transformers pytorch safetensors bert feature-extraction mteb sentence-transfomres en arxiv:2310.07554 arxiv:2309.07597 license:mit model-index text-embeddings-inference endpoints_compatible region:us
---
language:
- en
license: mit
tags:
- mteb
- sentence-transfomres
- transformers
model-index:
- name: bge-large-en
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      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:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      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:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.158
    - type: f1
      value: 45.78935185074774
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: 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:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 48.94652870403906
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 43.17257160340209
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 63.97867370559182
    - type: mrr
      value: 77.00820032537484
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      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:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 88.00000000000001
    - type: f1
      value: 87.99039027511853
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 41.36932844640705
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 38.34983239611985
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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:
      name: MTEB ClimateFEVER
      type: climate-fever
      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:
      name: MTEB DBPedia
      type: dbpedia-entity
      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:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 52.71
    - type: f1
      value: 47.74801556489574
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: 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:
      name: MTEB FiQA2018
      type: fiqa
      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:
      name: MTEB HotpotQA
      type: 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:
      name: MTEB ImdbClassification
      type: mteb/imdb
      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:
      name: MTEB MSMARCO
      type: 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:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 94.64888280893753
    - type: f1
      value: 94.41310774203512
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 79.72184222526221
    - type: f1
      value: 61.522034067350106
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 79.60659045057163
    - type: f1
      value: 77.268649687049
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 81.83254875588432
    - type: f1
      value: 81.61520635919082
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 36.31529875009507
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 31.734233714415073
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 30.994501713009452
    - type: mrr
      value: 32.13512850703073
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: 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:
      name: MTEB NQ
      type: 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:
      name: MTEB QuoraRetrieval
      type: quora
      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:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 56.96878497780007
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 65.31371347128074
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: 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:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      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:
      name: MTEB STS12
      type: mteb/sts12-sts
      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:
      name: MTEB STS13
      type: mteb/sts13-sts
      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:
      name: MTEB STS14
      type: mteb/sts14-sts
      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:
      name: MTEB STS15
      type: mteb/sts15-sts
      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:
      name: MTEB STS16
      type: mteb/sts16-sts
      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:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      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:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      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:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      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:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 87.6909022589666
    - type: mrr
      value: 96.43341952165481
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: 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:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      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:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 65.92560972698926
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 34.92797240259008
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 55.244624045597654
    - type: mrr
      value: 56.185303666921314
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: 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:
      name: MTEB TRECCOVID
      type: trec-covid
      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:
      name: MTEB Touche2020
      type: webis-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:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      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:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.34069043576683
    - type: f1
      value: 59.662041994618406
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 53.70780611078653
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      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:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      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
---


**Recommend switching to newest [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5), which has more reasonable similarity distribution and same method of usage.**

<h1 align="center">FlagEmbedding</h1>


<h4 align="center">
    <p>
        <a href=#model-list>Model List</a> | 
        <a href=#frequently-asked-questions>FAQ</a> |
        <a href=#usage>Usage</a>  |
        <a href="#evaluation">Evaluation</a> |
        <a href="#train">Train</a> |
        <a href="#contact">Contact</a> |
        <a href="#citation">Citation</a> |
        <a href="#license">License</a> 
    <p>
</h4>

More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).


[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)

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 databases for LLMs.

************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf)  :fire:  
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released 
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released 
- 09/12/2023: New models: 
    - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. 
    - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
 

<details>
  <summary>More</summary>
<!-- ### More -->
    
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. 
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).  
- 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!** :tada: :tada:   
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.  
  
</details>


## Model List

`bge` is short for `BAAI general embedding`.

|              Model              | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
|  [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder)  |   English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
|  [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)  |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] |   |
|  [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] |   |
|  [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
|  [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
|  [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution  | `Represent this sentence for searching relevant passages: `  |
|  [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
|  [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
|  [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: `  |
|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `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](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：`  |


[1\]: If you need to search the relevant passages to a query, we suggest 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** needs to be added to passages.

[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. 
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.

All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. 
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .


## Frequently asked questions

<details>
  <summary>1. How to fine-tune bge embedding model?</summary>

  <!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. 
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.

  
</details>

<details>
  <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>

  <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** 

Since we finetune the models by contrastive learning with a temperature of 0.01, 
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.

For downstream tasks, such as passage retrieval or semantic similarity, 
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold, 
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).

</details>

<details>
  <summary>3. When does the query instruction need to be used</summary>

  <!-- ### When does the query instruction need to be used -->

For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. 
No instruction only has a slight degradation in retrieval performance compared with using instruction. 
So you can generate embedding without instruction in all cases for convenience.
 
For a retrieval task that uses short queries to find long related documents, 
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction. 

</details>


## Usage 

### Usage for Embedding Model

Here are some examples for using `bge` models with 
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).

#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.

```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5', 
                  query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
                  use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

# for s2p(short query to long passage) retrieval task, suggest to 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
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). 

By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.


#### Using Sentence-Transformers

You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):

```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, 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](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
But the instruction is not needed for passages.
```python
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-v1.5')
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:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs,
    query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```


#### Using HuggingFace Transformers

With the 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 the first token (i.e., [CLS]) as the sentence embedding.

```python
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-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()

# 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)
```

### Usage for Reranker

Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. 
You can get a relevance score by inputting query and passage to the reranker. 
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.


#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```

Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

score = reranker.compute_score(['query', 'passage'])
print(score)

scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```


#### Using Huggingface transformers

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()

pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
    print(scores)
```

## Evaluation  

`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). 

- **MTEB**:   

| Model Name |  Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) |  STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 |  **64.23** | **54.29** |  46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |  
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |  768 | 512 | 63.55 | 53.25 |   45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |  
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |  384 | 512 | 62.17 |51.68 | 43.82 |  84.92 | 58.36 | 81.59 | 30.12 | 74.14 |  
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) |  1024 | 512 | 63.98 |  53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | 
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) |  768 | 512 |  63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | 
| [gte-large](https://huggingface.co/thenlper/gte-large) |  1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) 	|  768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) |  1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) |  384 | 512 | 62.11 |  51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |  
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) |  768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) |  768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) |  384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/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](https://huggingface.co/sentence-transformers/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](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	|  4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |



- **C-MTEB**:  
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. 
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
 
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 |  **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |  
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 |  63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |  
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |   
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |


- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.

| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|  
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |  
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |  
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |  
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |  
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |  
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |  
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |  
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |  
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |  
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |  

\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks

## Train

### BAAI Embedding 

We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. 
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).



### BGE Reranker

Cross-encoder will perform full-attention over the input pair, 
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data, 
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). 
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)


## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). 


## Citation

If you find this repository useful, please consider giving a star :star: and citation

```
@misc{bge_embedding,
      title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, 
      author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
      year={2023},
      eprint={2309.07597},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

