metadata

annotations_creators: []
language_creators: []
language:
  - de
  - en
  - fr
  - ru
  - zh
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
  - multilingual
pretty_name: MTEB Benchmark
configs:
  - config_name: default
    data_files:
      - path: test/*.jsonl.gz
        split: test
  - config_name: fr-en
    data_files:
      - path: test/fr-en.jsonl.gz
        split: test
  - config_name: ru-en
    data_files:
      - path: test/ru-en.jsonl.gz
        split: test
  - config_name: de-en
    data_files:
      - path: test/de-en.jsonl.gz
        split: test
  - config_name: zh-en
    data_files:
      - path: test/zh-en.jsonl.gz
        split: test

Dataset Card for MTEB Benchmark

Dataset Description

Homepage: https://github.com/embeddings-benchmark/mteb-draft
Repository: https://github.com/embeddings-benchmark/mteb-draft
Paper: soon
Leaderboard: https://docs.google.com/spreadsheets/d/14P8bdEzsIgTGGlp9oOlMw-THrQbn2fYfZEkZV4NUBos
Point of Contact: [email protected]

Dataset Summary

MTEB is a heterogeneous benchmark that has been built from diverse tasks:

BitextMining: BUCC, Tatoeba
Classification: AmazonCounterfactualClassification, AmazonPolarityClassification, AmazonReviewsClassification, Banking77Classification, EmotionClassification, ImdbClassification, MassiveIntentClassification, MassiveScenarioClassification, MTOPDomainClassification, MTOPIntentClassification, ToxicConversationsClassification, TweetSentimentExtractionClassification
Clustering: ArxivClusteringP2P, ArxivClusteringS2S, BiorxivClusteringP2P, BiorxivClusteringS2S, MedrxivClusteringP2P, MedrxivClusteringS2S, RedditClustering, RedditClusteringP2P, StackExchangeClustering, StackExchangeClusteringP2P, TwentyNewsgroupsClustering
Pair Classification: SprintDuplicateQuestions, TwitterSemEval2015, TwitterURLCorpus
Reranking: AskUbuntuDupQuestions, MindSmallReranking, SciDocs, StackOverflowDupQuestions
Retrieval: ArguAna, ClimateFEVER, CQADupstackRetrieval, DBPedia, FEVER, FiQA2018, HotpotQA, MSMARCO, MSMARCOv2, NFCorpus, NQ, QuoraRetrieval, SCIDOCS, SciFact, Touche2020, TRECCOVID
STS: BIOSSES, SICK-R, STS12, STS13, STS14, STS15, STS16, STS17, STS22, STSBenchmark
Summarization: SummEval

All these datasets have been preprocessed and can be used for your experiments.