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Upload BAAI/bge-large-en-v1.5 ctranslate2 weights
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metadata
tags:
  - ctranslate2
  - int8
  - float16
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - mteb
model-index:
  - name: bge-large-en-v1.5
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 75.8507462686567
          - type: ap
            value: 38.566457320228245
          - type: f1
            value: 69.69386648043475
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 92.416675
          - type: ap
            value: 89.1928861155922
          - type: f1
            value: 92.39477019574215
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 48.175999999999995
          - type: f1
            value: 47.80712792870253
      - task:
          type: Retrieval
        dataset:
          type: arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 40.184999999999995
          - type: map_at_10
            value: 55.654
          - type: map_at_100
            value: 56.25
          - type: map_at_1000
            value: 56.255
          - type: map_at_3
            value: 51.742999999999995
          - type: map_at_5
            value: 54.129000000000005
          - type: mrr_at_1
            value: 40.967
          - type: mrr_at_10
            value: 55.96
          - type: mrr_at_100
            value: 56.54900000000001
          - type: mrr_at_1000
            value: 56.554
          - type: mrr_at_3
            value: 51.980000000000004
          - type: mrr_at_5
            value: 54.44
          - type: ndcg_at_1
            value: 40.184999999999995
          - type: ndcg_at_10
            value: 63.542
          - type: ndcg_at_100
            value: 65.96499999999999
          - type: ndcg_at_1000
            value: 66.08699999999999
          - type: ndcg_at_3
            value: 55.582
          - type: ndcg_at_5
            value: 59.855000000000004
          - type: precision_at_1
            value: 40.184999999999995
          - type: precision_at_10
            value: 8.841000000000001
          - type: precision_at_100
            value: 0.987
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 22.238
          - type: precision_at_5
            value: 15.405
          - type: recall_at_1
            value: 40.184999999999995
          - type: recall_at_10
            value: 88.407
          - type: recall_at_100
            value: 98.72
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_3
            value: 66.714
          - type: recall_at_5
            value: 77.027
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 48.567077926750066
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 43.19453389182364
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 64.46555939623092
          - type: mrr
            value: 77.82361605768807
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 84.9554128814735
          - type: cos_sim_spearman
            value: 84.65373612172036
          - type: euclidean_pearson
            value: 83.2905059954138
          - type: euclidean_spearman
            value: 84.52240782811128
          - type: manhattan_pearson
            value: 82.99533802997436
          - type: manhattan_spearman
            value: 84.20673798475734
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 87.78896103896103
          - type: f1
            value: 87.77189310964883
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 39.714538337650495
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 36.90108349284447
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 32.795
          - type: map_at_10
            value: 43.669000000000004
          - type: map_at_100
            value: 45.151
          - type: map_at_1000
            value: 45.278
          - type: map_at_3
            value: 40.006
          - type: map_at_5
            value: 42.059999999999995
          - type: mrr_at_1
            value: 39.771
          - type: mrr_at_10
            value: 49.826
          - type: mrr_at_100
            value: 50.504000000000005
          - type: mrr_at_1000
            value: 50.549
          - type: mrr_at_3
            value: 47.115
          - type: mrr_at_5
            value: 48.832
          - type: ndcg_at_1
            value: 39.771
          - type: ndcg_at_10
            value: 50.217999999999996
          - type: ndcg_at_100
            value: 55.454
          - type: ndcg_at_1000
            value: 57.37
          - type: ndcg_at_3
            value: 44.885000000000005
          - type: ndcg_at_5
            value: 47.419
          - type: precision_at_1
            value: 39.771
          - type: precision_at_10
            value: 9.642000000000001
          - type: precision_at_100
            value: 1.538
          - type: precision_at_1000
            value: 0.198
          - type: precision_at_3
            value: 21.268
          - type: precision_at_5
            value: 15.536
          - type: recall_at_1
            value: 32.795
          - type: recall_at_10
            value: 62.580999999999996
          - type: recall_at_100
            value: 84.438
          - type: recall_at_1000
            value: 96.492
          - type: recall_at_3
            value: 47.071000000000005
          - type: recall_at_5
            value: 54.079
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 32.671
          - type: map_at_10
            value: 43.334
          - type: map_at_100
            value: 44.566
          - type: map_at_1000
            value: 44.702999999999996
          - type: map_at_3
            value: 40.343
          - type: map_at_5
            value: 41.983
          - type: mrr_at_1
            value: 40.764
          - type: mrr_at_10
            value: 49.382
          - type: mrr_at_100
            value: 49.988
          - type: mrr_at_1000
            value: 50.03300000000001
          - type: mrr_at_3
            value: 47.293
          - type: mrr_at_5
            value: 48.51
          - type: ndcg_at_1
            value: 40.764
          - type: ndcg_at_10
            value: 49.039
          - type: ndcg_at_100
            value: 53.259
          - type: ndcg_at_1000
            value: 55.253
          - type: ndcg_at_3
            value: 45.091
          - type: ndcg_at_5
            value: 46.839999999999996
          - type: precision_at_1
            value: 40.764
          - type: precision_at_10
            value: 9.191
          - type: precision_at_100
            value: 1.476
          - type: precision_at_1000
            value: 0.19499999999999998
          - type: precision_at_3
            value: 21.72
          - type: precision_at_5
            value: 15.299
          - type: recall_at_1
            value: 32.671
          - type: recall_at_10
            value: 58.816
          - type: recall_at_100
            value: 76.654
          - type: recall_at_1000
            value: 89.05999999999999
          - type: recall_at_3
            value: 46.743
          - type: recall_at_5
            value: 51.783
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 40.328
          - type: map_at_10
            value: 53.32599999999999
          - type: map_at_100
            value: 54.37499999999999
          - type: map_at_1000
            value: 54.429
          - type: map_at_3
            value: 49.902
          - type: map_at_5
            value: 52.002
          - type: mrr_at_1
            value: 46.332
          - type: mrr_at_10
            value: 56.858
          - type: mrr_at_100
            value: 57.522
          - type: mrr_at_1000
            value: 57.54899999999999
          - type: mrr_at_3
            value: 54.472
          - type: mrr_at_5
            value: 55.996
          - type: ndcg_at_1
            value: 46.332
          - type: ndcg_at_10
            value: 59.313
          - type: ndcg_at_100
            value: 63.266999999999996
          - type: ndcg_at_1000
            value: 64.36
          - type: ndcg_at_3
            value: 53.815000000000005
          - type: ndcg_at_5
            value: 56.814
          - type: precision_at_1
            value: 46.332
          - type: precision_at_10
            value: 9.53
          - type: precision_at_100
            value: 1.238
          - type: precision_at_1000
            value: 0.13699999999999998
          - type: precision_at_3
            value: 24.054000000000002
          - type: precision_at_5
            value: 16.589000000000002
          - type: recall_at_1
            value: 40.328
          - type: recall_at_10
            value: 73.421
          - type: recall_at_100
            value: 90.059
          - type: recall_at_1000
            value: 97.81
          - type: recall_at_3
            value: 59.009
          - type: recall_at_5
            value: 66.352
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 27.424
          - type: map_at_10
            value: 36.332
          - type: map_at_100
            value: 37.347
          - type: map_at_1000
            value: 37.422
          - type: map_at_3
            value: 33.743
          - type: map_at_5
            value: 35.176
          - type: mrr_at_1
            value: 29.153000000000002
          - type: mrr_at_10
            value: 38.233
          - type: mrr_at_100
            value: 39.109
          - type: mrr_at_1000
            value: 39.164
          - type: mrr_at_3
            value: 35.876000000000005
          - type: mrr_at_5
            value: 37.169000000000004
          - type: ndcg_at_1
            value: 29.153000000000002
          - type: ndcg_at_10
            value: 41.439
          - type: ndcg_at_100
            value: 46.42
          - type: ndcg_at_1000
            value: 48.242000000000004
          - type: ndcg_at_3
            value: 36.362
          - type: ndcg_at_5
            value: 38.743
          - type: precision_at_1
            value: 29.153000000000002
          - type: precision_at_10
            value: 6.315999999999999
          - type: precision_at_100
            value: 0.927
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: precision_at_3
            value: 15.443000000000001
          - type: precision_at_5
            value: 10.644
          - type: recall_at_1
            value: 27.424
          - type: recall_at_10
            value: 55.364000000000004
          - type: recall_at_100
            value: 78.211
          - type: recall_at_1000
            value: 91.74600000000001
          - type: recall_at_3
            value: 41.379
          - type: recall_at_5
            value: 47.14
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 19.601
          - type: map_at_10
            value: 27.826
          - type: map_at_100
            value: 29.017
          - type: map_at_1000
            value: 29.137
          - type: map_at_3
            value: 25.125999999999998
          - type: map_at_5
            value: 26.765
          - type: mrr_at_1
            value: 24.005000000000003
          - type: mrr_at_10
            value: 32.716
          - type: mrr_at_100
            value: 33.631
          - type: mrr_at_1000
            value: 33.694
          - type: mrr_at_3
            value: 29.934
          - type: mrr_at_5
            value: 31.630999999999997
          - type: ndcg_at_1
            value: 24.005000000000003
          - type: ndcg_at_10
            value: 33.158
          - type: ndcg_at_100
            value: 38.739000000000004
          - type: ndcg_at_1000
            value: 41.495
          - type: ndcg_at_3
            value: 28.185
          - type: ndcg_at_5
            value: 30.796
          - type: precision_at_1
            value: 24.005000000000003
          - type: precision_at_10
            value: 5.908
          - type: precision_at_100
            value: 1.005
          - type: precision_at_1000
            value: 0.13899999999999998
          - type: precision_at_3
            value: 13.391
          - type: precision_at_5
            value: 9.876
          - type: recall_at_1
            value: 19.601
          - type: recall_at_10
            value: 44.746
          - type: recall_at_100
            value: 68.82300000000001
          - type: recall_at_1000
            value: 88.215
          - type: recall_at_3
            value: 31.239
          - type: recall_at_5
            value: 37.695
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 30.130000000000003
          - type: map_at_10
            value: 40.96
          - type: map_at_100
            value: 42.282
          - type: map_at_1000
            value: 42.392
          - type: map_at_3
            value: 37.889
          - type: map_at_5
            value: 39.661
          - type: mrr_at_1
            value: 36.958999999999996
          - type: mrr_at_10
            value: 46.835
          - type: mrr_at_100
            value: 47.644
          - type: mrr_at_1000
            value: 47.688
          - type: mrr_at_3
            value: 44.562000000000005
          - type: mrr_at_5
            value: 45.938
          - type: ndcg_at_1
            value: 36.958999999999996
          - type: ndcg_at_10
            value: 47.06
          - type: ndcg_at_100
            value: 52.345
          - type: ndcg_at_1000
            value: 54.35
          - type: ndcg_at_3
            value: 42.301
          - type: ndcg_at_5
            value: 44.635999999999996
          - type: precision_at_1
            value: 36.958999999999996
          - type: precision_at_10
            value: 8.479000000000001
          - type: precision_at_100
            value: 1.284
          - type: precision_at_1000
            value: 0.163
          - type: precision_at_3
            value: 20.244
          - type: precision_at_5
            value: 14.224999999999998
          - type: recall_at_1
            value: 30.130000000000003
          - type: recall_at_10
            value: 59.27
          - type: recall_at_100
            value: 81.195
          - type: recall_at_1000
            value: 94.21199999999999
          - type: recall_at_3
            value: 45.885
          - type: recall_at_5
            value: 52.016
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.169999999999998
          - type: map_at_10
            value: 36.451
          - type: map_at_100
            value: 37.791000000000004
          - type: map_at_1000
            value: 37.897
          - type: map_at_3
            value: 33.109
          - type: map_at_5
            value: 34.937000000000005
          - type: mrr_at_1
            value: 32.877
          - type: mrr_at_10
            value: 42.368
          - type: mrr_at_100
            value: 43.201
          - type: mrr_at_1000
            value: 43.259
          - type: mrr_at_3
            value: 39.763999999999996
          - type: mrr_at_5
            value: 41.260000000000005
          - type: ndcg_at_1
            value: 32.877
          - type: ndcg_at_10
            value: 42.659000000000006
          - type: ndcg_at_100
            value: 48.161
          - type: ndcg_at_1000
            value: 50.345
          - type: ndcg_at_3
            value: 37.302
          - type: ndcg_at_5
            value: 39.722
          - type: precision_at_1
            value: 32.877
          - type: precision_at_10
            value: 7.9
          - type: precision_at_100
            value: 1.236
          - type: precision_at_1000
            value: 0.158
          - type: precision_at_3
            value: 17.846
          - type: precision_at_5
            value: 12.9
          - type: recall_at_1
            value: 26.169999999999998
          - type: recall_at_10
            value: 55.35
          - type: recall_at_100
            value: 78.755
          - type: recall_at_1000
            value: 93.518
          - type: recall_at_3
            value: 40.176
          - type: recall_at_5
            value: 46.589000000000006
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 27.15516666666667
          - type: map_at_10
            value: 36.65741666666667
          - type: map_at_100
            value: 37.84991666666666
          - type: map_at_1000
            value: 37.96316666666667
          - type: map_at_3
            value: 33.74974999999999
          - type: map_at_5
            value: 35.3765
          - type: mrr_at_1
            value: 32.08233333333334
          - type: mrr_at_10
            value: 41.033833333333334
          - type: mrr_at_100
            value: 41.84524999999999
          - type: mrr_at_1000
            value: 41.89983333333333
          - type: mrr_at_3
            value: 38.62008333333333
          - type: mrr_at_5
            value: 40.03441666666666
          - type: ndcg_at_1
            value: 32.08233333333334
          - type: ndcg_at_10
            value: 42.229
          - type: ndcg_at_100
            value: 47.26716666666667
          - type: ndcg_at_1000
            value: 49.43466666666667
          - type: ndcg_at_3
            value: 37.36408333333333
          - type: ndcg_at_5
            value: 39.6715
          - type: precision_at_1
            value: 32.08233333333334
          - type: precision_at_10
            value: 7.382583333333334
          - type: precision_at_100
            value: 1.16625
          - type: precision_at_1000
            value: 0.15408333333333332
          - type: precision_at_3
            value: 17.218
          - type: precision_at_5
            value: 12.21875
          - type: recall_at_1
            value: 27.15516666666667
          - type: recall_at_10
            value: 54.36683333333333
          - type: recall_at_100
            value: 76.37183333333333
          - type: recall_at_1000
            value: 91.26183333333333
          - type: recall_at_3
            value: 40.769916666666674
          - type: recall_at_5
            value: 46.702333333333335
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 25.749
          - type: map_at_10
            value: 33.001999999999995
          - type: map_at_100
            value: 33.891
          - type: map_at_1000
            value: 33.993
          - type: map_at_3
            value: 30.703999999999997
          - type: map_at_5
            value: 31.959
          - type: mrr_at_1
            value: 28.834
          - type: mrr_at_10
            value: 35.955
          - type: mrr_at_100
            value: 36.709
          - type: mrr_at_1000
            value: 36.779
          - type: mrr_at_3
            value: 33.947
          - type: mrr_at_5
            value: 35.089
          - type: ndcg_at_1
            value: 28.834
          - type: ndcg_at_10
            value: 37.329
          - type: ndcg_at_100
            value: 41.79
          - type: ndcg_at_1000
            value: 44.169000000000004
          - type: ndcg_at_3
            value: 33.184999999999995
          - type: ndcg_at_5
            value: 35.107
          - type: precision_at_1
            value: 28.834
          - type: precision_at_10
            value: 5.7669999999999995
          - type: precision_at_100
            value: 0.876
          - type: precision_at_1000
            value: 0.11399999999999999
          - type: precision_at_3
            value: 14.213000000000001
          - type: precision_at_5
            value: 9.754999999999999
          - type: recall_at_1
            value: 25.749
          - type: recall_at_10
            value: 47.791
          - type: recall_at_100
            value: 68.255
          - type: recall_at_1000
            value: 85.749
          - type: recall_at_3
            value: 36.199
          - type: recall_at_5
            value: 41.071999999999996
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 17.777
          - type: map_at_10
            value: 25.201
          - type: map_at_100
            value: 26.423999999999996
          - type: map_at_1000
            value: 26.544
          - type: map_at_3
            value: 22.869
          - type: map_at_5
            value: 24.023
          - type: mrr_at_1
            value: 21.473
          - type: mrr_at_10
            value: 29.12
          - type: mrr_at_100
            value: 30.144
          - type: mrr_at_1000
            value: 30.215999999999998
          - type: mrr_at_3
            value: 26.933
          - type: mrr_at_5
            value: 28.051
          - type: ndcg_at_1
            value: 21.473
          - type: ndcg_at_10
            value: 30.003
          - type: ndcg_at_100
            value: 35.766
          - type: ndcg_at_1000
            value: 38.501000000000005
          - type: ndcg_at_3
            value: 25.773000000000003
          - type: ndcg_at_5
            value: 27.462999999999997
          - type: precision_at_1
            value: 21.473
          - type: precision_at_10
            value: 5.482
          - type: precision_at_100
            value: 0.975
          - type: precision_at_1000
            value: 0.13799999999999998
          - type: precision_at_3
            value: 12.205
          - type: precision_at_5
            value: 8.692
          - type: recall_at_1
            value: 17.777
          - type: recall_at_10
            value: 40.582
          - type: recall_at_100
            value: 66.305
          - type: recall_at_1000
            value: 85.636
          - type: recall_at_3
            value: 28.687
          - type: recall_at_5
            value: 33.089
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.677
          - type: map_at_10
            value: 36.309000000000005
          - type: map_at_100
            value: 37.403999999999996
          - type: map_at_1000
            value: 37.496
          - type: map_at_3
            value: 33.382
          - type: map_at_5
            value: 34.98
          - type: mrr_at_1
            value: 31.343
          - type: mrr_at_10
            value: 40.549
          - type: mrr_at_100
            value: 41.342
          - type: mrr_at_1000
            value: 41.397
          - type: mrr_at_3
            value: 38.029
          - type: mrr_at_5
            value: 39.451
          - type: ndcg_at_1
            value: 31.343
          - type: ndcg_at_10
            value: 42.1
          - type: ndcg_at_100
            value: 47.089999999999996
          - type: ndcg_at_1000
            value: 49.222
          - type: ndcg_at_3
            value: 36.836999999999996
          - type: ndcg_at_5
            value: 39.21
          - type: precision_at_1
            value: 31.343
          - type: precision_at_10
            value: 7.164
          - type: precision_at_100
            value: 1.0959999999999999
          - type: precision_at_1000
            value: 0.13899999999999998
          - type: precision_at_3
            value: 16.915
          - type: precision_at_5
            value: 11.940000000000001
          - type: recall_at_1
            value: 26.677
          - type: recall_at_10
            value: 55.54599999999999
          - type: recall_at_100
            value: 77.094
          - type: recall_at_1000
            value: 92.01
          - type: recall_at_3
            value: 41.191
          - type: recall_at_5
            value: 47.006
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.501
          - type: map_at_10
            value: 33.102
          - type: map_at_100
            value: 34.676
          - type: map_at_1000
            value: 34.888000000000005
          - type: map_at_3
            value: 29.944
          - type: map_at_5
            value: 31.613999999999997
          - type: mrr_at_1
            value: 29.447000000000003
          - type: mrr_at_10
            value: 37.996
          - type: mrr_at_100
            value: 38.946
          - type: mrr_at_1000
            value: 38.995000000000005
          - type: mrr_at_3
            value: 35.079
          - type: mrr_at_5
            value: 36.69
          - type: ndcg_at_1
            value: 29.447000000000003
          - type: ndcg_at_10
            value: 39.232
          - type: ndcg_at_100
            value: 45.247
          - type: ndcg_at_1000
            value: 47.613
          - type: ndcg_at_3
            value: 33.922999999999995
          - type: ndcg_at_5
            value: 36.284
          - type: precision_at_1
            value: 29.447000000000003
          - type: precision_at_10
            value: 7.648000000000001
          - type: precision_at_100
            value: 1.516
          - type: precision_at_1000
            value: 0.23900000000000002
          - type: precision_at_3
            value: 16.008
          - type: precision_at_5
            value: 11.779
          - type: recall_at_1
            value: 24.501
          - type: recall_at_10
            value: 51.18899999999999
          - type: recall_at_100
            value: 78.437
          - type: recall_at_1000
            value: 92.842
          - type: recall_at_3
            value: 35.808
          - type: recall_at_5
            value: 42.197
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.039
          - type: map_at_10
            value: 30.377
          - type: map_at_100
            value: 31.275
          - type: map_at_1000
            value: 31.379
          - type: map_at_3
            value: 27.98
          - type: map_at_5
            value: 29.358
          - type: mrr_at_1
            value: 24.03
          - type: mrr_at_10
            value: 32.568000000000005
          - type: mrr_at_100
            value: 33.403
          - type: mrr_at_1000
            value: 33.475
          - type: mrr_at_3
            value: 30.436999999999998
          - type: mrr_at_5
            value: 31.796000000000003
          - type: ndcg_at_1
            value: 24.03
          - type: ndcg_at_10
            value: 35.198
          - type: ndcg_at_100
            value: 39.668
          - type: ndcg_at_1000
            value: 42.296
          - type: ndcg_at_3
            value: 30.709999999999997
          - type: ndcg_at_5
            value: 33.024
          - type: precision_at_1
            value: 24.03
          - type: precision_at_10
            value: 5.564
          - type: precision_at_100
            value: 0.828
          - type: precision_at_1000
            value: 0.117
          - type: precision_at_3
            value: 13.309000000000001
          - type: precision_at_5
            value: 9.39
          - type: recall_at_1
            value: 22.039
          - type: recall_at_10
            value: 47.746
          - type: recall_at_100
            value: 68.23599999999999
          - type: recall_at_1000
            value: 87.852
          - type: recall_at_3
            value: 35.852000000000004
          - type: recall_at_5
            value: 41.410000000000004
      - task:
          type: Retrieval
        dataset:
          type: climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 15.692999999999998
          - type: map_at_10
            value: 26.903
          - type: map_at_100
            value: 28.987000000000002
          - type: map_at_1000
            value: 29.176999999999996
          - type: map_at_3
            value: 22.137
          - type: map_at_5
            value: 24.758
          - type: mrr_at_1
            value: 35.57
          - type: mrr_at_10
            value: 47.821999999999996
          - type: mrr_at_100
            value: 48.608000000000004
          - type: mrr_at_1000
            value: 48.638999999999996
          - type: mrr_at_3
            value: 44.452000000000005
          - type: mrr_at_5
            value: 46.546
          - type: ndcg_at_1
            value: 35.57
          - type: ndcg_at_10
            value: 36.567
          - type: ndcg_at_100
            value: 44.085
          - type: ndcg_at_1000
            value: 47.24
          - type: ndcg_at_3
            value: 29.964000000000002
          - type: ndcg_at_5
            value: 32.511
          - type: precision_at_1
            value: 35.57
          - type: precision_at_10
            value: 11.485
          - type: precision_at_100
            value: 1.9619999999999997
          - type: precision_at_1000
            value: 0.256
          - type: precision_at_3
            value: 22.237000000000002
          - type: precision_at_5
            value: 17.471999999999998
          - type: recall_at_1
            value: 15.692999999999998
          - type: recall_at_10
            value: 43.056
          - type: recall_at_100
            value: 68.628
          - type: recall_at_1000
            value: 86.075
          - type: recall_at_3
            value: 26.918999999999997
          - type: recall_at_5
            value: 34.14
      - task:
          type: Retrieval
        dataset:
          type: dbpedia-entity
          name: MTEB DBPedia
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 9.53
          - type: map_at_10
            value: 20.951
          - type: map_at_100
            value: 30.136000000000003
          - type: map_at_1000
            value: 31.801000000000002
          - type: map_at_3
            value: 15.021
          - type: map_at_5
            value: 17.471999999999998
          - type: mrr_at_1
            value: 71
          - type: mrr_at_10
            value: 79.176
          - type: mrr_at_100
            value: 79.418
          - type: mrr_at_1000
            value: 79.426
          - type: mrr_at_3
            value: 78.125
          - type: mrr_at_5
            value: 78.61200000000001
          - type: ndcg_at_1
            value: 58.5
          - type: ndcg_at_10
            value: 44.106
          - type: ndcg_at_100
            value: 49.268
          - type: ndcg_at_1000
            value: 56.711999999999996
          - type: ndcg_at_3
            value: 48.934
          - type: ndcg_at_5
            value: 45.826
          - type: precision_at_1
            value: 71
          - type: precision_at_10
            value: 35
          - type: precision_at_100
            value: 11.360000000000001
          - type: precision_at_1000
            value: 2.046
          - type: precision_at_3
            value: 52.833
          - type: precision_at_5
            value: 44.15
          - type: recall_at_1
            value: 9.53
          - type: recall_at_10
            value: 26.811
          - type: recall_at_100
            value: 55.916999999999994
          - type: recall_at_1000
            value: 79.973
          - type: recall_at_3
            value: 16.413
          - type: recall_at_5
            value: 19.980999999999998
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 51.519999999999996
          - type: f1
            value: 46.36601294761231
      - task:
          type: Retrieval
        dataset:
          type: fever
          name: MTEB FEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 74.413
          - type: map_at_10
            value: 83.414
          - type: map_at_100
            value: 83.621
          - type: map_at_1000
            value: 83.635
          - type: map_at_3
            value: 82.337
          - type: map_at_5
            value: 83.039
          - type: mrr_at_1
            value: 80.19800000000001
          - type: mrr_at_10
            value: 87.715
          - type: mrr_at_100
            value: 87.778
          - type: mrr_at_1000
            value: 87.779
          - type: mrr_at_3
            value: 87.106
          - type: mrr_at_5
            value: 87.555
          - type: ndcg_at_1
            value: 80.19800000000001
          - type: ndcg_at_10
            value: 87.182
          - type: ndcg_at_100
            value: 87.90299999999999
          - type: ndcg_at_1000
            value: 88.143
          - type: ndcg_at_3
            value: 85.60600000000001
          - type: ndcg_at_5
            value: 86.541
          - type: precision_at_1
            value: 80.19800000000001
          - type: precision_at_10
            value: 10.531
          - type: precision_at_100
            value: 1.113
          - type: precision_at_1000
            value: 0.11499999999999999
          - type: precision_at_3
            value: 32.933
          - type: precision_at_5
            value: 20.429
          - type: recall_at_1
            value: 74.413
          - type: recall_at_10
            value: 94.363
          - type: recall_at_100
            value: 97.165
          - type: recall_at_1000
            value: 98.668
          - type: recall_at_3
            value: 90.108
          - type: recall_at_5
            value: 92.52
      - task:
          type: Retrieval
        dataset:
          type: fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.701
          - type: map_at_10
            value: 37.122
          - type: map_at_100
            value: 39.178000000000004
          - type: map_at_1000
            value: 39.326
          - type: map_at_3
            value: 32.971000000000004
          - type: map_at_5
            value: 35.332
          - type: mrr_at_1
            value: 44.753
          - type: mrr_at_10
            value: 53.452
          - type: mrr_at_100
            value: 54.198
          - type: mrr_at_1000
            value: 54.225
          - type: mrr_at_3
            value: 50.952
          - type: mrr_at_5
            value: 52.464
          - type: ndcg_at_1
            value: 44.753
          - type: ndcg_at_10
            value: 45.021
          - type: ndcg_at_100
            value: 52.028
          - type: ndcg_at_1000
            value: 54.596000000000004
          - type: ndcg_at_3
            value: 41.622
          - type: ndcg_at_5
            value: 42.736000000000004
          - type: precision_at_1
            value: 44.753
          - type: precision_at_10
            value: 12.284
          - type: precision_at_100
            value: 1.955
          - type: precision_at_1000
            value: 0.243
          - type: precision_at_3
            value: 27.828999999999997
          - type: precision_at_5
            value: 20.061999999999998
          - type: recall_at_1
            value: 22.701
          - type: recall_at_10
            value: 51.432
          - type: recall_at_100
            value: 77.009
          - type: recall_at_1000
            value: 92.511
          - type: recall_at_3
            value: 37.919000000000004
          - type: recall_at_5
            value: 44.131
      - task:
          type: Retrieval
        dataset:
          type: hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 40.189
          - type: map_at_10
            value: 66.24600000000001
          - type: map_at_100
            value: 67.098
          - type: map_at_1000
            value: 67.149
          - type: map_at_3
            value: 62.684
          - type: map_at_5
            value: 64.974
          - type: mrr_at_1
            value: 80.378
          - type: mrr_at_10
            value: 86.127
          - type: mrr_at_100
            value: 86.29299999999999
          - type: mrr_at_1000
            value: 86.297
          - type: mrr_at_3
            value: 85.31400000000001
          - type: mrr_at_5
            value: 85.858
          - type: ndcg_at_1
            value: 80.378
          - type: ndcg_at_10
            value: 74.101
          - type: ndcg_at_100
            value: 76.993
          - type: ndcg_at_1000
            value: 77.948
          - type: ndcg_at_3
            value: 69.232
          - type: ndcg_at_5
            value: 72.04599999999999
          - type: precision_at_1
            value: 80.378
          - type: precision_at_10
            value: 15.595999999999998
          - type: precision_at_100
            value: 1.7840000000000003
          - type: precision_at_1000
            value: 0.191
          - type: precision_at_3
            value: 44.884
          - type: precision_at_5
            value: 29.145
          - type: recall_at_1
            value: 40.189
          - type: recall_at_10
            value: 77.981
          - type: recall_at_100
            value: 89.21
          - type: recall_at_1000
            value: 95.48299999999999
          - type: recall_at_3
            value: 67.326
          - type: recall_at_5
            value: 72.863
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 92.84599999999999
          - type: ap
            value: 89.4710787567357
          - type: f1
            value: 92.83752676932258
      - task:
          type: Retrieval
        dataset:
          type: msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 23.132
          - type: map_at_10
            value: 35.543
          - type: map_at_100
            value: 36.702
          - type: map_at_1000
            value: 36.748999999999995
          - type: map_at_3
            value: 31.737
          - type: map_at_5
            value: 33.927
          - type: mrr_at_1
            value: 23.782
          - type: mrr_at_10
            value: 36.204
          - type: mrr_at_100
            value: 37.29
          - type: mrr_at_1000
            value: 37.330999999999996
          - type: mrr_at_3
            value: 32.458999999999996
          - type: mrr_at_5
            value: 34.631
          - type: ndcg_at_1
            value: 23.782
          - type: ndcg_at_10
            value: 42.492999999999995
          - type: ndcg_at_100
            value: 47.985
          - type: ndcg_at_1000
            value: 49.141
          - type: ndcg_at_3
            value: 34.748000000000005
          - type: ndcg_at_5
            value: 38.651
          - type: precision_at_1
            value: 23.782
          - type: precision_at_10
            value: 6.665
          - type: precision_at_100
            value: 0.941
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 14.776
          - type: precision_at_5
            value: 10.84
          - type: recall_at_1
            value: 23.132
          - type: recall_at_10
            value: 63.794
          - type: recall_at_100
            value: 89.027
          - type: recall_at_1000
            value: 97.807
          - type: recall_at_3
            value: 42.765
          - type: recall_at_5
            value: 52.11
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 94.59188326493388
          - type: f1
            value: 94.3842594786827
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 79.49384404924761
          - type: f1
            value: 59.7580539534629
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 77.56220578345663
          - type: f1
            value: 75.27228165561478
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 80.53463349024884
          - type: f1
            value: 80.4893958236536
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 32.56100273484962
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 31.470380028839607
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 32.06102792457849
          - type: mrr
            value: 33.30709199672238
      - task:
          type: Retrieval
        dataset:
          type: nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 6.776999999999999
          - type: map_at_10
            value: 14.924000000000001
          - type: map_at_100
            value: 18.955
          - type: map_at_1000
            value: 20.538999999999998
          - type: map_at_3
            value: 10.982
          - type: map_at_5
            value: 12.679000000000002
          - type: mrr_at_1
            value: 47.988
          - type: mrr_at_10
            value: 57.232000000000006
          - type: mrr_at_100
            value: 57.818999999999996
          - type: mrr_at_1000
            value: 57.847
          - type: mrr_at_3
            value: 54.901999999999994
          - type: mrr_at_5
            value: 56.481
          - type: ndcg_at_1
            value: 46.594
          - type: ndcg_at_10
            value: 38.129000000000005
          - type: ndcg_at_100
            value: 35.54
          - type: ndcg_at_1000
            value: 44.172
          - type: ndcg_at_3
            value: 43.025999999999996
          - type: ndcg_at_5
            value: 41.052
          - type: precision_at_1
            value: 47.988
          - type: precision_at_10
            value: 28.111000000000004
          - type: precision_at_100
            value: 8.929
          - type: precision_at_1000
            value: 2.185
          - type: precision_at_3
            value: 40.144000000000005
          - type: precision_at_5
            value: 35.232
          - type: recall_at_1
            value: 6.776999999999999
          - type: recall_at_10
            value: 19.289
          - type: recall_at_100
            value: 36.359
          - type: recall_at_1000
            value: 67.54
          - type: recall_at_3
            value: 11.869
          - type: recall_at_5
            value: 14.999
      - task:
          type: Retrieval
        dataset:
          type: nq
          name: MTEB NQ
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 31.108000000000004
          - type: map_at_10
            value: 47.126000000000005
          - type: map_at_100
            value: 48.171
          - type: map_at_1000
            value: 48.199
          - type: map_at_3
            value: 42.734
          - type: map_at_5
            value: 45.362
          - type: mrr_at_1
            value: 34.936
          - type: mrr_at_10
            value: 49.571
          - type: mrr_at_100
            value: 50.345
          - type: mrr_at_1000
            value: 50.363
          - type: mrr_at_3
            value: 45.959
          - type: mrr_at_5
            value: 48.165
          - type: ndcg_at_1
            value: 34.936
          - type: ndcg_at_10
            value: 55.028999999999996
          - type: ndcg_at_100
            value: 59.244
          - type: ndcg_at_1000
            value: 59.861
          - type: ndcg_at_3
            value: 46.872
          - type: ndcg_at_5
            value: 51.217999999999996
          - type: precision_at_1
            value: 34.936
          - type: precision_at_10
            value: 9.099
          - type: precision_at_100
            value: 1.145
          - type: precision_at_1000
            value: 0.12
          - type: precision_at_3
            value: 21.456
          - type: precision_at_5
            value: 15.411
          - type: recall_at_1
            value: 31.108000000000004
          - type: recall_at_10
            value: 76.53999999999999
          - type: recall_at_100
            value: 94.39
          - type: recall_at_1000
            value: 98.947
          - type: recall_at_3
            value: 55.572
          - type: recall_at_5
            value: 65.525
      - task:
          type: Retrieval
        dataset:
          type: quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 71.56400000000001
          - type: map_at_10
            value: 85.482
          - type: map_at_100
            value: 86.114
          - type: map_at_1000
            value: 86.13
          - type: map_at_3
            value: 82.607
          - type: map_at_5
            value: 84.405
          - type: mrr_at_1
            value: 82.42
          - type: mrr_at_10
            value: 88.304
          - type: mrr_at_100
            value: 88.399
          - type: mrr_at_1000
            value: 88.399
          - type: mrr_at_3
            value: 87.37
          - type: mrr_at_5
            value: 88.024
          - type: ndcg_at_1
            value: 82.45
          - type: ndcg_at_10
            value: 89.06500000000001
          - type: ndcg_at_100
            value: 90.232
          - type: ndcg_at_1000
            value: 90.305
          - type: ndcg_at_3
            value: 86.375
          - type: ndcg_at_5
            value: 87.85300000000001
          - type: precision_at_1
            value: 82.45
          - type: precision_at_10
            value: 13.486999999999998
          - type: precision_at_100
            value: 1.534
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 37.813
          - type: precision_at_5
            value: 24.773999999999997
          - type: recall_at_1
            value: 71.56400000000001
          - type: recall_at_10
            value: 95.812
          - type: recall_at_100
            value: 99.7
          - type: recall_at_1000
            value: 99.979
          - type: recall_at_3
            value: 87.966
          - type: recall_at_5
            value: 92.268
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 57.241876648614145
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 64.66212576446223
      - task:
          type: Retrieval
        dataset:
          type: scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 5.308
          - type: map_at_10
            value: 13.803
          - type: map_at_100
            value: 16.176
          - type: map_at_1000
            value: 16.561
          - type: map_at_3
            value: 9.761000000000001
          - type: map_at_5
            value: 11.802
          - type: mrr_at_1
            value: 26.200000000000003
          - type: mrr_at_10
            value: 37.621
          - type: mrr_at_100
            value: 38.767
          - type: mrr_at_1000
            value: 38.815
          - type: mrr_at_3
            value: 34.117
          - type: mrr_at_5
            value: 36.107
          - type: ndcg_at_1
            value: 26.200000000000003
          - type: ndcg_at_10
            value: 22.64
          - type: ndcg_at_100
            value: 31.567
          - type: ndcg_at_1000
            value: 37.623
          - type: ndcg_at_3
            value: 21.435000000000002
          - type: ndcg_at_5
            value: 18.87
          - type: precision_at_1
            value: 26.200000000000003
          - type: precision_at_10
            value: 11.74
          - type: precision_at_100
            value: 2.465
          - type: precision_at_1000
            value: 0.391
          - type: precision_at_3
            value: 20.033
          - type: precision_at_5
            value: 16.64
          - type: recall_at_1
            value: 5.308
          - type: recall_at_10
            value: 23.794999999999998
          - type: recall_at_100
            value: 50.015
          - type: recall_at_1000
            value: 79.283
          - type: recall_at_3
            value: 12.178
          - type: recall_at_5
            value: 16.882
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 84.93231134675553
          - type: cos_sim_spearman
            value: 81.68319292603205
          - type: euclidean_pearson
            value: 81.8396814380367
          - type: euclidean_spearman
            value: 81.24641903349945
          - type: manhattan_pearson
            value: 81.84698799204274
          - type: manhattan_spearman
            value: 81.24269997904105
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 86.73241671587446
          - type: cos_sim_spearman
            value: 79.05091082971826
          - type: euclidean_pearson
            value: 83.91146869578044
          - type: euclidean_spearman
            value: 79.87978465370936
          - type: manhattan_pearson
            value: 83.90888338917678
          - type: manhattan_spearman
            value: 79.87482848584241
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 85.14970731146177
          - type: cos_sim_spearman
            value: 86.37363490084627
          - type: euclidean_pearson
            value: 83.02154218530433
          - type: euclidean_spearman
            value: 83.80258761957367
          - type: manhattan_pearson
            value: 83.01664495119347
          - type: manhattan_spearman
            value: 83.77567458007952
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 83.40474139886784
          - type: cos_sim_spearman
            value: 82.77768789165984
          - type: euclidean_pearson
            value: 80.7065877443695
          - type: euclidean_spearman
            value: 81.375940662505
          - type: manhattan_pearson
            value: 80.6507552270278
          - type: manhattan_spearman
            value: 81.32782179098741
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 87.08585968722274
          - type: cos_sim_spearman
            value: 88.03110031451399
          - type: euclidean_pearson
            value: 85.74012019602384
          - type: euclidean_spearman
            value: 86.13592849438209
          - type: manhattan_pearson
            value: 85.74404842369206
          - type: manhattan_spearman
            value: 86.14492318960154
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 84.95069052788875
          - type: cos_sim_spearman
            value: 86.4867991595147
          - type: euclidean_pearson
            value: 84.31013325754635
          - type: euclidean_spearman
            value: 85.01529258006482
          - type: manhattan_pearson
            value: 84.26995570085374
          - type: manhattan_spearman
            value: 84.96982104986162
      - 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: 87.54617647971897
          - type: cos_sim_spearman
            value: 87.49834181751034
          - type: euclidean_pearson
            value: 86.01015322577122
          - type: euclidean_spearman
            value: 84.63362652063199
          - type: manhattan_pearson
            value: 86.13807574475706
          - type: manhattan_spearman
            value: 84.7772370721132
      - 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: 67.20047755786615
          - type: cos_sim_spearman
            value: 67.05324077987636
          - type: euclidean_pearson
            value: 66.91930642976601
          - type: euclidean_spearman
            value: 65.21491856099105
          - type: manhattan_pearson
            value: 66.78756851976624
          - type: manhattan_spearman
            value: 65.12356257740728
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 86.19852871539686
          - type: cos_sim_spearman
            value: 87.5161895296395
          - type: euclidean_pearson
            value: 84.59848645207485
          - type: euclidean_spearman
            value: 85.26427328757919
          - type: manhattan_pearson
            value: 84.59747366996524
          - type: manhattan_spearman
            value: 85.24045855146915
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 87.63320317811032
          - type: mrr
            value: 96.26242947321379
      - task:
          type: Retrieval
        dataset:
          type: scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 60.928000000000004
          - type: map_at_10
            value: 70.112
          - type: map_at_100
            value: 70.59299999999999
          - type: map_at_1000
            value: 70.623
          - type: map_at_3
            value: 66.846
          - type: map_at_5
            value: 68.447
          - type: mrr_at_1
            value: 64
          - type: mrr_at_10
            value: 71.212
          - type: mrr_at_100
            value: 71.616
          - type: mrr_at_1000
            value: 71.64500000000001
          - type: mrr_at_3
            value: 68.77799999999999
          - type: mrr_at_5
            value: 70.094
          - type: ndcg_at_1
            value: 64
          - type: ndcg_at_10
            value: 74.607
          - type: ndcg_at_100
            value: 76.416
          - type: ndcg_at_1000
            value: 77.102
          - type: ndcg_at_3
            value: 69.126
          - type: ndcg_at_5
            value: 71.41300000000001
          - type: precision_at_1
            value: 64
          - type: precision_at_10
            value: 9.933
          - type: precision_at_100
            value: 1.077
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 26.556
          - type: precision_at_5
            value: 17.467
          - type: recall_at_1
            value: 60.928000000000004
          - type: recall_at_10
            value: 87.322
          - type: recall_at_100
            value: 94.833
          - type: recall_at_1000
            value: 100
          - type: recall_at_3
            value: 72.628
          - type: recall_at_5
            value: 78.428
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.86237623762376
          - type: cos_sim_ap
            value: 96.72586477206649
          - type: cos_sim_f1
            value: 93.01858362631845
          - type: cos_sim_precision
            value: 93.4409687184662
          - type: cos_sim_recall
            value: 92.60000000000001
          - type: dot_accuracy
            value: 99.78019801980199
          - type: dot_ap
            value: 93.72748205246228
          - type: dot_f1
            value: 89.04109589041096
          - type: dot_precision
            value: 87.16475095785441
          - type: dot_recall
            value: 91
          - type: euclidean_accuracy
            value: 99.85445544554456
          - type: euclidean_ap
            value: 96.6661459876145
          - type: euclidean_f1
            value: 92.58337481333997
          - type: euclidean_precision
            value: 92.17046580773042
          - type: euclidean_recall
            value: 93
          - type: manhattan_accuracy
            value: 99.85445544554456
          - type: manhattan_ap
            value: 96.6883549244056
          - type: manhattan_f1
            value: 92.57598405580468
          - type: manhattan_precision
            value: 92.25422045680239
          - type: manhattan_recall
            value: 92.9
          - type: max_accuracy
            value: 99.86237623762376
          - type: max_ap
            value: 96.72586477206649
          - type: max_f1
            value: 93.01858362631845
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 66.39930057069995
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 34.96398659903402
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 55.946944700355395
          - type: mrr
            value: 56.97151398438164
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 31.541657650692905
          - type: cos_sim_spearman
            value: 31.605804192286303
          - type: dot_pearson
            value: 28.26905996736398
          - type: dot_spearman
            value: 27.864801765851187
      - task:
          type: Retrieval
        dataset:
          type: trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.22599999999999998
          - type: map_at_10
            value: 1.8870000000000002
          - type: map_at_100
            value: 9.78
          - type: map_at_1000
            value: 22.514
          - type: map_at_3
            value: 0.6669999999999999
          - type: map_at_5
            value: 1.077
          - type: mrr_at_1
            value: 82
          - type: mrr_at_10
            value: 89.86699999999999
          - type: mrr_at_100
            value: 89.86699999999999
          - type: mrr_at_1000
            value: 89.86699999999999
          - type: mrr_at_3
            value: 89.667
          - type: mrr_at_5
            value: 89.667
          - type: ndcg_at_1
            value: 79
          - type: ndcg_at_10
            value: 74.818
          - type: ndcg_at_100
            value: 53.715999999999994
          - type: ndcg_at_1000
            value: 47.082
          - type: ndcg_at_3
            value: 82.134
          - type: ndcg_at_5
            value: 79.81899999999999
          - type: precision_at_1
            value: 82
          - type: precision_at_10
            value: 78
          - type: precision_at_100
            value: 54.48
          - type: precision_at_1000
            value: 20.518
          - type: precision_at_3
            value: 87.333
          - type: precision_at_5
            value: 85.2
          - type: recall_at_1
            value: 0.22599999999999998
          - type: recall_at_10
            value: 2.072
          - type: recall_at_100
            value: 13.013
          - type: recall_at_1000
            value: 43.462
          - type: recall_at_3
            value: 0.695
          - type: recall_at_5
            value: 1.139
      - task:
          type: Retrieval
        dataset:
          type: webis-touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 2.328
          - type: map_at_10
            value: 9.795
          - type: map_at_100
            value: 15.801000000000002
          - type: map_at_1000
            value: 17.23
          - type: map_at_3
            value: 4.734
          - type: map_at_5
            value: 6.644
          - type: mrr_at_1
            value: 30.612000000000002
          - type: mrr_at_10
            value: 46.902
          - type: mrr_at_100
            value: 47.495
          - type: mrr_at_1000
            value: 47.495
          - type: mrr_at_3
            value: 41.156
          - type: mrr_at_5
            value: 44.218
          - type: ndcg_at_1
            value: 28.571
          - type: ndcg_at_10
            value: 24.806
          - type: ndcg_at_100
            value: 36.419000000000004
          - type: ndcg_at_1000
            value: 47.272999999999996
          - type: ndcg_at_3
            value: 25.666
          - type: ndcg_at_5
            value: 25.448999999999998
          - type: precision_at_1
            value: 30.612000000000002
          - type: precision_at_10
            value: 23.061
          - type: precision_at_100
            value: 7.714
          - type: precision_at_1000
            value: 1.484
          - type: precision_at_3
            value: 26.531
          - type: precision_at_5
            value: 26.122
          - type: recall_at_1
            value: 2.328
          - type: recall_at_10
            value: 16.524
          - type: recall_at_100
            value: 47.179
          - type: recall_at_1000
            value: 81.22200000000001
          - type: recall_at_3
            value: 5.745
          - type: recall_at_5
            value: 9.339
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 70.9142
          - type: ap
            value: 14.335574772555415
          - type: f1
            value: 54.62839595194111
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 59.94340690435768
          - type: f1
            value: 60.286487936731916
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 51.26597708987974
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 87.48882398521786
          - type: cos_sim_ap
            value: 79.04326607602204
          - type: cos_sim_f1
            value: 71.64566826860633
          - type: cos_sim_precision
            value: 70.55512918905092
          - type: cos_sim_recall
            value: 72.77044854881267
          - type: dot_accuracy
            value: 84.19264469213805
          - type: dot_ap
            value: 67.96360043562528
          - type: dot_f1
            value: 64.06418393006827
          - type: dot_precision
            value: 58.64941898706424
          - type: dot_recall
            value: 70.58047493403694
          - type: euclidean_accuracy
            value: 87.45902127913214
          - type: euclidean_ap
            value: 78.9742237648272
          - type: euclidean_f1
            value: 71.5553235908142
          - type: euclidean_precision
            value: 70.77955601445535
          - type: euclidean_recall
            value: 72.34828496042216
          - type: manhattan_accuracy
            value: 87.41729749061214
          - type: manhattan_ap
            value: 78.90073137580596
          - type: manhattan_f1
            value: 71.3942611553533
          - type: manhattan_precision
            value: 68.52705653967483
          - type: manhattan_recall
            value: 74.51187335092348
          - type: max_accuracy
            value: 87.48882398521786
          - type: max_ap
            value: 79.04326607602204
          - type: max_f1
            value: 71.64566826860633
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.68125897465751
          - type: cos_sim_ap
            value: 85.6003454431979
          - type: cos_sim_f1
            value: 77.6957163958641
          - type: cos_sim_precision
            value: 73.0110366307807
          - type: cos_sim_recall
            value: 83.02279026793964
          - type: dot_accuracy
            value: 87.7672992587418
          - type: dot_ap
            value: 82.4971301112899
          - type: dot_f1
            value: 75.90528233151184
          - type: dot_precision
            value: 72.0370626469368
          - type: dot_recall
            value: 80.21250384970742
          - type: euclidean_accuracy
            value: 88.4503434625684
          - type: euclidean_ap
            value: 84.91949884748384
          - type: euclidean_f1
            value: 76.92365018444684
          - type: euclidean_precision
            value: 74.53245721712759
          - type: euclidean_recall
            value: 79.47336002463813
          - type: manhattan_accuracy
            value: 88.47556952691427
          - type: manhattan_ap
            value: 84.8963689101517
          - type: manhattan_f1
            value: 76.85901249256395
          - type: manhattan_precision
            value: 74.31693989071039
          - type: manhattan_recall
            value: 79.58115183246073
          - type: max_accuracy
            value: 88.68125897465751
          - type: max_ap
            value: 85.6003454431979
          - type: max_f1
            value: 77.6957163958641
license: mit
language:
  - en

# Fast-Inference with Ctranslate2

Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.

quantized version of BAAI/bge-large-en-v1.5

pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-bge-large-en-v1.5"
model_name_orig="BAAI/bge-large-en-v1.5"

from hf_hub_ctranslate2 import EncoderCT2fromHfHub
model = EncoderCT2fromHfHub(
        # load in int8 on CUDA
        model_name_or_path=model_name,
        device="cuda",
        compute_type="int8_float16"
)
outputs = model.generate(
    text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
    max_length=64,
) # perform downstream tasks on outputs
outputs["pooler_output"]
outputs["last_hidden_state"]
outputs["attention_mask"]

# alternative, use SentenceTransformer Mix-In
# for end-to-end Sentence embeddings generation
# (not pulling from this CT2fast-HF repo)

from hf_hub_ctranslate2 import CT2SentenceTransformer
model = CT2SentenceTransformer(
    model_name_orig, compute_type="int8_float16", device="cuda"
)
embeddings = model.encode(
    ["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
    batch_size=32,
    convert_to_numpy=True,
    normalize_embeddings=True,
)
print(embeddings.shape, embeddings)
scores = (embeddings @ embeddings.T) * 100

# Hint: you can also host this code via REST API and
# via github.com/michaelfeil/infinity  

Checkpoint compatible to ctranslate2>=3.17.1 and hf-hub-ctranslate2>=2.12.0

  • compute_type=int8_float16 for device="cuda"
  • compute_type=int8 for device="cpu"

Converted on 2023-10-13 using

LLama-2 -> removed <pad> token.

Licence and other remarks:

This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.

Original description

FlagEmbedding

Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | 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 databases for LLMs.

************* 🌟Updates🌟 *************

  • 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper :fire:
  • 09/15/2023: The technical report of BGE has been released
  • 09/15/2023: The masive training data 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.
More
  • 09/07/2023: Update fine-tune code: 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; C-MTEB leaderboard is available.
  • 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 (C-MTEB), consisting of 31 test dataset.

Model List

bge is short for BAAI general embedding.

Model Language Description query instruction for retrieval [1]
BAAI/llm-embedder English Inference Fine-tune a unified embedding model to support diverse retrieval augmentation needs for LLMs See README
BAAI/bge-reranker-large Chinese and English Inference Fine-tune a cross-encoder model which is more accurate but less efficient [2]
BAAI/bge-reranker-base Chinese and English Inference Fine-tune a cross-encoder model which is more accurate but less efficient [2]
BAAI/bge-large-en-v1.5 English Inference Fine-tune version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-base-en-v1.5 English Inference Fine-tune version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-small-en-v1.5 English Inference Fine-tune version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-large-zh-v1.5 Chinese Inference Fine-tune 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 Chinese Inference Fine-tune 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 Chinese Inference Fine-tune 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 English Inference Fine-tune :trophy: rank 1st in MTEB leaderboard Represent this sentence for searching relevant passages:
BAAI/bge-base-en English Inference Fine-tune a base-scale model but with similar ability to bge-large-en Represent this sentence for searching relevant passages:
BAAI/bge-small-en English Inference Fine-tune a small-scale model but with competitive performance Represent this sentence for searching relevant passages:
BAAI/bge-large-zh Chinese Inference Fine-tune :trophy: 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-base-zh Chinese Inference Fine-tune 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 Chinese Inference Fine-tune 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

1. How to fine-tune bge embedding model?

Following this example to prepare data and fine-tune your model. Some suggestions:

  • Mine hard negatives following this example, 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.
2. 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).

3. 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.

Usage

Usage for Embedding Model

Here are some examples for using 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_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.

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:

pip install -U sentence-transformers
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). 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-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:

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.

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):

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

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.

  • 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 1024 512 64.23 54.29 46.08 87.12 60.03 83.11 31.61 75.97
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-v1.5 384 512 62.17 51.68 43.82 84.92 58.36 81.59 30.12 74.14
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
  • 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 for a detailed introduction.
Model Embedding dimension Avg Retrieval STS PairClassification Classification Reranking Clustering
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-v1.5 768 63.13 69.49 53.72 79.75 68.07 65.39 47.53
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 1024 64.20 71.53 54.98 78.94 68.32 65.11 48.39
bge-large-zh-noinstruct 1024 63.53 70.55 53 76.77 68.58 64.91 50.01
BAAI/bge-base-zh 768 62.96 69.53 54.12 77.5 67.07 64.91 47.63
multilingual-e5-large 1024 58.79 63.66 48.44 69.89 67.34 56.00 48.23
BAAI/bge-small-zh 512 58.27 63.07 49.45 70.35 63.64 61.48 45.09
m3e-base 768 57.10 56.91 50.47 63.99 67.52 59.34 47.68
m3e-large 1024 57.05 54.75 50.42 64.3 68.2 59.66 48.88
multilingual-e5-base 768 55.48 61.63 46.49 67.07 65.35 54.35 40.68
multilingual-e5-small 384 55.38 59.95 45.27 66.45 65.85 53.86 45.26
text-embedding-ada-002(OpenAI) 1536 53.02 52.0 43.35 69.56 64.31 54.28 45.68
luotuo 1024 49.37 44.4 42.78 66.62 61 49.25 44.39
text2vec-base 768 47.63 38.79 43.41 67.41 62.19 49.45 37.66
text2vec-large 1024 47.36 41.94 44.97 70.86 60.66 49.16 30.02
  • Reranking: See 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 67.28 63.95 60.45 35.46 81.26 84.1 65.42
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 and train them on large-scale pairs data using contrastive learning. You can fine-tune the embedding model on your data following our examples. We also provide a pre-train example. 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.

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. More details please refer to ./FlagEmbedding/reranker/README.md

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. The released models can be used for commercial purposes free of charge.