Add new SentenceTransformer model with an openvino backend (#1)

- Add new SentenceTransformer model with an openvino backend (2216880c1ab09f496c17372f0c8a35e41b8e1b24)

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,3014 @@
+---
+tags:
+- mteb
+- sentence transformers
+model-index:
+- name: bge-small-en
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 74.34328358208955
+    - type: ap
+      value: 37.59947775195661
+    - type: f1
+      value: 68.548415491933
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.04527499999999
+    - type: ap
+      value: 89.60696356772135
+    - type: f1
+      value: 93.03361469382438
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 46.08
+    - type: f1
+      value: 45.66249835363254
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 35.205999999999996
+    - type: map_at_10
+      value: 50.782000000000004
+    - type: map_at_100
+      value: 51.547
+    - type: map_at_1000
+      value: 51.554
+    - type: map_at_3
+      value: 46.515
+    - type: map_at_5
+      value: 49.296
+    - type: mrr_at_1
+      value: 35.632999999999996
+    - type: mrr_at_10
+      value: 50.958999999999996
+    - type: mrr_at_100
+      value: 51.724000000000004
+    - type: mrr_at_1000
+      value: 51.731
+    - type: mrr_at_3
+      value: 46.669
+    - type: mrr_at_5
+      value: 49.439
+    - type: ndcg_at_1
+      value: 35.205999999999996
+    - type: ndcg_at_10
+      value: 58.835
+    - type: ndcg_at_100
+      value: 62.095
+    - type: ndcg_at_1000
+      value: 62.255
+    - type: ndcg_at_3
+      value: 50.255
+    - type: ndcg_at_5
+      value: 55.296
+    - type: precision_at_1
+      value: 35.205999999999996
+    - type: precision_at_10
+      value: 8.421
+    - type: precision_at_100
+      value: 0.984
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.365
+    - type: precision_at_5
+      value: 14.680000000000001
+    - type: recall_at_1
+      value: 35.205999999999996
+    - type: recall_at_10
+      value: 84.211
+    - type: recall_at_100
+      value: 98.43499999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 61.095
+    - type: recall_at_5
+      value: 73.4
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.52644476278646
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 39.973045724188964
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.28285314871488
+    - type: mrr
+      value: 74.52743701358659
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.09041909160327
+    - type: cos_sim_spearman
+      value: 79.96266537706944
+    - type: euclidean_pearson
+      value: 79.50774978162241
+    - type: euclidean_spearman
+      value: 79.9144715078551
+    - type: manhattan_pearson
+      value: 79.2062139879302
+    - type: manhattan_spearman
+      value: 79.35000081468212
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.31493506493506
+    - type: f1
+      value: 85.2704557977762
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.6837242810816
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 35.38881249555897
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.884999999999998
+    - type: map_at_10
+      value: 39.574
+    - type: map_at_100
+      value: 40.993
+    - type: map_at_1000
+      value: 41.129
+    - type: map_at_3
+      value: 36.089
+    - type: map_at_5
+      value: 38.191
+    - type: mrr_at_1
+      value: 34.477999999999994
+    - type: mrr_at_10
+      value: 45.411
+    - type: mrr_at_100
+      value: 46.089999999999996
+    - type: mrr_at_1000
+      value: 46.147
+    - type: mrr_at_3
+      value: 42.346000000000004
+    - type: mrr_at_5
+      value: 44.292
+    - type: ndcg_at_1
+      value: 34.477999999999994
+    - type: ndcg_at_10
+      value: 46.123999999999995
+    - type: ndcg_at_100
+      value: 51.349999999999994
+    - type: ndcg_at_1000
+      value: 53.578
+    - type: ndcg_at_3
+      value: 40.824
+    - type: ndcg_at_5
+      value: 43.571
+    - type: precision_at_1
+      value: 34.477999999999994
+    - type: precision_at_10
+      value: 8.841000000000001
+    - type: precision_at_100
+      value: 1.4460000000000002
+    - type: precision_at_1000
+      value: 0.192
+    - type: precision_at_3
+      value: 19.742
+    - type: precision_at_5
+      value: 14.421000000000001
+    - type: recall_at_1
+      value: 27.884999999999998
+    - type: recall_at_10
+      value: 59.087
+    - type: recall_at_100
+      value: 80.609
+    - type: recall_at_1000
+      value: 95.054
+    - type: recall_at_3
+      value: 44.082
+    - type: recall_at_5
+      value: 51.593999999999994
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.639
+    - type: map_at_10
+      value: 40.047
+    - type: map_at_100
+      value: 41.302
+    - type: map_at_1000
+      value: 41.425
+    - type: map_at_3
+      value: 37.406
+    - type: map_at_5
+      value: 38.934000000000005
+    - type: mrr_at_1
+      value: 37.707
+    - type: mrr_at_10
+      value: 46.082
+    - type: mrr_at_100
+      value: 46.745
+    - type: mrr_at_1000
+      value: 46.786
+    - type: mrr_at_3
+      value: 43.980999999999995
+    - type: mrr_at_5
+      value: 45.287
+    - type: ndcg_at_1
+      value: 37.707
+    - type: ndcg_at_10
+      value: 45.525
+    - type: ndcg_at_100
+      value: 49.976
+    - type: ndcg_at_1000
+      value: 51.94499999999999
+    - type: ndcg_at_3
+      value: 41.704
+    - type: ndcg_at_5
+      value: 43.596000000000004
+    - type: precision_at_1
+      value: 37.707
+    - type: precision_at_10
+      value: 8.465
+    - type: precision_at_100
+      value: 1.375
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 19.979
+    - type: precision_at_5
+      value: 14.115
+    - type: recall_at_1
+      value: 30.639
+    - type: recall_at_10
+      value: 54.775
+    - type: recall_at_100
+      value: 73.678
+    - type: recall_at_1000
+      value: 86.142
+    - type: recall_at_3
+      value: 43.230000000000004
+    - type: recall_at_5
+      value: 48.622
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.038
+    - type: map_at_10
+      value: 49.922
+    - type: map_at_100
+      value: 51.032
+    - type: map_at_1000
+      value: 51.085
+    - type: map_at_3
+      value: 46.664
+    - type: map_at_5
+      value: 48.588
+    - type: mrr_at_1
+      value: 43.95
+    - type: mrr_at_10
+      value: 53.566
+    - type: mrr_at_100
+      value: 54.318999999999996
+    - type: mrr_at_1000
+      value: 54.348
+    - type: mrr_at_3
+      value: 51.066
+    - type: mrr_at_5
+      value: 52.649
+    - type: ndcg_at_1
+      value: 43.95
+    - type: ndcg_at_10
+      value: 55.676
+    - type: ndcg_at_100
+      value: 60.126000000000005
+    - type: ndcg_at_1000
+      value: 61.208
+    - type: ndcg_at_3
+      value: 50.20400000000001
+    - type: ndcg_at_5
+      value: 53.038
+    - type: precision_at_1
+      value: 43.95
+    - type: precision_at_10
+      value: 8.953
+    - type: precision_at_100
+      value: 1.2109999999999999
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 22.256999999999998
+    - type: precision_at_5
+      value: 15.524
+    - type: recall_at_1
+      value: 38.038
+    - type: recall_at_10
+      value: 69.15
+    - type: recall_at_100
+      value: 88.31599999999999
+    - type: recall_at_1000
+      value: 95.993
+    - type: recall_at_3
+      value: 54.663
+    - type: recall_at_5
+      value: 61.373
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.872
+    - type: map_at_10
+      value: 32.912
+    - type: map_at_100
+      value: 33.972
+    - type: map_at_1000
+      value: 34.046
+    - type: map_at_3
+      value: 30.361
+    - type: map_at_5
+      value: 31.704
+    - type: mrr_at_1
+      value: 26.779999999999998
+    - type: mrr_at_10
+      value: 34.812
+    - type: mrr_at_100
+      value: 35.754999999999995
+    - type: mrr_at_1000
+      value: 35.809000000000005
+    - type: mrr_at_3
+      value: 32.335
+    - type: mrr_at_5
+      value: 33.64
+    - type: ndcg_at_1
+      value: 26.779999999999998
+    - type: ndcg_at_10
+      value: 37.623
+    - type: ndcg_at_100
+      value: 42.924
+    - type: ndcg_at_1000
+      value: 44.856
+    - type: ndcg_at_3
+      value: 32.574
+    - type: ndcg_at_5
+      value: 34.842
+    - type: precision_at_1
+      value: 26.779999999999998
+    - type: precision_at_10
+      value: 5.729
+    - type: precision_at_100
+      value: 0.886
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 13.559
+    - type: precision_at_5
+      value: 9.469
+    - type: recall_at_1
+      value: 24.872
+    - type: recall_at_10
+      value: 50.400999999999996
+    - type: recall_at_100
+      value: 74.954
+    - type: recall_at_1000
+      value: 89.56
+    - type: recall_at_3
+      value: 36.726
+    - type: recall_at_5
+      value: 42.138999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.803
+    - type: map_at_10
+      value: 24.348
+    - type: map_at_100
+      value: 25.56
+    - type: map_at_1000
+      value: 25.668000000000003
+    - type: map_at_3
+      value: 21.811
+    - type: map_at_5
+      value: 23.287
+    - type: mrr_at_1
+      value: 20.771
+    - type: mrr_at_10
+      value: 28.961
+    - type: mrr_at_100
+      value: 29.979
+    - type: mrr_at_1000
+      value: 30.046
+    - type: mrr_at_3
+      value: 26.555
+    - type: mrr_at_5
+      value: 28.060000000000002
+    - type: ndcg_at_1
+      value: 20.771
+    - type: ndcg_at_10
+      value: 29.335
+    - type: ndcg_at_100
+      value: 35.188
+    - type: ndcg_at_1000
+      value: 37.812
+    - type: ndcg_at_3
+      value: 24.83
+    - type: ndcg_at_5
+      value: 27.119
+    - type: precision_at_1
+      value: 20.771
+    - type: precision_at_10
+      value: 5.4350000000000005
+    - type: precision_at_100
+      value: 0.9480000000000001
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 11.982
+    - type: precision_at_5
+      value: 8.831
+    - type: recall_at_1
+      value: 16.803
+    - type: recall_at_10
+      value: 40.039
+    - type: recall_at_100
+      value: 65.83200000000001
+    - type: recall_at_1000
+      value: 84.478
+    - type: recall_at_3
+      value: 27.682000000000002
+    - type: recall_at_5
+      value: 33.535
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.345
+    - type: map_at_10
+      value: 37.757000000000005
+    - type: map_at_100
+      value: 39.141
+    - type: map_at_1000
+      value: 39.262
+    - type: map_at_3
+      value: 35.183
+    - type: map_at_5
+      value: 36.592
+    - type: mrr_at_1
+      value: 34.649
+    - type: mrr_at_10
+      value: 43.586999999999996
+    - type: mrr_at_100
+      value: 44.481
+    - type: mrr_at_1000
+      value: 44.542
+    - type: mrr_at_3
+      value: 41.29
+    - type: mrr_at_5
+      value: 42.642
+    - type: ndcg_at_1
+      value: 34.649
+    - type: ndcg_at_10
+      value: 43.161
+    - type: ndcg_at_100
+      value: 48.734
+    - type: ndcg_at_1000
+      value: 51.046
+    - type: ndcg_at_3
+      value: 39.118
+    - type: ndcg_at_5
+      value: 41.022
+    - type: precision_at_1
+      value: 34.649
+    - type: precision_at_10
+      value: 7.603
+    - type: precision_at_100
+      value: 1.209
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 18.319
+    - type: precision_at_5
+      value: 12.839
+    - type: recall_at_1
+      value: 28.345
+    - type: recall_at_10
+      value: 53.367
+    - type: recall_at_100
+      value: 76.453
+    - type: recall_at_1000
+      value: 91.82000000000001
+    - type: recall_at_3
+      value: 41.636
+    - type: recall_at_5
+      value: 46.760000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.419
+    - type: map_at_10
+      value: 31.716
+    - type: map_at_100
+      value: 33.152
+    - type: map_at_1000
+      value: 33.267
+    - type: map_at_3
+      value: 28.74
+    - type: map_at_5
+      value: 30.48
+    - type: mrr_at_1
+      value: 28.310999999999996
+    - type: mrr_at_10
+      value: 37.039
+    - type: mrr_at_100
+      value: 38.09
+    - type: mrr_at_1000
+      value: 38.145
+    - type: mrr_at_3
+      value: 34.437
+    - type: mrr_at_5
+      value: 36.024
+    - type: ndcg_at_1
+      value: 28.310999999999996
+    - type: ndcg_at_10
+      value: 37.41
+    - type: ndcg_at_100
+      value: 43.647999999999996
+    - type: ndcg_at_1000
+      value: 46.007
+    - type: ndcg_at_3
+      value: 32.509
+    - type: ndcg_at_5
+      value: 34.943999999999996
+    - type: precision_at_1
+      value: 28.310999999999996
+    - type: precision_at_10
+      value: 6.963
+    - type: precision_at_100
+      value: 1.1860000000000002
+    - type: precision_at_1000
+      value: 0.154
+    - type: precision_at_3
+      value: 15.867999999999999
+    - type: precision_at_5
+      value: 11.507000000000001
+    - type: recall_at_1
+      value: 22.419
+    - type: recall_at_10
+      value: 49.28
+    - type: recall_at_100
+      value: 75.802
+    - type: recall_at_1000
+      value: 92.032
+    - type: recall_at_3
+      value: 35.399
+    - type: recall_at_5
+      value: 42.027
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.669249999999998
+    - type: map_at_10
+      value: 33.332583333333325
+    - type: map_at_100
+      value: 34.557833333333335
+    - type: map_at_1000
+      value: 34.67141666666666
+    - type: map_at_3
+      value: 30.663166666666662
+    - type: map_at_5
+      value: 32.14883333333333
+    - type: mrr_at_1
+      value: 29.193833333333334
+    - type: mrr_at_10
+      value: 37.47625
+    - type: mrr_at_100
+      value: 38.3545
+    - type: mrr_at_1000
+      value: 38.413166666666676
+    - type: mrr_at_3
+      value: 35.06741666666667
+    - type: mrr_at_5
+      value: 36.450666666666656
+    - type: ndcg_at_1
+      value: 29.193833333333334
+    - type: ndcg_at_10
+      value: 38.505416666666676
+    - type: ndcg_at_100
+      value: 43.81125
+    - type: ndcg_at_1000
+      value: 46.09558333333333
+    - type: ndcg_at_3
+      value: 33.90916666666667
+    - type: ndcg_at_5
+      value: 36.07666666666666
+    - type: precision_at_1
+      value: 29.193833333333334
+    - type: precision_at_10
+      value: 6.7251666666666665
+    - type: precision_at_100
+      value: 1.1058333333333332
+    - type: precision_at_1000
+      value: 0.14833333333333332
+    - type: precision_at_3
+      value: 15.554166666666665
+    - type: precision_at_5
+      value: 11.079250000000002
+    - type: recall_at_1
+      value: 24.669249999999998
+    - type: recall_at_10
+      value: 49.75583333333332
+    - type: recall_at_100
+      value: 73.06908333333332
+    - type: recall_at_1000
+      value: 88.91316666666667
+    - type: recall_at_3
+      value: 36.913250000000005
+    - type: recall_at_5
+      value: 42.48641666666666
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.044999999999998
+    - type: map_at_10
+      value: 30.349999999999998
+    - type: map_at_100
+      value: 31.273
+    - type: map_at_1000
+      value: 31.362000000000002
+    - type: map_at_3
+      value: 28.508
+    - type: map_at_5
+      value: 29.369
+    - type: mrr_at_1
+      value: 26.994
+    - type: mrr_at_10
+      value: 33.12
+    - type: mrr_at_100
+      value: 33.904
+    - type: mrr_at_1000
+      value: 33.967000000000006
+    - type: mrr_at_3
+      value: 31.365
+    - type: mrr_at_5
+      value: 32.124
+    - type: ndcg_at_1
+      value: 26.994
+    - type: ndcg_at_10
+      value: 34.214
+    - type: ndcg_at_100
+      value: 38.681
+    - type: ndcg_at_1000
+      value: 40.926
+    - type: ndcg_at_3
+      value: 30.725
+    - type: ndcg_at_5
+      value: 31.967000000000002
+    - type: precision_at_1
+      value: 26.994
+    - type: precision_at_10
+      value: 5.215
+    - type: precision_at_100
+      value: 0.807
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 12.986
+    - type: precision_at_5
+      value: 8.712
+    - type: recall_at_1
+      value: 24.044999999999998
+    - type: recall_at_10
+      value: 43.456
+    - type: recall_at_100
+      value: 63.675000000000004
+    - type: recall_at_1000
+      value: 80.05499999999999
+    - type: recall_at_3
+      value: 33.561
+    - type: recall_at_5
+      value: 36.767
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.672
+    - type: map_at_10
+      value: 22.641
+    - type: map_at_100
+      value: 23.75
+    - type: map_at_1000
+      value: 23.877000000000002
+    - type: map_at_3
+      value: 20.219
+    - type: map_at_5
+      value: 21.648
+    - type: mrr_at_1
+      value: 18.823
+    - type: mrr_at_10
+      value: 26.101999999999997
+    - type: mrr_at_100
+      value: 27.038
+    - type: mrr_at_1000
+      value: 27.118
+    - type: mrr_at_3
+      value: 23.669
+    - type: mrr_at_5
+      value: 25.173000000000002
+    - type: ndcg_at_1
+      value: 18.823
+    - type: ndcg_at_10
+      value: 27.176000000000002
+    - type: ndcg_at_100
+      value: 32.42
+    - type: ndcg_at_1000
+      value: 35.413
+    - type: ndcg_at_3
+      value: 22.756999999999998
+    - type: ndcg_at_5
+      value: 25.032
+    - type: precision_at_1
+      value: 18.823
+    - type: precision_at_10
+      value: 5.034000000000001
+    - type: precision_at_100
+      value: 0.895
+    - type: precision_at_1000
+      value: 0.132
+    - type: precision_at_3
+      value: 10.771
+    - type: precision_at_5
+      value: 8.1
+    - type: recall_at_1
+      value: 15.672
+    - type: recall_at_10
+      value: 37.296
+    - type: recall_at_100
+      value: 60.863
+    - type: recall_at_1000
+      value: 82.234
+    - type: recall_at_3
+      value: 25.330000000000002
+    - type: recall_at_5
+      value: 30.964000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.633
+    - type: map_at_10
+      value: 32.858
+    - type: map_at_100
+      value: 34.038000000000004
+    - type: map_at_1000
+      value: 34.141
+    - type: map_at_3
+      value: 30.209000000000003
+    - type: map_at_5
+      value: 31.567
+    - type: mrr_at_1
+      value: 28.358
+    - type: mrr_at_10
+      value: 36.433
+    - type: mrr_at_100
+      value: 37.352000000000004
+    - type: mrr_at_1000
+      value: 37.41
+    - type: mrr_at_3
+      value: 34.033
+    - type: mrr_at_5
+      value: 35.246
+    - type: ndcg_at_1
+      value: 28.358
+    - type: ndcg_at_10
+      value: 37.973
+    - type: ndcg_at_100
+      value: 43.411
+    - type: ndcg_at_1000
+      value: 45.747
+    - type: ndcg_at_3
+      value: 32.934999999999995
+    - type: ndcg_at_5
+      value: 35.013
+    - type: precision_at_1
+      value: 28.358
+    - type: precision_at_10
+      value: 6.418
+    - type: precision_at_100
+      value: 1.02
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 14.677000000000001
+    - type: precision_at_5
+      value: 10.335999999999999
+    - type: recall_at_1
+      value: 24.633
+    - type: recall_at_10
+      value: 50.048
+    - type: recall_at_100
+      value: 73.821
+    - type: recall_at_1000
+      value: 90.046
+    - type: recall_at_3
+      value: 36.284
+    - type: recall_at_5
+      value: 41.370000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.133
+    - type: map_at_10
+      value: 31.491999999999997
+    - type: map_at_100
+      value: 33.062000000000005
+    - type: map_at_1000
+      value: 33.256
+    - type: map_at_3
+      value: 28.886
+    - type: map_at_5
+      value: 30.262
+    - type: mrr_at_1
+      value: 28.063
+    - type: mrr_at_10
+      value: 36.144
+    - type: mrr_at_100
+      value: 37.14
+    - type: mrr_at_1000
+      value: 37.191
+    - type: mrr_at_3
+      value: 33.762
+    - type: mrr_at_5
+      value: 34.997
+    - type: ndcg_at_1
+      value: 28.063
+    - type: ndcg_at_10
+      value: 36.951
+    - type: ndcg_at_100
+      value: 43.287
+    - type: ndcg_at_1000
+      value: 45.777
+    - type: ndcg_at_3
+      value: 32.786
+    - type: ndcg_at_5
+      value: 34.65
+    - type: precision_at_1
+      value: 28.063
+    - type: precision_at_10
+      value: 7.055
+    - type: precision_at_100
+      value: 1.476
+    - type: precision_at_1000
+      value: 0.22899999999999998
+    - type: precision_at_3
+      value: 15.481
+    - type: precision_at_5
+      value: 11.186
+    - type: recall_at_1
+      value: 23.133
+    - type: recall_at_10
+      value: 47.285
+    - type: recall_at_100
+      value: 76.176
+    - type: recall_at_1000
+      value: 92.176
+    - type: recall_at_3
+      value: 35.223
+    - type: recall_at_5
+      value: 40.142
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.547
+    - type: map_at_10
+      value: 26.374
+    - type: map_at_100
+      value: 27.419
+    - type: map_at_1000
+      value: 27.539
+    - type: map_at_3
+      value: 23.882
+    - type: map_at_5
+      value: 25.163999999999998
+    - type: mrr_at_1
+      value: 21.442
+    - type: mrr_at_10
+      value: 28.458
+    - type: mrr_at_100
+      value: 29.360999999999997
+    - type: mrr_at_1000
+      value: 29.448999999999998
+    - type: mrr_at_3
+      value: 25.97
+    - type: mrr_at_5
+      value: 27.273999999999997
+    - type: ndcg_at_1
+      value: 21.442
+    - type: ndcg_at_10
+      value: 30.897000000000002
+    - type: ndcg_at_100
+      value: 35.99
+    - type: ndcg_at_1000
+      value: 38.832
+    - type: ndcg_at_3
+      value: 25.944
+    - type: ndcg_at_5
+      value: 28.126
+    - type: precision_at_1
+      value: 21.442
+    - type: precision_at_10
+      value: 4.9910000000000005
+    - type: precision_at_100
+      value: 0.8109999999999999
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 11.029
+    - type: precision_at_5
+      value: 7.911
+    - type: recall_at_1
+      value: 19.547
+    - type: recall_at_10
+      value: 42.886
+    - type: recall_at_100
+      value: 66.64999999999999
+    - type: recall_at_1000
+      value: 87.368
+    - type: recall_at_3
+      value: 29.143
+    - type: recall_at_5
+      value: 34.544000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.572
+    - type: map_at_10
+      value: 25.312
+    - type: map_at_100
+      value: 27.062
+    - type: map_at_1000
+      value: 27.253
+    - type: map_at_3
+      value: 21.601
+    - type: map_at_5
+      value: 23.473
+    - type: mrr_at_1
+      value: 34.984
+    - type: mrr_at_10
+      value: 46.406
+    - type: mrr_at_100
+      value: 47.179
+    - type: mrr_at_1000
+      value: 47.21
+    - type: mrr_at_3
+      value: 43.485
+    - type: mrr_at_5
+      value: 45.322
+    - type: ndcg_at_1
+      value: 34.984
+    - type: ndcg_at_10
+      value: 34.344
+    - type: ndcg_at_100
+      value: 41.015
+    - type: ndcg_at_1000
+      value: 44.366
+    - type: ndcg_at_3
+      value: 29.119
+    - type: ndcg_at_5
+      value: 30.825999999999997
+    - type: precision_at_1
+      value: 34.984
+    - type: precision_at_10
+      value: 10.358
+    - type: precision_at_100
+      value: 1.762
+    - type: precision_at_1000
+      value: 0.23900000000000002
+    - type: precision_at_3
+      value: 21.368000000000002
+    - type: precision_at_5
+      value: 15.948
+    - type: recall_at_1
+      value: 15.572
+    - type: recall_at_10
+      value: 39.367999999999995
+    - type: recall_at_100
+      value: 62.183
+    - type: recall_at_1000
+      value: 80.92200000000001
+    - type: recall_at_3
+      value: 26.131999999999998
+    - type: recall_at_5
+      value: 31.635999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.848
+    - type: map_at_10
+      value: 19.25
+    - type: map_at_100
+      value: 27.193
+    - type: map_at_1000
+      value: 28.721999999999998
+    - type: map_at_3
+      value: 13.968
+    - type: map_at_5
+      value: 16.283
+    - type: mrr_at_1
+      value: 68.75
+    - type: mrr_at_10
+      value: 76.25
+    - type: mrr_at_100
+      value: 76.534
+    - type: mrr_at_1000
+      value: 76.53999999999999
+    - type: mrr_at_3
+      value: 74.667
+    - type: mrr_at_5
+      value: 75.86699999999999
+    - type: ndcg_at_1
+      value: 56.00000000000001
+    - type: ndcg_at_10
+      value: 41.426
+    - type: ndcg_at_100
+      value: 45.660000000000004
+    - type: ndcg_at_1000
+      value: 53.02
+    - type: ndcg_at_3
+      value: 46.581
+    - type: ndcg_at_5
+      value: 43.836999999999996
+    - type: precision_at_1
+      value: 68.75
+    - type: precision_at_10
+      value: 32.800000000000004
+    - type: precision_at_100
+      value: 10.440000000000001
+    - type: precision_at_1000
+      value: 1.9980000000000002
+    - type: precision_at_3
+      value: 49.667
+    - type: precision_at_5
+      value: 42.25
+    - type: recall_at_1
+      value: 8.848
+    - type: recall_at_10
+      value: 24.467
+    - type: recall_at_100
+      value: 51.344
+    - type: recall_at_1000
+      value: 75.235
+    - type: recall_at_3
+      value: 15.329
+    - type: recall_at_5
+      value: 18.892999999999997
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 48.95
+    - type: f1
+      value: 43.44563593360779
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 78.036
+    - type: map_at_10
+      value: 85.639
+    - type: map_at_100
+      value: 85.815
+    - type: map_at_1000
+      value: 85.829
+    - type: map_at_3
+      value: 84.795
+    - type: map_at_5
+      value: 85.336
+    - type: mrr_at_1
+      value: 84.353
+    - type: mrr_at_10
+      value: 90.582
+    - type: mrr_at_100
+      value: 90.617
+    - type: mrr_at_1000
+      value: 90.617
+    - type: mrr_at_3
+      value: 90.132
+    - type: mrr_at_5
+      value: 90.447
+    - type: ndcg_at_1
+      value: 84.353
+    - type: ndcg_at_10
+      value: 89.003
+    - type: ndcg_at_100
+      value: 89.60000000000001
+    - type: ndcg_at_1000
+      value: 89.836
+    - type: ndcg_at_3
+      value: 87.81400000000001
+    - type: ndcg_at_5
+      value: 88.478
+    - type: precision_at_1
+      value: 84.353
+    - type: precision_at_10
+      value: 10.482
+    - type: precision_at_100
+      value: 1.099
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 33.257999999999996
+    - type: precision_at_5
+      value: 20.465
+    - type: recall_at_1
+      value: 78.036
+    - type: recall_at_10
+      value: 94.517
+    - type: recall_at_100
+      value: 96.828
+    - type: recall_at_1000
+      value: 98.261
+    - type: recall_at_3
+      value: 91.12
+    - type: recall_at_5
+      value: 92.946
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.191
+    - type: map_at_10
+      value: 32.369
+    - type: map_at_100
+      value: 34.123999999999995
+    - type: map_at_1000
+      value: 34.317
+    - type: map_at_3
+      value: 28.71
+    - type: map_at_5
+      value: 30.607
+    - type: mrr_at_1
+      value: 40.894999999999996
+    - type: mrr_at_10
+      value: 48.842
+    - type: mrr_at_100
+      value: 49.599
+    - type: mrr_at_1000
+      value: 49.647000000000006
+    - type: mrr_at_3
+      value: 46.785
+    - type: mrr_at_5
+      value: 47.672
+    - type: ndcg_at_1
+      value: 40.894999999999996
+    - type: ndcg_at_10
+      value: 39.872
+    - type: ndcg_at_100
+      value: 46.126
+    - type: ndcg_at_1000
+      value: 49.476
+    - type: ndcg_at_3
+      value: 37.153000000000006
+    - type: ndcg_at_5
+      value: 37.433
+    - type: precision_at_1
+      value: 40.894999999999996
+    - type: precision_at_10
+      value: 10.818
+    - type: precision_at_100
+      value: 1.73
+    - type: precision_at_1000
+      value: 0.231
+    - type: precision_at_3
+      value: 25.051000000000002
+    - type: precision_at_5
+      value: 17.531
+    - type: recall_at_1
+      value: 20.191
+    - type: recall_at_10
+      value: 45.768
+    - type: recall_at_100
+      value: 68.82000000000001
+    - type: recall_at_1000
+      value: 89.133
+    - type: recall_at_3
+      value: 33.296
+    - type: recall_at_5
+      value: 38.022
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.257
+    - type: map_at_10
+      value: 61.467000000000006
+    - type: map_at_100
+      value: 62.364
+    - type: map_at_1000
+      value: 62.424
+    - type: map_at_3
+      value: 58.228
+    - type: map_at_5
+      value: 60.283
+    - type: mrr_at_1
+      value: 78.515
+    - type: mrr_at_10
+      value: 84.191
+    - type: mrr_at_100
+      value: 84.378
+    - type: mrr_at_1000
+      value: 84.385
+    - type: mrr_at_3
+      value: 83.284
+    - type: mrr_at_5
+      value: 83.856
+    - type: ndcg_at_1
+      value: 78.515
+    - type: ndcg_at_10
+      value: 69.78999999999999
+    - type: ndcg_at_100
+      value: 72.886
+    - type: ndcg_at_1000
+      value: 74.015
+    - type: ndcg_at_3
+      value: 65.23
+    - type: ndcg_at_5
+      value: 67.80199999999999
+    - type: precision_at_1
+      value: 78.515
+    - type: precision_at_10
+      value: 14.519000000000002
+    - type: precision_at_100
+      value: 1.694
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 41.702
+    - type: precision_at_5
+      value: 27.046999999999997
+    - type: recall_at_1
+      value: 39.257
+    - type: recall_at_10
+      value: 72.59299999999999
+    - type: recall_at_100
+      value: 84.679
+    - type: recall_at_1000
+      value: 92.12
+    - type: recall_at_3
+      value: 62.552
+    - type: recall_at_5
+      value: 67.616
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 91.5152
+    - type: ap
+      value: 87.64584669595709
+    - type: f1
+      value: 91.50605576428437
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.926000000000002
+    - type: map_at_10
+      value: 34.049
+    - type: map_at_100
+      value: 35.213
+    - type: map_at_1000
+      value: 35.265
+    - type: map_at_3
+      value: 30.309
+    - type: map_at_5
+      value: 32.407000000000004
+    - type: mrr_at_1
+      value: 22.55
+    - type: mrr_at_10
+      value: 34.657
+    - type: mrr_at_100
+      value: 35.760999999999996
+    - type: mrr_at_1000
+      value: 35.807
+    - type: mrr_at_3
+      value: 30.989
+    - type: mrr_at_5
+      value: 33.039
+    - type: ndcg_at_1
+      value: 22.55
+    - type: ndcg_at_10
+      value: 40.842
+    - type: ndcg_at_100
+      value: 46.436
+    - type: ndcg_at_1000
+      value: 47.721999999999994
+    - type: ndcg_at_3
+      value: 33.209
+    - type: ndcg_at_5
+      value: 36.943
+    - type: precision_at_1
+      value: 22.55
+    - type: precision_at_10
+      value: 6.447
+    - type: precision_at_100
+      value: 0.9249999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.136000000000001
+    - type: precision_at_5
+      value: 10.381
+    - type: recall_at_1
+      value: 21.926000000000002
+    - type: recall_at_10
+      value: 61.724999999999994
+    - type: recall_at_100
+      value: 87.604
+    - type: recall_at_1000
+      value: 97.421
+    - type: recall_at_3
+      value: 40.944
+    - type: recall_at_5
+      value: 49.915
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.54765161878704
+    - type: f1
+      value: 93.3298945415573
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 75.71591427268582
+    - type: f1
+      value: 59.32113870474471
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 75.83053127101547
+    - type: f1
+      value: 73.60757944876475
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.72562205783457
+    - type: f1
+      value: 78.63761662505502
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.37935633767996
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.55270546130387
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.462692753143834
+    - type: mrr
+      value: 31.497569753511563
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.646
+    - type: map_at_10
+      value: 12.498
+    - type: map_at_100
+      value: 15.486
+    - type: map_at_1000
+      value: 16.805999999999997
+    - type: map_at_3
+      value: 9.325
+    - type: map_at_5
+      value: 10.751
+    - type: mrr_at_1
+      value: 43.034
+    - type: mrr_at_10
+      value: 52.662
+    - type: mrr_at_100
+      value: 53.189
+    - type: mrr_at_1000
+      value: 53.25
+    - type: mrr_at_3
+      value: 50.929
+    - type: mrr_at_5
+      value: 51.92
+    - type: ndcg_at_1
+      value: 41.796
+    - type: ndcg_at_10
+      value: 33.477000000000004
+    - type: ndcg_at_100
+      value: 29.996000000000002
+    - type: ndcg_at_1000
+      value: 38.864
+    - type: ndcg_at_3
+      value: 38.940000000000005
+    - type: ndcg_at_5
+      value: 36.689
+    - type: precision_at_1
+      value: 43.034
+    - type: precision_at_10
+      value: 24.799
+    - type: precision_at_100
+      value: 7.432999999999999
+    - type: precision_at_1000
+      value: 1.9929999999999999
+    - type: precision_at_3
+      value: 36.842000000000006
+    - type: precision_at_5
+      value: 32.135999999999996
+    - type: recall_at_1
+      value: 5.646
+    - type: recall_at_10
+      value: 15.963
+    - type: recall_at_100
+      value: 29.492
+    - type: recall_at_1000
+      value: 61.711000000000006
+    - type: recall_at_3
+      value: 10.585
+    - type: recall_at_5
+      value: 12.753999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.602
+    - type: map_at_10
+      value: 41.545
+    - type: map_at_100
+      value: 42.644999999999996
+    - type: map_at_1000
+      value: 42.685
+    - type: map_at_3
+      value: 37.261
+    - type: map_at_5
+      value: 39.706
+    - type: mrr_at_1
+      value: 31.141000000000002
+    - type: mrr_at_10
+      value: 44.139
+    - type: mrr_at_100
+      value: 44.997
+    - type: mrr_at_1000
+      value: 45.025999999999996
+    - type: mrr_at_3
+      value: 40.503
+    - type: mrr_at_5
+      value: 42.64
+    - type: ndcg_at_1
+      value: 31.141000000000002
+    - type: ndcg_at_10
+      value: 48.995
+    - type: ndcg_at_100
+      value: 53.788000000000004
+    - type: ndcg_at_1000
+      value: 54.730000000000004
+    - type: ndcg_at_3
+      value: 40.844
+    - type: ndcg_at_5
+      value: 44.955
+    - type: precision_at_1
+      value: 31.141000000000002
+    - type: precision_at_10
+      value: 8.233
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 18.579
+    - type: precision_at_5
+      value: 13.533999999999999
+    - type: recall_at_1
+      value: 27.602
+    - type: recall_at_10
+      value: 69.216
+    - type: recall_at_100
+      value: 90.252
+    - type: recall_at_1000
+      value: 97.27
+    - type: recall_at_3
+      value: 47.987
+    - type: recall_at_5
+      value: 57.438
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.949
+    - type: map_at_10
+      value: 84.89999999999999
+    - type: map_at_100
+      value: 85.531
+    - type: map_at_1000
+      value: 85.548
+    - type: map_at_3
+      value: 82.027
+    - type: map_at_5
+      value: 83.853
+    - type: mrr_at_1
+      value: 81.69999999999999
+    - type: mrr_at_10
+      value: 87.813
+    - type: mrr_at_100
+      value: 87.917
+    - type: mrr_at_1000
+      value: 87.91799999999999
+    - type: mrr_at_3
+      value: 86.938
+    - type: mrr_at_5
+      value: 87.53999999999999
+    - type: ndcg_at_1
+      value: 81.75
+    - type: ndcg_at_10
+      value: 88.55499999999999
+    - type: ndcg_at_100
+      value: 89.765
+    - type: ndcg_at_1000
+      value: 89.871
+    - type: ndcg_at_3
+      value: 85.905
+    - type: ndcg_at_5
+      value: 87.41
+    - type: precision_at_1
+      value: 81.75
+    - type: precision_at_10
+      value: 13.403
+    - type: precision_at_100
+      value: 1.528
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.597
+    - type: precision_at_5
+      value: 24.69
+    - type: recall_at_1
+      value: 70.949
+    - type: recall_at_10
+      value: 95.423
+    - type: recall_at_100
+      value: 99.509
+    - type: recall_at_1000
+      value: 99.982
+    - type: recall_at_3
+      value: 87.717
+    - type: recall_at_5
+      value: 92.032
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 51.76962893449579
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 62.32897690686379
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.478
+    - type: map_at_10
+      value: 11.994
+    - type: map_at_100
+      value: 13.977
+    - type: map_at_1000
+      value: 14.295
+    - type: map_at_3
+      value: 8.408999999999999
+    - type: map_at_5
+      value: 10.024
+    - type: mrr_at_1
+      value: 22.1
+    - type: mrr_at_10
+      value: 33.526
+    - type: mrr_at_100
+      value: 34.577000000000005
+    - type: mrr_at_1000
+      value: 34.632000000000005
+    - type: mrr_at_3
+      value: 30.217
+    - type: mrr_at_5
+      value: 31.962000000000003
+    - type: ndcg_at_1
+      value: 22.1
+    - type: ndcg_at_10
+      value: 20.191
+    - type: ndcg_at_100
+      value: 27.954
+    - type: ndcg_at_1000
+      value: 33.491
+    - type: ndcg_at_3
+      value: 18.787000000000003
+    - type: ndcg_at_5
+      value: 16.378999999999998
+    - type: precision_at_1
+      value: 22.1
+    - type: precision_at_10
+      value: 10.69
+    - type: precision_at_100
+      value: 2.1919999999999997
+    - type: precision_at_1000
+      value: 0.35200000000000004
+    - type: precision_at_3
+      value: 17.732999999999997
+    - type: precision_at_5
+      value: 14.499999999999998
+    - type: recall_at_1
+      value: 4.478
+    - type: recall_at_10
+      value: 21.657
+    - type: recall_at_100
+      value: 44.54
+    - type: recall_at_1000
+      value: 71.542
+    - type: recall_at_3
+      value: 10.778
+    - type: recall_at_5
+      value: 14.687
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.82325259156718
+    - type: cos_sim_spearman
+      value: 79.2463589100662
+    - type: euclidean_pearson
+      value: 80.48318380496771
+    - type: euclidean_spearman
+      value: 79.34451935199979
+    - type: manhattan_pearson
+      value: 80.39041824178759
+    - type: manhattan_spearman
+      value: 79.23002892700211
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.74130231431258
+    - type: cos_sim_spearman
+      value: 78.36856568042397
+    - type: euclidean_pearson
+      value: 82.48301631890303
+    - type: euclidean_spearman
+      value: 78.28376980722732
+    - type: manhattan_pearson
+      value: 82.43552075450525
+    - type: manhattan_spearman
+      value: 78.22702443947126
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.96138619461459
+    - type: cos_sim_spearman
+      value: 81.85436343502379
+    - type: euclidean_pearson
+      value: 81.82895226665367
+    - type: euclidean_spearman
+      value: 82.22707349602916
+    - type: manhattan_pearson
+      value: 81.66303369445873
+    - type: manhattan_spearman
+      value: 82.05030197179455
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.05481244198648
+    - type: cos_sim_spearman
+      value: 80.85052504637808
+    - type: euclidean_pearson
+      value: 80.86728419744497
+    - type: euclidean_spearman
+      value: 81.033786401512
+    - type: manhattan_pearson
+      value: 80.90107531061103
+    - type: manhattan_spearman
+      value: 81.11374116827795
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.615220756399
+    - type: cos_sim_spearman
+      value: 86.46858500002092
+    - type: euclidean_pearson
+      value: 86.08307800247586
+    - type: euclidean_spearman
+      value: 86.72691443870013
+    - type: manhattan_pearson
+      value: 85.96155594487269
+    - type: manhattan_spearman
+      value: 86.605909505275
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.14363913634436
+    - type: cos_sim_spearman
+      value: 84.48430226487102
+    - type: euclidean_pearson
+      value: 83.75303424801902
+    - type: euclidean_spearman
+      value: 84.56762380734538
+    - type: manhattan_pearson
+      value: 83.6135447165928
+    - type: manhattan_spearman
+      value: 84.39898212616731
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.09909252554525
+    - type: cos_sim_spearman
+      value: 85.70951402743276
+    - type: euclidean_pearson
+      value: 87.1991936239908
+    - type: euclidean_spearman
+      value: 86.07745840612071
+    - type: manhattan_pearson
+      value: 87.25039137549952
+    - type: manhattan_spearman
+      value: 85.99938746659761
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 63.529332093413615
+    - type: cos_sim_spearman
+      value: 65.38177340147439
+    - type: euclidean_pearson
+      value: 66.35278011412136
+    - type: euclidean_spearman
+      value: 65.47147267032997
+    - type: manhattan_pearson
+      value: 66.71804682408693
+    - type: manhattan_spearman
+      value: 65.67406521423597
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.45802942885662
+    - type: cos_sim_spearman
+      value: 84.8853341842566
+    - type: euclidean_pearson
+      value: 84.60915021096707
+    - type: euclidean_spearman
+      value: 85.11181242913666
+    - type: manhattan_pearson
+      value: 84.38600521210364
+    - type: manhattan_spearman
+      value: 84.89045417981723
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 85.92793380635129
+    - type: mrr
+      value: 95.85834191226348
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 55.74400000000001
+    - type: map_at_10
+      value: 65.455
+    - type: map_at_100
+      value: 66.106
+    - type: map_at_1000
+      value: 66.129
+    - type: map_at_3
+      value: 62.719
+    - type: map_at_5
+      value: 64.441
+    - type: mrr_at_1
+      value: 58.667
+    - type: mrr_at_10
+      value: 66.776
+    - type: mrr_at_100
+      value: 67.363
+    - type: mrr_at_1000
+      value: 67.384
+    - type: mrr_at_3
+      value: 64.889
+    - type: mrr_at_5
+      value: 66.122
+    - type: ndcg_at_1
+      value: 58.667
+    - type: ndcg_at_10
+      value: 69.904
+    - type: ndcg_at_100
+      value: 72.807
+    - type: ndcg_at_1000
+      value: 73.423
+    - type: ndcg_at_3
+      value: 65.405
+    - type: ndcg_at_5
+      value: 67.86999999999999
+    - type: precision_at_1
+      value: 58.667
+    - type: precision_at_10
+      value: 9.3
+    - type: precision_at_100
+      value: 1.08
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 25.444
+    - type: precision_at_5
+      value: 17
+    - type: recall_at_1
+      value: 55.74400000000001
+    - type: recall_at_10
+      value: 82.122
+    - type: recall_at_100
+      value: 95.167
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 70.14399999999999
+    - type: recall_at_5
+      value: 76.417
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.86534653465347
+    - type: cos_sim_ap
+      value: 96.54142419791388
+    - type: cos_sim_f1
+      value: 93.07535641547861
+    - type: cos_sim_precision
+      value: 94.81327800829875
+    - type: cos_sim_recall
+      value: 91.4
+    - type: dot_accuracy
+      value: 99.86435643564356
+    - type: dot_ap
+      value: 96.53682260449868
+    - type: dot_f1
+      value: 92.98515104966718
+    - type: dot_precision
+      value: 95.27806925498426
+    - type: dot_recall
+      value: 90.8
+    - type: euclidean_accuracy
+      value: 99.86336633663366
+    - type: euclidean_ap
+      value: 96.5228676185697
+    - type: euclidean_f1
+      value: 92.9735234215886
+    - type: euclidean_precision
+      value: 94.70954356846472
+    - type: euclidean_recall
+      value: 91.3
+    - type: manhattan_accuracy
+      value: 99.85841584158416
+    - type: manhattan_ap
+      value: 96.50392760934032
+    - type: manhattan_f1
+      value: 92.84642321160581
+    - type: manhattan_precision
+      value: 92.8928928928929
+    - type: manhattan_recall
+      value: 92.80000000000001
+    - type: max_accuracy
+      value: 99.86534653465347
+    - type: max_ap
+      value: 96.54142419791388
+    - type: max_f1
+      value: 93.07535641547861
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 61.08285408766616
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.640675309010604
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.20333913710715
+    - type: mrr
+      value: 54.088813555725324
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.79465221925075
+    - type: cos_sim_spearman
+      value: 30.530816059163634
+    - type: dot_pearson
+      value: 31.364837244718043
+    - type: dot_spearman
+      value: 30.79726823684003
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.22599999999999998
+    - type: map_at_10
+      value: 1.735
+    - type: map_at_100
+      value: 8.978
+    - type: map_at_1000
+      value: 20.851
+    - type: map_at_3
+      value: 0.613
+    - type: map_at_5
+      value: 0.964
+    - type: mrr_at_1
+      value: 88
+    - type: mrr_at_10
+      value: 92.867
+    - type: mrr_at_100
+      value: 92.867
+    - type: mrr_at_1000
+      value: 92.867
+    - type: mrr_at_3
+      value: 92.667
+    - type: mrr_at_5
+      value: 92.667
+    - type: ndcg_at_1
+      value: 82
+    - type: ndcg_at_10
+      value: 73.164
+    - type: ndcg_at_100
+      value: 51.878
+    - type: ndcg_at_1000
+      value: 44.864
+    - type: ndcg_at_3
+      value: 79.184
+    - type: ndcg_at_5
+      value: 76.39
+    - type: precision_at_1
+      value: 88
+    - type: precision_at_10
+      value: 76.2
+    - type: precision_at_100
+      value: 52.459999999999994
+    - type: precision_at_1000
+      value: 19.692
+    - type: precision_at_3
+      value: 82.667
+    - type: precision_at_5
+      value: 80
+    - type: recall_at_1
+      value: 0.22599999999999998
+    - type: recall_at_10
+      value: 1.942
+    - type: recall_at_100
+      value: 12.342
+    - type: recall_at_1000
+      value: 41.42
+    - type: recall_at_3
+      value: 0.637
+    - type: recall_at_5
+      value: 1.034
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 3.567
+    - type: map_at_10
+      value: 13.116
+    - type: map_at_100
+      value: 19.39
+    - type: map_at_1000
+      value: 20.988
+    - type: map_at_3
+      value: 7.109
+    - type: map_at_5
+      value: 9.950000000000001
+    - type: mrr_at_1
+      value: 42.857
+    - type: mrr_at_10
+      value: 57.404999999999994
+    - type: mrr_at_100
+      value: 58.021
+    - type: mrr_at_1000
+      value: 58.021
+    - type: mrr_at_3
+      value: 54.762
+    - type: mrr_at_5
+      value: 56.19
+    - type: ndcg_at_1
+      value: 38.775999999999996
+    - type: ndcg_at_10
+      value: 30.359
+    - type: ndcg_at_100
+      value: 41.284
+    - type: ndcg_at_1000
+      value: 52.30200000000001
+    - type: ndcg_at_3
+      value: 36.744
+    - type: ndcg_at_5
+      value: 34.326
+    - type: precision_at_1
+      value: 42.857
+    - type: precision_at_10
+      value: 26.122
+    - type: precision_at_100
+      value: 8.082
+    - type: precision_at_1000
+      value: 1.559
+    - type: precision_at_3
+      value: 40.136
+    - type: precision_at_5
+      value: 35.510000000000005
+    - type: recall_at_1
+      value: 3.567
+    - type: recall_at_10
+      value: 19.045
+    - type: recall_at_100
+      value: 49.979
+    - type: recall_at_1000
+      value: 84.206
+    - type: recall_at_3
+      value: 8.52
+    - type: recall_at_5
+      value: 13.103000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 68.8394
+    - type: ap
+      value: 13.454399712443099
+    - type: f1
+      value: 53.04963076364322
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 60.546123372948514
+    - type: f1
+      value: 60.86952793277713
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.10042955060234
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.03308100375514
+    - type: cos_sim_ap
+      value: 71.08284605869684
+    - type: cos_sim_f1
+      value: 65.42539436255494
+    - type: cos_sim_precision
+      value: 64.14807302231237
+    - type: cos_sim_recall
+      value: 66.75461741424802
+    - type: dot_accuracy
+      value: 84.68736961316088
+    - type: dot_ap
+      value: 69.20524036530992
+    - type: dot_f1
+      value: 63.54893953365829
+    - type: dot_precision
+      value: 63.45698500394633
+    - type: dot_recall
+      value: 63.641160949868066
+    - type: euclidean_accuracy
+      value: 85.07480479227513
+    - type: euclidean_ap
+      value: 71.14592761009864
+    - type: euclidean_f1
+      value: 65.43814432989691
+    - type: euclidean_precision
+      value: 63.95465994962216
+    - type: euclidean_recall
+      value: 66.99208443271768
+    - type: manhattan_accuracy
+      value: 85.06288370984085
+    - type: manhattan_ap
+      value: 71.07289742593868
+    - type: manhattan_f1
+      value: 65.37585421412301
+    - type: manhattan_precision
+      value: 62.816147859922175
+    - type: manhattan_recall
+      value: 68.15303430079156
+    - type: max_accuracy
+      value: 85.07480479227513
+    - type: max_ap
+      value: 71.14592761009864
+    - type: max_f1
+      value: 65.43814432989691
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.79058485659952
+    - type: cos_sim_ap
+      value: 83.7183187008759
+    - type: cos_sim_f1
+      value: 75.86921142180798
+    - type: cos_sim_precision
+      value: 73.00683371298405
+    - type: cos_sim_recall
+      value: 78.96519864490298
+    - type: dot_accuracy
+      value: 87.0085768618776
+    - type: dot_ap
+      value: 81.87467488474279
+    - type: dot_f1
+      value: 74.04188363990559
+    - type: dot_precision
+      value: 72.10507114191901
+    - type: dot_recall
+      value: 76.08561749307053
+    - type: euclidean_accuracy
+      value: 87.8332751193387
+    - type: euclidean_ap
+      value: 83.83585648120315
+    - type: euclidean_f1
+      value: 76.02582177042369
+    - type: euclidean_precision
+      value: 73.36388371759989
+    - type: euclidean_recall
+      value: 78.88820449645827
+    - type: manhattan_accuracy
+      value: 87.87208444910156
+    - type: manhattan_ap
+      value: 83.8101950642973
+    - type: manhattan_f1
+      value: 75.90454195535027
+    - type: manhattan_precision
+      value: 72.44419564761039
+    - type: manhattan_recall
+      value: 79.71204188481676
+    - type: max_accuracy
+      value: 87.87208444910156
+    - type: max_ap
+      value: 83.83585648120315
+    - type: max_f1
+      value: 76.02582177042369
+license: mit
+language:
+- en
+---
+
+
+**Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
+
+<h1 align="center">FlagEmbedding</h1>
+
+
+<h4 align="center">
+    <p>
+        <a href=#model-list>Model List</a> | 
+        <a href=#frequently-asked-questions>FAQ</a> |
+        <a href=#usage>Usage</a>  |
+        <a href="#evaluation">Evaluation</a> |
+        <a href="#train">Train</a> |
+        <a href="#citation">Citation</a> |
+        <a href="#license">License</a> 
+    <p>
+</h4>
+
+More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
+
+
+[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
+
+FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently:
+
+- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
+- **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
+- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
+
+## News 
+
+- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:  
+- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
+- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released 
+- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released 
+- 09/12/2023: New models: 
+    - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. 
+    - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
+ 
+
+<details>
+  <summary>More</summary>
+<!-- ### More -->
+    
+- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. 
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).  
+- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**  
+- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:   
+- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.  
+  
+</details>
+
+
+## Model List
+
+`bge` is short for `BAAI general embedding`.
+
+|              Model              | Language | | Description | query instruction for retrieval [1] |
+|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
+| [LM-Cocktail](https://huggingface.co/Shitao)                   |   English |  | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail |  |
+|  [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder)  |   English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
+|  [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)  |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] |   |
+|  [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] |   |
+|  [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+
+
+[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
+
+[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. 
+For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
+
+All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. 
+If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
+
+
+## Frequently asked questions
+
+<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#hard-negatives), which can improve the retrieval performance.
+- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
+- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
+
+  
+</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 the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. 
+No instruction only has a slight degradation in retrieval performance compared with using instruction. 
+So you can generate embedding without instruction in all cases for convenience.
+ 
+For a retrieval task that uses short queries to find long related documents, 
+it is recommended to add instructions for these short queries.
+**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
+In all cases, the documents/passages do not need to add the instruction. 
+
+</details>
+
+
+## Usage 
+
+### Usage for Embedding Model
+
+Here are some examples for using `bge` models with 
+[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
+
+```python
+from FlagEmbedding import FlagModel
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
+model = FlagModel('BAAI/bge-large-zh-v1.5', 
+                  query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
+                  use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+embeddings_1 = model.encode(sentences_1)
+embeddings_2 = model.encode(sentences_2)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+
+# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
+# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+q_embeddings = model.encode_queries(queries)
+p_embeddings = model.encode(passages)
+scores = q_embeddings @ p_embeddings.T
+```
+For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). 
+
+By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
+You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
+
+
+#### Using Sentence-Transformers
+
+You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
+
+```
+pip install -U sentence-transformers
+```
+```python
+from sentence_transformers import SentenceTransformer
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
+model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
+embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+For s2p(short query to long passage) retrieval task, 
+each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
+But the instruction is not needed for passages.
+```python
+from sentence_transformers import SentenceTransformer
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+instruction = "为这个句子生成表示以用于检索相关文章："
+
+model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
+q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
+p_embeddings = model.encode(passages, normalize_embeddings=True)
+scores = q_embeddings @ p_embeddings.T
+```
+
+#### Using Langchain 
+
+You can use `bge` in langchain like this:
+```python
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+model_name = "BAAI/bge-large-en-v1.5"
+model_kwargs = {'device': 'cuda'}
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+model = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs,
+    query_instruction="为这个句子生成表示以用于检索相关文章："
+)
+model.query_instruction = "为这个句子生成表示以用于检索相关文章："
+```
+
+
+#### Using HuggingFace Transformers
+
+With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Sentences we want sentence embeddings for
+sentences = ["样例数据-1", "样例数据-2"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
+model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
+model.eval()
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
+# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+    # Perform pooling. In this case, cls pooling.
+    sentence_embeddings = model_output[0][:, 0]
+# normalize embeddings
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+print("Sentence embeddings:", sentence_embeddings)
+```
+
+### Usage for Reranker
+
+Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. 
+You can get a relevance score by inputting query and passage to the reranker. 
+The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
+
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+
+Get relevance scores (higher scores indicate more relevance):
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+
+
+#### Using Huggingface transformers
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+    print(scores)
+```
+
+## Evaluation  
+
+`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
+For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). 
+
+- **MTEB**:   
+
+| Model Name |  Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) |  STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 |  **64.23** | **54.29** |  46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |  
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |  768 | 512 | 63.55 | 53.25 |   45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |  
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |  384 | 512 | 62.17 |51.68 | 43.82 |  84.92 | 58.36 | 81.59 | 30.12 | 74.14 |  
+| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) |  1024 | 512 | 63.98 |  53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | 
+| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) |  768 | 512 |  63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | 
+| [gte-large](https://huggingface.co/thenlper/gte-large) |  1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
+| [gte-base](https://huggingface.co/thenlper/gte-base) 	|  768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) |  1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
+| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) |  384 | 512 | 62.11 |  51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |  
+| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) |  768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) |  768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
+| [gte-small](https://huggingface.co/thenlper/gte-small) |  384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) |  768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	|  768 | 514 	| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	|  4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
+
+
+
+- **C-MTEB**:  
+We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. 
+Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
+ 
+| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 |  **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |  
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 |  63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |  
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |   
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
+| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
+| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
+| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
+| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
+| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
+| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
+| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
+| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
+| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
+| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
+
+
+- **Reranking**:
+See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
+
+| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|  
+| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |  
+| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |  
+| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |  
+| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |  
+| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |  
+| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |  
+| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |  
+| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |  
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |  
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |  
+
+\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
+
+## Train
+
+### BAAI Embedding 
+
+We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. 
+**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
+We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
+Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
+More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
+
+
+
+### BGE Reranker
+
+Cross-encoder will perform full-attention over the input pair, 
+which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
+Therefore, it can be used to re-rank the top-k documents returned by embedding model.
+We train the cross-encoder on a multilingual pair data, 
+The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). 
+More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
+
+
+
+## Citation
+
+If you find this repository useful, please consider giving a star :star: and citation
+
+```
+@misc{bge_embedding,
+      title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, 
+      author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
+      year={2023},
+      eprint={2309.07597},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+## License
+FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
+

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.0",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.1+cpu"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

openvino/openvino_model.bin

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+oid sha256:edeeffb0e749faee0457e5b456274f4362a7963905ee17dee5d3d3336997b534
+size 132852896

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}