{"benchmarkName":"RTEB(beta)","taskTypes":[],"tasks":["AILACasedocs","EnglishFinance1Retrieval","AILAStatutes","LegalSummarization","MIRACLRetrievalHardNegatives","SWEbenchCodeRetrieval","GermanLegal1Retrieval","German1Retrieval","FreshStackRetrieval","AppsRetrieval","FinanceBenchRetrieval","EnglishFinance2Retrieval","FinQARetrieval","Code1Retrieval","DS1000Retrieval","French1Retrieval","WikiSQLRetrieval","LegalQuAD","HC3FinanceRetrieval","EnglishHealthcare1Retrieval","EnglishFinance3Retrieval","HumanEvalRetrieval","ChatDoctorRetrieval","MBPPRetrieval","JapaneseCode1Retrieval","CUREv1","EnglishFinance4Retrieval","GermanHealthcare1Retrieval","JapaneseLegal1Retrieval","FrenchLegal1Retrieval"],"tasksMeta":[{"name":"AILACasedocs","type":"Retrieval","simplifiedType":"retrieval","languages":["English"],"domains":["Legal","Written"],"modalities":["text"],"description":"The task is to retrieve the case document that most closely matches or is most relevant to the scenario described in the 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Queries are numerical reasoning questions while the corpus contains financial text passages with embedded tables, figures, and quantitative financial data from earnings reports.","reference":"https://huggingface.co/datasets/embedding-benchmark/FinQA","citation":"\n@article{chen2021finqa,\n  author = {Chen, Zhiyu and Chen, Wenhu and Smiley, Charese and Shah, Sameena and Borova, Iana and Langdon, Dylan and Moussa, Reema and Beane, Matt and Huang, Ting-Hao and Routledge, Bryan and Wang, William Yang},\n  journal = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},\n  title = {FinQA: A Dataset of Numerical Reasoning over Financial Data},\n  year = {2021},\n}\n","isPublic":true,"sourceDataset":"mteb/FinQARetrieval","license":"mit","dateFrom":"2021-01-01","dateTo":"2021-12-31","annotationsCreators":"expert-annotated","dialect":[],"sampleCreation":"found","mainScore":"ndcg_at_10","numModels":0},{"name":"AppsRetrieval","type":"Retrieval","simplifiedType":"retrieval","languages":["English","python"],"domains":["Programming","Written"],"modalities":["text"],"description":"The dataset is a collection of natural language queries and their corresponding code snippets. 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Each query is a natural language question (e.g., 'What is the name of the team that has scored the most goals?'), and the corpus contains SQL query implementations. The task is to retrieve the correct SQL query that answers the natural language question. 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Each query is a natural language description of a programming task (e.g., 'Write a function to reverse a string using recursion'), and the corpus contains code implementations in Python, JavaScript, and Go. The task is to retrieve the correct code snippet that solves the described problem. 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