update

README.md

@@ -1,2609 +1,16 @@
 ---
+pipeline_tag: sentence-similarity
 tags:
-- mteb
-- sentence-transfomres
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
 - transformers
-model-index:
-- name: bge-large-en
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 76.94029850746269
-    - type: ap
-      value: 40.00228964744091
-    - type: f1
-      value: 70.86088267934595
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 91.93745
-    - type: ap
-      value: 88.24758534667426
-    - type: f1
-      value: 91.91033034217591
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 46.158
-    - type: f1
-      value: 45.78935185074774
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 39.972
-    - type: map_at_10
-      value: 54.874
-    - type: map_at_100
-      value: 55.53399999999999
-    - type: map_at_1000
-      value: 55.539
-    - type: map_at_3
-      value: 51.031000000000006
-    - type: map_at_5
-      value: 53.342999999999996
-    - type: mrr_at_1
-      value: 40.541
-    - type: mrr_at_10
-      value: 55.096000000000004
-    - type: mrr_at_100
-      value: 55.75599999999999
-    - type: mrr_at_1000
-      value: 55.761
-    - type: mrr_at_3
-      value: 51.221000000000004
-    - type: mrr_at_5
-      value: 53.568000000000005
-    - type: ndcg_at_1
-      value: 39.972
-    - type: ndcg_at_10
-      value: 62.456999999999994
-    - type: ndcg_at_100
-      value: 65.262
-    - type: ndcg_at_1000
-      value: 65.389
-    - type: ndcg_at_3
-      value: 54.673
-    - type: ndcg_at_5
-      value: 58.80499999999999
-    - type: precision_at_1
-      value: 39.972
-    - type: precision_at_10
-      value: 8.634
-    - type: precision_at_100
-      value: 0.9860000000000001
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_3
-      value: 21.740000000000002
-    - type: precision_at_5
-      value: 15.036
-    - type: recall_at_1
-      value: 39.972
-    - type: recall_at_10
-      value: 86.344
-    - type: recall_at_100
-      value: 98.578
-    - type: recall_at_1000
-      value: 99.57300000000001
-    - type: recall_at_3
-      value: 65.22
-    - type: recall_at_5
-      value: 75.178
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 48.94652870403906
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 43.17257160340209
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 63.97867370559182
-    - type: mrr
-      value: 77.00820032537484
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 80.00986015960616
-    - type: cos_sim_spearman
-      value: 80.36387933827882
-    - type: euclidean_pearson
-      value: 80.32305287257296
-    - type: euclidean_spearman
-      value: 82.0524720308763
-    - type: manhattan_pearson
-      value: 80.19847473906454
-    - type: manhattan_spearman
-      value: 81.87957652506985
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 88.00000000000001
-    - type: f1
-      value: 87.99039027511853
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 41.36932844640705
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 38.34983239611985
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 32.257999999999996
-    - type: map_at_10
-      value: 42.937
-    - type: map_at_100
-      value: 44.406
-    - type: map_at_1000
-      value: 44.536
-    - type: map_at_3
-      value: 39.22
-    - type: map_at_5
-      value: 41.458
-    - type: mrr_at_1
-      value: 38.769999999999996
-    - type: mrr_at_10
-      value: 48.701
-    - type: mrr_at_100
-      value: 49.431000000000004
-    - type: mrr_at_1000
-      value: 49.476
-    - type: mrr_at_3
-      value: 45.875
-    - type: mrr_at_5
-      value: 47.67
-    - type: ndcg_at_1
-      value: 38.769999999999996
-    - type: ndcg_at_10
-      value: 49.35
-    - type: ndcg_at_100
-      value: 54.618
-    - type: ndcg_at_1000
-      value: 56.655
-    - type: ndcg_at_3
-      value: 43.826
-    - type: ndcg_at_5
-      value: 46.72
-    - type: precision_at_1
-      value: 38.769999999999996
-    - type: precision_at_10
-      value: 9.328
-    - type: precision_at_100
-      value: 1.484
-    - type: precision_at_1000
-      value: 0.196
-    - type: precision_at_3
-      value: 20.649
-    - type: precision_at_5
-      value: 15.25
-    - type: recall_at_1
-      value: 32.257999999999996
-    - type: recall_at_10
-      value: 61.849
-    - type: recall_at_100
-      value: 83.70400000000001
-    - type: recall_at_1000
-      value: 96.344
-    - type: recall_at_3
-      value: 46.037
-    - type: recall_at_5
-      value: 53.724000000000004
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 32.979
-    - type: map_at_10
-      value: 43.376999999999995
-    - type: map_at_100
-      value: 44.667
-    - type: map_at_1000
-      value: 44.794
-    - type: map_at_3
-      value: 40.461999999999996
-    - type: map_at_5
-      value: 42.138
-    - type: mrr_at_1
-      value: 41.146
-    - type: mrr_at_10
-      value: 49.575
-    - type: mrr_at_100
-      value: 50.187000000000005
-    - type: mrr_at_1000
-      value: 50.231
-    - type: mrr_at_3
-      value: 47.601
-    - type: mrr_at_5
-      value: 48.786
-    - type: ndcg_at_1
-      value: 41.146
-    - type: ndcg_at_10
-      value: 48.957
-    - type: ndcg_at_100
-      value: 53.296
-    - type: ndcg_at_1000
-      value: 55.254000000000005
-    - type: ndcg_at_3
-      value: 45.235
-    - type: ndcg_at_5
-      value: 47.014
-    - type: precision_at_1
-      value: 41.146
-    - type: precision_at_10
-      value: 9.107999999999999
-    - type: precision_at_100
-      value: 1.481
-    - type: precision_at_1000
-      value: 0.193
-    - type: precision_at_3
-      value: 21.783
-    - type: precision_at_5
-      value: 15.274
-    - type: recall_at_1
-      value: 32.979
-    - type: recall_at_10
-      value: 58.167
-    - type: recall_at_100
-      value: 76.374
-    - type: recall_at_1000
-      value: 88.836
-    - type: recall_at_3
-      value: 46.838
-    - type: recall_at_5
-      value: 52.006
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 40.326
-    - type: map_at_10
-      value: 53.468
-    - type: map_at_100
-      value: 54.454
-    - type: map_at_1000
-      value: 54.508
-    - type: map_at_3
-      value: 50.12799999999999
-    - type: map_at_5
-      value: 51.991
-    - type: mrr_at_1
-      value: 46.394999999999996
-    - type: mrr_at_10
-      value: 57.016999999999996
-    - type: mrr_at_100
-      value: 57.67099999999999
-    - type: mrr_at_1000
-      value: 57.699999999999996
-    - type: mrr_at_3
-      value: 54.65
-    - type: mrr_at_5
-      value: 56.101
-    - type: ndcg_at_1
-      value: 46.394999999999996
-    - type: ndcg_at_10
-      value: 59.507
-    - type: ndcg_at_100
-      value: 63.31099999999999
-    - type: ndcg_at_1000
-      value: 64.388
-    - type: ndcg_at_3
-      value: 54.04600000000001
-    - type: ndcg_at_5
-      value: 56.723
-    - type: precision_at_1
-      value: 46.394999999999996
-    - type: precision_at_10
-      value: 9.567
-    - type: precision_at_100
-      value: 1.234
-    - type: precision_at_1000
-      value: 0.13699999999999998
-    - type: precision_at_3
-      value: 24.117
-    - type: precision_at_5
-      value: 16.426
-    - type: recall_at_1
-      value: 40.326
-    - type: recall_at_10
-      value: 73.763
-    - type: recall_at_100
-      value: 89.927
-    - type: recall_at_1000
-      value: 97.509
-    - type: recall_at_3
-      value: 59.34
-    - type: recall_at_5
-      value: 65.915
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.661
-    - type: map_at_10
-      value: 35.522
-    - type: map_at_100
-      value: 36.619
-    - type: map_at_1000
-      value: 36.693999999999996
-    - type: map_at_3
-      value: 33.154
-    - type: map_at_5
-      value: 34.353
-    - type: mrr_at_1
-      value: 28.362
-    - type: mrr_at_10
-      value: 37.403999999999996
-    - type: mrr_at_100
-      value: 38.374
-    - type: mrr_at_1000
-      value: 38.428000000000004
-    - type: mrr_at_3
-      value: 35.235
-    - type: mrr_at_5
-      value: 36.269
-    - type: ndcg_at_1
-      value: 28.362
-    - type: ndcg_at_10
-      value: 40.431
-    - type: ndcg_at_100
-      value: 45.745999999999995
-    - type: ndcg_at_1000
-      value: 47.493
-    - type: ndcg_at_3
-      value: 35.733
-    - type: ndcg_at_5
-      value: 37.722
-    - type: precision_at_1
-      value: 28.362
-    - type: precision_at_10
-      value: 6.101999999999999
-    - type: precision_at_100
-      value: 0.922
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_3
-      value: 15.140999999999998
-    - type: precision_at_5
-      value: 10.305
-    - type: recall_at_1
-      value: 26.661
-    - type: recall_at_10
-      value: 53.675
-    - type: recall_at_100
-      value: 77.891
-    - type: recall_at_1000
-      value: 90.72
-    - type: recall_at_3
-      value: 40.751
-    - type: recall_at_5
-      value: 45.517
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 18.886
-    - type: map_at_10
-      value: 27.288
-    - type: map_at_100
-      value: 28.327999999999996
-    - type: map_at_1000
-      value: 28.438999999999997
-    - type: map_at_3
-      value: 24.453
-    - type: map_at_5
-      value: 25.959
-    - type: mrr_at_1
-      value: 23.134
-    - type: mrr_at_10
-      value: 32.004
-    - type: mrr_at_100
-      value: 32.789
-    - type: mrr_at_1000
-      value: 32.857
-    - type: mrr_at_3
-      value: 29.084
-    - type: mrr_at_5
-      value: 30.614
-    - type: ndcg_at_1
-      value: 23.134
-    - type: ndcg_at_10
-      value: 32.852
-    - type: ndcg_at_100
-      value: 37.972
-    - type: ndcg_at_1000
-      value: 40.656
-    - type: ndcg_at_3
-      value: 27.435
-    - type: ndcg_at_5
-      value: 29.823
-    - type: precision_at_1
-      value: 23.134
-    - type: precision_at_10
-      value: 6.032
-    - type: precision_at_100
-      value: 0.9950000000000001
-    - type: precision_at_1000
-      value: 0.136
-    - type: precision_at_3
-      value: 13.017999999999999
-    - type: precision_at_5
-      value: 9.501999999999999
-    - type: recall_at_1
-      value: 18.886
-    - type: recall_at_10
-      value: 45.34
-    - type: recall_at_100
-      value: 67.947
-    - type: recall_at_1000
-      value: 86.924
-    - type: recall_at_3
-      value: 30.535
-    - type: recall_at_5
-      value: 36.451
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 28.994999999999997
-    - type: map_at_10
-      value: 40.04
-    - type: map_at_100
-      value: 41.435
-    - type: map_at_1000
-      value: 41.537
-    - type: map_at_3
-      value: 37.091
-    - type: map_at_5
-      value: 38.802
-    - type: mrr_at_1
-      value: 35.034
-    - type: mrr_at_10
-      value: 45.411
-    - type: mrr_at_100
-      value: 46.226
-    - type: mrr_at_1000
-      value: 46.27
-    - type: mrr_at_3
-      value: 43.086
-    - type: mrr_at_5
-      value: 44.452999999999996
-    - type: ndcg_at_1
-      value: 35.034
-    - type: ndcg_at_10
-      value: 46.076
-    - type: ndcg_at_100
-      value: 51.483000000000004
-    - type: ndcg_at_1000
-      value: 53.433
-    - type: ndcg_at_3
-      value: 41.304
-    - type: ndcg_at_5
-      value: 43.641999999999996
-    - type: precision_at_1
-      value: 35.034
-    - type: precision_at_10
-      value: 8.258000000000001
-    - type: precision_at_100
-      value: 1.268
-    - type: precision_at_1000
-      value: 0.161
-    - type: precision_at_3
-      value: 19.57
-    - type: precision_at_5
-      value: 13.782
-    - type: recall_at_1
-      value: 28.994999999999997
-    - type: recall_at_10
-      value: 58.538000000000004
-    - type: recall_at_100
-      value: 80.72399999999999
-    - type: recall_at_1000
-      value: 93.462
-    - type: recall_at_3
-      value: 45.199
-    - type: recall_at_5
-      value: 51.237
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.795
-    - type: map_at_10
-      value: 34.935
-    - type: map_at_100
-      value: 36.306
-    - type: map_at_1000
-      value: 36.417
-    - type: map_at_3
-      value: 31.831
-    - type: map_at_5
-      value: 33.626
-    - type: mrr_at_1
-      value: 30.479
-    - type: mrr_at_10
-      value: 40.225
-    - type: mrr_at_100
-      value: 41.055
-    - type: mrr_at_1000
-      value: 41.114
-    - type: mrr_at_3
-      value: 37.538
-    - type: mrr_at_5
-      value: 39.073
-    - type: ndcg_at_1
-      value: 30.479
-    - type: ndcg_at_10
-      value: 40.949999999999996
-    - type: ndcg_at_100
-      value: 46.525
-    - type: ndcg_at_1000
-      value: 48.892
-    - type: ndcg_at_3
-      value: 35.79
-    - type: ndcg_at_5
-      value: 38.237
-    - type: precision_at_1
-      value: 30.479
-    - type: precision_at_10
-      value: 7.6259999999999994
-    - type: precision_at_100
-      value: 1.203
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 17.199
-    - type: precision_at_5
-      value: 12.466000000000001
-    - type: recall_at_1
-      value: 24.795
-    - type: recall_at_10
-      value: 53.421
-    - type: recall_at_100
-      value: 77.189
-    - type: recall_at_1000
-      value: 93.407
-    - type: recall_at_3
-      value: 39.051
-    - type: recall_at_5
-      value: 45.462
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.853499999999997
-    - type: map_at_10
-      value: 36.20433333333333
-    - type: map_at_100
-      value: 37.40391666666667
-    - type: map_at_1000
-      value: 37.515
-    - type: map_at_3
-      value: 33.39975
-    - type: map_at_5
-      value: 34.9665
-    - type: mrr_at_1
-      value: 31.62666666666667
-    - type: mrr_at_10
-      value: 40.436749999999996
-    - type: mrr_at_100
-      value: 41.260333333333335
-    - type: mrr_at_1000
-      value: 41.31525
-    - type: mrr_at_3
-      value: 38.06733333333332
-    - type: mrr_at_5
-      value: 39.41541666666667
-    - type: ndcg_at_1
-      value: 31.62666666666667
-    - type: ndcg_at_10
-      value: 41.63341666666667
-    - type: ndcg_at_100
-      value: 46.704166666666666
-    - type: ndcg_at_1000
-      value: 48.88483333333335
-    - type: ndcg_at_3
-      value: 36.896
-    - type: ndcg_at_5
-      value: 39.11891666666667
-    - type: precision_at_1
-      value: 31.62666666666667
-    - type: precision_at_10
-      value: 7.241083333333333
-    - type: precision_at_100
-      value: 1.1488333333333334
-    - type: precision_at_1000
-      value: 0.15250000000000002
-    - type: precision_at_3
-      value: 16.908333333333335
-    - type: precision_at_5
-      value: 11.942833333333333
-    - type: recall_at_1
-      value: 26.853499999999997
-    - type: recall_at_10
-      value: 53.461333333333336
-    - type: recall_at_100
-      value: 75.63633333333333
-    - type: recall_at_1000
-      value: 90.67016666666666
-    - type: recall_at_3
-      value: 40.24241666666667
-    - type: recall_at_5
-      value: 45.98608333333333
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.241999999999997
-    - type: map_at_10
-      value: 31.863999999999997
-    - type: map_at_100
-      value: 32.835
-    - type: map_at_1000
-      value: 32.928000000000004
-    - type: map_at_3
-      value: 29.694
-    - type: map_at_5
-      value: 30.978
-    - type: mrr_at_1
-      value: 28.374
-    - type: mrr_at_10
-      value: 34.814
-    - type: mrr_at_100
-      value: 35.596
-    - type: mrr_at_1000
-      value: 35.666
-    - type: mrr_at_3
-      value: 32.745000000000005
-    - type: mrr_at_5
-      value: 34.049
-    - type: ndcg_at_1
-      value: 28.374
-    - type: ndcg_at_10
-      value: 35.969
-    - type: ndcg_at_100
-      value: 40.708
-    - type: ndcg_at_1000
-      value: 43.08
-    - type: ndcg_at_3
-      value: 31.968999999999998
-    - type: ndcg_at_5
-      value: 34.069
-    - type: precision_at_1
-      value: 28.374
-    - type: precision_at_10
-      value: 5.583
-    - type: precision_at_100
-      value: 0.8630000000000001
-    - type: precision_at_1000
-      value: 0.11299999999999999
-    - type: precision_at_3
-      value: 13.547999999999998
-    - type: precision_at_5
-      value: 9.447999999999999
-    - type: recall_at_1
-      value: 25.241999999999997
-    - type: recall_at_10
-      value: 45.711
-    - type: recall_at_100
-      value: 67.482
-    - type: recall_at_1000
-      value: 85.13300000000001
-    - type: recall_at_3
-      value: 34.622
-    - type: recall_at_5
-      value: 40.043
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 17.488999999999997
-    - type: map_at_10
-      value: 25.142999999999997
-    - type: map_at_100
-      value: 26.244
-    - type: map_at_1000
-      value: 26.363999999999997
-    - type: map_at_3
-      value: 22.654
-    - type: map_at_5
-      value: 24.017
-    - type: mrr_at_1
-      value: 21.198
-    - type: mrr_at_10
-      value: 28.903000000000002
-    - type: mrr_at_100
-      value: 29.860999999999997
-    - type: mrr_at_1000
-      value: 29.934
-    - type: mrr_at_3
-      value: 26.634999999999998
-    - type: mrr_at_5
-      value: 27.903
-    - type: ndcg_at_1
-      value: 21.198
-    - type: ndcg_at_10
-      value: 29.982999999999997
-    - type: ndcg_at_100
-      value: 35.275
-    - type: ndcg_at_1000
-      value: 38.074000000000005
-    - type: ndcg_at_3
-      value: 25.502999999999997
-    - type: ndcg_at_5
-      value: 27.557
-    - type: precision_at_1
-      value: 21.198
-    - type: precision_at_10
-      value: 5.502
-    - type: precision_at_100
-      value: 0.942
-    - type: precision_at_1000
-      value: 0.136
-    - type: precision_at_3
-      value: 12.044
-    - type: precision_at_5
-      value: 8.782
-    - type: recall_at_1
-      value: 17.488999999999997
-    - type: recall_at_10
-      value: 40.821000000000005
-    - type: recall_at_100
-      value: 64.567
-    - type: recall_at_1000
-      value: 84.452
-    - type: recall_at_3
-      value: 28.351
-    - type: recall_at_5
-      value: 33.645
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 27.066000000000003
-    - type: map_at_10
-      value: 36.134
-    - type: map_at_100
-      value: 37.285000000000004
-    - type: map_at_1000
-      value: 37.389
-    - type: map_at_3
-      value: 33.522999999999996
-    - type: map_at_5
-      value: 34.905
-    - type: mrr_at_1
-      value: 31.436999999999998
-    - type: mrr_at_10
-      value: 40.225
-    - type: mrr_at_100
-      value: 41.079
-    - type: mrr_at_1000
-      value: 41.138000000000005
-    - type: mrr_at_3
-      value: 38.074999999999996
-    - type: mrr_at_5
-      value: 39.190000000000005
-    - type: ndcg_at_1
-      value: 31.436999999999998
-    - type: ndcg_at_10
-      value: 41.494
-    - type: ndcg_at_100
-      value: 46.678999999999995
-    - type: ndcg_at_1000
-      value: 48.964
-    - type: ndcg_at_3
-      value: 36.828
-    - type: ndcg_at_5
-      value: 38.789
-    - type: precision_at_1
-      value: 31.436999999999998
-    - type: precision_at_10
-      value: 6.931
-    - type: precision_at_100
-      value: 1.072
-    - type: precision_at_1000
-      value: 0.13799999999999998
-    - type: precision_at_3
-      value: 16.729
-    - type: precision_at_5
-      value: 11.567
-    - type: recall_at_1
-      value: 27.066000000000003
-    - type: recall_at_10
-      value: 53.705000000000005
-    - type: recall_at_100
-      value: 75.968
-    - type: recall_at_1000
-      value: 91.937
-    - type: recall_at_3
-      value: 40.865
-    - type: recall_at_5
-      value: 45.739999999999995
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.979000000000003
-    - type: map_at_10
-      value: 32.799
-    - type: map_at_100
-      value: 34.508
-    - type: map_at_1000
-      value: 34.719
-    - type: map_at_3
-      value: 29.947000000000003
-    - type: map_at_5
-      value: 31.584
-    - type: mrr_at_1
-      value: 30.237000000000002
-    - type: mrr_at_10
-      value: 37.651
-    - type: mrr_at_100
-      value: 38.805
-    - type: mrr_at_1000
-      value: 38.851
-    - type: mrr_at_3
-      value: 35.046
-    - type: mrr_at_5
-      value: 36.548
-    - type: ndcg_at_1
-      value: 30.237000000000002
-    - type: ndcg_at_10
-      value: 38.356
-    - type: ndcg_at_100
-      value: 44.906
-    - type: ndcg_at_1000
-      value: 47.299
-    - type: ndcg_at_3
-      value: 33.717999999999996
-    - type: ndcg_at_5
-      value: 35.946
-    - type: precision_at_1
-      value: 30.237000000000002
-    - type: precision_at_10
-      value: 7.292
-    - type: precision_at_100
-      value: 1.496
-    - type: precision_at_1000
-      value: 0.23600000000000002
-    - type: precision_at_3
-      value: 15.547
-    - type: precision_at_5
-      value: 11.344
-    - type: recall_at_1
-      value: 24.979000000000003
-    - type: recall_at_10
-      value: 48.624
-    - type: recall_at_100
-      value: 77.932
-    - type: recall_at_1000
-      value: 92.66499999999999
-    - type: recall_at_3
-      value: 35.217
-    - type: recall_at_5
-      value: 41.394
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.566
-    - type: map_at_10
-      value: 30.945
-    - type: map_at_100
-      value: 31.759999999999998
-    - type: map_at_1000
-      value: 31.855
-    - type: map_at_3
-      value: 28.64
-    - type: map_at_5
-      value: 29.787000000000003
-    - type: mrr_at_1
-      value: 24.954
-    - type: mrr_at_10
-      value: 33.311
-    - type: mrr_at_100
-      value: 34.050000000000004
-    - type: mrr_at_1000
-      value: 34.117999999999995
-    - type: mrr_at_3
-      value: 31.238
-    - type: mrr_at_5
-      value: 32.329
-    - type: ndcg_at_1
-      value: 24.954
-    - type: ndcg_at_10
-      value: 35.676
-    - type: ndcg_at_100
-      value: 39.931
-    - type: ndcg_at_1000
-      value: 42.43
-    - type: ndcg_at_3
-      value: 31.365
-    - type: ndcg_at_5
-      value: 33.184999999999995
-    - type: precision_at_1
-      value: 24.954
-    - type: precision_at_10
-      value: 5.564
-    - type: precision_at_100
-      value: 0.826
-    - type: precision_at_1000
-      value: 0.116
-    - type: precision_at_3
-      value: 13.555
-    - type: precision_at_5
-      value: 9.168
-    - type: recall_at_1
-      value: 22.566
-    - type: recall_at_10
-      value: 47.922
-    - type: recall_at_100
-      value: 67.931
-    - type: recall_at_1000
-      value: 86.653
-    - type: recall_at_3
-      value: 36.103
-    - type: recall_at_5
-      value: 40.699000000000005
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 16.950000000000003
-    - type: map_at_10
-      value: 28.612
-    - type: map_at_100
-      value: 30.476999999999997
-    - type: map_at_1000
-      value: 30.674
-    - type: map_at_3
-      value: 24.262
-    - type: map_at_5
-      value: 26.554
-    - type: mrr_at_1
-      value: 38.241
-    - type: mrr_at_10
-      value: 50.43
-    - type: mrr_at_100
-      value: 51.059
-    - type: mrr_at_1000
-      value: 51.090999999999994
-    - type: mrr_at_3
-      value: 47.514
-    - type: mrr_at_5
-      value: 49.246
-    - type: ndcg_at_1
-      value: 38.241
-    - type: ndcg_at_10
-      value: 38.218
-    - type: ndcg_at_100
-      value: 45.003
-    - type: ndcg_at_1000
-      value: 48.269
-    - type: ndcg_at_3
-      value: 32.568000000000005
-    - type: ndcg_at_5
-      value: 34.400999999999996
-    - type: precision_at_1
-      value: 38.241
-    - type: precision_at_10
-      value: 11.674
-    - type: precision_at_100
-      value: 1.913
-    - type: precision_at_1000
-      value: 0.252
-    - type: precision_at_3
-      value: 24.387
-    - type: precision_at_5
-      value: 18.163
-    - type: recall_at_1
-      value: 16.950000000000003
-    - type: recall_at_10
-      value: 43.769000000000005
-    - type: recall_at_100
-      value: 66.875
-    - type: recall_at_1000
-      value: 84.92699999999999
-    - type: recall_at_3
-      value: 29.353
-    - type: recall_at_5
-      value: 35.467
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 9.276
-    - type: map_at_10
-      value: 20.848
-    - type: map_at_100
-      value: 29.804000000000002
-    - type: map_at_1000
-      value: 31.398
-    - type: map_at_3
-      value: 14.886
-    - type: map_at_5
-      value: 17.516000000000002
-    - type: mrr_at_1
-      value: 71
-    - type: mrr_at_10
-      value: 78.724
-    - type: mrr_at_100
-      value: 78.976
-    - type: mrr_at_1000
-      value: 78.986
-    - type: mrr_at_3
-      value: 77.333
-    - type: mrr_at_5
-      value: 78.021
-    - type: ndcg_at_1
-      value: 57.875
-    - type: ndcg_at_10
-      value: 43.855
-    - type: ndcg_at_100
-      value: 48.99
-    - type: ndcg_at_1000
-      value: 56.141
-    - type: ndcg_at_3
-      value: 48.914
-    - type: ndcg_at_5
-      value: 45.961
-    - type: precision_at_1
-      value: 71
-    - type: precision_at_10
-      value: 34.575
-    - type: precision_at_100
-      value: 11.182
-    - type: precision_at_1000
-      value: 2.044
-    - type: precision_at_3
-      value: 52.5
-    - type: precision_at_5
-      value: 44.2
-    - type: recall_at_1
-      value: 9.276
-    - type: recall_at_10
-      value: 26.501
-    - type: recall_at_100
-      value: 55.72899999999999
-    - type: recall_at_1000
-      value: 78.532
-    - type: recall_at_3
-      value: 16.365
-    - type: recall_at_5
-      value: 20.154
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 52.71
-    - type: f1
-      value: 47.74801556489574
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 73.405
-    - type: map_at_10
-      value: 82.822
-    - type: map_at_100
-      value: 83.042
-    - type: map_at_1000
-      value: 83.055
-    - type: map_at_3
-      value: 81.65299999999999
-    - type: map_at_5
-      value: 82.431
-    - type: mrr_at_1
-      value: 79.178
-    - type: mrr_at_10
-      value: 87.02
-    - type: mrr_at_100
-      value: 87.095
-    - type: mrr_at_1000
-      value: 87.09700000000001
-    - type: mrr_at_3
-      value: 86.309
-    - type: mrr_at_5
-      value: 86.824
-    - type: ndcg_at_1
-      value: 79.178
-    - type: ndcg_at_10
-      value: 86.72
-    - type: ndcg_at_100
-      value: 87.457
-    - type: ndcg_at_1000
-      value: 87.691
-    - type: ndcg_at_3
-      value: 84.974
-    - type: ndcg_at_5
-      value: 86.032
-    - type: precision_at_1
-      value: 79.178
-    - type: precision_at_10
-      value: 10.548
-    - type: precision_at_100
-      value: 1.113
-    - type: precision_at_1000
-      value: 0.11499999999999999
-    - type: precision_at_3
-      value: 32.848
-    - type: precision_at_5
-      value: 20.45
-    - type: recall_at_1
-      value: 73.405
-    - type: recall_at_10
-      value: 94.39699999999999
-    - type: recall_at_100
-      value: 97.219
-    - type: recall_at_1000
-      value: 98.675
-    - type: recall_at_3
-      value: 89.679
-    - type: recall_at_5
-      value: 92.392
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.651
-    - type: map_at_10
-      value: 36.886
-    - type: map_at_100
-      value: 38.811
-    - type: map_at_1000
-      value: 38.981
-    - type: map_at_3
-      value: 32.538
-    - type: map_at_5
-      value: 34.763
-    - type: mrr_at_1
-      value: 44.444
-    - type: mrr_at_10
-      value: 53.168000000000006
-    - type: mrr_at_100
-      value: 53.839000000000006
-    - type: mrr_at_1000
-      value: 53.869
-    - type: mrr_at_3
-      value: 50.54
-    - type: mrr_at_5
-      value: 52.068000000000005
-    - type: ndcg_at_1
-      value: 44.444
-    - type: ndcg_at_10
-      value: 44.994
-    - type: ndcg_at_100
-      value: 51.599
-    - type: ndcg_at_1000
-      value: 54.339999999999996
-    - type: ndcg_at_3
-      value: 41.372
-    - type: ndcg_at_5
-      value: 42.149
-    - type: precision_at_1
-      value: 44.444
-    - type: precision_at_10
-      value: 12.407
-    - type: precision_at_100
-      value: 1.9269999999999998
-    - type: precision_at_1000
-      value: 0.242
-    - type: precision_at_3
-      value: 27.726
-    - type: precision_at_5
-      value: 19.814999999999998
-    - type: recall_at_1
-      value: 22.651
-    - type: recall_at_10
-      value: 52.075
-    - type: recall_at_100
-      value: 76.51400000000001
-    - type: recall_at_1000
-      value: 92.852
-    - type: recall_at_3
-      value: 37.236000000000004
-    - type: recall_at_5
-      value: 43.175999999999995
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 40.777
-    - type: map_at_10
-      value: 66.79899999999999
-    - type: map_at_100
-      value: 67.65299999999999
-    - type: map_at_1000
-      value: 67.706
-    - type: map_at_3
-      value: 63.352
-    - type: map_at_5
-      value: 65.52900000000001
-    - type: mrr_at_1
-      value: 81.553
-    - type: mrr_at_10
-      value: 86.983
-    - type: mrr_at_100
-      value: 87.132
-    - type: mrr_at_1000
-      value: 87.136
-    - type: mrr_at_3
-      value: 86.156
-    - type: mrr_at_5
-      value: 86.726
-    - type: ndcg_at_1
-      value: 81.553
-    - type: ndcg_at_10
-      value: 74.64
-    - type: ndcg_at_100
-      value: 77.459
-    - type: ndcg_at_1000
-      value: 78.43
-    - type: ndcg_at_3
-      value: 69.878
-    - type: ndcg_at_5
-      value: 72.59400000000001
-    - type: precision_at_1
-      value: 81.553
-    - type: precision_at_10
-      value: 15.654000000000002
-    - type: precision_at_100
-      value: 1.783
-    - type: precision_at_1000
-      value: 0.191
-    - type: precision_at_3
-      value: 45.199
-    - type: precision_at_5
-      value: 29.267
-    - type: recall_at_1
-      value: 40.777
-    - type: recall_at_10
-      value: 78.271
-    - type: recall_at_100
-      value: 89.129
-    - type: recall_at_1000
-      value: 95.49
-    - type: recall_at_3
-      value: 67.79899999999999
-    - type: recall_at_5
-      value: 73.167
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 93.5064
-    - type: ap
-      value: 90.25495114444111
-    - type: f1
-      value: 93.5012434973381
-  - task:
-      type: Retrieval
-    dataset:
-      type: msmarco
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.301
-    - type: map_at_10
-      value: 35.657
-    - type: map_at_100
-      value: 36.797000000000004
-    - type: map_at_1000
-      value: 36.844
-    - type: map_at_3
-      value: 31.743
-    - type: map_at_5
-      value: 34.003
-    - type: mrr_at_1
-      value: 23.854
-    - type: mrr_at_10
-      value: 36.242999999999995
-    - type: mrr_at_100
-      value: 37.32
-    - type: mrr_at_1000
-      value: 37.361
-    - type: mrr_at_3
-      value: 32.4
-    - type: mrr_at_5
-      value: 34.634
-    - type: ndcg_at_1
-      value: 23.868000000000002
-    - type: ndcg_at_10
-      value: 42.589
-    - type: ndcg_at_100
-      value: 48.031
-    - type: ndcg_at_1000
-      value: 49.189
-    - type: ndcg_at_3
-      value: 34.649
-    - type: ndcg_at_5
-      value: 38.676
-    - type: precision_at_1
-      value: 23.868000000000002
-    - type: precision_at_10
-      value: 6.6850000000000005
-    - type: precision_at_100
-      value: 0.9400000000000001
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 14.651
-    - type: precision_at_5
-      value: 10.834000000000001
-    - type: recall_at_1
-      value: 23.301
-    - type: recall_at_10
-      value: 63.88700000000001
-    - type: recall_at_100
-      value: 88.947
-    - type: recall_at_1000
-      value: 97.783
-    - type: recall_at_3
-      value: 42.393
-    - type: recall_at_5
-      value: 52.036
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 94.64888280893753
-    - type: f1
-      value: 94.41310774203512
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 79.72184222526221
-    - type: f1
-      value: 61.522034067350106
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 79.60659045057163
-    - type: f1
-      value: 77.268649687049
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 81.83254875588432
-    - type: f1
-      value: 81.61520635919082
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-p2p
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 36.31529875009507
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-s2s
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 31.734233714415073
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/mind_small
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 30.994501713009452
-    - type: mrr
-      value: 32.13512850703073
-  - task:
-      type: Retrieval
-    dataset:
-      type: nfcorpus
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 6.603000000000001
-    - type: map_at_10
-      value: 13.767999999999999
-    - type: map_at_100
-      value: 17.197000000000003
-    - type: map_at_1000
-      value: 18.615000000000002
-    - type: map_at_3
-      value: 10.567
-    - type: map_at_5
-      value: 12.078999999999999
-    - type: mrr_at_1
-      value: 44.891999999999996
-    - type: mrr_at_10
-      value: 53.75299999999999
-    - type: mrr_at_100
-      value: 54.35
-    - type: mrr_at_1000
-      value: 54.388000000000005
-    - type: mrr_at_3
-      value: 51.495999999999995
-    - type: mrr_at_5
-      value: 52.688
-    - type: ndcg_at_1
-      value: 43.189
-    - type: ndcg_at_10
-      value: 34.567
-    - type: ndcg_at_100
-      value: 32.273
-    - type: ndcg_at_1000
-      value: 41.321999999999996
-    - type: ndcg_at_3
-      value: 40.171
-    - type: ndcg_at_5
-      value: 37.502
-    - type: precision_at_1
-      value: 44.582
-    - type: precision_at_10
-      value: 25.139
-    - type: precision_at_100
-      value: 7.739999999999999
-    - type: precision_at_1000
-      value: 2.054
-    - type: precision_at_3
-      value: 37.152
-    - type: precision_at_5
-      value: 31.826999999999998
-    - type: recall_at_1
-      value: 6.603000000000001
-    - type: recall_at_10
-      value: 17.023
-    - type: recall_at_100
-      value: 32.914
-    - type: recall_at_1000
-      value: 64.44800000000001
-    - type: recall_at_3
-      value: 11.457
-    - type: recall_at_5
-      value: 13.816
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 30.026000000000003
-    - type: map_at_10
-      value: 45.429
-    - type: map_at_100
-      value: 46.45
-    - type: map_at_1000
-      value: 46.478
-    - type: map_at_3
-      value: 41.147
-    - type: map_at_5
-      value: 43.627
-    - type: mrr_at_1
-      value: 33.951
-    - type: mrr_at_10
-      value: 47.953
-    - type: mrr_at_100
-      value: 48.731
-    - type: mrr_at_1000
-      value: 48.751
-    - type: mrr_at_3
-      value: 44.39
-    - type: mrr_at_5
-      value: 46.533
-    - type: ndcg_at_1
-      value: 33.951
-    - type: ndcg_at_10
-      value: 53.24100000000001
-    - type: ndcg_at_100
-      value: 57.599999999999994
-    - type: ndcg_at_1000
-      value: 58.270999999999994
-    - type: ndcg_at_3
-      value: 45.190999999999995
-    - type: ndcg_at_5
-      value: 49.339
-    - type: precision_at_1
-      value: 33.951
-    - type: precision_at_10
-      value: 8.856
-    - type: precision_at_100
-      value: 1.133
-    - type: precision_at_1000
-      value: 0.12
-    - type: precision_at_3
-      value: 20.713
-    - type: precision_at_5
-      value: 14.838000000000001
-    - type: recall_at_1
-      value: 30.026000000000003
-    - type: recall_at_10
-      value: 74.512
-    - type: recall_at_100
-      value: 93.395
-    - type: recall_at_1000
-      value: 98.402
-    - type: recall_at_3
-      value: 53.677
-    - type: recall_at_5
-      value: 63.198
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 71.41300000000001
-    - type: map_at_10
-      value: 85.387
-    - type: map_at_100
-      value: 86.027
-    - type: map_at_1000
-      value: 86.041
-    - type: map_at_3
-      value: 82.543
-    - type: map_at_5
-      value: 84.304
-    - type: mrr_at_1
-      value: 82.35
-    - type: mrr_at_10
-      value: 88.248
-    - type: mrr_at_100
-      value: 88.348
-    - type: mrr_at_1000
-      value: 88.349
-    - type: mrr_at_3
-      value: 87.348
-    - type: mrr_at_5
-      value: 87.96300000000001
-    - type: ndcg_at_1
-      value: 82.37
-    - type: ndcg_at_10
-      value: 88.98
-    - type: ndcg_at_100
-      value: 90.16499999999999
-    - type: ndcg_at_1000
-      value: 90.239
-    - type: ndcg_at_3
-      value: 86.34100000000001
-    - type: ndcg_at_5
-      value: 87.761
-    - type: precision_at_1
-      value: 82.37
-    - type: precision_at_10
-      value: 13.471
-    - type: precision_at_100
-      value: 1.534
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 37.827
-    - type: precision_at_5
-      value: 24.773999999999997
-    - type: recall_at_1
-      value: 71.41300000000001
-    - type: recall_at_10
-      value: 95.748
-    - type: recall_at_100
-      value: 99.69200000000001
-    - type: recall_at_1000
-      value: 99.98
-    - type: recall_at_3
-      value: 87.996
-    - type: recall_at_5
-      value: 92.142
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 56.96878497780007
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 65.31371347128074
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 5.287
-    - type: map_at_10
-      value: 13.530000000000001
-    - type: map_at_100
-      value: 15.891
-    - type: map_at_1000
-      value: 16.245
-    - type: map_at_3
-      value: 9.612
-    - type: map_at_5
-      value: 11.672
-    - type: mrr_at_1
-      value: 26
-    - type: mrr_at_10
-      value: 37.335
-    - type: mrr_at_100
-      value: 38.443
-    - type: mrr_at_1000
-      value: 38.486
-    - type: mrr_at_3
-      value: 33.783
-    - type: mrr_at_5
-      value: 36.028
-    - type: ndcg_at_1
-      value: 26
-    - type: ndcg_at_10
-      value: 22.215
-    - type: ndcg_at_100
-      value: 31.101
-    - type: ndcg_at_1000
-      value: 36.809
-    - type: ndcg_at_3
-      value: 21.104
-    - type: ndcg_at_5
-      value: 18.759999999999998
-    - type: precision_at_1
-      value: 26
-    - type: precision_at_10
-      value: 11.43
-    - type: precision_at_100
-      value: 2.424
-    - type: precision_at_1000
-      value: 0.379
-    - type: precision_at_3
-      value: 19.7
-    - type: precision_at_5
-      value: 16.619999999999997
-    - type: recall_at_1
-      value: 5.287
-    - type: recall_at_10
-      value: 23.18
-    - type: recall_at_100
-      value: 49.208
-    - type: recall_at_1000
-      value: 76.85300000000001
-    - type: recall_at_3
-      value: 11.991999999999999
-    - type: recall_at_5
-      value: 16.85
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.87834913790886
-    - type: cos_sim_spearman
-      value: 81.04583513112122
-    - type: euclidean_pearson
-      value: 81.20484174558065
-    - type: euclidean_spearman
-      value: 80.76430832561769
-    - type: manhattan_pearson
-      value: 81.21416730978615
-    - type: manhattan_spearman
-      value: 80.7797637394211
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 86.56143998865157
-    - type: cos_sim_spearman
-      value: 79.75387012744471
-    - type: euclidean_pearson
-      value: 83.7877519997019
-    - type: euclidean_spearman
-      value: 79.90489748003296
-    - type: manhattan_pearson
-      value: 83.7540590666095
-    - type: manhattan_spearman
-      value: 79.86434577931573
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.92102564177941
-    - type: cos_sim_spearman
-      value: 84.98234585939103
-    - type: euclidean_pearson
-      value: 84.47729567593696
-    - type: euclidean_spearman
-      value: 85.09490696194469
-    - type: manhattan_pearson
-      value: 84.38622951588229
-    - type: manhattan_spearman
-      value: 85.02507171545574
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 80.1891164763377
-    - type: cos_sim_spearman
-      value: 80.7997969966883
-    - type: euclidean_pearson
-      value: 80.48572256162396
-    - type: euclidean_spearman
-      value: 80.57851903536378
-    - type: manhattan_pearson
-      value: 80.4324819433651
-    - type: manhattan_spearman
-      value: 80.5074526239062
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.64319975116025
-    - type: cos_sim_spearman
-      value: 84.88671197763652
-    - type: euclidean_pearson
-      value: 84.74692193293231
-    - type: euclidean_spearman
-      value: 85.27151722073653
-    - type: manhattan_pearson
-      value: 84.72460516785438
-    - type: manhattan_spearman
-      value: 85.26518899786687
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.24687565822381
-    - type: cos_sim_spearman
-      value: 85.60418454111263
-    - type: euclidean_pearson
-      value: 84.85829740169851
-    - type: euclidean_spearman
-      value: 85.66378014138306
-    - type: manhattan_pearson
-      value: 84.84672408808835
-    - type: manhattan_spearman
-      value: 85.63331924364891
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-en)
-      config: en-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.87758895415485
-    - type: cos_sim_spearman
-      value: 85.8193745617297
-    - type: euclidean_pearson
-      value: 85.78719118848134
-    - type: euclidean_spearman
-      value: 84.35797575385688
-    - type: manhattan_pearson
-      value: 85.97919844815692
-    - type: manhattan_spearman
-      value: 84.58334745175151
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (en)
-      config: en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 67.27076035963599
-    - type: cos_sim_spearman
-      value: 67.21433656439973
-    - type: euclidean_pearson
-      value: 68.07434078679324
-    - type: euclidean_spearman
-      value: 66.0249731719049
-    - type: manhattan_pearson
-      value: 67.95495198947476
-    - type: manhattan_spearman
-      value: 65.99893908331886
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.22437747056817
-    - type: cos_sim_spearman
-      value: 85.0995685206174
-    - type: euclidean_pearson
-      value: 84.08616925603394
-    - type: euclidean_spearman
-      value: 84.89633925691658
-    - type: manhattan_pearson
-      value: 84.08332675923133
-    - type: manhattan_spearman
-      value: 84.8858228112915
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 87.6909022589666
-    - type: mrr
-      value: 96.43341952165481
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 57.660999999999994
-    - type: map_at_10
-      value: 67.625
-    - type: map_at_100
-      value: 68.07600000000001
-    - type: map_at_1000
-      value: 68.10199999999999
-    - type: map_at_3
-      value: 64.50399999999999
-    - type: map_at_5
-      value: 66.281
-    - type: mrr_at_1
-      value: 61
-    - type: mrr_at_10
-      value: 68.953
-    - type: mrr_at_100
-      value: 69.327
-    - type: mrr_at_1000
-      value: 69.352
-    - type: mrr_at_3
-      value: 66.833
-    - type: mrr_at_5
-      value: 68.05
-    - type: ndcg_at_1
-      value: 61
-    - type: ndcg_at_10
-      value: 72.369
-    - type: ndcg_at_100
-      value: 74.237
-    - type: ndcg_at_1000
-      value: 74.939
-    - type: ndcg_at_3
-      value: 67.284
-    - type: ndcg_at_5
-      value: 69.72500000000001
-    - type: precision_at_1
-      value: 61
-    - type: precision_at_10
-      value: 9.733
-    - type: precision_at_100
-      value: 1.0670000000000002
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_3
-      value: 26.222
-    - type: precision_at_5
-      value: 17.4
-    - type: recall_at_1
-      value: 57.660999999999994
-    - type: recall_at_10
-      value: 85.656
-    - type: recall_at_100
-      value: 93.833
-    - type: recall_at_1000
-      value: 99.333
-    - type: recall_at_3
-      value: 71.961
-    - type: recall_at_5
-      value: 78.094
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.86930693069307
-    - type: cos_sim_ap
-      value: 96.76685487950894
-    - type: cos_sim_f1
-      value: 93.44587884806354
-    - type: cos_sim_precision
-      value: 92.80078895463511
-    - type: cos_sim_recall
-      value: 94.1
-    - type: dot_accuracy
-      value: 99.54356435643564
-    - type: dot_ap
-      value: 81.18659960405607
-    - type: dot_f1
-      value: 75.78008915304605
-    - type: dot_precision
-      value: 75.07360157016683
-    - type: dot_recall
-      value: 76.5
-    - type: euclidean_accuracy
-      value: 99.87326732673267
-    - type: euclidean_ap
-      value: 96.8102411908941
-    - type: euclidean_f1
-      value: 93.6127744510978
-    - type: euclidean_precision
-      value: 93.42629482071713
-    - type: euclidean_recall
-      value: 93.8
-    - type: manhattan_accuracy
-      value: 99.87425742574257
-    - type: manhattan_ap
-      value: 96.82857341435529
-    - type: manhattan_f1
-      value: 93.62129583124059
-    - type: manhattan_precision
-      value: 94.04641775983855
-    - type: manhattan_recall
-      value: 93.2
-    - type: max_accuracy
-      value: 99.87425742574257
-    - type: max_ap
-      value: 96.82857341435529
-    - type: max_f1
-      value: 93.62129583124059
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 65.92560972698926
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 34.92797240259008
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 55.244624045597654
-    - type: mrr
-      value: 56.185303666921314
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 31.02491987312937
-    - type: cos_sim_spearman
-      value: 32.055592206679734
-    - type: dot_pearson
-      value: 24.731627575422557
-    - type: dot_spearman
-      value: 24.308029077069733
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.231
-    - type: map_at_10
-      value: 1.899
-    - type: map_at_100
-      value: 9.498
-    - type: map_at_1000
-      value: 20.979999999999997
-    - type: map_at_3
-      value: 0.652
-    - type: map_at_5
-      value: 1.069
-    - type: mrr_at_1
-      value: 88
-    - type: mrr_at_10
-      value: 93.4
-    - type: mrr_at_100
-      value: 93.4
-    - type: mrr_at_1000
-      value: 93.4
-    - type: mrr_at_3
-      value: 93
-    - type: mrr_at_5
-      value: 93.4
-    - type: ndcg_at_1
-      value: 86
-    - type: ndcg_at_10
-      value: 75.375
-    - type: ndcg_at_100
-      value: 52.891999999999996
-    - type: ndcg_at_1000
-      value: 44.952999999999996
-    - type: ndcg_at_3
-      value: 81.05
-    - type: ndcg_at_5
-      value: 80.175
-    - type: precision_at_1
-      value: 88
-    - type: precision_at_10
-      value: 79
-    - type: precision_at_100
-      value: 53.16
-    - type: precision_at_1000
-      value: 19.408
-    - type: precision_at_3
-      value: 85.333
-    - type: precision_at_5
-      value: 84
-    - type: recall_at_1
-      value: 0.231
-    - type: recall_at_10
-      value: 2.078
-    - type: recall_at_100
-      value: 12.601
-    - type: recall_at_1000
-      value: 41.296
-    - type: recall_at_3
-      value: 0.6779999999999999
-    - type: recall_at_5
-      value: 1.1360000000000001
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 2.782
-    - type: map_at_10
-      value: 10.204
-    - type: map_at_100
-      value: 16.176
-    - type: map_at_1000
-      value: 17.456
-    - type: map_at_3
-      value: 5.354
-    - type: map_at_5
-      value: 7.503
-    - type: mrr_at_1
-      value: 40.816
-    - type: mrr_at_10
-      value: 54.010000000000005
-    - type: mrr_at_100
-      value: 54.49
-    - type: mrr_at_1000
-      value: 54.49
-    - type: mrr_at_3
-      value: 48.980000000000004
-    - type: mrr_at_5
-      value: 51.735
-    - type: ndcg_at_1
-      value: 36.735
-    - type: ndcg_at_10
-      value: 26.61
-    - type: ndcg_at_100
-      value: 36.967
-    - type: ndcg_at_1000
-      value: 47.274
-    - type: ndcg_at_3
-      value: 30.363
-    - type: ndcg_at_5
-      value: 29.448999999999998
-    - type: precision_at_1
-      value: 40.816
-    - type: precision_at_10
-      value: 23.878
-    - type: precision_at_100
-      value: 7.693999999999999
-    - type: precision_at_1000
-      value: 1.4489999999999998
-    - type: precision_at_3
-      value: 31.293
-    - type: precision_at_5
-      value: 29.796
-    - type: recall_at_1
-      value: 2.782
-    - type: recall_at_10
-      value: 16.485
-    - type: recall_at_100
-      value: 46.924
-    - type: recall_at_1000
-      value: 79.365
-    - type: recall_at_3
-      value: 6.52
-    - type: recall_at_5
-      value: 10.48
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 70.08300000000001
-    - type: ap
-      value: 13.91559884590195
-    - type: f1
-      value: 53.956838444291364
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 59.34069043576683
-    - type: f1
-      value: 59.662041994618406
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 53.70780611078653
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 87.10734934732073
-    - type: cos_sim_ap
-      value: 77.58349999516054
-    - type: cos_sim_f1
-      value: 70.25391395868965
-    - type: cos_sim_precision
-      value: 70.06035161374967
-    - type: cos_sim_recall
-      value: 70.44854881266491
-    - type: dot_accuracy
-      value: 80.60439887941826
-    - type: dot_ap
-      value: 54.52935200483575
-    - type: dot_f1
-      value: 54.170444242973716
-    - type: dot_precision
-      value: 47.47715534366309
-    - type: dot_recall
-      value: 63.06068601583114
-    - type: euclidean_accuracy
-      value: 87.26828396018358
-    - type: euclidean_ap
-      value: 78.00158454104036
-    - type: euclidean_f1
-      value: 70.70292457670601
-    - type: euclidean_precision
-      value: 68.79680479281079
-    - type: euclidean_recall
-      value: 72.71767810026385
-    - type: manhattan_accuracy
-      value: 87.11330988853788
-    - type: manhattan_ap
-      value: 77.92527099601855
-    - type: manhattan_f1
-      value: 70.76488706365502
-    - type: manhattan_precision
-      value: 68.89055472263868
-    - type: manhattan_recall
-      value: 72.74406332453826
-    - type: max_accuracy
-      value: 87.26828396018358
-    - type: max_ap
-      value: 78.00158454104036
-    - type: max_f1
-      value: 70.76488706365502
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 87.80804905499282
-    - type: cos_sim_ap
-      value: 83.06187782630936
-    - type: cos_sim_f1
-      value: 74.99716435403985
-    - type: cos_sim_precision
-      value: 73.67951860931579
-    - type: cos_sim_recall
-      value: 76.36279642747151
-    - type: dot_accuracy
-      value: 81.83141227151008
-    - type: dot_ap
-      value: 67.18241090841795
-    - type: dot_f1
-      value: 62.216037571751606
-    - type: dot_precision
-      value: 56.749381227391005
-    - type: dot_recall
-      value: 68.84816753926701
-    - type: euclidean_accuracy
-      value: 87.91671517832887
-    - type: euclidean_ap
-      value: 83.56538942001427
-    - type: euclidean_f1
-      value: 75.7327253337256
-    - type: euclidean_precision
-      value: 72.48856036606828
-    - type: euclidean_recall
-      value: 79.28087465352634
-    - type: manhattan_accuracy
-      value: 87.86626304963713
-    - type: manhattan_ap
-      value: 83.52939841172832
-    - type: manhattan_f1
-      value: 75.73635656329888
-    - type: manhattan_precision
-      value: 72.99150182103836
-    - type: manhattan_recall
-      value: 78.69571912534647
-    - type: max_accuracy
-      value: 87.91671517832887
-    - type: max_ap
-      value: 83.56538942001427
-    - type: max_f1
-      value: 75.73635656329888
 license: mit
 language:
 - en
-pipeline_tag: sentence-similarity
 ---
 
+
 <h1 align="center">FlagEmbedding</h1>
 
 
@@ -2613,11 +20,14 @@ pipeline_tag: sentence-similarity
         <a href=#usage>Usage</a>  |
         <a href="#evaluation">Evaluation</a> |
         <a href="#train">Train</a> |
+        <a href="#contact">Contact</a> |
         <a href="#license">License</a> 
     <p>
 </h4>
 
-For more details please refer to our GitHub repo: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
+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)
 
@@ -2625,6 +35,11 @@ FlagEmbedding can map any text to a low-dimensional dense vector which can be us
 And it also can be used in vector databases for LLMs.
 
 ************* 🌟**Updates**🌟 *************
+- 09/12/2023: New Release: 
+    - **New reranker model**: release a cross-encoder model bge-reranker-base, which is more powerful than embedding model. We recommend to use/fine-tune it to re-rank top-k documents returned by embedding models. 
+    - **update embedding model**: release bge-*-v1.5 embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
+- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).  
 - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
 - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: 
 - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.   
@@ -2634,88 +49,182 @@ And it also can be used in vector databases for LLMs.
 
 `bge` is short for `BAAI general embedding`.
 
-|              Model              | Language | Description | query instruction for retrieval |
-|:-------------------------------|:--------:| :--------:| :--------:|
-|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English |  :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English |  rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
-|  [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) |   Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark |   |
-|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
-|  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+|              Model              | Language | | Description | query instruction for retrieval\* |
+|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
+|  [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient \** |   |
+|  [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient \** |   |
+|  [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相���文章：`  |
+|  [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+
+
+\*: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
+
+\**: To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. 
+For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
+
+
+## Frequently asked questions
+
+<details>
+  <summary>1. How to fine-tune bge embedding model?</summary>
+
+  <!-- ### How to fine-tune bge embedding model? -->
+Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. 
+Some suggestions:
+- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#data-format), which can improve the retrieval performance.
+- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
+- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
+
+  
+</details>
+
+<details>
+  <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
+
+  <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
+**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** 
+
+Since we finetune the models by contrastive learning with a temperature of 0.01, 
+the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
+So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
 
+For downstream tasks, such as passage retrieval or semantic similarity, 
+**what matters is the relative order of the scores, not the absolute value.**
+If you need to filter similar sentences based on a similarity threshold, 
+please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
+
+</details>
+
+<details>
+  <summary>3. When does the query instruction need to be used</summary>
+
+  <!-- ### When does the query instruction need to be used -->
+  
+For a retrieval task that uses short queries to find long related documents, 
+it is recommended to add instructions for these short queries.
+**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
+In all cases, the documents/passages do not need to add the instruction. 
+
+</details>
 
 
 ## Usage 
 
-* **Using FlagEmbedding**
+### 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
 ```
-See [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
+If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
 
 ```python
 from FlagEmbedding import FlagModel
-sentences = ["样例数据-1", "样例数据-2"]
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
 model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：")
-embeddings = model.encode(sentences)
-print(embeddings)
-# for retrieval task, please use encode_queries() which will automatically add the instruction to each query
-# corpus in retrieval task can still use encode() or encode_corpus()
+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 = ["样例段落-1", "样例段落-2"]
+passages = ["样例文档-1", "样例文档-2"]
 q_embeddings = model.encode_queries(queries)
 p_embeddings = model.encode(passages)
 scores = q_embeddings @ p_embeddings.T
 ```
-The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). 
+For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). 
 
-FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU.
+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**
+#### Using Sentence-Transformers
 
-Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+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 = ["样例数据-1", "样例数据-2"]
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
 model = SentenceTransformer('BAAI/bge-large-zh')
-embeddings = model.encode(sentences, normalize_embeddings=True)
-print(embeddings)
+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 retrieval task, 
-each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
+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 = ["手机开不了机怎么办？"]
-passages = ["样例段落-1", "样例段落-2"]
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
 instruction = "为这个句子生成表示以用于检索相关文章："
+
 model = SentenceTransformer('BAAI/bge-large-zh')
 q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
 p_embeddings = model.encode(passages, normalize_embeddings=True)
 scores = q_embeddings @ p_embeddings.T
 ```
 
-* **Using HuggingFace Transformers**
+#### Using Langchain 
+
+You can use `bge` in langchain like this:
+```python
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+model_name = "BAAI/bge-small-en"
+model_kwargs = {'device': 'cuda'}
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+model = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs,
+    query_instruction="为这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："
+```
+
 
-With transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of first token (i.e., [CLS]) as the sentence embedding.
+#### 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')
 model = AutoModel.from_pretrained('BAAI/bge-large-zh')
+model.eval()
+
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# for retrieval task, add an instruction to query
+# 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)
@@ -2726,21 +235,65 @@ sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, di
 print("Sentence embeddings:", sentence_embeddings)
 ```
 
+### Usage for Reranker
+
+You can get a relevance score by inputting query and passage to the reranker. 
+The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
+
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+
+Get relevance score:
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) #use fp16 can speed up computing
+
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+
+
+#### Using Huggingface transformers
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, BatchEncoding, PreTrainedTokenizerFast
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-base')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+    print(scores)
+```
 
 ## Evaluation  
+
 `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
-More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). 
+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) |
 |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| [**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 | 
+| [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 |  
+| [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 |
@@ -2749,85 +302,80 @@ More details and evaluation tools see our [scripts](https://github.com/FlagOpen/
 | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) |  768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
 | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	|  768 | 514 	| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
 | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	|  4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
-| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 	|  384 | 512 	| 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 |
-| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 	|  384 | 512 	| 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 |
-| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) 	|  768 | 512 	| 56.00 | 41.88 | 41.1 	| 82.54 | 53.14 | 76.51 | 30.36 | 66.68 |
-| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	|  768 | 512 	| 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 |
 
 
 
 - **C-MTEB**:  
-We create a benchmark C-MTEB for Chinese text embedding which consists of  31 datasets from 6 tasks. 
+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 |
 |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
-| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 |  
-| [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** |   
-| [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) |  768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 |  
-| [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 |  
-| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 |  
-| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 |  
-| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 |  
-| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | 
-| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 |  
-| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 |  
-
-
+| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 |  **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |  
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 |  63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |  
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |   
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
+| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
+| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
+| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
+| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
+| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
+| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
+| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
+| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
+| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
+| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
+
+
+- **Reranking**:
+See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
+
+| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MmarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|  
+| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |  
+| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |  
+| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |  
+| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |  
+| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |  
+| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |  
+| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |  
+| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |  
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |  
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |  
+
+\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval task
 
 ## Train
-This section will introduce the way we used to train the general embedding. 
-The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), 
-and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md).
-
 
-**1. RetroMAE Pre-train**  
-We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), 
-which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). 
-The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. 
-In retromae, the mask ratio of encoder and decoder are 0.3, and 0.5 respectively.
-We used the AdamW optimizer and the learning rate is 2e-5.
+### BAAI Embedding 
 
-**Pre-training data**:
-- English: 
-    - [Pile](https://pile.eleuther.ai/)
-    - [wikipedia](https://huggingface.co/datasets/wikipedia)
-    - [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus)
-- Chinese: 
-    - Subset of [wudao](https://github.com/BAAI-WuDao/Data)
-    - [baidu-baike](https://baike.baidu.com/)
+We pre-train the models using retromae and train them on large-scale pairs data using contrastive learning. 
+**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
+We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
+Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
+More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
 
 
-**2. Finetune**  
-We fine-tune the model using a contrastive objective. 
-The format of input data is a triple`(query, positive, negative)`. 
-Besides the negative in the triple, we also adopt in-batch negatives strategy. 
-We employ the cross-device negatives sharing method to share negatives among different GPUs, 
-which can dramatically **increase the number of negatives**.
 
-We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). 
-We used the AdamW optimizer and the learning rate is 1e-5.
-The temperature for contrastive loss is 0.01.
+### BGE Reranker
 
-For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training. 
-For english, the instruction is `Represent this sentence for searching relevant passages: `;
-For chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`.
-In the evaluation, the instruction should be added for sentence to passages retrieval task, not be added for other tasks.
+Cross-encoder will perform full-attention over the input pair, 
+which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
+Therefore, it can be used to re-rank the top-k documents returned by embedding model.
+We train the cross-encoder on a multilingual pair data, 
+The data format is the same as embedding model, so you can fine-tune it easily following our example. 
+More details pelease refer to [./FlagEmbedding/reranker/README.md](./FlagEmbedding/reranker/README.md)
 
 
-The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). 
-You can easily finetune your model with it.
+## Contact
+If you have any question or suggestion related to this project, feel free to open an issue or pull request.
+You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). 
 
-**Training data**:
 
-- For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on.
-
-- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on.
+## 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 data collection is to be released in the future.**
 
-We will continually update the embedding models and training codes, 
-hoping to promote the development of the embedding model community.
 
-## License
-FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.