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README.md

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+---
+tags:
+- mteb
+- sentence transformers
+model-index:
+- name: bge-small-en
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 74.34328358208955
+    - type: ap
+      value: 37.59947775195661
+    - type: f1
+      value: 68.548415491933
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.04527499999999
+    - type: ap
+      value: 89.60696356772135
+    - type: f1
+      value: 93.03361469382438
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 46.08
+    - type: f1
+      value: 45.66249835363254
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 35.205999999999996
+    - type: map_at_10
+      value: 50.782000000000004
+    - type: map_at_100
+      value: 51.547
+    - type: map_at_1000
+      value: 51.554
+    - type: map_at_3
+      value: 46.515
+    - type: map_at_5
+      value: 49.296
+    - type: mrr_at_1
+      value: 35.632999999999996
+    - type: mrr_at_10
+      value: 50.958999999999996
+    - type: mrr_at_100
+      value: 51.724000000000004
+    - type: mrr_at_1000
+      value: 51.731
+    - type: mrr_at_3
+      value: 46.669
+    - type: mrr_at_5
+      value: 49.439
+    - type: ndcg_at_1
+      value: 35.205999999999996
+    - type: ndcg_at_10
+      value: 58.835
+    - type: ndcg_at_100
+      value: 62.095
+    - type: ndcg_at_1000
+      value: 62.255
+    - type: ndcg_at_3
+      value: 50.255
+    - type: ndcg_at_5
+      value: 55.296
+    - type: precision_at_1
+      value: 35.205999999999996
+    - type: precision_at_10
+      value: 8.421
+    - type: precision_at_100
+      value: 0.984
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.365
+    - type: precision_at_5
+      value: 14.680000000000001
+    - type: recall_at_1
+      value: 35.205999999999996
+    - type: recall_at_10
+      value: 84.211
+    - type: recall_at_100
+      value: 98.43499999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 61.095
+    - type: recall_at_5
+      value: 73.4
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.52644476278646
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 39.973045724188964
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.28285314871488
+    - type: mrr
+      value: 74.52743701358659
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.09041909160327
+    - type: cos_sim_spearman
+      value: 79.96266537706944
+    - type: euclidean_pearson
+      value: 79.50774978162241
+    - type: euclidean_spearman
+      value: 79.9144715078551
+    - type: manhattan_pearson
+      value: 79.2062139879302
+    - type: manhattan_spearman
+      value: 79.35000081468212
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.31493506493506
+    - type: f1
+      value: 85.2704557977762
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.6837242810816
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 35.38881249555897
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.884999999999998
+    - type: map_at_10
+      value: 39.574
+    - type: map_at_100
+      value: 40.993
+    - type: map_at_1000
+      value: 41.129
+    - type: map_at_3
+      value: 36.089
+    - type: map_at_5
+      value: 38.191
+    - type: mrr_at_1
+      value: 34.477999999999994
+    - type: mrr_at_10
+      value: 45.411
+    - type: mrr_at_100
+      value: 46.089999999999996
+    - type: mrr_at_1000
+      value: 46.147
+    - type: mrr_at_3
+      value: 42.346000000000004
+    - type: mrr_at_5
+      value: 44.292
+    - type: ndcg_at_1
+      value: 34.477999999999994
+    - type: ndcg_at_10
+      value: 46.123999999999995
+    - type: ndcg_at_100
+      value: 51.349999999999994
+    - type: ndcg_at_1000
+      value: 53.578
+    - type: ndcg_at_3
+      value: 40.824
+    - type: ndcg_at_5
+      value: 43.571
+    - type: precision_at_1
+      value: 34.477999999999994
+    - type: precision_at_10
+      value: 8.841000000000001
+    - type: precision_at_100
+      value: 1.4460000000000002
+    - type: precision_at_1000
+      value: 0.192
+    - type: precision_at_3
+      value: 19.742
+    - type: precision_at_5
+      value: 14.421000000000001
+    - type: recall_at_1
+      value: 27.884999999999998
+    - type: recall_at_10
+      value: 59.087
+    - type: recall_at_100
+      value: 80.609
+    - type: recall_at_1000
+      value: 95.054
+    - type: recall_at_3
+      value: 44.082
+    - type: recall_at_5
+      value: 51.593999999999994
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.639
+    - type: map_at_10
+      value: 40.047
+    - type: map_at_100
+      value: 41.302
+    - type: map_at_1000
+      value: 41.425
+    - type: map_at_3
+      value: 37.406
+    - type: map_at_5
+      value: 38.934000000000005
+    - type: mrr_at_1
+      value: 37.707
+    - type: mrr_at_10
+      value: 46.082
+    - type: mrr_at_100
+      value: 46.745
+    - type: mrr_at_1000
+      value: 46.786
+    - type: mrr_at_3
+      value: 43.980999999999995
+    - type: mrr_at_5
+      value: 45.287
+    - type: ndcg_at_1
+      value: 37.707
+    - type: ndcg_at_10
+      value: 45.525
+    - type: ndcg_at_100
+      value: 49.976
+    - type: ndcg_at_1000
+      value: 51.94499999999999
+    - type: ndcg_at_3
+      value: 41.704
+    - type: ndcg_at_5
+      value: 43.596000000000004
+    - type: precision_at_1
+      value: 37.707
+    - type: precision_at_10
+      value: 8.465
+    - type: precision_at_100
+      value: 1.375
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 19.979
+    - type: precision_at_5
+      value: 14.115
+    - type: recall_at_1
+      value: 30.639
+    - type: recall_at_10
+      value: 54.775
+    - type: recall_at_100
+      value: 73.678
+    - type: recall_at_1000
+      value: 86.142
+    - type: recall_at_3
+      value: 43.230000000000004
+    - type: recall_at_5
+      value: 48.622
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.038
+    - type: map_at_10
+      value: 49.922
+    - type: map_at_100
+      value: 51.032
+    - type: map_at_1000
+      value: 51.085
+    - type: map_at_3
+      value: 46.664
+    - type: map_at_5
+      value: 48.588
+    - type: mrr_at_1
+      value: 43.95
+    - type: mrr_at_10
+      value: 53.566
+    - type: mrr_at_100
+      value: 54.318999999999996
+    - type: mrr_at_1000
+      value: 54.348
+    - type: mrr_at_3
+      value: 51.066
+    - type: mrr_at_5
+      value: 52.649
+    - type: ndcg_at_1
+      value: 43.95
+    - type: ndcg_at_10
+      value: 55.676
+    - type: ndcg_at_100
+      value: 60.126000000000005
+    - type: ndcg_at_1000
+      value: 61.208
+    - type: ndcg_at_3
+      value: 50.20400000000001
+    - type: ndcg_at_5
+      value: 53.038
+    - type: precision_at_1
+      value: 43.95
+    - type: precision_at_10
+      value: 8.953
+    - type: precision_at_100
+      value: 1.2109999999999999
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 22.256999999999998
+    - type: precision_at_5
+      value: 15.524
+    - type: recall_at_1
+      value: 38.038
+    - type: recall_at_10
+      value: 69.15
+    - type: recall_at_100
+      value: 88.31599999999999
+    - type: recall_at_1000
+      value: 95.993
+    - type: recall_at_3
+      value: 54.663
+    - type: recall_at_5
+      value: 61.373
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.872
+    - type: map_at_10
+      value: 32.912
+    - type: map_at_100
+      value: 33.972
+    - type: map_at_1000
+      value: 34.046
+    - type: map_at_3
+      value: 30.361
+    - type: map_at_5
+      value: 31.704
+    - type: mrr_at_1
+      value: 26.779999999999998
+    - type: mrr_at_10
+      value: 34.812
+    - type: mrr_at_100
+      value: 35.754999999999995
+    - type: mrr_at_1000
+      value: 35.809000000000005
+    - type: mrr_at_3
+      value: 32.335
+    - type: mrr_at_5
+      value: 33.64
+    - type: ndcg_at_1
+      value: 26.779999999999998
+    - type: ndcg_at_10
+      value: 37.623
+    - type: ndcg_at_100
+      value: 42.924
+    - type: ndcg_at_1000
+      value: 44.856
+    - type: ndcg_at_3
+      value: 32.574
+    - type: ndcg_at_5
+      value: 34.842
+    - type: precision_at_1
+      value: 26.779999999999998
+    - type: precision_at_10
+      value: 5.729
+    - type: precision_at_100
+      value: 0.886
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 13.559
+    - type: precision_at_5
+      value: 9.469
+    - type: recall_at_1
+      value: 24.872
+    - type: recall_at_10
+      value: 50.400999999999996
+    - type: recall_at_100
+      value: 74.954
+    - type: recall_at_1000
+      value: 89.56
+    - type: recall_at_3
+      value: 36.726
+    - type: recall_at_5
+      value: 42.138999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.803
+    - type: map_at_10
+      value: 24.348
+    - type: map_at_100
+      value: 25.56
+    - type: map_at_1000
+      value: 25.668000000000003
+    - type: map_at_3
+      value: 21.811
+    - type: map_at_5
+      value: 23.287
+    - type: mrr_at_1
+      value: 20.771
+    - type: mrr_at_10
+      value: 28.961
+    - type: mrr_at_100
+      value: 29.979
+    - type: mrr_at_1000
+      value: 30.046
+    - type: mrr_at_3
+      value: 26.555
+    - type: mrr_at_5
+      value: 28.060000000000002
+    - type: ndcg_at_1
+      value: 20.771
+    - type: ndcg_at_10
+      value: 29.335
+    - type: ndcg_at_100
+      value: 35.188
+    - type: ndcg_at_1000
+      value: 37.812
+    - type: ndcg_at_3
+      value: 24.83
+    - type: ndcg_at_5
+      value: 27.119
+    - type: precision_at_1
+      value: 20.771
+    - type: precision_at_10
+      value: 5.4350000000000005
+    - type: precision_at_100
+      value: 0.9480000000000001
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 11.982
+    - type: precision_at_5
+      value: 8.831
+    - type: recall_at_1
+      value: 16.803
+    - type: recall_at_10
+      value: 40.039
+    - type: recall_at_100
+      value: 65.83200000000001
+    - type: recall_at_1000
+      value: 84.478
+    - type: recall_at_3
+      value: 27.682000000000002
+    - type: recall_at_5
+      value: 33.535
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.345
+    - type: map_at_10
+      value: 37.757000000000005
+    - type: map_at_100
+      value: 39.141
+    - type: map_at_1000
+      value: 39.262
+    - type: map_at_3
+      value: 35.183
+    - type: map_at_5
+      value: 36.592
+    - type: mrr_at_1
+      value: 34.649
+    - type: mrr_at_10
+      value: 43.586999999999996
+    - type: mrr_at_100
+      value: 44.481
+    - type: mrr_at_1000
+      value: 44.542
+    - type: mrr_at_3
+      value: 41.29
+    - type: mrr_at_5
+      value: 42.642
+    - type: ndcg_at_1
+      value: 34.649
+    - type: ndcg_at_10
+      value: 43.161
+    - type: ndcg_at_100
+      value: 48.734
+    - type: ndcg_at_1000
+      value: 51.046
+    - type: ndcg_at_3
+      value: 39.118
+    - type: ndcg_at_5
+      value: 41.022
+    - type: precision_at_1
+      value: 34.649
+    - type: precision_at_10
+      value: 7.603
+    - type: precision_at_100
+      value: 1.209
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 18.319
+    - type: precision_at_5
+      value: 12.839
+    - type: recall_at_1
+      value: 28.345
+    - type: recall_at_10
+      value: 53.367
+    - type: recall_at_100
+      value: 76.453
+    - type: recall_at_1000
+      value: 91.82000000000001
+    - type: recall_at_3
+      value: 41.636
+    - type: recall_at_5
+      value: 46.760000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.419
+    - type: map_at_10
+      value: 31.716
+    - type: map_at_100
+      value: 33.152
+    - type: map_at_1000
+      value: 33.267
+    - type: map_at_3
+      value: 28.74
+    - type: map_at_5
+      value: 30.48
+    - type: mrr_at_1
+      value: 28.310999999999996
+    - type: mrr_at_10
+      value: 37.039
+    - type: mrr_at_100
+      value: 38.09
+    - type: mrr_at_1000
+      value: 38.145
+    - type: mrr_at_3
+      value: 34.437
+    - type: mrr_at_5
+      value: 36.024
+    - type: ndcg_at_1
+      value: 28.310999999999996
+    - type: ndcg_at_10
+      value: 37.41
+    - type: ndcg_at_100
+      value: 43.647999999999996
+    - type: ndcg_at_1000
+      value: 46.007
+    - type: ndcg_at_3
+      value: 32.509
+    - type: ndcg_at_5
+      value: 34.943999999999996
+    - type: precision_at_1
+      value: 28.310999999999996
+    - type: precision_at_10
+      value: 6.963
+    - type: precision_at_100
+      value: 1.1860000000000002
+    - type: precision_at_1000
+      value: 0.154
+    - type: precision_at_3
+      value: 15.867999999999999
+    - type: precision_at_5
+      value: 11.507000000000001
+    - type: recall_at_1
+      value: 22.419
+    - type: recall_at_10
+      value: 49.28
+    - type: recall_at_100
+      value: 75.802
+    - type: recall_at_1000
+      value: 92.032
+    - type: recall_at_3
+      value: 35.399
+    - type: recall_at_5
+      value: 42.027
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.669249999999998
+    - type: map_at_10
+      value: 33.332583333333325
+    - type: map_at_100
+      value: 34.557833333333335
+    - type: map_at_1000
+      value: 34.67141666666666
+    - type: map_at_3
+      value: 30.663166666666662
+    - type: map_at_5
+      value: 32.14883333333333
+    - type: mrr_at_1
+      value: 29.193833333333334
+    - type: mrr_at_10
+      value: 37.47625
+    - type: mrr_at_100
+      value: 38.3545
+    - type: mrr_at_1000
+      value: 38.413166666666676
+    - type: mrr_at_3
+      value: 35.06741666666667
+    - type: mrr_at_5
+      value: 36.450666666666656
+    - type: ndcg_at_1
+      value: 29.193833333333334
+    - type: ndcg_at_10
+      value: 38.505416666666676
+    - type: ndcg_at_100
+      value: 43.81125
+    - type: ndcg_at_1000
+      value: 46.09558333333333
+    - type: ndcg_at_3
+      value: 33.90916666666667
+    - type: ndcg_at_5
+      value: 36.07666666666666
+    - type: precision_at_1
+      value: 29.193833333333334
+    - type: precision_at_10
+      value: 6.7251666666666665
+    - type: precision_at_100
+      value: 1.1058333333333332
+    - type: precision_at_1000
+      value: 0.14833333333333332
+    - type: precision_at_3
+      value: 15.554166666666665
+    - type: precision_at_5
+      value: 11.079250000000002
+    - type: recall_at_1
+      value: 24.669249999999998
+    - type: recall_at_10
+      value: 49.75583333333332
+    - type: recall_at_100
+      value: 73.06908333333332
+    - type: recall_at_1000
+      value: 88.91316666666667
+    - type: recall_at_3
+      value: 36.913250000000005
+    - type: recall_at_5
+      value: 42.48641666666666
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.044999999999998
+    - type: map_at_10
+      value: 30.349999999999998
+    - type: map_at_100
+      value: 31.273
+    - type: map_at_1000
+      value: 31.362000000000002
+    - type: map_at_3
+      value: 28.508
+    - type: map_at_5
+      value: 29.369
+    - type: mrr_at_1
+      value: 26.994
+    - type: mrr_at_10
+      value: 33.12
+    - type: mrr_at_100
+      value: 33.904
+    - type: mrr_at_1000
+      value: 33.967000000000006
+    - type: mrr_at_3
+      value: 31.365
+    - type: mrr_at_5
+      value: 32.124
+    - type: ndcg_at_1
+      value: 26.994
+    - type: ndcg_at_10
+      value: 34.214
+    - type: ndcg_at_100
+      value: 38.681
+    - type: ndcg_at_1000
+      value: 40.926
+    - type: ndcg_at_3
+      value: 30.725
+    - type: ndcg_at_5
+      value: 31.967000000000002
+    - type: precision_at_1
+      value: 26.994
+    - type: precision_at_10
+      value: 5.215
+    - type: precision_at_100
+      value: 0.807
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 12.986
+    - type: precision_at_5
+      value: 8.712
+    - type: recall_at_1
+      value: 24.044999999999998
+    - type: recall_at_10
+      value: 43.456
+    - type: recall_at_100
+      value: 63.675000000000004
+    - type: recall_at_1000
+      value: 80.05499999999999
+    - type: recall_at_3
+      value: 33.561
+    - type: recall_at_5
+      value: 36.767
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.672
+    - type: map_at_10
+      value: 22.641
+    - type: map_at_100
+      value: 23.75
+    - type: map_at_1000
+      value: 23.877000000000002
+    - type: map_at_3
+      value: 20.219
+    - type: map_at_5
+      value: 21.648
+    - type: mrr_at_1
+      value: 18.823
+    - type: mrr_at_10
+      value: 26.101999999999997
+    - type: mrr_at_100
+      value: 27.038
+    - type: mrr_at_1000
+      value: 27.118
+    - type: mrr_at_3
+      value: 23.669
+    - type: mrr_at_5
+      value: 25.173000000000002
+    - type: ndcg_at_1
+      value: 18.823
+    - type: ndcg_at_10
+      value: 27.176000000000002
+    - type: ndcg_at_100
+      value: 32.42
+    - type: ndcg_at_1000
+      value: 35.413
+    - type: ndcg_at_3
+      value: 22.756999999999998
+    - type: ndcg_at_5
+      value: 25.032
+    - type: precision_at_1
+      value: 18.823
+    - type: precision_at_10
+      value: 5.034000000000001
+    - type: precision_at_100
+      value: 0.895
+    - type: precision_at_1000
+      value: 0.132
+    - type: precision_at_3
+      value: 10.771
+    - type: precision_at_5
+      value: 8.1
+    - type: recall_at_1
+      value: 15.672
+    - type: recall_at_10
+      value: 37.296
+    - type: recall_at_100
+      value: 60.863
+    - type: recall_at_1000
+      value: 82.234
+    - type: recall_at_3
+      value: 25.330000000000002
+    - type: recall_at_5
+      value: 30.964000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.633
+    - type: map_at_10
+      value: 32.858
+    - type: map_at_100
+      value: 34.038000000000004
+    - type: map_at_1000
+      value: 34.141
+    - type: map_at_3
+      value: 30.209000000000003
+    - type: map_at_5
+      value: 31.567
+    - type: mrr_at_1
+      value: 28.358
+    - type: mrr_at_10
+      value: 36.433
+    - type: mrr_at_100
+      value: 37.352000000000004
+    - type: mrr_at_1000
+      value: 37.41
+    - type: mrr_at_3
+      value: 34.033
+    - type: mrr_at_5
+      value: 35.246
+    - type: ndcg_at_1
+      value: 28.358
+    - type: ndcg_at_10
+      value: 37.973
+    - type: ndcg_at_100
+      value: 43.411
+    - type: ndcg_at_1000
+      value: 45.747
+    - type: ndcg_at_3
+      value: 32.934999999999995
+    - type: ndcg_at_5
+      value: 35.013
+    - type: precision_at_1
+      value: 28.358
+    - type: precision_at_10
+      value: 6.418
+    - type: precision_at_100
+      value: 1.02
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 14.677000000000001
+    - type: precision_at_5
+      value: 10.335999999999999
+    - type: recall_at_1
+      value: 24.633
+    - type: recall_at_10
+      value: 50.048
+    - type: recall_at_100
+      value: 73.821
+    - type: recall_at_1000
+      value: 90.046
+    - type: recall_at_3
+      value: 36.284
+    - type: recall_at_5
+      value: 41.370000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.133
+    - type: map_at_10
+      value: 31.491999999999997
+    - type: map_at_100
+      value: 33.062000000000005
+    - type: map_at_1000
+      value: 33.256
+    - type: map_at_3
+      value: 28.886
+    - type: map_at_5
+      value: 30.262
+    - type: mrr_at_1
+      value: 28.063
+    - type: mrr_at_10
+      value: 36.144
+    - type: mrr_at_100
+      value: 37.14
+    - type: mrr_at_1000
+      value: 37.191
+    - type: mrr_at_3
+      value: 33.762
+    - type: mrr_at_5
+      value: 34.997
+    - type: ndcg_at_1
+      value: 28.063
+    - type: ndcg_at_10
+      value: 36.951
+    - type: ndcg_at_100
+      value: 43.287
+    - type: ndcg_at_1000
+      value: 45.777
+    - type: ndcg_at_3
+      value: 32.786
+    - type: ndcg_at_5
+      value: 34.65
+    - type: precision_at_1
+      value: 28.063
+    - type: precision_at_10
+      value: 7.055
+    - type: precision_at_100
+      value: 1.476
+    - type: precision_at_1000
+      value: 0.22899999999999998
+    - type: precision_at_3
+      value: 15.481
+    - type: precision_at_5
+      value: 11.186
+    - type: recall_at_1
+      value: 23.133
+    - type: recall_at_10
+      value: 47.285
+    - type: recall_at_100
+      value: 76.176
+    - type: recall_at_1000
+      value: 92.176
+    - type: recall_at_3
+      value: 35.223
+    - type: recall_at_5
+      value: 40.142
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.547
+    - type: map_at_10
+      value: 26.374
+    - type: map_at_100
+      value: 27.419
+    - type: map_at_1000
+      value: 27.539
+    - type: map_at_3
+      value: 23.882
+    - type: map_at_5
+      value: 25.163999999999998
+    - type: mrr_at_1
+      value: 21.442
+    - type: mrr_at_10
+      value: 28.458
+    - type: mrr_at_100
+      value: 29.360999999999997
+    - type: mrr_at_1000
+      value: 29.448999999999998
+    - type: mrr_at_3
+      value: 25.97
+    - type: mrr_at_5
+      value: 27.273999999999997
+    - type: ndcg_at_1
+      value: 21.442
+    - type: ndcg_at_10
+      value: 30.897000000000002
+    - type: ndcg_at_100
+      value: 35.99
+    - type: ndcg_at_1000
+      value: 38.832
+    - type: ndcg_at_3
+      value: 25.944
+    - type: ndcg_at_5
+      value: 28.126
+    - type: precision_at_1
+      value: 21.442
+    - type: precision_at_10
+      value: 4.9910000000000005
+    - type: precision_at_100
+      value: 0.8109999999999999
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 11.029
+    - type: precision_at_5
+      value: 7.911
+    - type: recall_at_1
+      value: 19.547
+    - type: recall_at_10
+      value: 42.886
+    - type: recall_at_100
+      value: 66.64999999999999
+    - type: recall_at_1000
+      value: 87.368
+    - type: recall_at_3
+      value: 29.143
+    - type: recall_at_5
+      value: 34.544000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.572
+    - type: map_at_10
+      value: 25.312
+    - type: map_at_100
+      value: 27.062
+    - type: map_at_1000
+      value: 27.253
+    - type: map_at_3
+      value: 21.601
+    - type: map_at_5
+      value: 23.473
+    - type: mrr_at_1
+      value: 34.984
+    - type: mrr_at_10
+      value: 46.406
+    - type: mrr_at_100
+      value: 47.179
+    - type: mrr_at_1000
+      value: 47.21
+    - type: mrr_at_3
+      value: 43.485
+    - type: mrr_at_5
+      value: 45.322
+    - type: ndcg_at_1
+      value: 34.984
+    - type: ndcg_at_10
+      value: 34.344
+    - type: ndcg_at_100
+      value: 41.015
+    - type: ndcg_at_1000
+      value: 44.366
+    - type: ndcg_at_3
+      value: 29.119
+    - type: ndcg_at_5
+      value: 30.825999999999997
+    - type: precision_at_1
+      value: 34.984
+    - type: precision_at_10
+      value: 10.358
+    - type: precision_at_100
+      value: 1.762
+    - type: precision_at_1000
+      value: 0.23900000000000002
+    - type: precision_at_3
+      value: 21.368000000000002
+    - type: precision_at_5
+      value: 15.948
+    - type: recall_at_1
+      value: 15.572
+    - type: recall_at_10
+      value: 39.367999999999995
+    - type: recall_at_100
+      value: 62.183
+    - type: recall_at_1000
+      value: 80.92200000000001
+    - type: recall_at_3
+      value: 26.131999999999998
+    - type: recall_at_5
+      value: 31.635999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.848
+    - type: map_at_10
+      value: 19.25
+    - type: map_at_100
+      value: 27.193
+    - type: map_at_1000
+      value: 28.721999999999998
+    - type: map_at_3
+      value: 13.968
+    - type: map_at_5
+      value: 16.283
+    - type: mrr_at_1
+      value: 68.75
+    - type: mrr_at_10
+      value: 76.25
+    - type: mrr_at_100
+      value: 76.534
+    - type: mrr_at_1000
+      value: 76.53999999999999
+    - type: mrr_at_3
+      value: 74.667
+    - type: mrr_at_5
+      value: 75.86699999999999
+    - type: ndcg_at_1
+      value: 56.00000000000001
+    - type: ndcg_at_10
+      value: 41.426
+    - type: ndcg_at_100
+      value: 45.660000000000004
+    - type: ndcg_at_1000
+      value: 53.02
+    - type: ndcg_at_3
+      value: 46.581
+    - type: ndcg_at_5
+      value: 43.836999999999996
+    - type: precision_at_1
+      value: 68.75
+    - type: precision_at_10
+      value: 32.800000000000004
+    - type: precision_at_100
+      value: 10.440000000000001
+    - type: precision_at_1000
+      value: 1.9980000000000002
+    - type: precision_at_3
+      value: 49.667
+    - type: precision_at_5
+      value: 42.25
+    - type: recall_at_1
+      value: 8.848
+    - type: recall_at_10
+      value: 24.467
+    - type: recall_at_100
+      value: 51.344
+    - type: recall_at_1000
+      value: 75.235
+    - type: recall_at_3
+      value: 15.329
+    - type: recall_at_5
+      value: 18.892999999999997
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 48.95
+    - type: f1
+      value: 43.44563593360779
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 78.036
+    - type: map_at_10
+      value: 85.639
+    - type: map_at_100
+      value: 85.815
+    - type: map_at_1000
+      value: 85.829
+    - type: map_at_3
+      value: 84.795
+    - type: map_at_5
+      value: 85.336
+    - type: mrr_at_1
+      value: 84.353
+    - type: mrr_at_10
+      value: 90.582
+    - type: mrr_at_100
+      value: 90.617
+    - type: mrr_at_1000
+      value: 90.617
+    - type: mrr_at_3
+      value: 90.132
+    - type: mrr_at_5
+      value: 90.447
+    - type: ndcg_at_1
+      value: 84.353
+    - type: ndcg_at_10
+      value: 89.003
+    - type: ndcg_at_100
+      value: 89.60000000000001
+    - type: ndcg_at_1000
+      value: 89.836
+    - type: ndcg_at_3
+      value: 87.81400000000001
+    - type: ndcg_at_5
+      value: 88.478
+    - type: precision_at_1
+      value: 84.353
+    - type: precision_at_10
+      value: 10.482
+    - type: precision_at_100
+      value: 1.099
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 33.257999999999996
+    - type: precision_at_5
+      value: 20.465
+    - type: recall_at_1
+      value: 78.036
+    - type: recall_at_10
+      value: 94.517
+    - type: recall_at_100
+      value: 96.828
+    - type: recall_at_1000
+      value: 98.261
+    - type: recall_at_3
+      value: 91.12
+    - type: recall_at_5
+      value: 92.946
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.191
+    - type: map_at_10
+      value: 32.369
+    - type: map_at_100
+      value: 34.123999999999995
+    - type: map_at_1000
+      value: 34.317
+    - type: map_at_3
+      value: 28.71
+    - type: map_at_5
+      value: 30.607
+    - type: mrr_at_1
+      value: 40.894999999999996
+    - type: mrr_at_10
+      value: 48.842
+    - type: mrr_at_100
+      value: 49.599
+    - type: mrr_at_1000
+      value: 49.647000000000006
+    - type: mrr_at_3
+      value: 46.785
+    - type: mrr_at_5
+      value: 47.672
+    - type: ndcg_at_1
+      value: 40.894999999999996
+    - type: ndcg_at_10
+      value: 39.872
+    - type: ndcg_at_100
+      value: 46.126
+    - type: ndcg_at_1000
+      value: 49.476
+    - type: ndcg_at_3
+      value: 37.153000000000006
+    - type: ndcg_at_5
+      value: 37.433
+    - type: precision_at_1
+      value: 40.894999999999996
+    - type: precision_at_10
+      value: 10.818
+    - type: precision_at_100
+      value: 1.73
+    - type: precision_at_1000
+      value: 0.231
+    - type: precision_at_3
+      value: 25.051000000000002
+    - type: precision_at_5
+      value: 17.531
+    - type: recall_at_1
+      value: 20.191
+    - type: recall_at_10
+      value: 45.768
+    - type: recall_at_100
+      value: 68.82000000000001
+    - type: recall_at_1000
+      value: 89.133
+    - type: recall_at_3
+      value: 33.296
+    - type: recall_at_5
+      value: 38.022
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.257
+    - type: map_at_10
+      value: 61.467000000000006
+    - type: map_at_100
+      value: 62.364
+    - type: map_at_1000
+      value: 62.424
+    - type: map_at_3
+      value: 58.228
+    - type: map_at_5
+      value: 60.283
+    - type: mrr_at_1
+      value: 78.515
+    - type: mrr_at_10
+      value: 84.191
+    - type: mrr_at_100
+      value: 84.378
+    - type: mrr_at_1000
+      value: 84.385
+    - type: mrr_at_3
+      value: 83.284
+    - type: mrr_at_5
+      value: 83.856
+    - type: ndcg_at_1
+      value: 78.515
+    - type: ndcg_at_10
+      value: 69.78999999999999
+    - type: ndcg_at_100
+      value: 72.886
+    - type: ndcg_at_1000
+      value: 74.015
+    - type: ndcg_at_3
+      value: 65.23
+    - type: ndcg_at_5
+      value: 67.80199999999999
+    - type: precision_at_1
+      value: 78.515
+    - type: precision_at_10
+      value: 14.519000000000002
+    - type: precision_at_100
+      value: 1.694
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 41.702
+    - type: precision_at_5
+      value: 27.046999999999997
+    - type: recall_at_1
+      value: 39.257
+    - type: recall_at_10
+      value: 72.59299999999999
+    - type: recall_at_100
+      value: 84.679
+    - type: recall_at_1000
+      value: 92.12
+    - type: recall_at_3
+      value: 62.552
+    - type: recall_at_5
+      value: 67.616
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 91.5152
+    - type: ap
+      value: 87.64584669595709
+    - type: f1
+      value: 91.50605576428437
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.926000000000002
+    - type: map_at_10
+      value: 34.049
+    - type: map_at_100
+      value: 35.213
+    - type: map_at_1000
+      value: 35.265
+    - type: map_at_3
+      value: 30.309
+    - type: map_at_5
+      value: 32.407000000000004
+    - type: mrr_at_1
+      value: 22.55
+    - type: mrr_at_10
+      value: 34.657
+    - type: mrr_at_100
+      value: 35.760999999999996
+    - type: mrr_at_1000
+      value: 35.807
+    - type: mrr_at_3
+      value: 30.989
+    - type: mrr_at_5
+      value: 33.039
+    - type: ndcg_at_1
+      value: 22.55
+    - type: ndcg_at_10
+      value: 40.842
+    - type: ndcg_at_100
+      value: 46.436
+    - type: ndcg_at_1000
+      value: 47.721999999999994
+    - type: ndcg_at_3
+      value: 33.209
+    - type: ndcg_at_5
+      value: 36.943
+    - type: precision_at_1
+      value: 22.55
+    - type: precision_at_10
+      value: 6.447
+    - type: precision_at_100
+      value: 0.9249999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.136000000000001
+    - type: precision_at_5
+      value: 10.381
+    - type: recall_at_1
+      value: 21.926000000000002
+    - type: recall_at_10
+      value: 61.724999999999994
+    - type: recall_at_100
+      value: 87.604
+    - type: recall_at_1000
+      value: 97.421
+    - type: recall_at_3
+      value: 40.944
+    - type: recall_at_5
+      value: 49.915
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.54765161878704
+    - type: f1
+      value: 93.3298945415573
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 75.71591427268582
+    - type: f1
+      value: 59.32113870474471
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 75.83053127101547
+    - type: f1
+      value: 73.60757944876475
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.72562205783457
+    - type: f1
+      value: 78.63761662505502
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.37935633767996
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.55270546130387
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.462692753143834
+    - type: mrr
+      value: 31.497569753511563
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.646
+    - type: map_at_10
+      value: 12.498
+    - type: map_at_100
+      value: 15.486
+    - type: map_at_1000
+      value: 16.805999999999997
+    - type: map_at_3
+      value: 9.325
+    - type: map_at_5
+      value: 10.751
+    - type: mrr_at_1
+      value: 43.034
+    - type: mrr_at_10
+      value: 52.662
+    - type: mrr_at_100
+      value: 53.189
+    - type: mrr_at_1000
+      value: 53.25
+    - type: mrr_at_3
+      value: 50.929
+    - type: mrr_at_5
+      value: 51.92
+    - type: ndcg_at_1
+      value: 41.796
+    - type: ndcg_at_10
+      value: 33.477000000000004
+    - type: ndcg_at_100
+      value: 29.996000000000002
+    - type: ndcg_at_1000
+      value: 38.864
+    - type: ndcg_at_3
+      value: 38.940000000000005
+    - type: ndcg_at_5
+      value: 36.689
+    - type: precision_at_1
+      value: 43.034
+    - type: precision_at_10
+      value: 24.799
+    - type: precision_at_100
+      value: 7.432999999999999
+    - type: precision_at_1000
+      value: 1.9929999999999999
+    - type: precision_at_3
+      value: 36.842000000000006
+    - type: precision_at_5
+      value: 32.135999999999996
+    - type: recall_at_1
+      value: 5.646
+    - type: recall_at_10
+      value: 15.963
+    - type: recall_at_100
+      value: 29.492
+    - type: recall_at_1000
+      value: 61.711000000000006
+    - type: recall_at_3
+      value: 10.585
+    - type: recall_at_5
+      value: 12.753999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.602
+    - type: map_at_10
+      value: 41.545
+    - type: map_at_100
+      value: 42.644999999999996
+    - type: map_at_1000
+      value: 42.685
+    - type: map_at_3
+      value: 37.261
+    - type: map_at_5
+      value: 39.706
+    - type: mrr_at_1
+      value: 31.141000000000002
+    - type: mrr_at_10
+      value: 44.139
+    - type: mrr_at_100
+      value: 44.997
+    - type: mrr_at_1000
+      value: 45.025999999999996
+    - type: mrr_at_3
+      value: 40.503
+    - type: mrr_at_5
+      value: 42.64
+    - type: ndcg_at_1
+      value: 31.141000000000002
+    - type: ndcg_at_10
+      value: 48.995
+    - type: ndcg_at_100
+      value: 53.788000000000004
+    - type: ndcg_at_1000
+      value: 54.730000000000004
+    - type: ndcg_at_3
+      value: 40.844
+    - type: ndcg_at_5
+      value: 44.955
+    - type: precision_at_1
+      value: 31.141000000000002
+    - type: precision_at_10
+      value: 8.233
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 18.579
+    - type: precision_at_5
+      value: 13.533999999999999
+    - type: recall_at_1
+      value: 27.602
+    - type: recall_at_10
+      value: 69.216
+    - type: recall_at_100
+      value: 90.252
+    - type: recall_at_1000
+      value: 97.27
+    - type: recall_at_3
+      value: 47.987
+    - type: recall_at_5
+      value: 57.438
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.949
+    - type: map_at_10
+      value: 84.89999999999999
+    - type: map_at_100
+      value: 85.531
+    - type: map_at_1000
+      value: 85.548
+    - type: map_at_3
+      value: 82.027
+    - type: map_at_5
+      value: 83.853
+    - type: mrr_at_1
+      value: 81.69999999999999
+    - type: mrr_at_10
+      value: 87.813
+    - type: mrr_at_100
+      value: 87.917
+    - type: mrr_at_1000
+      value: 87.91799999999999
+    - type: mrr_at_3
+      value: 86.938
+    - type: mrr_at_5
+      value: 87.53999999999999
+    - type: ndcg_at_1
+      value: 81.75
+    - type: ndcg_at_10
+      value: 88.55499999999999
+    - type: ndcg_at_100
+      value: 89.765
+    - type: ndcg_at_1000
+      value: 89.871
+    - type: ndcg_at_3
+      value: 85.905
+    - type: ndcg_at_5
+      value: 87.41
+    - type: precision_at_1
+      value: 81.75
+    - type: precision_at_10
+      value: 13.403
+    - type: precision_at_100
+      value: 1.528
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.597
+    - type: precision_at_5
+      value: 24.69
+    - type: recall_at_1
+      value: 70.949
+    - type: recall_at_10
+      value: 95.423
+    - type: recall_at_100
+      value: 99.509
+    - type: recall_at_1000
+      value: 99.982
+    - type: recall_at_3
+      value: 87.717
+    - type: recall_at_5
+      value: 92.032
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 51.76962893449579
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 62.32897690686379
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.478
+    - type: map_at_10
+      value: 11.994
+    - type: map_at_100
+      value: 13.977
+    - type: map_at_1000
+      value: 14.295
+    - type: map_at_3
+      value: 8.408999999999999
+    - type: map_at_5
+      value: 10.024
+    - type: mrr_at_1
+      value: 22.1
+    - type: mrr_at_10
+      value: 33.526
+    - type: mrr_at_100
+      value: 34.577000000000005
+    - type: mrr_at_1000
+      value: 34.632000000000005
+    - type: mrr_at_3
+      value: 30.217
+    - type: mrr_at_5
+      value: 31.962000000000003
+    - type: ndcg_at_1
+      value: 22.1
+    - type: ndcg_at_10
+      value: 20.191
+    - type: ndcg_at_100
+      value: 27.954
+    - type: ndcg_at_1000
+      value: 33.491
+    - type: ndcg_at_3
+      value: 18.787000000000003
+    - type: ndcg_at_5
+      value: 16.378999999999998
+    - type: precision_at_1
+      value: 22.1
+    - type: precision_at_10
+      value: 10.69
+    - type: precision_at_100
+      value: 2.1919999999999997
+    - type: precision_at_1000
+      value: 0.35200000000000004
+    - type: precision_at_3
+      value: 17.732999999999997
+    - type: precision_at_5
+      value: 14.499999999999998
+    - type: recall_at_1
+      value: 4.478
+    - type: recall_at_10
+      value: 21.657
+    - type: recall_at_100
+      value: 44.54
+    - type: recall_at_1000
+      value: 71.542
+    - type: recall_at_3
+      value: 10.778
+    - type: recall_at_5
+      value: 14.687
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.82325259156718
+    - type: cos_sim_spearman
+      value: 79.2463589100662
+    - type: euclidean_pearson
+      value: 80.48318380496771
+    - type: euclidean_spearman
+      value: 79.34451935199979
+    - type: manhattan_pearson
+      value: 80.39041824178759
+    - type: manhattan_spearman
+      value: 79.23002892700211
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.74130231431258
+    - type: cos_sim_spearman
+      value: 78.36856568042397
+    - type: euclidean_pearson
+      value: 82.48301631890303
+    - type: euclidean_spearman
+      value: 78.28376980722732
+    - type: manhattan_pearson
+      value: 82.43552075450525
+    - type: manhattan_spearman
+      value: 78.22702443947126
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.96138619461459
+    - type: cos_sim_spearman
+      value: 81.85436343502379
+    - type: euclidean_pearson
+      value: 81.82895226665367
+    - type: euclidean_spearman
+      value: 82.22707349602916
+    - type: manhattan_pearson
+      value: 81.66303369445873
+    - type: manhattan_spearman
+      value: 82.05030197179455
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.05481244198648
+    - type: cos_sim_spearman
+      value: 80.85052504637808
+    - type: euclidean_pearson
+      value: 80.86728419744497
+    - type: euclidean_spearman
+      value: 81.033786401512
+    - type: manhattan_pearson
+      value: 80.90107531061103
+    - type: manhattan_spearman
+      value: 81.11374116827795
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.615220756399
+    - type: cos_sim_spearman
+      value: 86.46858500002092
+    - type: euclidean_pearson
+      value: 86.08307800247586
+    - type: euclidean_spearman
+      value: 86.72691443870013
+    - type: manhattan_pearson
+      value: 85.96155594487269
+    - type: manhattan_spearman
+      value: 86.605909505275
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.14363913634436
+    - type: cos_sim_spearman
+      value: 84.48430226487102
+    - type: euclidean_pearson
+      value: 83.75303424801902
+    - type: euclidean_spearman
+      value: 84.56762380734538
+    - type: manhattan_pearson
+      value: 83.6135447165928
+    - type: manhattan_spearman
+      value: 84.39898212616731
+  - 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: 85.09909252554525
+    - type: cos_sim_spearman
+      value: 85.70951402743276
+    - type: euclidean_pearson
+      value: 87.1991936239908
+    - type: euclidean_spearman
+      value: 86.07745840612071
+    - type: manhattan_pearson
+      value: 87.25039137549952
+    - type: manhattan_spearman
+      value: 85.99938746659761
+  - 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: 63.529332093413615
+    - type: cos_sim_spearman
+      value: 65.38177340147439
+    - type: euclidean_pearson
+      value: 66.35278011412136
+    - type: euclidean_spearman
+      value: 65.47147267032997
+    - type: manhattan_pearson
+      value: 66.71804682408693
+    - type: manhattan_spearman
+      value: 65.67406521423597
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.45802942885662
+    - type: cos_sim_spearman
+      value: 84.8853341842566
+    - type: euclidean_pearson
+      value: 84.60915021096707
+    - type: euclidean_spearman
+      value: 85.11181242913666
+    - type: manhattan_pearson
+      value: 84.38600521210364
+    - type: manhattan_spearman
+      value: 84.89045417981723
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 85.92793380635129
+    - type: mrr
+      value: 95.85834191226348
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 55.74400000000001
+    - type: map_at_10
+      value: 65.455
+    - type: map_at_100
+      value: 66.106
+    - type: map_at_1000
+      value: 66.129
+    - type: map_at_3
+      value: 62.719
+    - type: map_at_5
+      value: 64.441
+    - type: mrr_at_1
+      value: 58.667
+    - type: mrr_at_10
+      value: 66.776
+    - type: mrr_at_100
+      value: 67.363
+    - type: mrr_at_1000
+      value: 67.384
+    - type: mrr_at_3
+      value: 64.889
+    - type: mrr_at_5
+      value: 66.122
+    - type: ndcg_at_1
+      value: 58.667
+    - type: ndcg_at_10
+      value: 69.904
+    - type: ndcg_at_100
+      value: 72.807
+    - type: ndcg_at_1000
+      value: 73.423
+    - type: ndcg_at_3
+      value: 65.405
+    - type: ndcg_at_5
+      value: 67.86999999999999
+    - type: precision_at_1
+      value: 58.667
+    - type: precision_at_10
+      value: 9.3
+    - type: precision_at_100
+      value: 1.08
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 25.444
+    - type: precision_at_5
+      value: 17
+    - type: recall_at_1
+      value: 55.74400000000001
+    - type: recall_at_10
+      value: 82.122
+    - type: recall_at_100
+      value: 95.167
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 70.14399999999999
+    - type: recall_at_5
+      value: 76.417
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.86534653465347
+    - type: cos_sim_ap
+      value: 96.54142419791388
+    - type: cos_sim_f1
+      value: 93.07535641547861
+    - type: cos_sim_precision
+      value: 94.81327800829875
+    - type: cos_sim_recall
+      value: 91.4
+    - type: dot_accuracy
+      value: 99.86435643564356
+    - type: dot_ap
+      value: 96.53682260449868
+    - type: dot_f1
+      value: 92.98515104966718
+    - type: dot_precision
+      value: 95.27806925498426
+    - type: dot_recall
+      value: 90.8
+    - type: euclidean_accuracy
+      value: 99.86336633663366
+    - type: euclidean_ap
+      value: 96.5228676185697
+    - type: euclidean_f1
+      value: 92.9735234215886
+    - type: euclidean_precision
+      value: 94.70954356846472
+    - type: euclidean_recall
+      value: 91.3
+    - type: manhattan_accuracy
+      value: 99.85841584158416
+    - type: manhattan_ap
+      value: 96.50392760934032
+    - type: manhattan_f1
+      value: 92.84642321160581
+    - type: manhattan_precision
+      value: 92.8928928928929
+    - type: manhattan_recall
+      value: 92.80000000000001
+    - type: max_accuracy
+      value: 99.86534653465347
+    - type: max_ap
+      value: 96.54142419791388
+    - type: max_f1
+      value: 93.07535641547861
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 61.08285408766616
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.640675309010604
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.20333913710715
+    - type: mrr
+      value: 54.088813555725324
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.79465221925075
+    - type: cos_sim_spearman
+      value: 30.530816059163634
+    - type: dot_pearson
+      value: 31.364837244718043
+    - type: dot_spearman
+      value: 30.79726823684003
+  - 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: 1.735
+    - type: map_at_100
+      value: 8.978
+    - type: map_at_1000
+      value: 20.851
+    - type: map_at_3
+      value: 0.613
+    - type: map_at_5
+      value: 0.964
+    - type: mrr_at_1
+      value: 88
+    - type: mrr_at_10
+      value: 92.867
+    - type: mrr_at_100
+      value: 92.867
+    - type: mrr_at_1000
+      value: 92.867
+    - type: mrr_at_3
+      value: 92.667
+    - type: mrr_at_5
+      value: 92.667
+    - type: ndcg_at_1
+      value: 82
+    - type: ndcg_at_10
+      value: 73.164
+    - type: ndcg_at_100
+      value: 51.878
+    - type: ndcg_at_1000
+      value: 44.864
+    - type: ndcg_at_3
+      value: 79.184
+    - type: ndcg_at_5
+      value: 76.39
+    - type: precision_at_1
+      value: 88
+    - type: precision_at_10
+      value: 76.2
+    - type: precision_at_100
+      value: 52.459999999999994
+    - type: precision_at_1000
+      value: 19.692
+    - type: precision_at_3
+      value: 82.667
+    - type: precision_at_5
+      value: 80
+    - type: recall_at_1
+      value: 0.22599999999999998
+    - type: recall_at_10
+      value: 1.942
+    - type: recall_at_100
+      value: 12.342
+    - type: recall_at_1000
+      value: 41.42
+    - type: recall_at_3
+      value: 0.637
+    - type: recall_at_5
+      value: 1.034
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 3.567
+    - type: map_at_10
+      value: 13.116
+    - type: map_at_100
+      value: 19.39
+    - type: map_at_1000
+      value: 20.988
+    - type: map_at_3
+      value: 7.109
+    - type: map_at_5
+      value: 9.950000000000001
+    - type: mrr_at_1
+      value: 42.857
+    - type: mrr_at_10
+      value: 57.404999999999994
+    - type: mrr_at_100
+      value: 58.021
+    - type: mrr_at_1000
+      value: 58.021
+    - type: mrr_at_3
+      value: 54.762
+    - type: mrr_at_5
+      value: 56.19
+    - type: ndcg_at_1
+      value: 38.775999999999996
+    - type: ndcg_at_10
+      value: 30.359
+    - type: ndcg_at_100
+      value: 41.284
+    - type: ndcg_at_1000
+      value: 52.30200000000001
+    - type: ndcg_at_3
+      value: 36.744
+    - type: ndcg_at_5
+      value: 34.326
+    - type: precision_at_1
+      value: 42.857
+    - type: precision_at_10
+      value: 26.122
+    - type: precision_at_100
+      value: 8.082
+    - type: precision_at_1000
+      value: 1.559
+    - type: precision_at_3
+      value: 40.136
+    - type: precision_at_5
+      value: 35.510000000000005
+    - type: recall_at_1
+      value: 3.567
+    - type: recall_at_10
+      value: 19.045
+    - type: recall_at_100
+      value: 49.979
+    - type: recall_at_1000
+      value: 84.206
+    - type: recall_at_3
+      value: 8.52
+    - type: recall_at_5
+      value: 13.103000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 68.8394
+    - type: ap
+      value: 13.454399712443099
+    - type: f1
+      value: 53.04963076364322
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 60.546123372948514
+    - type: f1
+      value: 60.86952793277713
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.10042955060234
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.03308100375514
+    - type: cos_sim_ap
+      value: 71.08284605869684
+    - type: cos_sim_f1
+      value: 65.42539436255494
+    - type: cos_sim_precision
+      value: 64.14807302231237
+    - type: cos_sim_recall
+      value: 66.75461741424802
+    - type: dot_accuracy
+      value: 84.68736961316088
+    - type: dot_ap
+      value: 69.20524036530992
+    - type: dot_f1
+      value: 63.54893953365829
+    - type: dot_precision
+      value: 63.45698500394633
+    - type: dot_recall
+      value: 63.641160949868066
+    - type: euclidean_accuracy
+      value: 85.07480479227513
+    - type: euclidean_ap
+      value: 71.14592761009864
+    - type: euclidean_f1
+      value: 65.43814432989691
+    - type: euclidean_precision
+      value: 63.95465994962216
+    - type: euclidean_recall
+      value: 66.99208443271768
+    - type: manhattan_accuracy
+      value: 85.06288370984085
+    - type: manhattan_ap
+      value: 71.07289742593868
+    - type: manhattan_f1
+      value: 65.37585421412301
+    - type: manhattan_precision
+      value: 62.816147859922175
+    - type: manhattan_recall
+      value: 68.15303430079156
+    - type: max_accuracy
+      value: 85.07480479227513
+    - type: max_ap
+      value: 71.14592761009864
+    - type: max_f1
+      value: 65.43814432989691
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.79058485659952
+    - type: cos_sim_ap
+      value: 83.7183187008759
+    - type: cos_sim_f1
+      value: 75.86921142180798
+    - type: cos_sim_precision
+      value: 73.00683371298405
+    - type: cos_sim_recall
+      value: 78.96519864490298
+    - type: dot_accuracy
+      value: 87.0085768618776
+    - type: dot_ap
+      value: 81.87467488474279
+    - type: dot_f1
+      value: 74.04188363990559
+    - type: dot_precision
+      value: 72.10507114191901
+    - type: dot_recall
+      value: 76.08561749307053
+    - type: euclidean_accuracy
+      value: 87.8332751193387
+    - type: euclidean_ap
+      value: 83.83585648120315
+    - type: euclidean_f1
+      value: 76.02582177042369
+    - type: euclidean_precision
+      value: 73.36388371759989
+    - type: euclidean_recall
+      value: 78.88820449645827
+    - type: manhattan_accuracy
+      value: 87.87208444910156
+    - type: manhattan_ap
+      value: 83.8101950642973
+    - type: manhattan_f1
+      value: 75.90454195535027
+    - type: manhattan_precision
+      value: 72.44419564761039
+    - type: manhattan_recall
+      value: 79.71204188481676
+    - type: max_accuracy
+      value: 87.87208444910156
+    - type: max_ap
+      value: 83.83585648120315
+    - type: max_f1
+      value: 76.02582177042369
+license: mit
+language:
+- en
+pipeline_tag: sentence-similarity
+---
+
+
+<h1 align="center">FlagEmbedding</h1>
+
+
+<h4 align="center">
+    <p>
+        <a href=#model-list>Model List</a> | 
+        <a href=#usage>Usage</a>  |
+        <a href="#evaluation">Evaluation</a> |
+        <a href="#train">Train</a> |
+        <a href="#contact">Contact</a> |
+        <a href="#license">License</a> 
+    <p>
+</h4>
+
+More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
+
+[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
+
+FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification,  clustering, or semantic search.
+And it also can be used in vector database for LLMs.
+
+************* 🌟**Updates**🌟 *************
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [**this**](#using-langchain); C-MTEB **leaderboard** is [avaliable](https://huggingface.co/spaces/mteb/leaderboard).  
+- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
+- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!**  
+- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.   
+
+
+## Model List
+
+`bge` is short for `BAAI general embedding`.
+
+|              Model              | Language | Description | query instruction for retrieval\* |
+|:-------------------------------|:--------:| :--------:| :--------:|
+|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English |  rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English |  rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) |   Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark |   |
+|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+
+\*: If you need to search the **long** relevant passages to a **short** query (s2p retrieval task), you need to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** need to be added to passages.
+
+## Usage 
+
+Here are some examples to use `bge` models with 
+[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
+
+```python
+from FlagEmbedding import FlagModel
+sentences = ["样例数据-1", "样例数据-2"]
+model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：")
+embeddings_1 = model.encode(sentences)
+embeddings_2 = model.encode(sentences)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+
+# for s2p(short query to long passage) retrieval task, please use encode_queries() which will automatically add the instruction to each query
+# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+q_embeddings = model.encode_queries(queries)
+p_embeddings = model.encode(passages)
+scores = q_embeddings @ p_embeddings.T
+```
+The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). 
+
+FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU.
+
+
+#### Using Sentence-Transformers
+
+Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["样例数据-1", "样例数据-2"]
+model = SentenceTransformer('BAAI/bge-large-zh')
+embeddings_1 = model.encode(sentences, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+For s2p(short query to long passage) retrieval task, 
+each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
+But the instruction is not needed for passages.
+```python
+from sentence_transformers import SentenceTransformer
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+instruction = "为这个句子生成表示以用于检索相关文章："
+
+model = SentenceTransformer('BAAI/bge-large-zh')
+q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
+p_embeddings = model.encode(passages, normalize_embeddings=True)
+scores = q_embeddings @ p_embeddings.T
+```
+
+#### Using Langchain 
+
+You can use `bge` in langchain like this:
+```python
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+model_name = "BAAI/bge-small-en"
+model_kwargs = {'device': 'cuda'}
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+model_norm = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+```
+
+
+#### Using HuggingFace Transformers
+
+With transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of first token (i.e., [CLS]) as the sentence embedding.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Sentences we want sentence embeddings for
+sentences = ["样例数据-1", "样例数据-2"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh')
+model = AutoModel.from_pretrained('BAAI/bge-large-zh')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
+# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+    # Perform pooling. In this case, cls pooling.
+    sentence_embeddings = model_output[0][:, 0]
+# normalize embeddings
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+print("Sentence embeddings:", sentence_embeddings)
+```
+
+
+## Evaluation  
+`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
+More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). 
+
+- **MTEB**:   
+
+| Model Name |  Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) |  STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [**bge-large-en**](https://huggingface.co/BAAI/bge-large-en) |  1024 | 512 | **63.98** |  **53.9** | **46.98** | 85.8 | **59.48** | 81.56 | 32.06 | **76.21** | 
+| [**bge-base-en**](https://huggingface.co/BAAI/bge-base-en) |  768 | 512 |  63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | 
+| [gte-large](https://huggingface.co/thenlper/gte-large) |  1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
+| [gte-base](https://huggingface.co/thenlper/gte-base) 	|  768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) |  1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
+| [**bge-small-en**](https://huggingface.co/BAAI/bge-small-en) |  384 | 512 | 62.11 |  51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |  
+| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) |  768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) |  768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
+| [gte-small](https://huggingface.co/thenlper/gte-small) |  384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) |  768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	|  768 | 514 	| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	|  4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
+| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 	|  384 | 512 	| 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 |
+| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 	|  384 | 512 	| 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 |
+| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) 	|  768 | 512 	| 56.00 | 41.88 | 41.1 	| 82.54 | 53.14 | 76.51 | 30.36 | 66.68 |
+| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	|  768 | 512 	| 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 |
+
+
+
+- **C-MTEB**:  
+We create a benchmark C-MTEB for chinese text embedding which consists of  31 datasets from 6 tasks. 
+Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
+ 
+| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 |  
+| [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** |   
+| [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) |  768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 |  
+| [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 |  
+| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 |  
+| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 |  
+| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 |  
+| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | 
+| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 |  
+| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 |  
+
+
+
+## Train
+This section will introduce the way we used to train the general embedding. 
+The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), 
+and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md).
+
+
+**1. RetroMAE Pre-train**  
+We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), 
+which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). 
+The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. 
+In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively.
+We used the AdamW optimizer and the learning rate is 2e-5.
+
+**Pre-training data**:
+- English: 
+    - [Pile](https://pile.eleuther.ai/)
+    - [wikipedia](https://huggingface.co/datasets/wikipedia)
+    - [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus)
+- Chinese: 
+    - [wudao](https://github.com/BAAI-WuDao/Data)
+
+
+**2. Finetune**  
+We fine-tune the model using a contrastive objective. 
+The format of input data is a triple`(query, positive, negative)`. 
+Besides the negative in the triple, we also adopt in-batch negatives strategy. 
+We employ the cross-device negatives sharing method to share negatives among different GPUs, 
+which can dramatically **increase the number of negatives**.
+
+We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). 
+We used the AdamW optimizer and the learning rate is 1e-5.
+The temperature for contrastive loss is 0.01.
+
+Besides, we add instruction to the query for s2p(short query to long passage) retrieval task in the training (add nothing to passages). 
+For English, the instruction is `Represent this sentence for searching relevant passages: `;
+For Chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`.
+In the evaluation, the instruction should be added for queries in retrieval task, not be added for other tasks.
+Noted that the instruction is not needed for passages.
+
+The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). 
+You can easily finetune your model with it.
+
+**Training data**:
+
+- For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on.
+
+- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on.
+
+**The data collection is to be released in the future.**
+
+We will continually update the embedding models and training codes, 
+hoping to promote the development of the embedding model community.
+
+
+
+## License
+FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
+
+
+

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tokenizer_config.json

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+  "unk_token": "[UNK]"
+}