embeddings / README.md
Arnav
model with custom inference script
e9989e3
metadata
tags:
  - mteb
  - sentence-transfomres
  - transformers
model-index:
  - name: bge-large-en
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 76.94029850746269
          - type: ap
            value: 40.00228964744091
          - type: f1
            value: 70.86088267934595
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 91.93745
          - type: ap
            value: 88.24758534667426
          - type: f1
            value: 91.91033034217591
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 46.158
          - type: f1
            value: 45.78935185074774
      - task:
          type: Retrieval
        dataset:
          type: arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 39.972
          - type: map_at_10
            value: 54.874
          - type: map_at_100
            value: 55.53399999999999
          - type: map_at_1000
            value: 55.539
          - type: map_at_3
            value: 51.031000000000006
          - type: map_at_5
            value: 53.342999999999996
          - type: mrr_at_1
            value: 40.541
          - type: mrr_at_10
            value: 55.096000000000004
          - type: mrr_at_100
            value: 55.75599999999999
          - type: mrr_at_1000
            value: 55.761
          - type: mrr_at_3
            value: 51.221000000000004
          - type: mrr_at_5
            value: 53.568000000000005
          - type: ndcg_at_1
            value: 39.972
          - type: ndcg_at_10
            value: 62.456999999999994
          - type: ndcg_at_100
            value: 65.262
          - type: ndcg_at_1000
            value: 65.389
          - type: ndcg_at_3
            value: 54.673
          - type: ndcg_at_5
            value: 58.80499999999999
          - type: precision_at_1
            value: 39.972
          - type: precision_at_10
            value: 8.634
          - type: precision_at_100
            value: 0.9860000000000001
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 21.740000000000002
          - type: precision_at_5
            value: 15.036
          - type: recall_at_1
            value: 39.972
          - type: recall_at_10
            value: 86.344
          - type: recall_at_100
            value: 98.578
          - type: recall_at_1000
            value: 99.57300000000001
          - type: recall_at_3
            value: 65.22
          - type: recall_at_5
            value: 75.178
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 48.94652870403906
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 43.17257160340209
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 63.97867370559182
          - type: mrr
            value: 77.00820032537484
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 80.00986015960616
          - type: cos_sim_spearman
            value: 80.36387933827882
          - type: euclidean_pearson
            value: 80.32305287257296
          - type: euclidean_spearman
            value: 82.0524720308763
          - type: manhattan_pearson
            value: 80.19847473906454
          - type: manhattan_spearman
            value: 81.87957652506985
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 88.00000000000001
          - type: f1
            value: 87.99039027511853
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 41.36932844640705
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 38.34983239611985
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 32.257999999999996
          - type: map_at_10
            value: 42.937
          - type: map_at_100
            value: 44.406
          - type: map_at_1000
            value: 44.536
          - type: map_at_3
            value: 39.22
          - type: map_at_5
            value: 41.458
          - type: mrr_at_1
            value: 38.769999999999996
          - type: mrr_at_10
            value: 48.701
          - type: mrr_at_100
            value: 49.431000000000004
          - type: mrr_at_1000
            value: 49.476
          - type: mrr_at_3
            value: 45.875
          - type: mrr_at_5
            value: 47.67
          - type: ndcg_at_1
            value: 38.769999999999996
          - type: ndcg_at_10
            value: 49.35
          - type: ndcg_at_100
            value: 54.618
          - type: ndcg_at_1000
            value: 56.655
          - type: ndcg_at_3
            value: 43.826
          - type: ndcg_at_5
            value: 46.72
          - type: precision_at_1
            value: 38.769999999999996
          - type: precision_at_10
            value: 9.328
          - type: precision_at_100
            value: 1.484
          - type: precision_at_1000
            value: 0.196
          - type: precision_at_3
            value: 20.649
          - type: precision_at_5
            value: 15.25
          - type: recall_at_1
            value: 32.257999999999996
          - type: recall_at_10
            value: 61.849
          - type: recall_at_100
            value: 83.70400000000001
          - type: recall_at_1000
            value: 96.344
          - type: recall_at_3
            value: 46.037
          - type: recall_at_5
            value: 53.724000000000004
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 32.979
          - type: map_at_10
            value: 43.376999999999995
          - type: map_at_100
            value: 44.667
          - type: map_at_1000
            value: 44.794
          - type: map_at_3
            value: 40.461999999999996
          - type: map_at_5
            value: 42.138
          - type: mrr_at_1
            value: 41.146
          - type: mrr_at_10
            value: 49.575
          - type: mrr_at_100
            value: 50.187000000000005
          - type: mrr_at_1000
            value: 50.231
          - type: mrr_at_3
            value: 47.601
          - type: mrr_at_5
            value: 48.786
          - type: ndcg_at_1
            value: 41.146
          - type: ndcg_at_10
            value: 48.957
          - type: ndcg_at_100
            value: 53.296
          - type: ndcg_at_1000
            value: 55.254000000000005
          - type: ndcg_at_3
            value: 45.235
          - type: ndcg_at_5
            value: 47.014
          - type: precision_at_1
            value: 41.146
          - type: precision_at_10
            value: 9.107999999999999
          - type: precision_at_100
            value: 1.481
          - type: precision_at_1000
            value: 0.193
          - type: precision_at_3
            value: 21.783
          - type: precision_at_5
            value: 15.274
          - type: recall_at_1
            value: 32.979
          - type: recall_at_10
            value: 58.167
          - type: recall_at_100
            value: 76.374
          - type: recall_at_1000
            value: 88.836
          - type: recall_at_3
            value: 46.838
          - type: recall_at_5
            value: 52.006
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 40.326
          - type: map_at_10
            value: 53.468
          - type: map_at_100
            value: 54.454
          - type: map_at_1000
            value: 54.508
          - type: map_at_3
            value: 50.12799999999999
          - type: map_at_5
            value: 51.991
          - type: mrr_at_1
            value: 46.394999999999996
          - type: mrr_at_10
            value: 57.016999999999996
          - type: mrr_at_100
            value: 57.67099999999999
          - type: mrr_at_1000
            value: 57.699999999999996
          - type: mrr_at_3
            value: 54.65
          - type: mrr_at_5
            value: 56.101
          - type: ndcg_at_1
            value: 46.394999999999996
          - type: ndcg_at_10
            value: 59.507
          - type: ndcg_at_100
            value: 63.31099999999999
          - type: ndcg_at_1000
            value: 64.388
          - type: ndcg_at_3
            value: 54.04600000000001
          - type: ndcg_at_5
            value: 56.723
          - type: precision_at_1
            value: 46.394999999999996
          - type: precision_at_10
            value: 9.567
          - type: precision_at_100
            value: 1.234
          - type: precision_at_1000
            value: 0.13699999999999998
          - type: precision_at_3
            value: 24.117
          - type: precision_at_5
            value: 16.426
          - type: recall_at_1
            value: 40.326
          - type: recall_at_10
            value: 73.763
          - type: recall_at_100
            value: 89.927
          - type: recall_at_1000
            value: 97.509
          - type: recall_at_3
            value: 59.34
          - type: recall_at_5
            value: 65.915
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.661
          - type: map_at_10
            value: 35.522
          - type: map_at_100
            value: 36.619
          - type: map_at_1000
            value: 36.693999999999996
          - type: map_at_3
            value: 33.154
          - type: map_at_5
            value: 34.353
          - type: mrr_at_1
            value: 28.362
          - type: mrr_at_10
            value: 37.403999999999996
          - type: mrr_at_100
            value: 38.374
          - type: mrr_at_1000
            value: 38.428000000000004
          - type: mrr_at_3
            value: 35.235
          - type: mrr_at_5
            value: 36.269
          - type: ndcg_at_1
            value: 28.362
          - type: ndcg_at_10
            value: 40.431
          - type: ndcg_at_100
            value: 45.745999999999995
          - type: ndcg_at_1000
            value: 47.493
          - type: ndcg_at_3
            value: 35.733
          - type: ndcg_at_5
            value: 37.722
          - type: precision_at_1
            value: 28.362
          - type: precision_at_10
            value: 6.101999999999999
          - type: precision_at_100
            value: 0.922
          - type: precision_at_1000
            value: 0.11100000000000002
          - type: precision_at_3
            value: 15.140999999999998
          - type: precision_at_5
            value: 10.305
          - type: recall_at_1
            value: 26.661
          - type: recall_at_10
            value: 53.675
          - type: recall_at_100
            value: 77.891
          - type: recall_at_1000
            value: 90.72
          - type: recall_at_3
            value: 40.751
          - type: recall_at_5
            value: 45.517
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 18.886
          - type: map_at_10
            value: 27.288
          - type: map_at_100
            value: 28.327999999999996
          - type: map_at_1000
            value: 28.438999999999997
          - type: map_at_3
            value: 24.453
          - type: map_at_5
            value: 25.959
          - type: mrr_at_1
            value: 23.134
          - type: mrr_at_10
            value: 32.004
          - type: mrr_at_100
            value: 32.789
          - type: mrr_at_1000
            value: 32.857
          - type: mrr_at_3
            value: 29.084
          - type: mrr_at_5
            value: 30.614
          - type: ndcg_at_1
            value: 23.134
          - type: ndcg_at_10
            value: 32.852
          - type: ndcg_at_100
            value: 37.972
          - type: ndcg_at_1000
            value: 40.656
          - type: ndcg_at_3
            value: 27.435
          - type: ndcg_at_5
            value: 29.823
          - type: precision_at_1
            value: 23.134
          - type: precision_at_10
            value: 6.032
          - type: precision_at_100
            value: 0.9950000000000001
          - type: precision_at_1000
            value: 0.136
          - type: precision_at_3
            value: 13.017999999999999
          - type: precision_at_5
            value: 9.501999999999999
          - type: recall_at_1
            value: 18.886
          - type: recall_at_10
            value: 45.34
          - type: recall_at_100
            value: 67.947
          - type: recall_at_1000
            value: 86.924
          - type: recall_at_3
            value: 30.535
          - type: recall_at_5
            value: 36.451
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 28.994999999999997
          - type: map_at_10
            value: 40.04
          - type: map_at_100
            value: 41.435
          - type: map_at_1000
            value: 41.537
          - type: map_at_3
            value: 37.091
          - type: map_at_5
            value: 38.802
          - type: mrr_at_1
            value: 35.034
          - type: mrr_at_10
            value: 45.411
          - type: mrr_at_100
            value: 46.226
          - type: mrr_at_1000
            value: 46.27
          - type: mrr_at_3
            value: 43.086
          - type: mrr_at_5
            value: 44.452999999999996
          - type: ndcg_at_1
            value: 35.034
          - type: ndcg_at_10
            value: 46.076
          - type: ndcg_at_100
            value: 51.483000000000004
          - type: ndcg_at_1000
            value: 53.433
          - type: ndcg_at_3
            value: 41.304
          - type: ndcg_at_5
            value: 43.641999999999996
          - type: precision_at_1
            value: 35.034
          - type: precision_at_10
            value: 8.258000000000001
          - type: precision_at_100
            value: 1.268
          - type: precision_at_1000
            value: 0.161
          - type: precision_at_3
            value: 19.57
          - type: precision_at_5
            value: 13.782
          - type: recall_at_1
            value: 28.994999999999997
          - type: recall_at_10
            value: 58.538000000000004
          - type: recall_at_100
            value: 80.72399999999999
          - type: recall_at_1000
            value: 93.462
          - type: recall_at_3
            value: 45.199
          - type: recall_at_5
            value: 51.237
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.795
          - type: map_at_10
            value: 34.935
          - type: map_at_100
            value: 36.306
          - type: map_at_1000
            value: 36.417
          - type: map_at_3
            value: 31.831
          - type: map_at_5
            value: 33.626
          - type: mrr_at_1
            value: 30.479
          - type: mrr_at_10
            value: 40.225
          - type: mrr_at_100
            value: 41.055
          - type: mrr_at_1000
            value: 41.114
          - type: mrr_at_3
            value: 37.538
          - type: mrr_at_5
            value: 39.073
          - type: ndcg_at_1
            value: 30.479
          - type: ndcg_at_10
            value: 40.949999999999996
          - type: ndcg_at_100
            value: 46.525
          - type: ndcg_at_1000
            value: 48.892
          - type: ndcg_at_3
            value: 35.79
          - type: ndcg_at_5
            value: 38.237
          - type: precision_at_1
            value: 30.479
          - type: precision_at_10
            value: 7.6259999999999994
          - type: precision_at_100
            value: 1.203
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 17.199
          - type: precision_at_5
            value: 12.466000000000001
          - type: recall_at_1
            value: 24.795
          - type: recall_at_10
            value: 53.421
          - type: recall_at_100
            value: 77.189
          - type: recall_at_1000
            value: 93.407
          - type: recall_at_3
            value: 39.051
          - type: recall_at_5
            value: 45.462
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.853499999999997
          - type: map_at_10
            value: 36.20433333333333
          - type: map_at_100
            value: 37.40391666666667
          - type: map_at_1000
            value: 37.515
          - type: map_at_3
            value: 33.39975
          - type: map_at_5
            value: 34.9665
          - type: mrr_at_1
            value: 31.62666666666667
          - type: mrr_at_10
            value: 40.436749999999996
          - type: mrr_at_100
            value: 41.260333333333335
          - type: mrr_at_1000
            value: 41.31525
          - type: mrr_at_3
            value: 38.06733333333332
          - type: mrr_at_5
            value: 39.41541666666667
          - type: ndcg_at_1
            value: 31.62666666666667
          - type: ndcg_at_10
            value: 41.63341666666667
          - type: ndcg_at_100
            value: 46.704166666666666
          - type: ndcg_at_1000
            value: 48.88483333333335
          - type: ndcg_at_3
            value: 36.896
          - type: ndcg_at_5
            value: 39.11891666666667
          - type: precision_at_1
            value: 31.62666666666667
          - type: precision_at_10
            value: 7.241083333333333
          - type: precision_at_100
            value: 1.1488333333333334
          - type: precision_at_1000
            value: 0.15250000000000002
          - type: precision_at_3
            value: 16.908333333333335
          - type: precision_at_5
            value: 11.942833333333333
          - type: recall_at_1
            value: 26.853499999999997
          - type: recall_at_10
            value: 53.461333333333336
          - type: recall_at_100
            value: 75.63633333333333
          - type: recall_at_1000
            value: 90.67016666666666
          - type: recall_at_3
            value: 40.24241666666667
          - type: recall_at_5
            value: 45.98608333333333
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 25.241999999999997
          - type: map_at_10
            value: 31.863999999999997
          - type: map_at_100
            value: 32.835
          - type: map_at_1000
            value: 32.928000000000004
          - type: map_at_3
            value: 29.694
          - type: map_at_5
            value: 30.978
          - type: mrr_at_1
            value: 28.374
          - type: mrr_at_10
            value: 34.814
          - type: mrr_at_100
            value: 35.596
          - type: mrr_at_1000
            value: 35.666
          - type: mrr_at_3
            value: 32.745000000000005
          - type: mrr_at_5
            value: 34.049
          - type: ndcg_at_1
            value: 28.374
          - type: ndcg_at_10
            value: 35.969
          - type: ndcg_at_100
            value: 40.708
          - type: ndcg_at_1000
            value: 43.08
          - type: ndcg_at_3
            value: 31.968999999999998
          - type: ndcg_at_5
            value: 34.069
          - type: precision_at_1
            value: 28.374
          - type: precision_at_10
            value: 5.583
          - type: precision_at_100
            value: 0.8630000000000001
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 13.547999999999998
          - type: precision_at_5
            value: 9.447999999999999
          - type: recall_at_1
            value: 25.241999999999997
          - type: recall_at_10
            value: 45.711
          - type: recall_at_100
            value: 67.482
          - type: recall_at_1000
            value: 85.13300000000001
          - type: recall_at_3
            value: 34.622
          - type: recall_at_5
            value: 40.043
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 17.488999999999997
          - type: map_at_10
            value: 25.142999999999997
          - type: map_at_100
            value: 26.244
          - type: map_at_1000
            value: 26.363999999999997
          - type: map_at_3
            value: 22.654
          - type: map_at_5
            value: 24.017
          - type: mrr_at_1
            value: 21.198
          - type: mrr_at_10
            value: 28.903000000000002
          - type: mrr_at_100
            value: 29.860999999999997
          - type: mrr_at_1000
            value: 29.934
          - type: mrr_at_3
            value: 26.634999999999998
          - type: mrr_at_5
            value: 27.903
          - type: ndcg_at_1
            value: 21.198
          - type: ndcg_at_10
            value: 29.982999999999997
          - type: ndcg_at_100
            value: 35.275
          - type: ndcg_at_1000
            value: 38.074000000000005
          - type: ndcg_at_3
            value: 25.502999999999997
          - type: ndcg_at_5
            value: 27.557
          - type: precision_at_1
            value: 21.198
          - type: precision_at_10
            value: 5.502
          - type: precision_at_100
            value: 0.942
          - type: precision_at_1000
            value: 0.136
          - type: precision_at_3
            value: 12.044
          - type: precision_at_5
            value: 8.782
          - type: recall_at_1
            value: 17.488999999999997
          - type: recall_at_10
            value: 40.821000000000005
          - type: recall_at_100
            value: 64.567
          - type: recall_at_1000
            value: 84.452
          - type: recall_at_3
            value: 28.351
          - type: recall_at_5
            value: 33.645
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 27.066000000000003
          - type: map_at_10
            value: 36.134
          - type: map_at_100
            value: 37.285000000000004
          - type: map_at_1000
            value: 37.389
          - type: map_at_3
            value: 33.522999999999996
          - type: map_at_5
            value: 34.905
          - type: mrr_at_1
            value: 31.436999999999998
          - type: mrr_at_10
            value: 40.225
          - type: mrr_at_100
            value: 41.079
          - type: mrr_at_1000
            value: 41.138000000000005
          - type: mrr_at_3
            value: 38.074999999999996
          - type: mrr_at_5
            value: 39.190000000000005
          - type: ndcg_at_1
            value: 31.436999999999998
          - type: ndcg_at_10
            value: 41.494
          - type: ndcg_at_100
            value: 46.678999999999995
          - type: ndcg_at_1000
            value: 48.964
          - type: ndcg_at_3
            value: 36.828
          - type: ndcg_at_5
            value: 38.789
          - type: precision_at_1
            value: 31.436999999999998
          - type: precision_at_10
            value: 6.931
          - type: precision_at_100
            value: 1.072
          - type: precision_at_1000
            value: 0.13799999999999998
          - type: precision_at_3
            value: 16.729
          - type: precision_at_5
            value: 11.567
          - type: recall_at_1
            value: 27.066000000000003
          - type: recall_at_10
            value: 53.705000000000005
          - type: recall_at_100
            value: 75.968
          - type: recall_at_1000
            value: 91.937
          - type: recall_at_3
            value: 40.865
          - type: recall_at_5
            value: 45.739999999999995
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.979000000000003
          - type: map_at_10
            value: 32.799
          - type: map_at_100
            value: 34.508
          - type: map_at_1000
            value: 34.719
          - type: map_at_3
            value: 29.947000000000003
          - type: map_at_5
            value: 31.584
          - type: mrr_at_1
            value: 30.237000000000002
          - type: mrr_at_10
            value: 37.651
          - type: mrr_at_100
            value: 38.805
          - type: mrr_at_1000
            value: 38.851
          - type: mrr_at_3
            value: 35.046
          - type: mrr_at_5
            value: 36.548
          - type: ndcg_at_1
            value: 30.237000000000002
          - type: ndcg_at_10
            value: 38.356
          - type: ndcg_at_100
            value: 44.906
          - type: ndcg_at_1000
            value: 47.299
          - type: ndcg_at_3
            value: 33.717999999999996
          - type: ndcg_at_5
            value: 35.946
          - type: precision_at_1
            value: 30.237000000000002
          - type: precision_at_10
            value: 7.292
          - type: precision_at_100
            value: 1.496
          - type: precision_at_1000
            value: 0.23600000000000002
          - type: precision_at_3
            value: 15.547
          - type: precision_at_5
            value: 11.344
          - type: recall_at_1
            value: 24.979000000000003
          - type: recall_at_10
            value: 48.624
          - type: recall_at_100
            value: 77.932
          - type: recall_at_1000
            value: 92.66499999999999
          - type: recall_at_3
            value: 35.217
          - type: recall_at_5
            value: 41.394
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.566
          - type: map_at_10
            value: 30.945
          - type: map_at_100
            value: 31.759999999999998
          - type: map_at_1000
            value: 31.855
          - type: map_at_3
            value: 28.64
          - type: map_at_5
            value: 29.787000000000003
          - type: mrr_at_1
            value: 24.954
          - type: mrr_at_10
            value: 33.311
          - type: mrr_at_100
            value: 34.050000000000004
          - type: mrr_at_1000
            value: 34.117999999999995
          - type: mrr_at_3
            value: 31.238
          - type: mrr_at_5
            value: 32.329
          - type: ndcg_at_1
            value: 24.954
          - type: ndcg_at_10
            value: 35.676
          - type: ndcg_at_100
            value: 39.931
          - type: ndcg_at_1000
            value: 42.43
          - type: ndcg_at_3
            value: 31.365
          - type: ndcg_at_5
            value: 33.184999999999995
          - type: precision_at_1
            value: 24.954
          - type: precision_at_10
            value: 5.564
          - type: precision_at_100
            value: 0.826
          - type: precision_at_1000
            value: 0.116
          - type: precision_at_3
            value: 13.555
          - type: precision_at_5
            value: 9.168
          - type: recall_at_1
            value: 22.566
          - type: recall_at_10
            value: 47.922
          - type: recall_at_100
            value: 67.931
          - type: recall_at_1000
            value: 86.653
          - type: recall_at_3
            value: 36.103
          - type: recall_at_5
            value: 40.699000000000005
      - task:
          type: Retrieval
        dataset:
          type: climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 16.950000000000003
          - type: map_at_10
            value: 28.612
          - type: map_at_100
            value: 30.476999999999997
          - type: map_at_1000
            value: 30.674
          - type: map_at_3
            value: 24.262
          - type: map_at_5
            value: 26.554
          - type: mrr_at_1
            value: 38.241
          - type: mrr_at_10
            value: 50.43
          - type: mrr_at_100
            value: 51.059
          - type: mrr_at_1000
            value: 51.090999999999994
          - type: mrr_at_3
            value: 47.514
          - type: mrr_at_5
            value: 49.246
          - type: ndcg_at_1
            value: 38.241
          - type: ndcg_at_10
            value: 38.218
          - type: ndcg_at_100
            value: 45.003
          - type: ndcg_at_1000
            value: 48.269
          - type: ndcg_at_3
            value: 32.568000000000005
          - type: ndcg_at_5
            value: 34.400999999999996
          - type: precision_at_1
            value: 38.241
          - type: precision_at_10
            value: 11.674
          - type: precision_at_100
            value: 1.913
          - type: precision_at_1000
            value: 0.252
          - type: precision_at_3
            value: 24.387
          - type: precision_at_5
            value: 18.163
          - type: recall_at_1
            value: 16.950000000000003
          - type: recall_at_10
            value: 43.769000000000005
          - type: recall_at_100
            value: 66.875
          - type: recall_at_1000
            value: 84.92699999999999
          - type: recall_at_3
            value: 29.353
          - type: recall_at_5
            value: 35.467
      - task:
          type: Retrieval
        dataset:
          type: dbpedia-entity
          name: MTEB DBPedia
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 9.276
          - type: map_at_10
            value: 20.848
          - type: map_at_100
            value: 29.804000000000002
          - type: map_at_1000
            value: 31.398
          - type: map_at_3
            value: 14.886
          - type: map_at_5
            value: 17.516000000000002
          - type: mrr_at_1
            value: 71
          - type: mrr_at_10
            value: 78.724
          - type: mrr_at_100
            value: 78.976
          - type: mrr_at_1000
            value: 78.986
          - type: mrr_at_3
            value: 77.333
          - type: mrr_at_5
            value: 78.021
          - type: ndcg_at_1
            value: 57.875
          - type: ndcg_at_10
            value: 43.855
          - type: ndcg_at_100
            value: 48.99
          - type: ndcg_at_1000
            value: 56.141
          - type: ndcg_at_3
            value: 48.914
          - type: ndcg_at_5
            value: 45.961
          - type: precision_at_1
            value: 71
          - type: precision_at_10
            value: 34.575
          - type: precision_at_100
            value: 11.182
          - type: precision_at_1000
            value: 2.044
          - type: precision_at_3
            value: 52.5
          - type: precision_at_5
            value: 44.2
          - type: recall_at_1
            value: 9.276
          - type: recall_at_10
            value: 26.501
          - type: recall_at_100
            value: 55.72899999999999
          - type: recall_at_1000
            value: 78.532
          - type: recall_at_3
            value: 16.365
          - type: recall_at_5
            value: 20.154
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 52.71
          - type: f1
            value: 47.74801556489574
      - task:
          type: Retrieval
        dataset:
          type: fever
          name: MTEB FEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 73.405
          - type: map_at_10
            value: 82.822
          - type: map_at_100
            value: 83.042
          - type: map_at_1000
            value: 83.055
          - type: map_at_3
            value: 81.65299999999999
          - type: map_at_5
            value: 82.431
          - type: mrr_at_1
            value: 79.178
          - type: mrr_at_10
            value: 87.02
          - type: mrr_at_100
            value: 87.095
          - type: mrr_at_1000
            value: 87.09700000000001
          - type: mrr_at_3
            value: 86.309
          - type: mrr_at_5
            value: 86.824
          - type: ndcg_at_1
            value: 79.178
          - type: ndcg_at_10
            value: 86.72
          - type: ndcg_at_100
            value: 87.457
          - type: ndcg_at_1000
            value: 87.691
          - type: ndcg_at_3
            value: 84.974
          - type: ndcg_at_5
            value: 86.032
          - type: precision_at_1
            value: 79.178
          - type: precision_at_10
            value: 10.548
          - type: precision_at_100
            value: 1.113
          - type: precision_at_1000
            value: 0.11499999999999999
          - type: precision_at_3
            value: 32.848
          - type: precision_at_5
            value: 20.45
          - type: recall_at_1
            value: 73.405
          - type: recall_at_10
            value: 94.39699999999999
          - type: recall_at_100
            value: 97.219
          - type: recall_at_1000
            value: 98.675
          - type: recall_at_3
            value: 89.679
          - type: recall_at_5
            value: 92.392
      - task:
          type: Retrieval
        dataset:
          type: fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.651
          - type: map_at_10
            value: 36.886
          - type: map_at_100
            value: 38.811
          - type: map_at_1000
            value: 38.981
          - type: map_at_3
            value: 32.538
          - type: map_at_5
            value: 34.763
          - type: mrr_at_1
            value: 44.444
          - type: mrr_at_10
            value: 53.168000000000006
          - type: mrr_at_100
            value: 53.839000000000006
          - type: mrr_at_1000
            value: 53.869
          - type: mrr_at_3
            value: 50.54
          - type: mrr_at_5
            value: 52.068000000000005
          - type: ndcg_at_1
            value: 44.444
          - type: ndcg_at_10
            value: 44.994
          - type: ndcg_at_100
            value: 51.599
          - type: ndcg_at_1000
            value: 54.339999999999996
          - type: ndcg_at_3
            value: 41.372
          - type: ndcg_at_5
            value: 42.149
          - type: precision_at_1
            value: 44.444
          - type: precision_at_10
            value: 12.407
          - type: precision_at_100
            value: 1.9269999999999998
          - type: precision_at_1000
            value: 0.242
          - type: precision_at_3
            value: 27.726
          - type: precision_at_5
            value: 19.814999999999998
          - type: recall_at_1
            value: 22.651
          - type: recall_at_10
            value: 52.075
          - type: recall_at_100
            value: 76.51400000000001
          - type: recall_at_1000
            value: 92.852
          - type: recall_at_3
            value: 37.236000000000004
          - type: recall_at_5
            value: 43.175999999999995
      - task:
          type: Retrieval
        dataset:
          type: hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 40.777
          - type: map_at_10
            value: 66.79899999999999
          - type: map_at_100
            value: 67.65299999999999
          - type: map_at_1000
            value: 67.706
          - type: map_at_3
            value: 63.352
          - type: map_at_5
            value: 65.52900000000001
          - type: mrr_at_1
            value: 81.553
          - type: mrr_at_10
            value: 86.983
          - type: mrr_at_100
            value: 87.132
          - type: mrr_at_1000
            value: 87.136
          - type: mrr_at_3
            value: 86.156
          - type: mrr_at_5
            value: 86.726
          - type: ndcg_at_1
            value: 81.553
          - type: ndcg_at_10
            value: 74.64
          - type: ndcg_at_100
            value: 77.459
          - type: ndcg_at_1000
            value: 78.43
          - type: ndcg_at_3
            value: 69.878
          - type: ndcg_at_5
            value: 72.59400000000001
          - type: precision_at_1
            value: 81.553
          - type: precision_at_10
            value: 15.654000000000002
          - type: precision_at_100
            value: 1.783
          - type: precision_at_1000
            value: 0.191
          - type: precision_at_3
            value: 45.199
          - type: precision_at_5
            value: 29.267
          - type: recall_at_1
            value: 40.777
          - type: recall_at_10
            value: 78.271
          - type: recall_at_100
            value: 89.129
          - type: recall_at_1000
            value: 95.49
          - type: recall_at_3
            value: 67.79899999999999
          - type: recall_at_5
            value: 73.167
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 93.5064
          - type: ap
            value: 90.25495114444111
          - type: f1
            value: 93.5012434973381
      - task:
          type: Retrieval
        dataset:
          type: msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 23.301
          - type: map_at_10
            value: 35.657
          - type: map_at_100
            value: 36.797000000000004
          - type: map_at_1000
            value: 36.844
          - type: map_at_3
            value: 31.743
          - type: map_at_5
            value: 34.003
          - type: mrr_at_1
            value: 23.854
          - type: mrr_at_10
            value: 36.242999999999995
          - type: mrr_at_100
            value: 37.32
          - type: mrr_at_1000
            value: 37.361
          - type: mrr_at_3
            value: 32.4
          - type: mrr_at_5
            value: 34.634
          - type: ndcg_at_1
            value: 23.868000000000002
          - type: ndcg_at_10
            value: 42.589
          - type: ndcg_at_100
            value: 48.031
          - type: ndcg_at_1000
            value: 49.189
          - type: ndcg_at_3
            value: 34.649
          - type: ndcg_at_5
            value: 38.676
          - type: precision_at_1
            value: 23.868000000000002
          - type: precision_at_10
            value: 6.6850000000000005
          - type: precision_at_100
            value: 0.9400000000000001
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 14.651
          - type: precision_at_5
            value: 10.834000000000001
          - type: recall_at_1
            value: 23.301
          - type: recall_at_10
            value: 63.88700000000001
          - type: recall_at_100
            value: 88.947
          - type: recall_at_1000
            value: 97.783
          - type: recall_at_3
            value: 42.393
          - type: recall_at_5
            value: 52.036
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 94.64888280893753
          - type: f1
            value: 94.41310774203512
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 79.72184222526221
          - type: f1
            value: 61.522034067350106
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 79.60659045057163
          - type: f1
            value: 77.268649687049
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 81.83254875588432
          - type: f1
            value: 81.61520635919082
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 36.31529875009507
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 31.734233714415073
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 30.994501713009452
          - type: mrr
            value: 32.13512850703073
      - task:
          type: Retrieval
        dataset:
          type: nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 6.603000000000001
          - type: map_at_10
            value: 13.767999999999999
          - type: map_at_100
            value: 17.197000000000003
          - type: map_at_1000
            value: 18.615000000000002
          - type: map_at_3
            value: 10.567
          - type: map_at_5
            value: 12.078999999999999
          - type: mrr_at_1
            value: 44.891999999999996
          - type: mrr_at_10
            value: 53.75299999999999
          - type: mrr_at_100
            value: 54.35
          - type: mrr_at_1000
            value: 54.388000000000005
          - type: mrr_at_3
            value: 51.495999999999995
          - type: mrr_at_5
            value: 52.688
          - type: ndcg_at_1
            value: 43.189
          - type: ndcg_at_10
            value: 34.567
          - type: ndcg_at_100
            value: 32.273
          - type: ndcg_at_1000
            value: 41.321999999999996
          - type: ndcg_at_3
            value: 40.171
          - type: ndcg_at_5
            value: 37.502
          - type: precision_at_1
            value: 44.582
          - type: precision_at_10
            value: 25.139
          - type: precision_at_100
            value: 7.739999999999999
          - type: precision_at_1000
            value: 2.054
          - type: precision_at_3
            value: 37.152
          - type: precision_at_5
            value: 31.826999999999998
          - type: recall_at_1
            value: 6.603000000000001
          - type: recall_at_10
            value: 17.023
          - type: recall_at_100
            value: 32.914
          - type: recall_at_1000
            value: 64.44800000000001
          - type: recall_at_3
            value: 11.457
          - type: recall_at_5
            value: 13.816
      - task:
          type: Retrieval
        dataset:
          type: nq
          name: MTEB NQ
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 30.026000000000003
          - type: map_at_10
            value: 45.429
          - type: map_at_100
            value: 46.45
          - type: map_at_1000
            value: 46.478
          - type: map_at_3
            value: 41.147
          - type: map_at_5
            value: 43.627
          - type: mrr_at_1
            value: 33.951
          - type: mrr_at_10
            value: 47.953
          - type: mrr_at_100
            value: 48.731
          - type: mrr_at_1000
            value: 48.751
          - type: mrr_at_3
            value: 44.39
          - type: mrr_at_5
            value: 46.533
          - type: ndcg_at_1
            value: 33.951
          - type: ndcg_at_10
            value: 53.24100000000001
          - type: ndcg_at_100
            value: 57.599999999999994
          - type: ndcg_at_1000
            value: 58.270999999999994
          - type: ndcg_at_3
            value: 45.190999999999995
          - type: ndcg_at_5
            value: 49.339
          - type: precision_at_1
            value: 33.951
          - type: precision_at_10
            value: 8.856
          - type: precision_at_100
            value: 1.133
          - type: precision_at_1000
            value: 0.12
          - type: precision_at_3
            value: 20.713
          - type: precision_at_5
            value: 14.838000000000001
          - type: recall_at_1
            value: 30.026000000000003
          - type: recall_at_10
            value: 74.512
          - type: recall_at_100
            value: 93.395
          - type: recall_at_1000
            value: 98.402
          - type: recall_at_3
            value: 53.677
          - type: recall_at_5
            value: 63.198
      - task:
          type: Retrieval
        dataset:
          type: quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 71.41300000000001
          - type: map_at_10
            value: 85.387
          - type: map_at_100
            value: 86.027
          - type: map_at_1000
            value: 86.041
          - type: map_at_3
            value: 82.543
          - type: map_at_5
            value: 84.304
          - type: mrr_at_1
            value: 82.35
          - type: mrr_at_10
            value: 88.248
          - type: mrr_at_100
            value: 88.348
          - type: mrr_at_1000
            value: 88.349
          - type: mrr_at_3
            value: 87.348
          - type: mrr_at_5
            value: 87.96300000000001
          - type: ndcg_at_1
            value: 82.37
          - type: ndcg_at_10
            value: 88.98
          - type: ndcg_at_100
            value: 90.16499999999999
          - type: ndcg_at_1000
            value: 90.239
          - type: ndcg_at_3
            value: 86.34100000000001
          - type: ndcg_at_5
            value: 87.761
          - type: precision_at_1
            value: 82.37
          - type: precision_at_10
            value: 13.471
          - type: precision_at_100
            value: 1.534
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 37.827
          - type: precision_at_5
            value: 24.773999999999997
          - type: recall_at_1
            value: 71.41300000000001
          - type: recall_at_10
            value: 95.748
          - type: recall_at_100
            value: 99.69200000000001
          - type: recall_at_1000
            value: 99.98
          - type: recall_at_3
            value: 87.996
          - type: recall_at_5
            value: 92.142
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 56.96878497780007
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 65.31371347128074
      - task:
          type: Retrieval
        dataset:
          type: scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 5.287
          - type: map_at_10
            value: 13.530000000000001
          - type: map_at_100
            value: 15.891
          - type: map_at_1000
            value: 16.245
          - type: map_at_3
            value: 9.612
          - type: map_at_5
            value: 11.672
          - type: mrr_at_1
            value: 26
          - type: mrr_at_10
            value: 37.335
          - type: mrr_at_100
            value: 38.443
          - type: mrr_at_1000
            value: 38.486
          - type: mrr_at_3
            value: 33.783
          - type: mrr_at_5
            value: 36.028
          - type: ndcg_at_1
            value: 26
          - type: ndcg_at_10
            value: 22.215
          - type: ndcg_at_100
            value: 31.101
          - type: ndcg_at_1000
            value: 36.809
          - type: ndcg_at_3
            value: 21.104
          - type: ndcg_at_5
            value: 18.759999999999998
          - type: precision_at_1
            value: 26
          - type: precision_at_10
            value: 11.43
          - type: precision_at_100
            value: 2.424
          - type: precision_at_1000
            value: 0.379
          - type: precision_at_3
            value: 19.7
          - type: precision_at_5
            value: 16.619999999999997
          - type: recall_at_1
            value: 5.287
          - type: recall_at_10
            value: 23.18
          - type: recall_at_100
            value: 49.208
          - type: recall_at_1000
            value: 76.85300000000001
          - type: recall_at_3
            value: 11.991999999999999
          - type: recall_at_5
            value: 16.85
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 83.87834913790886
          - type: cos_sim_spearman
            value: 81.04583513112122
          - type: euclidean_pearson
            value: 81.20484174558065
          - type: euclidean_spearman
            value: 80.76430832561769
          - type: manhattan_pearson
            value: 81.21416730978615
          - type: manhattan_spearman
            value: 80.7797637394211
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 86.56143998865157
          - type: cos_sim_spearman
            value: 79.75387012744471
          - type: euclidean_pearson
            value: 83.7877519997019
          - type: euclidean_spearman
            value: 79.90489748003296
          - type: manhattan_pearson
            value: 83.7540590666095
          - type: manhattan_spearman
            value: 79.86434577931573
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 83.92102564177941
          - type: cos_sim_spearman
            value: 84.98234585939103
          - type: euclidean_pearson
            value: 84.47729567593696
          - type: euclidean_spearman
            value: 85.09490696194469
          - type: manhattan_pearson
            value: 84.38622951588229
          - type: manhattan_spearman
            value: 85.02507171545574
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 80.1891164763377
          - type: cos_sim_spearman
            value: 80.7997969966883
          - type: euclidean_pearson
            value: 80.48572256162396
          - type: euclidean_spearman
            value: 80.57851903536378
          - type: manhattan_pearson
            value: 80.4324819433651
          - type: manhattan_spearman
            value: 80.5074526239062
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 82.64319975116025
          - type: cos_sim_spearman
            value: 84.88671197763652
          - type: euclidean_pearson
            value: 84.74692193293231
          - type: euclidean_spearman
            value: 85.27151722073653
          - type: manhattan_pearson
            value: 84.72460516785438
          - type: manhattan_spearman
            value: 85.26518899786687
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 83.24687565822381
          - type: cos_sim_spearman
            value: 85.60418454111263
          - type: euclidean_pearson
            value: 84.85829740169851
          - type: euclidean_spearman
            value: 85.66378014138306
          - type: manhattan_pearson
            value: 84.84672408808835
          - type: manhattan_spearman
            value: 85.63331924364891
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-en)
          config: en-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 84.87758895415485
          - type: cos_sim_spearman
            value: 85.8193745617297
          - type: euclidean_pearson
            value: 85.78719118848134
          - type: euclidean_spearman
            value: 84.35797575385688
          - type: manhattan_pearson
            value: 85.97919844815692
          - type: manhattan_spearman
            value: 84.58334745175151
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 67.27076035963599
          - type: cos_sim_spearman
            value: 67.21433656439973
          - type: euclidean_pearson
            value: 68.07434078679324
          - type: euclidean_spearman
            value: 66.0249731719049
          - type: manhattan_pearson
            value: 67.95495198947476
          - type: manhattan_spearman
            value: 65.99893908331886
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 82.22437747056817
          - type: cos_sim_spearman
            value: 85.0995685206174
          - type: euclidean_pearson
            value: 84.08616925603394
          - type: euclidean_spearman
            value: 84.89633925691658
          - type: manhattan_pearson
            value: 84.08332675923133
          - type: manhattan_spearman
            value: 84.8858228112915
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 87.6909022589666
          - type: mrr
            value: 96.43341952165481
      - task:
          type: Retrieval
        dataset:
          type: scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 57.660999999999994
          - type: map_at_10
            value: 67.625
          - type: map_at_100
            value: 68.07600000000001
          - type: map_at_1000
            value: 68.10199999999999
          - type: map_at_3
            value: 64.50399999999999
          - type: map_at_5
            value: 66.281
          - type: mrr_at_1
            value: 61
          - type: mrr_at_10
            value: 68.953
          - type: mrr_at_100
            value: 69.327
          - type: mrr_at_1000
            value: 69.352
          - type: mrr_at_3
            value: 66.833
          - type: mrr_at_5
            value: 68.05
          - type: ndcg_at_1
            value: 61
          - type: ndcg_at_10
            value: 72.369
          - type: ndcg_at_100
            value: 74.237
          - type: ndcg_at_1000
            value: 74.939
          - type: ndcg_at_3
            value: 67.284
          - type: ndcg_at_5
            value: 69.72500000000001
          - type: precision_at_1
            value: 61
          - type: precision_at_10
            value: 9.733
          - type: precision_at_100
            value: 1.0670000000000002
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: precision_at_3
            value: 26.222
          - type: precision_at_5
            value: 17.4
          - type: recall_at_1
            value: 57.660999999999994
          - type: recall_at_10
            value: 85.656
          - type: recall_at_100
            value: 93.833
          - type: recall_at_1000
            value: 99.333
          - type: recall_at_3
            value: 71.961
          - type: recall_at_5
            value: 78.094
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.86930693069307
          - type: cos_sim_ap
            value: 96.76685487950894
          - type: cos_sim_f1
            value: 93.44587884806354
          - type: cos_sim_precision
            value: 92.80078895463511
          - type: cos_sim_recall
            value: 94.1
          - type: dot_accuracy
            value: 99.54356435643564
          - type: dot_ap
            value: 81.18659960405607
          - type: dot_f1
            value: 75.78008915304605
          - type: dot_precision
            value: 75.07360157016683
          - type: dot_recall
            value: 76.5
          - type: euclidean_accuracy
            value: 99.87326732673267
          - type: euclidean_ap
            value: 96.8102411908941
          - type: euclidean_f1
            value: 93.6127744510978
          - type: euclidean_precision
            value: 93.42629482071713
          - type: euclidean_recall
            value: 93.8
          - type: manhattan_accuracy
            value: 99.87425742574257
          - type: manhattan_ap
            value: 96.82857341435529
          - type: manhattan_f1
            value: 93.62129583124059
          - type: manhattan_precision
            value: 94.04641775983855
          - type: manhattan_recall
            value: 93.2
          - type: max_accuracy
            value: 99.87425742574257
          - type: max_ap
            value: 96.82857341435529
          - type: max_f1
            value: 93.62129583124059
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 65.92560972698926
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 34.92797240259008
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 55.244624045597654
          - type: mrr
            value: 56.185303666921314
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 31.02491987312937
          - type: cos_sim_spearman
            value: 32.055592206679734
          - type: dot_pearson
            value: 24.731627575422557
          - type: dot_spearman
            value: 24.308029077069733
      - task:
          type: Retrieval
        dataset:
          type: trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.231
          - type: map_at_10
            value: 1.899
          - type: map_at_100
            value: 9.498
          - type: map_at_1000
            value: 20.979999999999997
          - type: map_at_3
            value: 0.652
          - type: map_at_5
            value: 1.069
          - type: mrr_at_1
            value: 88
          - type: mrr_at_10
            value: 93.4
          - type: mrr_at_100
            value: 93.4
          - type: mrr_at_1000
            value: 93.4
          - type: mrr_at_3
            value: 93
          - type: mrr_at_5
            value: 93.4
          - type: ndcg_at_1
            value: 86
          - type: ndcg_at_10
            value: 75.375
          - type: ndcg_at_100
            value: 52.891999999999996
          - type: ndcg_at_1000
            value: 44.952999999999996
          - type: ndcg_at_3
            value: 81.05
          - type: ndcg_at_5
            value: 80.175
          - type: precision_at_1
            value: 88
          - type: precision_at_10
            value: 79
          - type: precision_at_100
            value: 53.16
          - type: precision_at_1000
            value: 19.408
          - type: precision_at_3
            value: 85.333
          - type: precision_at_5
            value: 84
          - type: recall_at_1
            value: 0.231
          - type: recall_at_10
            value: 2.078
          - type: recall_at_100
            value: 12.601
          - type: recall_at_1000
            value: 41.296
          - type: recall_at_3
            value: 0.6779999999999999
          - type: recall_at_5
            value: 1.1360000000000001
      - task:
          type: Retrieval
        dataset:
          type: webis-touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 2.782
          - type: map_at_10
            value: 10.204
          - type: map_at_100
            value: 16.176
          - type: map_at_1000
            value: 17.456
          - type: map_at_3
            value: 5.354
          - type: map_at_5
            value: 7.503
          - type: mrr_at_1
            value: 40.816
          - type: mrr_at_10
            value: 54.010000000000005
          - type: mrr_at_100
            value: 54.49
          - type: mrr_at_1000
            value: 54.49
          - type: mrr_at_3
            value: 48.980000000000004
          - type: mrr_at_5
            value: 51.735
          - type: ndcg_at_1
            value: 36.735
          - type: ndcg_at_10
            value: 26.61
          - type: ndcg_at_100
            value: 36.967
          - type: ndcg_at_1000
            value: 47.274
          - type: ndcg_at_3
            value: 30.363
          - type: ndcg_at_5
            value: 29.448999999999998
          - type: precision_at_1
            value: 40.816
          - type: precision_at_10
            value: 23.878
          - type: precision_at_100
            value: 7.693999999999999
          - type: precision_at_1000
            value: 1.4489999999999998
          - type: precision_at_3
            value: 31.293
          - type: precision_at_5
            value: 29.796
          - type: recall_at_1
            value: 2.782
          - type: recall_at_10
            value: 16.485
          - type: recall_at_100
            value: 46.924
          - type: recall_at_1000
            value: 79.365
          - type: recall_at_3
            value: 6.52
          - type: recall_at_5
            value: 10.48
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 70.08300000000001
          - type: ap
            value: 13.91559884590195
          - type: f1
            value: 53.956838444291364
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 59.34069043576683
          - type: f1
            value: 59.662041994618406
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 53.70780611078653
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 87.10734934732073
          - type: cos_sim_ap
            value: 77.58349999516054
          - type: cos_sim_f1
            value: 70.25391395868965
          - type: cos_sim_precision
            value: 70.06035161374967
          - type: cos_sim_recall
            value: 70.44854881266491
          - type: dot_accuracy
            value: 80.60439887941826
          - type: dot_ap
            value: 54.52935200483575
          - type: dot_f1
            value: 54.170444242973716
          - type: dot_precision
            value: 47.47715534366309
          - type: dot_recall
            value: 63.06068601583114
          - type: euclidean_accuracy
            value: 87.26828396018358
          - type: euclidean_ap
            value: 78.00158454104036
          - type: euclidean_f1
            value: 70.70292457670601
          - type: euclidean_precision
            value: 68.79680479281079
          - type: euclidean_recall
            value: 72.71767810026385
          - type: manhattan_accuracy
            value: 87.11330988853788
          - type: manhattan_ap
            value: 77.92527099601855
          - type: manhattan_f1
            value: 70.76488706365502
          - type: manhattan_precision
            value: 68.89055472263868
          - type: manhattan_recall
            value: 72.74406332453826
          - type: max_accuracy
            value: 87.26828396018358
          - type: max_ap
            value: 78.00158454104036
          - type: max_f1
            value: 70.76488706365502
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 87.80804905499282
          - type: cos_sim_ap
            value: 83.06187782630936
          - type: cos_sim_f1
            value: 74.99716435403985
          - type: cos_sim_precision
            value: 73.67951860931579
          - type: cos_sim_recall
            value: 76.36279642747151
          - type: dot_accuracy
            value: 81.83141227151008
          - type: dot_ap
            value: 67.18241090841795
          - type: dot_f1
            value: 62.216037571751606
          - type: dot_precision
            value: 56.749381227391005
          - type: dot_recall
            value: 68.84816753926701
          - type: euclidean_accuracy
            value: 87.91671517832887
          - type: euclidean_ap
            value: 83.56538942001427
          - type: euclidean_f1
            value: 75.7327253337256
          - type: euclidean_precision
            value: 72.48856036606828
          - type: euclidean_recall
            value: 79.28087465352634
          - type: manhattan_accuracy
            value: 87.86626304963713
          - type: manhattan_ap
            value: 83.52939841172832
          - type: manhattan_f1
            value: 75.73635656329888
          - type: manhattan_precision
            value: 72.99150182103836
          - type: manhattan_recall
            value: 78.69571912534647
          - type: max_accuracy
            value: 87.91671517832887
          - type: max_ap
            value: 83.56538942001427
          - type: max_f1
            value: 75.73635656329888
license: mit
language:
  - en
pipeline_tag: sentence-similarity

FlagEmbedding

Model List | Usage | Evaluation | Train | Contact | License

More details please refer to our Github: FlagEmbedding.

English | 中文

FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector database for LLMs.

************* 🌟Updates🌟 *************

  • 08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is avaliable.
  • 08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗
  • 08/02/2023: Release bge-large-*(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark!
  • 08/01/2023: We release the Chinese Massive Text Embedding Benchmark (C-MTEB), consisting of 31 test dataset.

Model List

bge is short for BAAI general embedding.

Model Language Description query instruction for retrieval*
BAAI/bge-large-en English rank 1st in MTEB leaderboard Represent this sentence for searching relevant passages:
BAAI/bge-base-en English rank 2nd in MTEB leaderboard Represent this sentence for searching relevant passages:
BAAI/bge-small-en English a small-scale model but with competitive performance Represent this sentence for searching relevant passages:
BAAI/bge-large-zh Chinese rank 1st in C-MTEB benchmark 为这个句子生成表示以用于检索相关文章:
BAAI/bge-large-zh-noinstruct Chinese This model is trained without instruction, and rank 2nd in C-MTEB benchmark
BAAI/bge-base-zh Chinese a base-scale model but has similar ability with bge-large-zh 为这个句子生成表示以用于检索相关文章:
BAAI/bge-small-zh Chinese a small-scale model but with competitive performance 为这个句子生成表示以用于检索相关文章:

*: If you need to search the long relevant passages to a short query (s2p retrieval task), you need to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, no instruction need to be added to passages.

Usage

Here are some examples to use bge models with FlagEmbedding, Sentence-Transformers, Langchain, or Huggingface Transformers.

Using FlagEmbedding

pip install -U FlagEmbedding

If it doesn't work for you, you can see FlagEmbedding for more methods to install FlagEmbedding.

from FlagEmbedding import FlagModel
sentences = ["样例数据-1", "样例数据-2"]
model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章:")
embeddings_1 = model.encode(sentences)
embeddings_2 = model.encode(sentences)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

# for s2p(short query to long passage) retrieval task, please use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T

The value of argument query_instruction_for_retrieval see Model List.

FlagModel will use all available GPUs when encoding, please set os.environ["CUDA_VISIBLE_DEVICES"] to choose GPU.

Using Sentence-Transformers

Using this model also is easy when you have sentence-transformers installed:

pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
sentences = ["样例数据-1", "样例数据-2"]
model = SentenceTransformer('BAAI/bge-large-zh')
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see Model List). But the instruction is not needed for passages.

from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章:"

model = SentenceTransformer('BAAI/bge-large-zh')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T

Using Langchain

You can use bge in langchain like this:

from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-small-en"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model_norm = HuggingFaceBgeEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

Using HuggingFace Transformers

With transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of first token (i.e., [CLS]) as the sentence embedding.

from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh')
model = AutoModel.from_pretrained('BAAI/bge-large-zh')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
    # Perform pooling. In this case, cls pooling.
    sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)

Evaluation

baai-general-embedding models achieve state-of-the-art performance on both MTEB and C-MTEB leaderboard! More details and evaluation tools see our scripts.

  • MTEB:
Model Name Dimension Sequence Length Average (56) Retrieval (15) Clustering (11) Pair Classification (3) Reranking (4) STS (10) Summarization (1) Classification (12)
bge-large-en 1024 512 63.98 53.9 46.98 85.8 59.48 81.56 32.06 76.21
bge-base-en 768 512 63.36 53.0 46.32 85.86 58.7 81.84 29.27 75.27
gte-large 1024 512 63.13 52.22 46.84 85.00 59.13 83.35 31.66 73.33
gte-base 768 512 62.39 51.14 46.2 84.57 58.61 82.3 31.17 73.01
e5-large-v2 1024 512 62.25 50.56 44.49 86.03 56.61 82.05 30.19 75.24
bge-small-en 384 512 62.11 51.82 44.31 83.78 57.97 80.72 30.53 74.37
instructor-xl 768 512 61.79 49.26 44.74 86.62 57.29 83.06 32.32 61.79
e5-base-v2 768 512 61.5 50.29 43.80 85.73 55.91 81.05 30.28 73.84
gte-small 384 512 61.36 49.46 44.89 83.54 57.7 82.07 30.42 72.31
text-embedding-ada-002 1536 8192 60.99 49.25 45.9 84.89 56.32 80.97 30.8 70.93
e5-small-v2 384 512 59.93 49.04 39.92 84.67 54.32 80.39 31.16 72.94
sentence-t5-xxl 768 512 59.51 42.24 43.72 85.06 56.42 82.63 30.08 73.42
all-mpnet-base-v2 768 514 57.78 43.81 43.69 83.04 59.36 80.28 27.49 65.07
sgpt-bloom-7b1-msmarco 4096 2048 57.59 48.22 38.93 81.9 55.65 77.74 33.6 66.19
all-MiniLM-L12-v2 384 512 56.53 42.69 41.81 82.41 58.44 79.8 27.9 63.21
all-MiniLM-L6-v2 384 512 56.26 41.95 42.35 82.37 58.04 78.9 30.81 63.05
contriever-base-msmarco 768 512 56.00 41.88 41.1 82.54 53.14 76.51 30.36 66.68
sentence-t5-base 768 512 55.27 33.63 40.21 85.18 53.09 81.14 31.39 69.81
  • C-MTEB:
    We create a benchmark C-MTEB for chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to C_MTEB for a detailed introduction.
Model Embedding dimension Avg Retrieval STS PairClassification Classification Reranking Clustering
bge-large-zh 1024 64.20 71.53 53.23 78.94 72.26 65.11 48.39
bge-large-zh-noinstruct 1024 63.53 70.55 50.98 76.77 72.49 64.91 50.01
BAAI/bge-base-zh 768 62.96 69.53 52.05 77.5 70.98 64.91 47.63
BAAI/bge-small-zh 512 58.27 63.07 46.87 70.35 67.78 61.48 45.09
m3e-base 768 57.10 56.91 48.15 63.99 70.28 59.34 47.68
m3e-large 1024 57.05 54.75 48.64 64.3 71.22 59.66 48.88
text-embedding-ada-002(OpenAI) 1536 53.02 52.0 40.61 69.56 67.38 54.28 45.68
luotuo 1024 49.37 44.4 39.41 66.62 65.29 49.25 44.39
text2vec 768 47.63 38.79 41.71 67.41 65.18 49.45 37.66
text2vec-large 1024 47.36 41.94 41.98 70.86 63.42 49.16 30.02

Train

This section will introduce the way we used to train the general embedding. The training scripts are in FlagEmbedding, and we provide some examples to do pre-train and fine-tune.

1. RetroMAE Pre-train
We pre-train the model following the method retromae, which shows promising improvement in retrieval task (paper). The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively. We used the AdamW optimizer and the learning rate is 2e-5.

Pre-training data:

2. Finetune
We fine-tune the model using a contrastive objective. The format of input data is a triple(query, positive, negative). Besides the negative in the triple, we also adopt in-batch negatives strategy. We employ the cross-device negatives sharing method to share negatives among different GPUs, which can dramatically increase the number of negatives.

We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are 65,535 negatives for each query in a batch). We used the AdamW optimizer and the learning rate is 1e-5. The temperature for contrastive loss is 0.01.

Besides, we add instruction to the query for s2p(short query to long passage) retrieval task in the training (add nothing to passages). For English, the instruction is Represent this sentence for searching relevant passages: ; For Chinese, the instruction is 为这个句子生成表示以用于检索相关文章:. In the evaluation, the instruction should be added for queries in retrieval task, not be added for other tasks. Noted that the instruction is not needed for passages.

The finetune script is accessible in this repository: FlagEmbedding. You can easily finetune your model with it.

Training data:

  • For English, we collect 230M text pairs from wikipedia, cc-net, and so on.

  • For chinese, we collect 120M text pairs from wudao, simclue and so on.

The data collection is to be released in the future.

We will continually update the embedding models and training codes, hoping to promote the development of the embedding model community.

License

FlagEmbedding is licensed under MIT License. The released models can be used for commercial purposes free of charge.