diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -2,3013 +2,436 @@
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
+- openvino
license: mit
language:
- en
+base_model:
+- BAAI/bge-small-en
+base_model_relation: quantized
---
-
-
-**Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
-
-FlagEmbedding
-
-
-
-
- Model List |
- FAQ |
- Usage |
- Evaluation |
- Train |
- Citation |
- License
-
-
-
-More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
-
-
-[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
-
-FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently:
-
-- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
-- **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
-- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
-
-
-## News
-
-- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
-- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
-- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
-- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
-- 09/12/2023: New models:
- - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
-
-
-
- More
-
-
-- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
-- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
-- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
-- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
-- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
-
-
-
-
-## Model List
-
-`bge` is short for `BAAI general embedding`.
-
-| Model | Language | | Description | query instruction for retrieval [1] |
-|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
-| [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |
-| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
-| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
-| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
-| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
-| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
-| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
-| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
-| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
-| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
-
-
-[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
-
-[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
-For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
-
-All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
-If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
-
-
-## Frequently asked questions
-
-
- 1. How to fine-tune bge embedding model?
-
-
-Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
-Some suggestions:
-- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
-- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
-- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
-
-
-
-
-
- 2. The similarity score between two dissimilar sentences is higher than 0.5
-
-
-**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
-
-Since we finetune the models by contrastive learning with a temperature of 0.01,
-the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
-So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
-
-For downstream tasks, such as passage retrieval or semantic similarity,
-**what matters is the relative order of the scores, not the absolute value.**
-If you need to filter similar sentences based on a similarity threshold,
-please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
-
-
-
-
- 3. When does the query instruction need to be used
-
-
-
-For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
-No instruction only has a slight degradation in retrieval performance compared with using instruction.
-So you can generate embedding without instruction in all cases for convenience.
-
-For a retrieval task that uses short queries to find long related documents,
-it is recommended to add instructions for these short queries.
-**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
-In all cases, the documents/passages do not need to add the instruction.
-
-
-
-
-## Usage
-
-### Usage for Embedding Model
-
-Here are some examples for using `bge` models with
-[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
-
-#### Using FlagEmbedding
-```
-pip install -U FlagEmbedding
-```
-If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
-
-```python
-from FlagEmbedding import FlagModel
-sentences_1 = ["样例数据-1", "样例数据-2"]
-sentences_2 = ["样例数据-3", "样例数据-4"]
-model = FlagModel('BAAI/bge-large-zh-v1.5',
- query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
- use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
-embeddings_1 = model.encode(sentences_1)
-embeddings_2 = model.encode(sentences_2)
-similarity = embeddings_1 @ embeddings_2.T
-print(similarity)
-
-# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
-# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
-queries = ['query_1', 'query_2']
-passages = ["样例��档-1", "样例文档-2"]
-q_embeddings = model.encode_queries(queries)
-p_embeddings = model.encode(passages)
-scores = q_embeddings @ p_embeddings.T
-```
-For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
-
-By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
-You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
-
-
-#### Using Sentence-Transformers
-
-You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
-
-```
-pip install -U sentence-transformers
-```
-```python
-from sentence_transformers import SentenceTransformer
-sentences_1 = ["样例数据-1", "样例数据-2"]
-sentences_2 = ["样例数据-3", "样例数据-4"]
-model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
-embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
-embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
-similarity = embeddings_1 @ embeddings_2.T
-print(similarity)
-```
-For s2p(short query to long passage) retrieval task,
-each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
-But the instruction is not needed for passages.
-```python
-from sentence_transformers import SentenceTransformer
-queries = ['query_1', 'query_2']
-passages = ["样例文档-1", "样例文档-2"]
-instruction = "为这个句子生成表示以用于检索相关文章："
-
-model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
-q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
-p_embeddings = model.encode(passages, normalize_embeddings=True)
-scores = q_embeddings @ p_embeddings.T
-```
-
-#### Using Langchain
-
-You can use `bge` in langchain like this:
-```python
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-model_name = "BAAI/bge-large-en-v1.5"
-model_kwargs = {'device': 'cuda'}
-encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
-model = HuggingFaceBgeEmbeddings(
- model_name=model_name,
- model_kwargs=model_kwargs,
- encode_kwargs=encode_kwargs,
- query_instruction="为这个句子生成表示以用于检索相关文章："
-)
-model.query_instruction = "为这个句子生成表示以用于检索相关文章："
-```
-
-
-#### Using HuggingFace Transformers
-
-With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-# Sentences we want sentence embeddings for
-sentences = ["样例数据-1", "样例数据-2"]
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
-model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
-model.eval()
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
-# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
- model_output = model(**encoded_input)
- # Perform pooling. In this case, cls pooling.
- sentence_embeddings = model_output[0][:, 0]
-# normalize embeddings
-sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
-print("Sentence embeddings:", sentence_embeddings)
-```
-
-### Usage for Reranker
-
-Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
-You can get a relevance score by inputting query and passage to the reranker.
-The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
-
-
-#### Using FlagEmbedding
-```
-pip install -U FlagEmbedding
-```
-
-Get relevance scores (higher scores indicate more relevance):
-```python
-from FlagEmbedding import FlagReranker
-reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
-
-score = reranker.compute_score(['query', 'passage'])
-print(score)
-
-scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
-print(scores)
-```
-
-
-#### Using Huggingface transformers
-
-```python
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
-model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
-model.eval()
-
-pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
-with torch.no_grad():
- inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
- scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
- print(scores)
-```
-
-## Evaluation
-
-`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
-For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
-
-- **MTEB**:
-
-| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
-|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
-| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
-| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
-| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
-| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
-| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
-| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
-| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
-| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
-| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
-| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
-| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
-| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
-| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
-| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
-| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
-| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
-
-
-
-- **C-MTEB**:
-We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
-Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
-
-| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
-|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
-| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
-| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
-| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
-| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
-| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
-| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
-| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
-| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
-| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
-| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
-| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
-| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
-| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
-| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
-| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
-| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
-
-
-- **Reranking**:
-See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
-
-| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
-|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
-| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
-| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
-| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
-| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
-| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
-| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
-| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
-| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
-| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
-| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
-
-\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
-
-## Train
-
-### BAAI Embedding
-
-We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
-**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
-We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
-Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
-More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
-
-
-
-### BGE Reranker
-
-Cross-encoder will perform full-attention over the input pair,
-which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
-Therefore, it can be used to re-rank the top-k documents returned by embedding model.
-We train the cross-encoder on a multilingual pair data,
-The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
-More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
-
-
-
-
-## Citation
-
-If you find this repository useful, please consider giving a star :star: and citation
-
-```
-@misc{bge_embedding,
- title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
- author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
- year={2023},
- eprint={2309.07597},
- archivePrefix={arXiv},
- primaryClass={cs.CL}
-}
-```
-
-## License
-FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
-
+
+The [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) converted for [openvino backend](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#openvino).
+
+```
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("aliakseilabanau/bge-small-en", backend="openvino")
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+embeddings = model.encode(sentences)
+```
+
+
+The original model card is below:
+
+--------
+
+**Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
+
+
FlagEmbedding
+
+
+
+
+ Model List |
+ FAQ |
+ Usage |
+ Evaluation |
+ Train |
+ Citation |
+ License
+
+
+
+More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
+
+
+[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
+
+FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently:
+
+- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
+- **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
+- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
+
+## News
+
+- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
+- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
+- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
+- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
+- 09/12/2023: New models:
+ - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
+ - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
+
+
+
+ More
+
+
+- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
+- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
+- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
+- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
+
+
+
+
+## Model List
+
+`bge` is short for `BAAI general embedding`.
+
+| Model | Language | | Description | query instruction for retrieval [1] |
+|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
+| [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |
+| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
+
+
+[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
+
+[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
+For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
+
+All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
+If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
+
+
+## Frequently asked questions
+
+
+ 1. How to fine-tune bge embedding model?
+
+
+Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
+Some suggestions:
+- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
+- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
+- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
+
+
+
+
+
+ 2. The similarity score between two dissimilar sentences is higher than 0.5
+
+
+**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
+
+Since we finetune the models by contrastive learning with a temperature of 0.01,
+the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
+So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
+
+For downstream tasks, such as passage retrieval or semantic similarity,
+**what matters is the relative order of the scores, not the absolute value.**
+If you need to filter similar sentences based on a similarity threshold,
+please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
+
+
+
+
+ 3. When does the query instruction need to be used
+
+
+
+For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
+No instruction only has a slight degradation in retrieval performance compared with using instruction.
+So you can generate embedding without instruction in all cases for convenience.
+
+For a retrieval task that uses short queries to find long related documents,
+it is recommended to add instructions for these short queries.
+**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
+In all cases, the documents/passages do not need to add the instruction.
+
+
+
+
+## Usage
+
+### Usage for Embedding Model
+
+Here are some examples for using `bge` models with
+[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
+
+```python
+from FlagEmbedding import FlagModel
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
+model = FlagModel('BAAI/bge-large-zh-v1.5',
+ query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
+ use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+embeddings_1 = model.encode(sentences_1)
+embeddings_2 = model.encode(sentences_2)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+
+# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
+# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+q_embeddings = model.encode_queries(queries)
+p_embeddings = model.encode(passages)
+scores = q_embeddings @ p_embeddings.T
+```
+For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
+
+By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
+You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
+
+
+#### Using Sentence-Transformers
+
+You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
+
+```
+pip install -U sentence-transformers
+```
+```python
+from sentence_transformers import SentenceTransformer
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
+model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
+embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+For s2p(short query to long passage) retrieval task,
+each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
+But the instruction is not needed for passages.
+```python
+from sentence_transformers import SentenceTransformer
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+instruction = "为这个句子生成表示以用于检索相关文章："
+
+model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
+q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
+p_embeddings = model.encode(passages, normalize_embeddings=True)
+scores = q_embeddings @ p_embeddings.T
+```
+
+#### Using Langchain
+
+You can use `bge` in langchain like this:
+```python
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+model_name = "BAAI/bge-large-en-v1.5"
+model_kwargs = {'device': 'cuda'}
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+model = HuggingFaceBgeEmbeddings(
+ model_name=model_name,
+ model_kwargs=model_kwargs,
+ encode_kwargs=encode_kwargs,
+ query_instruction="为这个句子生成表示以用于检索相关文章："
+)
+model.query_instruction = "为这个句子生成表示以用于检索相关文章："
+```
+
+
+#### Using HuggingFace Transformers
+
+With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Sentences we want sentence embeddings for
+sentences = ["样例数据-1", "样例数据-2"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
+model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
+model.eval()
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
+# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+ model_output = model(**encoded_input)
+ # Perform pooling. In this case, cls pooling.
+ sentence_embeddings = model_output[0][:, 0]
+# normalize embeddings
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+print("Sentence embeddings:", sentence_embeddings)
+```
+
+### Usage for Reranker
+
+Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
+You can get a relevance score by inputting query and passage to the reranker.
+The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
+
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+
+Get relevance scores (higher scores indicate more relevance):
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+
+
+#### Using Huggingface transformers
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+ inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+ scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+ print(scores)
+```
+
+## Evaluation
+
+`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
+For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
+
+- **MTEB**:
+
+| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
+| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
+| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
+| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
+| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
+| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
+| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
+| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
+
+
+
+- **C-MTEB**:
+We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
+Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
+
+| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
+| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
+| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
+| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
+| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
+| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
+| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
+| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
+| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
+| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
+| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
+
+
+- **Reranking**:
+See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
+
+| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
+| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
+| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
+| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
+| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
+| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
+| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
+| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
+
+\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
+
+## Train
+
+### BAAI Embedding
+
+We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
+**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
+We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
+Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
+More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
+
+
+
+### BGE Reranker
+
+Cross-encoder will perform full-attention over the input pair,
+which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
+Therefore, it can be used to re-rank the top-k documents returned by embedding model.
+We train the cross-encoder on a multilingual pair data,
+The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
+More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
+
+
+
+## Citation
+
+If you find this repository useful, please consider giving a star :star: and citation
+
+```
+@misc{bge_embedding,
+ title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
+ author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
+ year={2023},
+ eprint={2309.07597},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
+}
+```
+
+## License
+FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
\ No newline at end of file