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Models/Contriever

Contriever

Reported on 8 benchmarks across 6 tasks · 3 papers

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing5 results

  • Information RetrievalonEntityQuestions
    Recall@20· 2023-03-09
    0.647
    best: 0.838 (TOME-2)
    Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?arXiv:2303.05153
  • Text ClusteringonMTEB
    V-Measure· 2022-10-13
    41.1
    best: 43.71 (ST5-XXL)
    MTEB: Massive Text Embedding BenchmarkarXiv:2210.07316
  • Text ClassificationonMTEB
    Accuracy· 2022-10-13
    66.68
    best: 73.42 (ST5-XXL)
    MTEB: Massive Text Embedding BenchmarkarXiv:2210.07316
  • Information RetrievalonPeerQA
    MRR· 2021-12-16
    0.3624
    best: 0.4845 (Dragon+)
    Unsupervised Dense Information Retrieval with Contrastive LearningarXiv:2112.09118
  • Information RetrievalonPeerQA
    Recall@10· 2021-12-16
    0.5567
    best: 0.6851 (SPLADEv3)
    Unsupervised Dense Information Retrieval with Contrastive LearningarXiv:2112.09118

Methodology2 results

  • RetrievalonMTEB
    nDCG@10· 2022-10-13
    41.88
    best: 50.25 (SGPT-5.8B-msmarco)
    MTEB: Massive Text Embedding BenchmarkarXiv:2210.07316
  • ClassificationonMTEB
    Accuracy· 2022-10-13
    66.68
    best: 73.42 (ST5-XXL)
    MTEB: Massive Text Embedding BenchmarkarXiv:2210.07316

Knowledge Base1 result

  • Text SummarizationonMTEB
    Spearman Correlation· 2022-10-13
    30.36
    best: 31.57 (MPNet-multilingual)
    MTEB: Massive Text Embedding BenchmarkarXiv:2210.07316