BAAI
/

bge-small-en-v1.5

@@ -1,2610 +1,16 @@
 ---
 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>
@@ -2614,11 +20,14 @@ pipeline_tag: sentence-similarity
         <a href=#usage>Usage</a>  |
         <a href="#evaluation">Evaluation</a> |
         <a href="#train">Train</a> |
         <a href="#license">License</a>
     <p>
 </h4>
-For more details please refer to our GitHub repo: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
 [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
@@ -2626,6 +35,11 @@ FlagEmbedding can map any text to a low-dimensional dense vector which can be us
 And it also can be used in vector databases for LLMs.
 ************* 🌟**Updates**🌟 *************
 - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
 - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
 - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
@@ -2635,66 +49,136 @@ And it also can be used in vector databases for LLMs.
 `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 |  :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English |  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 | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
-|  [BAAI/bge-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：`  |
 ## Usage
-* **Using FlagEmbedding**
 ```
 pip install -U FlagEmbedding
 ```
-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 = model.encode(sentences)
-print(embeddings)
-# for 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()
 queries = ['query_1', 'query_2']
-passages = ["样例段落-1", "样例段落-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 = model.encode(sentences, normalize_embeddings=True)
-print(embeddings)
 ```
-For retrieval task,
-each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
 ```python
 from sentence_transformers import SentenceTransformer
-queries = ["手机开不了机怎么办？"]
-passages = ["样例段落-1", "样例段落-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')
@@ -2703,9 +187,27 @@ p_embeddings = model.encode(passages, normalize_embeddings=True)
 scores = q_embeddings @ p_embeddings.T
 ```
-* **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
@@ -2716,10 +218,11 @@ 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 retrieval task, add an instruction to query
 # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
 # Compute token embeddings
@@ -2732,21 +235,65 @@ sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, di
 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 |
@@ -2755,85 +302,80 @@ More details and evaluation tools see our [scripts](https://github.com/FlagOpen/
 | [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, and 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:
-    - Subset of [wudao](https://github.com/BAAI-WuDao/Data)
-    - [baidu-baike](https://baike.baidu.com/)
-**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.
-For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training.
-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 sentence to passages retrieval task, not be added for other tasks.
-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.

 ---
+pipeline_tag: sentence-similarity
 tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
 license: mit
 language:
 - en
 ---
 <h1 align="center">FlagEmbedding</h1>
         <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)
 And it also can be used in vector databases for LLMs.
 ************* 🌟**Updates**🌟 *************
+- 09/12/2023: New Release:
+    - **New reranker model**: release a cross-encoder model bge-reranker-base, which is more powerful than embedding model. We recommend to use/fine-tune it 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.
+- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
 - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
 - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
 - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
 `bge` is short for `BAAI general embedding`.
+|              Model              | Language | | Description | query instruction for retrieval\* |
+|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
+|  [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient \** |   |
+|  [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient \** |   |
+|  [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+\*: 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.
+\**: 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.
+## Frequently asked questions
+<details>
+  <summary>1. How to fine-tune bge embedding model?</summary>
+  <!-- ### How to fine-tune bge embedding model? -->
+Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
+Some suggestions:
+- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#data-format), which can improve the retrieval performance.
+- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
+- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
+</details>
+<details>
+  <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
+  <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
+**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
+Since we finetune the models by contrastive learning with a temperature of 0.01,
+the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
+So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
+For downstream tasks, such as passage retrieval or semantic similarity,
+**what matters is the relative order of the scores, not the absolute value.**
+If you need to filter similar sentences based on a similarity threshold,
+please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
+</details>
+<details>
+  <summary>3. When does the query instruction need to be used</summary>
+  <!-- ### When does the query instruction need to be used -->
+For a retrieval task that uses short queries to find long related documents,
+it is recommended to add instructions for these short queries.
+**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
+In all cases, the documents/passages do not need to add the instruction.
+</details>
 ## Usage
+### Usage for Embedding Model
+Here are some examples for using `bge` models with
+[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
+#### Using FlagEmbedding
 ```
 pip install -U FlagEmbedding
 ```
+If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
 ```python
 from FlagEmbedding import FlagModel
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
 model = FlagModel('BAAI/bge-large-zh', 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_1)
+embeddings_2 = model.encode(sentences_2)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
+# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
 queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
 q_embeddings = model.encode_queries(queries)
 p_embeddings = model.encode(passages)
 scores = q_embeddings @ p_embeddings.T
 ```
+For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
+By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
+You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
+#### Using Sentence-Transformers
+You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
 ```
 pip install -U sentence-transformers
 ```
 ```python
 from sentence_transformers import SentenceTransformer
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
 model = SentenceTransformer('BAAI/bge-large-zh')
+embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
 ```
+For s2p(short query to long passage) retrieval task,
+each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
+But the instruction is not needed for passages.
 ```python
 from sentence_transformers import SentenceTransformer
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
 instruction = "为这个句子生成表示以用于检索相关文章："
 model = SentenceTransformer('BAAI/bge-large-zh')
 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 = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs,
+    query_instruction="为这���句子生成表示以用于检索相关文章："
+)
+model.query_instruction = "为这个句子生成表示以用于检索相关文章："
+```
+#### Using HuggingFace Transformers
+With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
 ```python
 from transformers import AutoTokenizer, AutoModel
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh')
 model = AutoModel.from_pretrained('BAAI/bge-large-zh')
+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
 print("Sentence embeddings:", sentence_embeddings)
 ```
+### Usage for Reranker
+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 score:
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) #use fp16 can speed up computing
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+#### Using Huggingface transformers
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, BatchEncoding, PreTrainedTokenizerFast
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-base')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base')
+model.eval()
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+    print(scores)
+```
 ## Evaluation
 `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
+For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
 - **MTEB**:
 | Model Name |  Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) |  STS (10) | Summarization (1) | Classification (12) |
 |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 |  **64.23** | **54.29** |  46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |  768 | 512 | 63.55 | 53.25 |   45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |  384 | 512 | 62.17 |51.68 | 43.82 |  84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
+| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) |  1024 | 512 | 63.98 |  53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
+| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) |  768 | 512 |  63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
 | [gte-large](https://huggingface.co/thenlper/gte-large) |  1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
 | [gte-base](https://huggingface.co/thenlper/gte-base) 	|  768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
 | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) |  1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
+| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) |  384 | 512 | 62.11 |  51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
 | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) |  768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
 | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) |  768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
 | [gte-small](https://huggingface.co/thenlper/gte-small) |  384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
 | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) |  768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
 | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	|  768 | 514 	| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
 | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	|  4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
 - **C-MTEB**:
+We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
 Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
 | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
 |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 |  **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 |  63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
+| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
+| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
+| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
+| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
+| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
+| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
+| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
+| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
+| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
+| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
+- **Reranking**:
+See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
+| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MmarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
+| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
+| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
+| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
+| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
+| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
+| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
+| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
+\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval task
 ## 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](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
+We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
+Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
+More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
+### BGE Reranker
+Cross-encoder will perform full-attention over the input pair,
+which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
+Therefore, it can be used to re-rank the top-k documents returned by embedding model.
+We train the cross-encoder on a multilingual pair data,
+The data format is the same as embedding model, so you can fine-tune it easily following our example.
+More details pelease refer to [./FlagEmbedding/reranker/README.md](./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).
+## License
+FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.