikiCitiesClustering": "Classify the following city description by country.",
    "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.",
    "BiorxivClusteringP2P.v2": "Identify the main category of bioRxiv papers based on the titles and abstracts.",
    "MedrxivClusteringP2P.v2": "Identify the main category of medRxiv papers based on the titles and abstracts.",
    "StackExchangeClustering.v2": "Identify the topic or theme of StackExchange posts based on the titles.",
    "StackExchangeClusteringP2P.v2": "Identify the topic or theme of StackExchange posts based on the given paragraphs.",
    "MedrxivClusteringS2S.v2": "Identify the main category of medRxiv papers based on the titles.",
    "TwentyNewsgroupsClustering.v2": "Identify the topic or theme of the given news articles.",
    "SprintDuplicateQuestions": "Retrieve duplicate questions from Sprint forum.",
    "TwitterURLCorpus": "Retrieve tweets that are semantically similar to the given tweet.",
    "ArmenianParaphrasePC": "Retrieve paraphrases of the given sentence.",
    "OpusparcusPC": "Retrieve paraphrases of the given sentence.",
    "PawsXPairClassification": "Retrieve paraphrases of the given sentence.",
    "PpcPC": "Retrieve paraphrases of the given sentence.",
    "TwitterSemEval2015": "Retrieve tweets that are semantically similar to the given tweet.",
    "KorHateSpeechMLClassification": "Classify the sentiment of the given text.",
    "CEDRClassification": "Classify the emotion expressed in the given comment into: joy, sadness, surprise, fear, and anger.",
    "SummEvalSummarization.v2": "Given a news summary, retrieve other semantically similar summaries.",
    "AppsRetrieval": "Retrieve the most relevant code snippet for the given query.",
    "CodeEditSearchRetrieval": "Retrieve the most relevant code edit.",
    "CodeFeedbackMT": "Retrieve the most relevant response for the given query.",
    "CodeFeedbackST": "Retrieve the most relevant response for the given query.",
    "CodeSearchNetCCRetrieval": "Retrieve the most relevant code snippet for the given code snippet.",
    "CodeSearchNetRetrieval": "Retrieve the most relevant code snippet for the given query.",
    "CodeTransOceanContest": "Retrieve similar code to the given source code.",
    "CodeTransOceanDL": "Retrieve similar code to the given source code.",
    "CosQA": "Retrieve the most relevant code snippet for the given query.",
    "COIRCodeSearchNetRetrieval": "Retrieve the most relevant code summary for the given code snippet.",
    "StackOverflowQA": "Retrieve the most relevant response for the given query.",
    "SyntheticText2SQL": "Retrieve the most relevant sql code snippet for the given query.",
    "AILAStatutes": "Identify the most relevant statutes for the given situation.",
    "ArguAna": "Given a claim, find documents that refute the claim.",
    "LegalBenchCorporateLobbying": "Given a bill title, retrieve the corresponding bill summary.",
    "SCIDOCS": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.",
    "TRECCOVID": "Given a query on COVID-19, retrieve documents that answer the query.",
    "CovidRetrieval": "Given a query on COVID-19, retrieve documents that answer the query.",
    "CQADupstackGamingRetrieval": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackUnixRetrieval": "Given a question, retrieve questions that are semantically equivalent.",
    "ClimateFEVERHardNegatives": "Given a claim about climate change, 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 passages that answer the question.",
    "HotpotQAHardNegatives": "Given a multi-hop question, retrieve passages that answer the question.",
    "Touche2020Retrieval.v3": "Given a question, retrieve passages that answer the question.",
    "WebLINXCandidatesReranking": "Given a web navigation step, retrieve relevant elements.",
    "AskUbuntuDupQuestions": "Retrieve duplicate questions from AskUbuntu forum.",
    "MindSmallReranking": "Retrieve relevant news articles based on user browsing history.",
    "NFCorpus": "Given a question, retrieve passages that answer the question.",
    "TRECCOVID-PL": "Given a query on COVID-19, retrieve documents that answer the query.",
    "SciFact": "Given a scientific claim, retrieve passages that support or refute the claim.",
    "SciFact-PL": "Given a scientific claim, retrieve passages that support or refute the claim.",
    "CmedqaRetrieval": "Given a question, retrieve passages that answer the question.",
    "CMedQAv2-reranking": "Given a question, retrieve passages that answer the question.",
    "AngryTweetsClassification": "Classify the sentiment of the given text.",
    "DanishPoliticalCommentsClassification": "Classify the sentiment of the given text.",
    "DKHateClassification": "Classify the given comments as either offensive or not offensive.",
    "LccSentimentClassification": "Classify the sentiment of the given text.",
    "NoRecClassification": "Classify the sentiment of the given text.",
    "NorwegianParliamentClassification": "Classify the sentiment of the given text.",
    "SwedishSentimentClassification": "Classify the sentiment of the given text.",
    "SweRecClassification": "Classify the sentiment of the given text.",
    "DanFeverRetrieval": "Given a claim, retrieve documents that support or refute the claim.",
    "SNLRetrieval": "Given a summary, retrieve the original article.",
    "SwednRetrieval": "Given a summary, retrieve the original article.",
    "TV2Nordretrieval": "Given a news summary, retrieve the original article.",
    "BengaliSentimentAnalysis": "Classify the sentiment of the given text.",
    "HindiDiscourseClassification": "Classify the given text into one of the five discourse modes: argumentative, narrative, descriptive, dialogic, and informative.",
    "MTOPIntentClassification": "Classify the intent of the given utterance in task-oriented conversation.",
    "TweetSentimentClassification": "Classify the sentiment of the given text.",
    "UrduRomanSentimentClassification": "Classify the sentiment of the given text as either positive, negative, or neutral.",
    "AmazonReviewsClassification": "Classify the given Amazon review into its appropriate rating category.",
    "BlurbsClusteringP2P": "Classify the given book title and blurb into its genre.",
    "BlurbsClusteringS2S": "Classify the given book title into its genre.",
    "FalseFriendsGermanEnglish": "Retrieve translations of the following text.",
    "XMarket": "Given a product name search, retrieve the corresponding product description.",
    "GerDaLIR": "Retrieve documents that are referenced by the given text.",
    "MLSUMClusteringP2P": "Classify the topic of the given news article.",
    "SummEvalFr": "Given a news summary, retrieve other semantically similar summaries.",
    "AllegroReviews": "Classify the sentiment of the given text.",
    "CBD": "Classify the given text as either cyberbullying or not.",
    "PolEmo2.0-IN": "Classify the sentiment of the given text.",
    "PSC": "Retrieve semantically similar text.",
    "EcomRetrieval": "Given a product name query, retrieve the corresponding product description.",
    "MedicalRetrieval": "Retrieve the most relevant response for the given query.",
    "VideoRetrieval": "Given a video search query, retrieve the titles of relevant videos.",
    "CMedQAv1-reranking": "Retrieve the most relevant response for the given query.",
    "Waimai": "Classify the sentiment of the given review as either positive or negative.",
    "OnlineShopping": "Classify the sentiment of the given review as either positive or negative.",
    "JDReview": "Classify the sentiment of the given review as either positive or negative.",
    "MultilingualSentiment": "Classify the sentiment of the given review as either positive, negative, or neutral.",
    "ToxicChatClassification": "Classify the given text as either toxic or not toxic.",
    "JapaneseSentimentClassification": "Classify the sentiment of the given text.",
    "WRIMEClassification": "Classify the sentiment of the given text.",
    "NLPJournalTitleAbsRetrieval.V2": "Given a paper's title, retrieve the corresponding abstract.",
    "NLPJournalTitleIntroRetrieval.V2": "Given a paper's title, retrieve the corresponding introduction.",
    "NLPJournalAbsIntroRetrieval.V2": "Given a paper's abstract, retrieve the corresponding introduction.",
    "NLPJournalAbsArticleRetrieval.V2": "Given a paper's abstract, retrieve the corresponding paper.",
    "ESCIReranking": "Given a product name query, retrieve the corresponding product description.",
    "DutchBookReviewSentimentClassification.v2": "Classify the sentiment of the given text.",
    "VaccinChatNLClassification": "Classify the intent of the given utterance.",
    "DutchColaClassification": "Classify the given sentence as linguistically acceptable or not acceptable.",
    "DutchGovernmentBiasClassification": "Classify the given government document as biased or unbiased.",
    "DutchSarcasticHeadlinesClassification": "Classify the given newspaper headline as sarcastic or not sarcastic.",
    "XLWICNLPairClassification": "Retrieve semantically similar text.",
    "CovidDisinformationNLMultiLabelClassification": "Classify the given social media post related to COVID-19 into its misinformation category.",
    "VABBClusteringS2S": "Identify the main category of the given paper based on the title.",
    "VABBClusteringP2P": "Identify the main category of the given paper based on the title and abstract.",
    "ArguAna-NL.v2": "Given a claim, find documents that refute the claim.",
    "SCIDOCS-NL.v2": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.",
    "SciFact-NL.v2": "Given a scientific claim, retrieve passages that support or refute the claim.",
    "DutchNewsArticlesRetrieval": "Given a news title, retrieve the original article.",
    "OpenTenderRetrieval": "Given a title, retrieve the corresponding article.",
    "VABBRetrieval": "Given a paper's title, retrieve the corresponding abstract.",
    "GeoreviewClassification": "Classify the given review into its appropriate rating category.",
    "InappropriatenessClassification": "Classify the given message as either sensitive topic or not.",
    "KinopoiskClassification": "Classify the sentiment of the given movie review.",
    "RuReviewsClassification": "Classify the sentiment of the given review as either positive, negative, or neutral.",
    "RuSciBenchGRNTIClassification": "Identify the main category of the given paper based on the title and abstract.",
    "RuSciBenchOECDClassification": "Identify the main category of the given paper based on the title and abstract.",
    "GeoreviewClusteringP2P": "Identify the organization category based on the given review.",
    "RuSciBenchGRNTIClusteringP2P": "Identify the main category of the given paper based on the title and abstract.",
    "RuSciBenchOECDClusteringP2P": "Identify the main category of the given paper based on the title and abstract.",
    "SensitiveTopicsClassification": "Classify the given text into sensitive topics.",
    "RiaNewsRetrievalHardNegatives.v2": "Given a news title, retrieve the original article.",
    "PersianFoodSentimentClassification": "Classify the sentiment of the given text as either positive or negative.",
    "SynPerChatbotConvSAClassification": "Classify the sentiment of the given text.",
    "SynPerChatbotConvSAToneChatbotClassification": "Classify the sentiment of the given text.",
    "SynPerChatbotConvSAToneUserClassification": "Classify the sentiment of the given text.",
    "SynPerChatbotSatisfactionLevelClassification": "Classify the satisfaction level of the given text.",
    "SynPerTextToneClassification.v3": "Classify the tone of the given text.",
    "DeepSentiPers.v2": "Classify the sentiment of the given text.",
    "PersianTextEmotion.v2": "Classify the emotion expressed in the given text into: joy, sadness, surprise, fear, anger, and love.",
    "StyleClassification": "Classify the style of the given text as either formal or informal.",
    "PerShopDomainClassification": "Classify the domain of the given utterance in shopping dialogue.",
    "PerShopIntentClassification": "Classify the intent of the given utterance in shopping dialogue.",
    "SynPerChatbotRAGFAQPC": "Retrieve the most relevant response for the given query.",
    "FarsiParaphraseDetection": "Retrieve semantically similar text.",
    "SynPerTextKeywordsPC": "Identify keywords in the given text.",
    "SynPerQAPC": "Retrieve the most relevant response for the given query.",
    "ParsinluQueryParaphPC": "Retrieve semantically similar text.",
    "SynPerChatbotRAGFAQRetrieval": "Retrieve the most relevant response for the given query.",
    "HotpotQA-FaHardNegatives": "Given a multi-hop question, retrieve passages that answer the question.",
    "ArguAna-Fa.v2": "Given a claim, find documents that refute the claim.",
    "QuoraRetrieval-Fa.v2": "Retrieve questions that are semantically equivalent to the given one.",
    "SCIDOCS-Fa.v2": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.",
    "SciFact-Fa.v2": "Given a scientific claim, retrieve passages that support or refute the claim.",
    "TRECCOVID-Fa.v2": "Given a query on COVID-19, retrieve documents that answer the query.",
    "FEVER-FaHardNegatives": "Given a claim, retrieve documents that support or refute the claim.",
    "SAMSumFa": "Retrieve the most relevant summary for the given conversation.",
    "SynPerChatbotSumSRetrieval": "Retrieve the most relevant summary for the given conversation.",
    "SynPerChatbotRAGSumSRetrieval": "Retrieve the most relevant summary for the given conversation.",
    "ArguAna-VN": "Given a claim, find documents that refute the claim.",
    "SciFact-VN": "Given a scientific claim, retrieve passages that support or refute the claim.",
    "ClimateFEVER-VN": "Given a claim about climate change, retrieve documents that support or refute the claim.",
    "FEVER-VN": "Given a claim, retrieve documents that support or refute the claim.",
    "DBPedia-VN": "Given a query, retrieve relevant entity descriptions.",
    "HotpotQA-VN": "Given a multi-hop question, retrieve passages that answer the question.",
    "TRECCOVID-VN": "Given a query on COVID-19, retrieve documents that answer the query.",
    "SCIDOCS-VN": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.",
    "Quora-VN": "Retrieve questions that are semantically equivalent to the given one.",
    "CQADupstackAndroid-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackGis-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackMathematica-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackPhysics-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackProgrammers-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackStats-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackTex-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackUnix-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackWebmasters-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "CQADupstackWordpress-VN": "Given a question, retrieve questions that are semantically equivalent.",
    "Banking77VNClassification": "Given an online banking query, find the corresponding intents.",
    "EmotionVNClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise.",
    "AmazonCounterfactualVNClassification": "Classify a given Amazon customer review text as either counterfactual or not counterfactual.",
    "MTOPDomainVNClassification": "Classify the intent domain of the given utterance in task-oriented conversation.",
    "TweetSentimentExtractionVNClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral",
    "ToxicConversationsVNClassification": "Classify the given comments as either toxic or not toxic.",
    "ImdbVNClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset.",
    "MTOPIntentVNClassification": "Classify the intent of the given utterance in task-oriented conversation.",
    "MassiveScenarioVNClassification": "Given a user utterance as query, find the user scenarios.",
    "MassiveIntentVNClassification": "Given a user utterance as query, find the user intents.",
    "AmazonReviewsVNClassification": "Classify the given Amazon review into its appropriate rating category.",
    "AmazonPolarityVNClassification": "Classify the given Amazon review as either positive or negative.",
    "SprintDuplicateQuestions-VN": "Retrieve duplicate questions from Sprint forum.",
    "TwitterSemEval2015-VN": "Retrieve tweets that are semantically similar to the given tweet.",
    "TwitterURLCorpus-VN": "Retrieve tweets that are semantically similar to the given tweet.",
    "TwentyNewsgroupsClustering-VN": "Identify the topic or theme of the given news articles.",
    "RedditClusteringP2P-VN": "Identify the topic or theme of Reddit posts based on the titles and posts.",
    "StackExchangeClusteringP2P-VN": "Identify the topic or theme of StackExchange posts based on the given titles and paragraphs.",
    "StackExchangeClustering-VN": "Identify the topic or theme of StackExchange posts based on the titles.",
    "RedditClustering-VN": "Identify the topic or theme of Reddit posts based on the titles.",
    "SciDocsRR-VN": "Given a title of a scientific paper, retrieve the titles of other relevant papers.",
    "AskUbuntuDupQuestions-VN": "Retrieve duplicate questions from AskUbuntu forum.",
    "StackOverflowDupQuestions-VN": "Retrieve duplicate questions from StackOverflow forum."
  },
  "default_prompt_name": null,
  "similarity_fn_name": "cosine"
}