README.md

metadata

model-index:
  - name: no_model_name_available
    results:
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          config: default
          name: MTEB AFQMC (default)
          revision: b44c3b011063adb25877c13823db83bb193913c4
          split: validation
          type: C-MTEB/AFQMC
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          - type: pearson
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          - type: spearman
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        task:
          type: STS
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          config: default
          name: MTEB ATEC (default)
          revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
          split: test
          type: C-MTEB/ATEC
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            value: 40.89963324512739
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        task:
          type: STS
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          config: default
          name: MTEB ATEC (default)
          revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
          split: validation
          type: C-MTEB/ATEC
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            value: 40.97674579633659
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          - type: pearson
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          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
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          - type: main_score
            value: 74.1492537313433
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification (default)
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
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        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
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        task:
          type: Classification
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          config: zh
          name: MTEB AmazonReviewsClassification (zh)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
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        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: validation
          type: mteb/amazon_reviews_multi
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            value: 47.716
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          - type: f1_weighted
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          - type: main_score
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        task:
          type: Classification
      - dataset:
          config: zh
          name: MTEB AmazonReviewsClassification (zh)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: validation
          type: mteb/amazon_reviews_multi
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        task:
          type: Classification
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          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P (default)
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
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          - type: v_measure
            value: 48.31683216128774
          - type: v_measure_std
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        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S (default)
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
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            value: 40.2951016935384
          - type: v_measure
            value: 40.2951016935384
          - type: v_measure_std
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        task:
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      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions (default)
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
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          - type: map
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          - type: mrr
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        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES (default)
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: cosine_pearson
            value: 90.16604991710759
          - type: cosine_spearman
            value: 88.4670760233051
          - type: euclidean_pearson
            value: 89.02378164860428
          - type: euclidean_spearman
            value: 88.4670760233051
          - type: main_score
            value: 88.4670760233051
          - type: manhattan_pearson
            value: 88.8866912507422
          - type: manhattan_spearman
            value: 88.2755053931781
          - type: pearson
            value: 90.16604991710759
          - type: spearman
            value: 88.4670760233051
        task:
          type: STS
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          config: default
          name: MTEB BQ (default)
          revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
          split: test
          type: C-MTEB/BQ
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            value: 49.45233904713201
          - type: cosine_spearman
            value: 49.77342815602789
          - type: euclidean_pearson
            value: 49.13579036236359
          - type: euclidean_spearman
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          - type: main_score
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          - type: manhattan_pearson
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          - type: manhattan_spearman
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          - type: pearson
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          - type: spearman
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        task:
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      - dataset:
          config: default
          name: MTEB BQ (default)
          revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
          split: validation
          type: C-MTEB/BQ
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          - type: euclidean_pearson
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          - type: euclidean_spearman
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          - type: main_score
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          - type: manhattan_pearson
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          - type: manhattan_spearman
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          - type: pearson
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          - type: spearman
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        task:
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      - dataset:
          config: default
          name: MTEB Banking77Classification (default)
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
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          - type: f1
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          - type: f1_weighted
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          - type: main_score
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        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P (default)
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
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          - type: v_measure
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          - type: v_measure_std
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        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S (default)
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
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        task:
          type: Classification
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          name: MTEB MassiveScenarioClassification (en)
          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: test
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      - dataset:
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          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: test
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      - dataset:
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          name: MTEB MassiveScenarioClassification (en)
          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: validation
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        task:
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      - dataset:
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          name: MTEB MassiveScenarioClassification (zh-CN)
          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: validation
          type: mteb/amazon_massive_scenario
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          name: MTEB MedicalRetrieval (default)
          revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
          split: test
          type: C-MTEB/MedicalRetrieval
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        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB MedrxivClusteringP2P (default)
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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          type: mteb/medrxiv-clustering-p2p
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        task:
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      - dataset:
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          name: MTEB MedrxivClusteringS2S (default)
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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          type: mteb/medrxiv-clustering-s2s
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      - dataset:
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          name: MTEB MindSmallReranking (default)
          revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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          type: mteb/mind_small
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          revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
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          type: C-MTEB/MultilingualSentiment-classification
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        task:
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          revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
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          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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          type: mteb/nfcorpus
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        task:
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          revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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          - type: recall_at_3
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        task:
          type: Retrieval
      - dataset:
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          name: MTEB Ocnli (default)
          revision: 66e76a618a34d6d565d5538088562851e6daa7ec
          split: validation
          type: C-MTEB/OCNLI
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          - type: cosine_precision
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          - type: cosine_recall
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          - type: dot_accuracy
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          - type: dot_ap
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          - type: dot_precision
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          - type: dot_recall
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          - type: euclidean_accuracy
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          - type: euclidean_accuracy_threshold
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          - type: euclidean_ap
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          - type: euclidean_f1_threshold
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          - type: euclidean_precision
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          - type: euclidean_recall
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          - type: main_score
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          - type: manhattan_f1_threshold
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          - type: manhattan_recall
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          - type: max_accuracy
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          - type: max_ap
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          - type: max_f1
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          - type: max_precision
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          - type: max_recall
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          - type: similarity_accuracy
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          - type: similarity_accuracy_threshold
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          - type: similarity_ap
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          - type: similarity_precision
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          - type: similarity_recall
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        task:
          type: PairClassification
      - dataset:
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          name: MTEB OnlineShopping (default)
          revision: e610f2ebd179a8fda30ae534c3878750a96db120
          split: test
          type: C-MTEB/OnlineShopping-classification
        metrics:
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          - type: ap_weighted
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          - type: f1
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          - type: main_score
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        task:
          type: Classification
      - dataset:
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          name: MTEB PAWSX (default)
          revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
          split: test
          type: C-MTEB/PAWSX
        metrics:
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          - type: cosine_spearman
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          - type: euclidean_pearson
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          - type: euclidean_spearman
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          - type: main_score
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          - type: manhattan_pearson
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          - type: manhattan_spearman
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          - type: pearson
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          - type: spearman
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        task:
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      - dataset:
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          revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
          split: test
          type: C-MTEB/QBQTC
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          - type: euclidean_pearson
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        task:
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      - dataset:
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          name: MTEB QuoraRetrieval (default)
          revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
          split: test
          type: mteb/quora
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            value: 80.05738628240671
          - type: spearman
            value: 74.67900925815495
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12 (default)
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
        metrics:
          - type: cosine_pearson
            value: 59.8887701896054
          - type: cosine_spearman
            value: 61.76924747645064
          - type: euclidean_pearson
            value: 59.33178599145238
          - type: euclidean_spearman
            value: 61.76932878335521
          - type: main_score
            value: 61.76924747645064
          - type: manhattan_pearson
            value: 59.07929980423876
          - type: manhattan_spearman
            value: 61.729703658805704
          - type: pearson
            value: 59.8887701896054
          - type: spearman
            value: 61.76924747645064
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS13 (default)
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
        metrics:
          - type: cosine_pearson
            value: 73.46830128106028
          - type: cosine_spearman
            value: 75.65482017135808
          - type: euclidean_pearson
            value: 75.32146398293357
          - type: euclidean_spearman
            value: 75.65482017135808
          - type: main_score
            value: 75.65482017135808
          - type: manhattan_pearson
            value: 75.11839624254772
          - type: manhattan_spearman
            value: 75.52002809163668
          - type: pearson
            value: 73.46830128106028
          - type: spearman
            value: 75.65482017135808
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14 (default)
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
          - type: cosine_pearson
            value: 71.49707687668383
          - type: cosine_spearman
            value: 68.16858223090989
          - type: euclidean_pearson
            value: 71.22162204420484
          - type: euclidean_spearman
            value: 68.16860152985392
          - type: main_score
            value: 68.16858223090989
          - type: manhattan_pearson
            value: 70.91495914707767
          - type: manhattan_spearman
            value: 67.98861350196948
          - type: pearson
            value: 71.49707687668383
          - type: spearman
            value: 68.16858223090989
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15 (default)
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
          - type: cosine_pearson
            value: 76.4178409313179
          - type: cosine_spearman
            value: 78.88504631843803
          - type: euclidean_pearson
            value: 78.71618727706142
          - type: euclidean_spearman
            value: 78.88501053846501
          - type: main_score
            value: 78.88504631843803
          - type: manhattan_pearson
            value: 78.89331900480339
          - type: manhattan_spearman
            value: 79.04826734191282
          - type: pearson
            value: 76.4178409313179
          - type: spearman
            value: 78.88504631843803
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16 (default)
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
          - type: cosine_pearson
            value: 77.55495044464278
          - type: cosine_spearman
            value: 80.3363642808641
          - type: euclidean_pearson
            value: 79.96424489786347
          - type: euclidean_spearman
            value: 80.3363642808641
          - type: main_score
            value: 80.3363642808641
          - type: manhattan_pearson
            value: 80.05244658476923
          - type: manhattan_spearman
            value: 80.42606943747235
          - type: pearson
            value: 77.55495044464278
          - type: spearman
            value: 80.3363642808641
        task:
          type: STS
      - dataset:
          config: en-en
          name: MTEB STS17 (en-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 79.38935754316142
          - type: cosine_spearman
            value: 85.46849738990943
          - type: euclidean_pearson
            value: 83.64718180060812
          - type: euclidean_spearman
            value: 85.46849738990943
          - type: main_score
            value: 85.46849738990943
          - type: manhattan_pearson
            value: 83.67702948761875
          - type: manhattan_spearman
            value: 85.34710495908027
          - type: pearson
            value: 79.38935754316142
          - type: spearman
            value: 85.46849738990943
        task:
          type: STS
      - dataset:
          config: zh-en
          name: MTEB STS22 (zh-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 73.89485634957391
          - type: cosine_spearman
            value: 73.90825698961848
          - type: euclidean_pearson
            value: 75.25124910546262
          - type: euclidean_spearman
            value: 73.90825698961848
          - type: main_score
            value: 73.90825698961848
          - type: manhattan_pearson
            value: 75.11860084263171
          - type: manhattan_spearman
            value: 73.69593141677598
          - type: pearson
            value: 73.89485634957391
          - type: spearman
            value: 73.90825698961848
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 67.81059898950983
          - type: cosine_spearman
            value: 68.30115315444206
          - type: euclidean_pearson
            value: 69.27103790429173
          - type: euclidean_spearman
            value: 68.30115315444206
          - type: main_score
            value: 68.30115315444206
          - type: manhattan_pearson
            value: 69.46849620900602
          - type: manhattan_spearman
            value: 68.45651992521948
          - type: pearson
            value: 67.81059898950983
          - type: spearman
            value: 68.30115315444206
        task:
          type: STS
      - dataset:
          config: zh
          name: MTEB STS22 (zh)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 74.44033998959976
          - type: cosine_spearman
            value: 73.58772060484971
          - type: euclidean_pearson
            value: 73.06488074477468
          - type: euclidean_spearman
            value: 73.58772060484971
          - type: main_score
            value: 73.58772060484971
          - type: manhattan_pearson
            value: 73.00608049548906
          - type: manhattan_spearman
            value: 73.55105762622729
          - type: pearson
            value: 74.44033998959976
          - type: spearman
            value: 73.58772060484971
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSB (default)
          revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
          split: test
          type: C-MTEB/STSB
        metrics:
          - type: cosine_pearson
            value: 68.09021885283748
          - type: cosine_spearman
            value: 69.45189378034146
          - type: euclidean_pearson
            value: 69.51961611366887
          - type: euclidean_spearman
            value: 69.45189378034146
          - type: main_score
            value: 69.45189378034146
          - type: manhattan_pearson
            value: 69.30192429794056
          - type: manhattan_spearman
            value: 69.22518486689475
          - type: pearson
            value: 68.09021885283748
          - type: spearman
            value: 69.45189378034146
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSB (default)
          revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
          split: validation
          type: C-MTEB/STSB
        metrics:
          - type: cosine_pearson
            value: 69.85149506856348
          - type: cosine_spearman
            value: 72.05319943168429
          - type: euclidean_pearson
            value: 72.41468551288946
          - type: euclidean_spearman
            value: 72.05319943168429
          - type: main_score
            value: 72.05319943168429
          - type: manhattan_pearson
            value: 72.08871687183135
          - type: manhattan_spearman
            value: 71.63960073768047
          - type: pearson
            value: 69.85149506856348
          - type: spearman
            value: 72.05319943168429
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark (default)
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: cosine_pearson
            value: 71.64554885631986
          - type: cosine_spearman
            value: 74.23136916844818
          - type: euclidean_pearson
            value: 74.06782419242319
          - type: euclidean_spearman
            value: 74.23136916844818
          - type: main_score
            value: 74.23136916844818
          - type: manhattan_pearson
            value: 74.0008422515175
          - type: manhattan_spearman
            value: 74.10730250032161
          - type: pearson
            value: 71.64554885631986
          - type: spearman
            value: 74.23136916844818
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR (default)
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
          - type: main_score
            value: 84.06925933025143
          - type: map
            value: 84.06925933025143
          - type: mrr
            value: 95.48704382037715
          - type: nAUC_map_diff1
            value: -1.2531960147273353
          - type: nAUC_map_max
            value: 53.63794852890932
          - type: nAUC_map_std
            value: 66.72236818008908
          - type: nAUC_mrr_diff1
            value: 44.74963455842425
          - type: nAUC_mrr_max
            value: 85.95161801239465
          - type: nAUC_mrr_std
            value: 81.36111675398224
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact (default)
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
        metrics:
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            value: 76.10900000000001
          - type: map_at_1
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          - type: map_at_10
            value: 71.654
          - type: map_at_100
            value: 72.137
          - type: map_at_1000
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          - type: map_at_20
            value: 72.021
          - type: map_at_3
            value: 68.772
          - type: map_at_5
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          - type: mrr_at_1
            value: 63.66666666666667
          - type: mrr_at_10
            value: 72.70595238095237
          - type: mrr_at_100
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          - type: mrr_at_1000
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          - type: mrr_at_20
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          - type: mrr_at_3
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          - type: mrr_at_5
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          - type: nauc_map_at_1000_diff1
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            value: 55.197709395727045
          - type: nauc_mrr_at_3_std
            value: 10.04566210045661
          - type: nauc_mrr_at_5_diff1
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            value: 56.42653941908357
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          - type: nauc_ndcg_at_1000_diff1
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          - type: nauc_precision_at_3_diff1
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            value: 28.248258905786056
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          - type: nauc_precision_at_5_max
            value: 49.058382386827766
          - type: nauc_precision_at_5_std
            value: 42.45972725558781
          - type: nauc_recall_at_1000_diff1
            value: .nan
          - type: nauc_recall_at_1000_max
            value: .nan
          - type: nauc_recall_at_1000_std
            value: .nan
          - type: nauc_recall_at_100_diff1
            value: 59.20634920634924
          - type: nauc_recall_at_100_max
            value: 61.545284780578854
          - type: nauc_recall_at_100_std
            value: 20.144724556489304
          - type: nauc_recall_at_10_diff1
            value: 64.66373744374164
          - type: nauc_recall_at_10_max
            value: 63.97713233823764
          - type: nauc_recall_at_10_std
            value: 26.664192167250576
          - type: nauc_recall_at_1_diff1
            value: 73.89906137509777
          - type: nauc_recall_at_1_max
            value: 44.9910089561678
          - type: nauc_recall_at_1_std
            value: -3.560024114726528
          - type: nauc_recall_at_20_diff1
            value: 62.63616557734212
          - type: nauc_recall_at_20_max
            value: 67.73835460109973
          - type: nauc_recall_at_20_std
            value: 23.744164332399645
          - type: nauc_recall_at_3_diff1
            value: 66.22059143579988
          - type: nauc_recall_at_3_max
            value: 56.70083839895786
          - type: nauc_recall_at_3_std
            value: 8.353413350691936
          - type: nauc_recall_at_5_diff1
            value: 67.74494960835959
          - type: nauc_recall_at_5_max
            value: 65.60091243576522
          - type: nauc_recall_at_5_std
            value: 21.75399137900112
          - type: ndcg_at_1
            value: 63.666999999999994
          - type: ndcg_at_10
            value: 76.10900000000001
          - type: ndcg_at_100
            value: 77.989
          - type: ndcg_at_1000
            value: 78.391
          - type: ndcg_at_20
            value: 77.199
          - type: ndcg_at_3
            value: 71.53699999999999
          - type: ndcg_at_5
            value: 73.662
          - type: precision_at_1
            value: 63.666999999999994
          - type: precision_at_10
            value: 10.033
          - type: precision_at_100
            value: 1.097
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_20
            value: 5.267
          - type: precision_at_3
            value: 27.889000000000003
          - type: precision_at_5
            value: 18.267
          - type: recall_at_1
            value: 60.760999999999996
          - type: recall_at_10
            value: 88.43299999999999
          - type: recall_at_100
            value: 96.667
          - type: recall_at_1000
            value: 100
          - type: recall_at_20
            value: 92.5
          - type: recall_at_3
            value: 76.461
          - type: recall_at_5
            value: 81.678
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions (default)
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: cosine_accuracy
            value: 99.76831683168317
          - type: cosine_accuracy_threshold
            value: 75.98594427108765
          - type: cosine_ap
            value: 94.10650199330435
          - type: cosine_f1
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            value: 99.76831683168317
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            value: 99.76831683168317
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            value: 84.2
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            value: 99.76831683168317
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          name: MTEB StackExchangeClustering (default)
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
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          name: MTEB StackExchangeClusteringP2P (default)
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
          split: test
          type: mteb/stackexchange-clustering-p2p
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          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
          split: test
          type: mteb/stackoverflowdupquestions-reranking
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          name: MTEB SummEval (default)
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
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          type: C-MTEB/T2Reranking
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        task:
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      - dataset:
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          revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
          split: test
          type: C-MTEB/TNews-classification
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          - type: f1_weighted
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        task:
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      - dataset:
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          name: MTEB TNews (default)
          revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
          split: validation
          type: C-MTEB/TNews-classification
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        task:
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          revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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          type: mteb/trec-covid
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          revision: 5798586b105c0434e4f0fe5e767abe619442cf93
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          revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
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        task:
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      - dataset:
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          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
          split: test
          type: mteb/toxic_conversations_50k
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      - dataset:
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          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
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          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
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          type: mteb/twentynewsgroups-clustering
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      - dataset:
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          name: MTEB TwitterSemEval2015 (default)
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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          type: mteb/twittersemeval2015-pairclassification
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          - type: max_ap
            value: 71.56900157150807
          - type: max_f1
            value: 66.13515565679575
          - type: max_precision
            value: 64.43156037307789
          - type: max_recall
            value: 68.94459102902375
          - type: similarity_accuracy
            value: 85.17017345174942
          - type: similarity_accuracy_threshold
            value: 73.24392199516296
          - type: similarity_ap
            value: 71.5689998301689
          - type: similarity_f1
            value: 66.13515565679575
          - type: similarity_f1_threshold
            value: 69.43247318267822
          - type: similarity_precision
            value: 63.545719844357976
          - type: similarity_recall
            value: 68.94459102902375
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus (default)
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: cosine_accuracy
            value: 89.17995886211045
          - type: cosine_accuracy_threshold
            value: 70.09445428848267
          - type: cosine_ap
            value: 86.05864466686238
          - type: cosine_f1
            value: 78.36959806588094
          - type: cosine_f1_threshold
            value: 67.36087203025818
          - type: cosine_precision
            value: 76.92821121328983
          - type: cosine_recall
            value: 79.8660301817062
          - type: dot_accuracy
            value: 89.17995886211045
          - type: dot_accuracy_threshold
            value: 70.09446620941162
          - type: dot_ap
            value: 86.05863925325839
          - type: dot_f1
            value: 78.36959806588094
          - type: dot_f1_threshold
            value: 67.36087799072266
          - type: dot_precision
            value: 76.92821121328983
          - type: dot_recall
            value: 79.8660301817062
          - type: euclidean_accuracy
            value: 89.17995886211045
          - type: euclidean_accuracy_threshold
            value: 77.33762264251709
          - type: euclidean_ap
            value: 86.05864473129277
          - type: euclidean_f1
            value: 78.36959806588094
          - type: euclidean_f1_threshold
            value: 80.79496026039124
          - type: euclidean_precision
            value: 76.92821121328983
          - type: euclidean_recall
            value: 79.8660301817062
          - type: main_score
            value: 86.05864473129277
          - type: manhattan_accuracy
            value: 89.15279233127644
          - type: manhattan_accuracy_threshold
            value: 1960.135269165039
          - type: manhattan_ap
            value: 86.00803071652211
          - type: manhattan_f1
            value: 78.28386279840602
          - type: manhattan_f1_threshold
            value: 2062.195587158203
          - type: manhattan_precision
            value: 75.81331602106326
          - type: manhattan_recall
            value: 80.92085001539883
          - type: max_accuracy
            value: 89.17995886211045
          - type: max_ap
            value: 86.05864473129277
          - type: max_f1
            value: 78.36959806588094
          - type: max_precision
            value: 76.92821121328983
          - type: max_recall
            value: 80.92085001539883
          - type: similarity_accuracy
            value: 89.17995886211045
          - type: similarity_accuracy_threshold
            value: 70.09445428848267
          - type: similarity_ap
            value: 86.05864466686238
          - type: similarity_f1
            value: 78.36959806588094
          - type: similarity_f1_threshold
            value: 67.36087203025818
          - type: similarity_precision
            value: 76.92821121328983
          - type: similarity_recall
            value: 79.8660301817062
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB VideoRetrieval (default)
          revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
          split: test
          type: C-MTEB/VideoRetrieval
        metrics:
          - type: main_score
            value: 76.259
          - type: map_at_1
            value: 63.1
          - type: map_at_10
            value: 72.214
          - type: map_at_100
            value: 72.595
          - type: map_at_1000
            value: 72.604
          - type: map_at_20
            value: 72.482
          - type: map_at_3
            value: 70.45
          - type: map_at_5
            value: 71.565
          - type: mrr_at_1
            value: 63.1
          - type: mrr_at_10
            value: 72.2140873015873
          - type: mrr_at_100
            value: 72.59450024817937
          - type: mrr_at_1000
            value: 72.60392898169485
          - type: mrr_at_20
            value: 72.48216831972637
          - type: mrr_at_3
            value: 70.45000000000005
          - type: mrr_at_5
            value: 71.56500000000004
          - type: nauc_map_at_1000_diff1
            value: 74.28035434647866
          - type: nauc_map_at_1000_max
            value: 24.9986630946608
          - type: nauc_map_at_1000_std
            value: -39.39693868573278
          - type: nauc_map_at_100_diff1
            value: 74.26844900002635
          - type: nauc_map_at_100_max
            value: 25.018503837474537
          - type: nauc_map_at_100_std
            value: -39.37583943184342
          - type: nauc_map_at_10_diff1
            value: 74.26680601603067
          - type: nauc_map_at_10_max
            value: 25.09926459399667
          - type: nauc_map_at_10_std
            value: -39.89610049620442
          - type: nauc_map_at_1_diff1
            value: 77.10780012025063
          - type: nauc_map_at_1_max
            value: 19.806657111504965
          - type: nauc_map_at_1_std
            value: -37.2015516410441
          - type: nauc_map_at_20_diff1
            value: 74.21838781014907
          - type: nauc_map_at_20_max
            value: 25.063007078847527
          - type: nauc_map_at_20_std
            value: -39.43384966895805
          - type: nauc_map_at_3_diff1
            value: 74.18933637722046
          - type: nauc_map_at_3_max
            value: 24.353710639698495
          - type: nauc_map_at_3_std
            value: -40.981597611432065
          - type: nauc_map_at_5_diff1
            value: 74.10850958842639
          - type: nauc_map_at_5_max
            value: 24.97711616082726
          - type: nauc_map_at_5_std
            value: -40.679823827809464
          - type: nauc_mrr_at_1000_diff1
            value: 74.28035434647866
          - type: nauc_mrr_at_1000_max
            value: 24.9986630946608
          - type: nauc_mrr_at_1000_std
            value: -39.39693868573278
          - type: nauc_mrr_at_100_diff1
            value: 74.26844900002635
          - type: nauc_mrr_at_100_max
            value: 25.018503837474537
          - type: nauc_mrr_at_100_std
            value: -39.37583943184342
          - type: nauc_mrr_at_10_diff1
            value: 74.26680601603067
          - type: nauc_mrr_at_10_max
            value: 25.09926459399667
          - type: nauc_mrr_at_10_std
            value: -39.89610049620442
          - type: nauc_mrr_at_1_diff1
            value: 77.10780012025063
          - type: nauc_mrr_at_1_max
            value: 19.806657111504965
          - type: nauc_mrr_at_1_std
            value: -37.2015516410441
          - type: nauc_mrr_at_20_diff1
            value: 74.21838781014907
          - type: nauc_mrr_at_20_max
            value: 25.063007078847527
          - type: nauc_mrr_at_20_std
            value: -39.43384966895805
          - type: nauc_mrr_at_3_diff1
            value: 74.18933637722046
          - type: nauc_mrr_at_3_max
            value: 24.353710639698495
          - type: nauc_mrr_at_3_std
            value: -40.981597611432065
          - type: nauc_mrr_at_5_diff1
            value: 74.10850958842639
          - type: nauc_mrr_at_5_max
            value: 24.97711616082726
          - type: nauc_mrr_at_5_std
            value: -40.679823827809464
          - type: nauc_ndcg_at_1000_diff1
            value: 73.61936333916759
          - type: nauc_ndcg_at_1000_max
            value: 27.116854943289013
          - type: nauc_ndcg_at_1000_std
            value: -37.54463044764084
          - type: nauc_ndcg_at_100_diff1
            value: 73.34360970001946
          - type: nauc_ndcg_at_100_max
            value: 27.730908397954856
          - type: nauc_ndcg_at_100_std
            value: -36.847816086959746
          - type: nauc_ndcg_at_10_diff1
            value: 73.26740567929617
          - type: nauc_ndcg_at_10_max
            value: 28.07969523618901
          - type: nauc_ndcg_at_10_std
            value: -39.5613267347007
          - type: nauc_ndcg_at_1_diff1
            value: 77.10780012025063
          - type: nauc_ndcg_at_1_max
            value: 19.806657111504965
          - type: nauc_ndcg_at_1_std
            value: -37.2015516410441
          - type: nauc_ndcg_at_20_diff1
            value: 73.03206507305698
          - type: nauc_ndcg_at_20_max
            value: 27.998040243323953
          - type: nauc_ndcg_at_20_std
            value: -37.708810040181056
          - type: nauc_ndcg_at_3_diff1
            value: 73.14698395698991
          - type: nauc_ndcg_at_3_max
            value: 26.27353390787337
          - type: nauc_ndcg_at_3_std
            value: -42.26161377498877
          - type: nauc_ndcg_at_5_diff1
            value: 72.95385082418426
          - type: nauc_ndcg_at_5_max
            value: 27.616141938964926
          - type: nauc_ndcg_at_5_std
            value: -41.73620432852272
          - type: nauc_precision_at_1000_diff1
            value: 62.222222222221426
          - type: nauc_precision_at_1000_max
            value: 84.86150015561716
          - type: nauc_precision_at_1000_std
            value: 70.37659508247653
          - type: nauc_precision_at_100_diff1
            value: 57.83489866534799
          - type: nauc_precision_at_100_max
            value: 79.34448289119527
          - type: nauc_precision_at_100_std
            value: 41.03229527104971
          - type: nauc_precision_at_10_diff1
            value: 67.38577178030296
          - type: nauc_precision_at_10_max
            value: 47.06185741341976
          - type: nauc_precision_at_10_std
            value: -35.28392180735959
          - type: nauc_precision_at_1_diff1
            value: 77.10780012025063
          - type: nauc_precision_at_1_max
            value: 19.806657111504965
          - type: nauc_precision_at_1_std
            value: -37.2015516410441
          - type: nauc_precision_at_20_diff1
            value: 62.29131652661053
          - type: nauc_precision_at_20_max
            value: 54.85838779956423
          - type: nauc_precision_at_20_std
            value: -14.987239340181135
          - type: nauc_precision_at_3_diff1
            value: 69.30752094380478
          - type: nauc_precision_at_3_max
            value: 33.47195304146769
          - type: nauc_precision_at_3_std
            value: -46.94646447336974
          - type: nauc_precision_at_5_diff1
            value: 67.75036818851238
          - type: nauc_precision_at_5_max
            value: 39.96410162002936
          - type: nauc_precision_at_5_std
            value: -46.30185321551299
          - type: nauc_recall_at_1000_diff1
            value: 62.22222222222214
          - type: nauc_recall_at_1000_max
            value: 84.86150015561832
          - type: nauc_recall_at_1000_std
            value: 70.37659508247839
          - type: nauc_recall_at_100_diff1
            value: 57.83489866534869
          - type: nauc_recall_at_100_max
            value: 79.34448289119563
          - type: nauc_recall_at_100_std
            value: 41.03229527105021
          - type: nauc_recall_at_10_diff1
            value: 67.38577178030309
          - type: nauc_recall_at_10_max
            value: 47.061857413419816
          - type: nauc_recall_at_10_std
            value: -35.283921807359164
          - type: nauc_recall_at_1_diff1
            value: 77.10780012025063
          - type: nauc_recall_at_1_max
            value: 19.806657111504965
          - type: nauc_recall_at_1_std
            value: -37.2015516410441
          - type: nauc_recall_at_20_diff1
            value: 62.29131652661064
          - type: nauc_recall_at_20_max
            value: 54.858387799564234
          - type: nauc_recall_at_20_std
            value: -14.987239340180611
          - type: nauc_recall_at_3_diff1
            value: 69.30752094380468
          - type: nauc_recall_at_3_max
            value: 33.47195304146769
          - type: nauc_recall_at_3_std
            value: -46.94646447336982
          - type: nauc_recall_at_5_diff1
            value: 67.75036818851241
          - type: nauc_recall_at_5_max
            value: 39.96410162002941
          - type: nauc_recall_at_5_std
            value: -46.30185321551307
          - type: ndcg_at_1
            value: 63.1
          - type: ndcg_at_10
            value: 76.259
          - type: ndcg_at_100
            value: 77.985
          - type: ndcg_at_1000
            value: 78.227
          - type: ndcg_at_20
            value: 77.208
          - type: ndcg_at_3
            value: 72.684
          - type: ndcg_at_5
            value: 74.698
          - type: precision_at_1
            value: 63.1
          - type: precision_at_10
            value: 8.88
          - type: precision_at_100
            value: 0.966
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_20
            value: 4.625
          - type: precision_at_3
            value: 26.367
          - type: precision_at_5
            value: 16.8
          - type: recall_at_1
            value: 63.1
          - type: recall_at_10
            value: 88.8
          - type: recall_at_100
            value: 96.6
          - type: recall_at_1000
            value: 98.5
          - type: recall_at_20
            value: 92.5
          - type: recall_at_3
            value: 79.10000000000001
          - type: recall_at_5
            value: 84
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Waimai (default)
          revision: 339287def212450dcaa9df8c22bf93e9980c7023
          split: test
          type: C-MTEB/waimai-classification
        metrics:
          - type: accuracy
            value: 85.77999999999999
          - type: ap
            value: 68.2216414015039
          - type: ap_weighted
            value: 68.2216414015039
          - type: f1
            value: 83.82399258910729
          - type: f1_weighted
            value: 85.78923735869199
          - type: main_score
            value: 85.77999999999999
        task:
          type: Classification
pipeline_tag: feature-extraction
tags:
  - mteb
  - sentence-transformers
library_name: transformers

MiniCPM-Embedding-Light

MiniCPM-Embedding-Light 是面壁智能与清华大学自然语言处理实验室（THUNLP）、东北大学信息检索小组（NEUIR）共同开发的中英双语言文本嵌入模型，有如下特点：

• 出色的中文、英文检索能力。
• 出色的中英跨语言检索能力。
• 支持长文本（最长8192token）。
• 提供稠密向量与token级别的稀疏向量。
• 可变的稠密向量维度（套娃表征）。

MiniCPM-Embedding-Light结构上采取双向注意力和 Weighted Mean Pooling [1]。采取多阶段训练方式，共使用包括开源数据、机造数据、闭源数据在内的约 260M 条训练数据。

欢迎关注 UltraRAG 系列：

• 检索模型：MiniCPM-Embedding-Light
• 重排模型：MiniCPM-Reranker-Light
• 领域自适应RAG框架：UltraRAG

MiniCPM-Embedding-Light is a bilingual & cross-lingual text embedding model developed by ModelBest Inc. , THUNLP and NEUIR , featuring:

• Exceptional Chinese and English retrieval capabilities.
• Outstanding cross-lingual retrieval capabilities between Chinese and English.
• Long-text support (up to 8192 tokens).
• Dense vectors and token-level sparse vectors.
• Variable dense vector dimensions (Matryoshka representation [2]).

MiniCPM-Embedding-Light incorporates bidirectional attention and Weighted Mean Pooling [1] in its architecture. The model underwent multi-stage training using approximately 260 million training examples, including open-source, synthetic, and proprietary data.

We also invite you to explore the UltraRAG series:

• Retrieval Model: MiniCPM-Embedding-Light
• Re-ranking Model: MiniCPM-Reranker-Light
• Domain Adaptive RAG Framework: UltraRAG

[1] Muennighoff, N. (2022). Sgpt: Gpt sentence embeddings for semantic search. arXiv preprint arXiv:2202.08904. [2] Kusupati, Aditya, et al. "Matryoshka representation learning." Advances in Neural Information Processing Systems 35 (2022): 30233-30249.

模型信息 Model Information

• 模型大小：440M

• 嵌入维度：1024

• 最大输入token数：8192

• Model Size: 440M

• Embedding Dimension: 1024

• Max Input Tokens: 8192

使用方法 Usage

输入格式 Input Format

本模型支持 query 侧指令，格式如下：

MiniCPM-Embedding-Light supports query-side instructions in the following format:

Instruction: {{ instruction }} Query: {{ query }}

例如：

For example:

Instruction: 为这个医学问题检索相关回答。Query: 咽喉癌的成因是什么？

Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: However the warming trend is slower than most climate models have forecast.

也可以不提供指令，即采取如下格式：

MiniCPM-Embedding-Light also works in instruction-free mode in the following format:

Query: {{ query }}

环境要求 Requirements

transformers==4.37.2

示例脚本 Demo

Huggingface Transformers

from transformers import AutoModel
import torch

model_name = "openbmb/MiniCPM-Embedding-Light"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda")

# you can use flash_attention_2 for faster inference
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") 

model.eval()

queries = ["MiniCPM-o 2.6 A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone"]
passages = ["MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming."]

embeddings_query_dense, embeddings_query_sparse = model.encode_query(queries, return_sparse_vectors=True)
embeddings_doc_dense, embeddings_doc_sparse = model.encode_corpus(passages, return_sparse_vectors=True)

dense_scores = (embeddings_query_dense @ embeddings_doc_dense.T)
print(dense_scores.tolist())  # [[0.6512398719787598]]
print(model.compute_sparse_score_dicts(embeddings_query_sparse,  embeddings_doc_sparse)) # [[0.27202296]]

dense_scores, sparse_scores, mixed_scores = model.compute_score(queries, passages)
print(dense_scores) # [[0.65123993]]
print(sparse_scores) # [[0.27202296]]
print(mixed_scores) # [[0.73284686]]

Sentence Transformers

import torch
from sentence_transformers import SentenceTransformer


model_name = "openbmb/MiniCPM-Embedding-Light"
model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"torch_dtype": torch.float16})

# you can use flash_attention_2 for faster inference
# model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": torch.float16})

queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query: "

embeddings_query = model.encode(queries, prompt=INSTRUCTION)
embeddings_doc = model.encode(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]

Infinity

import asyncio
from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine
import numpy as np

array = AsyncEngineArray.from_args([
  EngineArgs(model_name_or_path = "openbmb/MiniCPM-Embedding-Light", engine="torch", dtype="float16", bettertransformer=False, pooling_method="mean", trust_remote_code=True),
])
queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query:"
queries = [f"{INSTRUCTION} {query}" for query in queries]


async def embed_text(engine: AsyncEmbeddingEngine,sentences): 
    async with engine: 
        embeddings, usage = await engine.embed(sentences=sentences)
    return embeddings

queries_embedding = asyncio.run(embed_text(array[0],queries))
passages_embedding = asyncio.run(embed_text(array[0],passages))

scores = (np.array(queries_embedding) @ np.array(passages_embedding).T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183443665504456]]

FlagEmbedding

from FlagEmbedding import FlagModel


model = FlagModel("openbmb/MiniCPM-Embedding-Light", 
                          query_instruction_for_retrieval="Query: ",
                          pooling_method="mean",
                          trust_remote_code=True,
                          normalize_embeddings=True,
                          use_fp16=True)
# You can hack the __init__() method of the FlagEmbedding BaseEmbedder class to use flash_attention_2 for faster inference
#  self.model = AutoModel.from_pretrained(
#             model_name_or_path,
#             trust_remote_code=trust_remote_code,
#             cache_dir=cache_dir,
#             # torch_dtype=torch.float16, # we need to add this line to use fp16
#             # attn_implementation="flash_attention_2", # we need to add this line to use flash_attention_2
#         )

queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"


embeddings_query = model.encode_queries(queries)
embeddings_doc = model.encode_corpus(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]

实验结果 Evaluation Results

中文与英文检索结果 CN/EN Retrieval Results

模型 Model	C-MTEB/Retrieval(NDCG@10)	BEIR(NDCG@10)
bge-large-zh-v1.5	70.46	-
gte-large-zh	72.49	-
Conan-embedding-v1	76.67
bge-large-en-v1.5	-	54.29
modernbert-embed-large	-	54.36
snowflake-arctic-embed-l	-	55.98
gte-en-large-v1.5	-	57.91
me5-large	63.66	51.43
bge-m3(Dense)	65.43	48.82
gte-multilingual-base(Dense)	71.95	51.08
jina-embeddings-v3	68.60	53.88
gte-Qwen2-1.5B-instruct	71.86	58.29
MiniCPM-Embedding	76.76	58.56
MiniCPM-Embedding-Light(Dense)	72.71	55.27
MiniCPM-Embedding-Light(Dense+Sparse)	73.13	56.31
MiniCPM-Embedding-Light(Dense+Sparse)+MiniCPM-Reranker-Light	76.34	61.49

中英跨语言检索结果 CN-EN Cross-lingual Retrieval Results

模型 Model	MKQA En-Zh_CN (Recall@20)	NeuCLIR22 (NDCG@10)	NeuCLIR23 (NDCG@10)
me5-large	44.3	9.01	25.33
bge-m3(Dense)	66.4	30.49	41.09
gte-multilingual-base(Dense)	68.2	39.46	45.86
MiniCPM-Embedding	72.95	52.65	49.95
MiniCPM-Embedding-Light(Dense)	68.29	41.17	45.83
MiniCPM-Embedding-Light(Dense)+MiniCPM-Reranker-Light	71.86	54.32	56.50

许可证 License

• 本仓库中代码依照 Apache-2.0 协议开源。
• MiniCPM-Embedding-Light 模型权重的使用则需要遵循 MiniCPM 模型协议。
• MiniCPM-Embedding-Light 模型权重对学术研究完全开放。如需将模型用于商业用途，请填写此问卷。

• The code in this repo is released under the Apache-2.0 License.
• The usage of MiniCPM-Embedding-Light model weights must strictly follow MiniCPM Model License.md.
• The models and weights of MiniCPM-Embedding-Light are completely free for academic research. After filling out a "questionnaire" for registration, MiniCPM-Embedding-Light weights are also available for free commercial use.