e5-base

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -1,3 +1,2725 @@
 ---
+tags:
+- mteb
+- Sentence Transformers
+- sentence-similarity
+- sentence-transformers
+model-index:
+- name: e5-base
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 79.71641791044777
+    - type: ap
+      value: 44.15426065428253
+    - type: f1
+      value: 73.89474407693241
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 87.9649
+    - type: ap
+      value: 84.10171551915973
+    - type: f1
+      value: 87.94148377827356
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 42.645999999999994
+    - type: f1
+      value: 42.230574673549
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.814
+    - type: map_at_10
+      value: 42.681999999999995
+    - type: map_at_100
+      value: 43.714
+    - type: map_at_1000
+      value: 43.724000000000004
+    - type: map_at_3
+      value: 38.11
+    - type: map_at_5
+      value: 40.666999999999994
+    - type: mrr_at_1
+      value: 27.168999999999997
+    - type: mrr_at_10
+      value: 42.84
+    - type: mrr_at_100
+      value: 43.864
+    - type: mrr_at_1000
+      value: 43.875
+    - type: mrr_at_3
+      value: 38.193
+    - type: mrr_at_5
+      value: 40.793
+    - type: ndcg_at_1
+      value: 26.814
+    - type: ndcg_at_10
+      value: 51.410999999999994
+    - type: ndcg_at_100
+      value: 55.713
+    - type: ndcg_at_1000
+      value: 55.957
+    - type: ndcg_at_3
+      value: 41.955
+    - type: ndcg_at_5
+      value: 46.558
+    - type: precision_at_1
+      value: 26.814
+    - type: precision_at_10
+      value: 7.922999999999999
+    - type: precision_at_100
+      value: 0.9780000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 17.71
+    - type: precision_at_5
+      value: 12.859000000000002
+    - type: recall_at_1
+      value: 26.814
+    - type: recall_at_10
+      value: 79.232
+    - type: recall_at_100
+      value: 97.795
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 53.129000000000005
+    - type: recall_at_5
+      value: 64.29599999999999
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 44.56933066536439
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 40.47647746165173
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 59.65675531567043
+    - type: mrr
+      value: 72.95255683067317
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.83147014162338
+    - type: cos_sim_spearman
+      value: 85.1031439521441
+    - type: euclidean_pearson
+      value: 83.53609085510973
+    - type: euclidean_spearman
+      value: 84.59650590202833
+    - type: manhattan_pearson
+      value: 83.14611947586386
+    - type: manhattan_spearman
+      value: 84.13384475757064
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 83.32792207792208
+    - type: f1
+      value: 83.32037485050513
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 36.18605446588703
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 32.72379130181917
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.659
+    - type: map_at_10
+      value: 40.333999999999996
+    - type: map_at_100
+      value: 41.763
+    - type: map_at_1000
+      value: 41.894
+    - type: map_at_3
+      value: 37.561
+    - type: map_at_5
+      value: 39.084
+    - type: mrr_at_1
+      value: 37.482
+    - type: mrr_at_10
+      value: 45.736
+    - type: mrr_at_100
+      value: 46.591
+    - type: mrr_at_1000
+      value: 46.644999999999996
+    - type: mrr_at_3
+      value: 43.491
+    - type: mrr_at_5
+      value: 44.75
+    - type: ndcg_at_1
+      value: 37.482
+    - type: ndcg_at_10
+      value: 45.606
+    - type: ndcg_at_100
+      value: 51.172
+    - type: ndcg_at_1000
+      value: 53.407000000000004
+    - type: ndcg_at_3
+      value: 41.808
+    - type: ndcg_at_5
+      value: 43.449
+    - type: precision_at_1
+      value: 37.482
+    - type: precision_at_10
+      value: 8.254999999999999
+    - type: precision_at_100
+      value: 1.3719999999999999
+    - type: precision_at_1000
+      value: 0.186
+    - type: precision_at_3
+      value: 19.695
+    - type: precision_at_5
+      value: 13.847999999999999
+    - type: recall_at_1
+      value: 30.659
+    - type: recall_at_10
+      value: 55.409
+    - type: recall_at_100
+      value: 78.687
+    - type: recall_at_1000
+      value: 93.068
+    - type: recall_at_3
+      value: 43.891999999999996
+    - type: recall_at_5
+      value: 48.678
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.977
+    - type: map_at_10
+      value: 40.296
+    - type: map_at_100
+      value: 41.453
+    - type: map_at_1000
+      value: 41.581
+    - type: map_at_3
+      value: 37.619
+    - type: map_at_5
+      value: 39.181
+    - type: mrr_at_1
+      value: 39.108
+    - type: mrr_at_10
+      value: 46.894000000000005
+    - type: mrr_at_100
+      value: 47.55
+    - type: mrr_at_1000
+      value: 47.598
+    - type: mrr_at_3
+      value: 44.766
+    - type: mrr_at_5
+      value: 46.062999999999995
+    - type: ndcg_at_1
+      value: 39.108
+    - type: ndcg_at_10
+      value: 45.717
+    - type: ndcg_at_100
+      value: 49.941
+    - type: ndcg_at_1000
+      value: 52.138
+    - type: ndcg_at_3
+      value: 42.05
+    - type: ndcg_at_5
+      value: 43.893
+    - type: precision_at_1
+      value: 39.108
+    - type: precision_at_10
+      value: 8.306
+    - type: precision_at_100
+      value: 1.3419999999999999
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 19.979
+    - type: precision_at_5
+      value: 14.038
+    - type: recall_at_1
+      value: 30.977
+    - type: recall_at_10
+      value: 54.688
+    - type: recall_at_100
+      value: 72.556
+    - type: recall_at_1000
+      value: 86.53800000000001
+    - type: recall_at_3
+      value: 43.388
+    - type: recall_at_5
+      value: 48.717
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.812
+    - type: map_at_10
+      value: 50.1
+    - type: map_at_100
+      value: 51.193999999999996
+    - type: map_at_1000
+      value: 51.258
+    - type: map_at_3
+      value: 47.510999999999996
+    - type: map_at_5
+      value: 48.891
+    - type: mrr_at_1
+      value: 45.266
+    - type: mrr_at_10
+      value: 53.459999999999994
+    - type: mrr_at_100
+      value: 54.19199999999999
+    - type: mrr_at_1000
+      value: 54.228
+    - type: mrr_at_3
+      value: 51.296
+    - type: mrr_at_5
+      value: 52.495999999999995
+    - type: ndcg_at_1
+      value: 45.266
+    - type: ndcg_at_10
+      value: 55.034000000000006
+    - type: ndcg_at_100
+      value: 59.458
+    - type: ndcg_at_1000
+      value: 60.862
+    - type: ndcg_at_3
+      value: 50.52799999999999
+    - type: ndcg_at_5
+      value: 52.564
+    - type: precision_at_1
+      value: 45.266
+    - type: precision_at_10
+      value: 8.483
+    - type: precision_at_100
+      value: 1.162
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 21.944
+    - type: precision_at_5
+      value: 14.721
+    - type: recall_at_1
+      value: 39.812
+    - type: recall_at_10
+      value: 66.36
+    - type: recall_at_100
+      value: 85.392
+    - type: recall_at_1000
+      value: 95.523
+    - type: recall_at_3
+      value: 54.127
+    - type: recall_at_5
+      value: 59.245000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.186
+    - type: map_at_10
+      value: 33.18
+    - type: map_at_100
+      value: 34.052
+    - type: map_at_1000
+      value: 34.149
+    - type: map_at_3
+      value: 31.029
+    - type: map_at_5
+      value: 32.321
+    - type: mrr_at_1
+      value: 28.136
+    - type: mrr_at_10
+      value: 35.195
+    - type: mrr_at_100
+      value: 35.996
+    - type: mrr_at_1000
+      value: 36.076
+    - type: mrr_at_3
+      value: 33.051
+    - type: mrr_at_5
+      value: 34.407
+    - type: ndcg_at_1
+      value: 28.136
+    - type: ndcg_at_10
+      value: 37.275999999999996
+    - type: ndcg_at_100
+      value: 41.935
+    - type: ndcg_at_1000
+      value: 44.389
+    - type: ndcg_at_3
+      value: 33.059
+    - type: ndcg_at_5
+      value: 35.313
+    - type: precision_at_1
+      value: 28.136
+    - type: precision_at_10
+      value: 5.457999999999999
+    - type: precision_at_100
+      value: 0.826
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 13.522
+    - type: precision_at_5
+      value: 9.424000000000001
+    - type: recall_at_1
+      value: 26.186
+    - type: recall_at_10
+      value: 47.961999999999996
+    - type: recall_at_100
+      value: 70.072
+    - type: recall_at_1000
+      value: 88.505
+    - type: recall_at_3
+      value: 36.752
+    - type: recall_at_5
+      value: 42.168
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.586000000000002
+    - type: map_at_10
+      value: 23.637
+    - type: map_at_100
+      value: 24.82
+    - type: map_at_1000
+      value: 24.95
+    - type: map_at_3
+      value: 21.428
+    - type: map_at_5
+      value: 22.555
+    - type: mrr_at_1
+      value: 20.771
+    - type: mrr_at_10
+      value: 27.839999999999996
+    - type: mrr_at_100
+      value: 28.887
+    - type: mrr_at_1000
+      value: 28.967
+    - type: mrr_at_3
+      value: 25.56
+    - type: mrr_at_5
+      value: 26.723000000000003
+    - type: ndcg_at_1
+      value: 20.771
+    - type: ndcg_at_10
+      value: 28.255000000000003
+    - type: ndcg_at_100
+      value: 33.886
+    - type: ndcg_at_1000
+      value: 36.963
+    - type: ndcg_at_3
+      value: 24.056
+    - type: ndcg_at_5
+      value: 25.818
+    - type: precision_at_1
+      value: 20.771
+    - type: precision_at_10
+      value: 5.1
+    - type: precision_at_100
+      value: 0.9119999999999999
+    - type: precision_at_1000
+      value: 0.132
+    - type: precision_at_3
+      value: 11.526
+    - type: precision_at_5
+      value: 8.158999999999999
+    - type: recall_at_1
+      value: 16.586000000000002
+    - type: recall_at_10
+      value: 38.456
+    - type: recall_at_100
+      value: 62.666
+    - type: recall_at_1000
+      value: 84.47
+    - type: recall_at_3
+      value: 26.765
+    - type: recall_at_5
+      value: 31.297000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.831
+    - type: map_at_10
+      value: 37.545
+    - type: map_at_100
+      value: 38.934999999999995
+    - type: map_at_1000
+      value: 39.044000000000004
+    - type: map_at_3
+      value: 34.601
+    - type: map_at_5
+      value: 36.302
+    - type: mrr_at_1
+      value: 34.264
+    - type: mrr_at_10
+      value: 42.569
+    - type: mrr_at_100
+      value: 43.514
+    - type: mrr_at_1000
+      value: 43.561
+    - type: mrr_at_3
+      value: 40.167
+    - type: mrr_at_5
+      value: 41.678
+    - type: ndcg_at_1
+      value: 34.264
+    - type: ndcg_at_10
+      value: 42.914
+    - type: ndcg_at_100
+      value: 48.931999999999995
+    - type: ndcg_at_1000
+      value: 51.004000000000005
+    - type: ndcg_at_3
+      value: 38.096999999999994
+    - type: ndcg_at_5
+      value: 40.509
+    - type: precision_at_1
+      value: 34.264
+    - type: precision_at_10
+      value: 7.642
+    - type: precision_at_100
+      value: 1.258
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 17.453
+    - type: precision_at_5
+      value: 12.608
+    - type: recall_at_1
+      value: 28.831
+    - type: recall_at_10
+      value: 53.56999999999999
+    - type: recall_at_100
+      value: 79.26100000000001
+    - type: recall_at_1000
+      value: 92.862
+    - type: recall_at_3
+      value: 40.681
+    - type: recall_at_5
+      value: 46.597
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.461000000000002
+    - type: map_at_10
+      value: 35.885
+    - type: map_at_100
+      value: 37.039
+    - type: map_at_1000
+      value: 37.16
+    - type: map_at_3
+      value: 33.451
+    - type: map_at_5
+      value: 34.807
+    - type: mrr_at_1
+      value: 34.018
+    - type: mrr_at_10
+      value: 41.32
+    - type: mrr_at_100
+      value: 42.157
+    - type: mrr_at_1000
+      value: 42.223
+    - type: mrr_at_3
+      value: 39.288000000000004
+    - type: mrr_at_5
+      value: 40.481
+    - type: ndcg_at_1
+      value: 34.018
+    - type: ndcg_at_10
+      value: 40.821000000000005
+    - type: ndcg_at_100
+      value: 46.053
+    - type: ndcg_at_1000
+      value: 48.673
+    - type: ndcg_at_3
+      value: 36.839
+    - type: ndcg_at_5
+      value: 38.683
+    - type: precision_at_1
+      value: 34.018
+    - type: precision_at_10
+      value: 7.009
+    - type: precision_at_100
+      value: 1.123
+    - type: precision_at_1000
+      value: 0.153
+    - type: precision_at_3
+      value: 16.933
+    - type: precision_at_5
+      value: 11.826
+    - type: recall_at_1
+      value: 27.461000000000002
+    - type: recall_at_10
+      value: 50.285000000000004
+    - type: recall_at_100
+      value: 73.25500000000001
+    - type: recall_at_1000
+      value: 91.17699999999999
+    - type: recall_at_3
+      value: 39.104
+    - type: recall_at_5
+      value: 43.968
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.980083333333337
+    - type: map_at_10
+      value: 34.47208333333333
+    - type: map_at_100
+      value: 35.609249999999996
+    - type: map_at_1000
+      value: 35.72833333333333
+    - type: map_at_3
+      value: 32.189416666666666
+    - type: map_at_5
+      value: 33.44683333333334
+    - type: mrr_at_1
+      value: 31.731666666666662
+    - type: mrr_at_10
+      value: 38.518
+    - type: mrr_at_100
+      value: 39.38166666666667
+    - type: mrr_at_1000
+      value: 39.446999999999996
+    - type: mrr_at_3
+      value: 36.49966666666668
+    - type: mrr_at_5
+      value: 37.639916666666664
+    - type: ndcg_at_1
+      value: 31.731666666666662
+    - type: ndcg_at_10
+      value: 38.92033333333333
+    - type: ndcg_at_100
+      value: 44.01675
+    - type: ndcg_at_1000
+      value: 46.51075
+    - type: ndcg_at_3
+      value: 35.09766666666667
+    - type: ndcg_at_5
+      value: 36.842999999999996
+    - type: precision_at_1
+      value: 31.731666666666662
+    - type: precision_at_10
+      value: 6.472583333333332
+    - type: precision_at_100
+      value: 1.0665
+    - type: precision_at_1000
+      value: 0.14725000000000002
+    - type: precision_at_3
+      value: 15.659083333333331
+    - type: precision_at_5
+      value: 10.878833333333333
+    - type: recall_at_1
+      value: 26.980083333333337
+    - type: recall_at_10
+      value: 48.13925
+    - type: recall_at_100
+      value: 70.70149999999998
+    - type: recall_at_1000
+      value: 88.10775000000001
+    - type: recall_at_3
+      value: 37.30091666666667
+    - type: recall_at_5
+      value: 41.90358333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.607999999999997
+    - type: map_at_10
+      value: 30.523
+    - type: map_at_100
+      value: 31.409
+    - type: map_at_1000
+      value: 31.507
+    - type: map_at_3
+      value: 28.915000000000003
+    - type: map_at_5
+      value: 29.756
+    - type: mrr_at_1
+      value: 28.681
+    - type: mrr_at_10
+      value: 33.409
+    - type: mrr_at_100
+      value: 34.241
+    - type: mrr_at_1000
+      value: 34.313
+    - type: mrr_at_3
+      value: 32.029999999999994
+    - type: mrr_at_5
+      value: 32.712
+    - type: ndcg_at_1
+      value: 28.681
+    - type: ndcg_at_10
+      value: 33.733000000000004
+    - type: ndcg_at_100
+      value: 38.32
+    - type: ndcg_at_1000
+      value: 40.937
+    - type: ndcg_at_3
+      value: 30.898999999999997
+    - type: ndcg_at_5
+      value: 32.088
+    - type: precision_at_1
+      value: 28.681
+    - type: precision_at_10
+      value: 4.968999999999999
+    - type: precision_at_100
+      value: 0.79
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 12.73
+    - type: precision_at_5
+      value: 8.558
+    - type: recall_at_1
+      value: 25.607999999999997
+    - type: recall_at_10
+      value: 40.722
+    - type: recall_at_100
+      value: 61.956999999999994
+    - type: recall_at_1000
+      value: 81.43
+    - type: recall_at_3
+      value: 32.785
+    - type: recall_at_5
+      value: 35.855
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.399
+    - type: map_at_10
+      value: 25.968000000000004
+    - type: map_at_100
+      value: 26.985999999999997
+    - type: map_at_1000
+      value: 27.105
+    - type: map_at_3
+      value: 24.215
+    - type: map_at_5
+      value: 25.157
+    - type: mrr_at_1
+      value: 24.708
+    - type: mrr_at_10
+      value: 29.971999999999998
+    - type: mrr_at_100
+      value: 30.858
+    - type: mrr_at_1000
+      value: 30.934
+    - type: mrr_at_3
+      value: 28.304000000000002
+    - type: mrr_at_5
+      value: 29.183999999999997
+    - type: ndcg_at_1
+      value: 24.708
+    - type: ndcg_at_10
+      value: 29.676000000000002
+    - type: ndcg_at_100
+      value: 34.656
+    - type: ndcg_at_1000
+      value: 37.588
+    - type: ndcg_at_3
+      value: 26.613
+    - type: ndcg_at_5
+      value: 27.919
+    - type: precision_at_1
+      value: 24.708
+    - type: precision_at_10
+      value: 5.01
+    - type: precision_at_100
+      value: 0.876
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 11.975
+    - type: precision_at_5
+      value: 8.279
+    - type: recall_at_1
+      value: 20.399
+    - type: recall_at_10
+      value: 36.935
+    - type: recall_at_100
+      value: 59.532
+    - type: recall_at_1000
+      value: 80.58
+    - type: recall_at_3
+      value: 27.979
+    - type: recall_at_5
+      value: 31.636999999999997
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.606
+    - type: map_at_10
+      value: 34.213
+    - type: map_at_100
+      value: 35.339999999999996
+    - type: map_at_1000
+      value: 35.458
+    - type: map_at_3
+      value: 31.987
+    - type: map_at_5
+      value: 33.322
+    - type: mrr_at_1
+      value: 31.53
+    - type: mrr_at_10
+      value: 37.911
+    - type: mrr_at_100
+      value: 38.879000000000005
+    - type: mrr_at_1000
+      value: 38.956
+    - type: mrr_at_3
+      value: 35.868
+    - type: mrr_at_5
+      value: 37.047999999999995
+    - type: ndcg_at_1
+      value: 31.53
+    - type: ndcg_at_10
+      value: 38.312000000000005
+    - type: ndcg_at_100
+      value: 43.812
+    - type: ndcg_at_1000
+      value: 46.414
+    - type: ndcg_at_3
+      value: 34.319
+    - type: ndcg_at_5
+      value: 36.312
+    - type: precision_at_1
+      value: 31.53
+    - type: precision_at_10
+      value: 5.970000000000001
+    - type: precision_at_100
+      value: 0.9939999999999999
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 14.738999999999999
+    - type: precision_at_5
+      value: 10.242999999999999
+    - type: recall_at_1
+      value: 27.606
+    - type: recall_at_10
+      value: 47.136
+    - type: recall_at_100
+      value: 71.253
+    - type: recall_at_1000
+      value: 89.39399999999999
+    - type: recall_at_3
+      value: 36.342
+    - type: recall_at_5
+      value: 41.388999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.855
+    - type: map_at_10
+      value: 31.963
+    - type: map_at_100
+      value: 33.371
+    - type: map_at_1000
+      value: 33.584
+    - type: map_at_3
+      value: 29.543999999999997
+    - type: map_at_5
+      value: 30.793
+    - type: mrr_at_1
+      value: 29.644
+    - type: mrr_at_10
+      value: 35.601
+    - type: mrr_at_100
+      value: 36.551
+    - type: mrr_at_1000
+      value: 36.623
+    - type: mrr_at_3
+      value: 33.399
+    - type: mrr_at_5
+      value: 34.575
+    - type: ndcg_at_1
+      value: 29.644
+    - type: ndcg_at_10
+      value: 36.521
+    - type: ndcg_at_100
+      value: 42.087
+    - type: ndcg_at_1000
+      value: 45.119
+    - type: ndcg_at_3
+      value: 32.797
+    - type: ndcg_at_5
+      value: 34.208
+    - type: precision_at_1
+      value: 29.644
+    - type: precision_at_10
+      value: 6.7
+    - type: precision_at_100
+      value: 1.374
+    - type: precision_at_1000
+      value: 0.22899999999999998
+    - type: precision_at_3
+      value: 15.152
+    - type: precision_at_5
+      value: 10.671999999999999
+    - type: recall_at_1
+      value: 24.855
+    - type: recall_at_10
+      value: 45.449
+    - type: recall_at_100
+      value: 70.921
+    - type: recall_at_1000
+      value: 90.629
+    - type: recall_at_3
+      value: 33.526
+    - type: recall_at_5
+      value: 37.848
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.781
+    - type: map_at_10
+      value: 30.020999999999997
+    - type: map_at_100
+      value: 30.948999999999998
+    - type: map_at_1000
+      value: 31.05
+    - type: map_at_3
+      value: 28.412
+    - type: map_at_5
+      value: 29.193
+    - type: mrr_at_1
+      value: 27.172
+    - type: mrr_at_10
+      value: 32.309
+    - type: mrr_at_100
+      value: 33.164
+    - type: mrr_at_1000
+      value: 33.239999999999995
+    - type: mrr_at_3
+      value: 30.775999999999996
+    - type: mrr_at_5
+      value: 31.562
+    - type: ndcg_at_1
+      value: 27.172
+    - type: ndcg_at_10
+      value: 33.178999999999995
+    - type: ndcg_at_100
+      value: 37.949
+    - type: ndcg_at_1000
+      value: 40.635
+    - type: ndcg_at_3
+      value: 30.107
+    - type: ndcg_at_5
+      value: 31.36
+    - type: precision_at_1
+      value: 27.172
+    - type: precision_at_10
+      value: 4.769
+    - type: precision_at_100
+      value: 0.769
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 12.261
+    - type: precision_at_5
+      value: 8.17
+    - type: recall_at_1
+      value: 24.781
+    - type: recall_at_10
+      value: 40.699000000000005
+    - type: recall_at_100
+      value: 62.866
+    - type: recall_at_1000
+      value: 83.11699999999999
+    - type: recall_at_3
+      value: 32.269999999999996
+    - type: recall_at_5
+      value: 35.443999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.2139999999999995
+    - type: map_at_10
+      value: 9.986
+    - type: map_at_100
+      value: 11.343
+    - type: map_at_1000
+      value: 11.55
+    - type: map_at_3
+      value: 7.961
+    - type: map_at_5
+      value: 8.967
+    - type: mrr_at_1
+      value: 12.052
+    - type: mrr_at_10
+      value: 20.165
+    - type: mrr_at_100
+      value: 21.317
+    - type: mrr_at_1000
+      value: 21.399
+    - type: mrr_at_3
+      value: 17.079
+    - type: mrr_at_5
+      value: 18.695
+    - type: ndcg_at_1
+      value: 12.052
+    - type: ndcg_at_10
+      value: 15.375
+    - type: ndcg_at_100
+      value: 21.858
+    - type: ndcg_at_1000
+      value: 26.145000000000003
+    - type: ndcg_at_3
+      value: 11.334
+    - type: ndcg_at_5
+      value: 12.798000000000002
+    - type: precision_at_1
+      value: 12.052
+    - type: precision_at_10
+      value: 5.16
+    - type: precision_at_100
+      value: 1.206
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_3
+      value: 8.73
+    - type: precision_at_5
+      value: 7.114
+    - type: recall_at_1
+      value: 5.2139999999999995
+    - type: recall_at_10
+      value: 20.669999999999998
+    - type: recall_at_100
+      value: 43.901
+    - type: recall_at_1000
+      value: 68.447
+    - type: recall_at_3
+      value: 11.049000000000001
+    - type: recall_at_5
+      value: 14.652999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.511000000000001
+    - type: map_at_10
+      value: 19.503
+    - type: map_at_100
+      value: 27.46
+    - type: map_at_1000
+      value: 29.187
+    - type: map_at_3
+      value: 14.030999999999999
+    - type: map_at_5
+      value: 16.329
+    - type: mrr_at_1
+      value: 63.74999999999999
+    - type: mrr_at_10
+      value: 73.419
+    - type: mrr_at_100
+      value: 73.691
+    - type: mrr_at_1000
+      value: 73.697
+    - type: mrr_at_3
+      value: 71.792
+    - type: mrr_at_5
+      value: 72.979
+    - type: ndcg_at_1
+      value: 53.125
+    - type: ndcg_at_10
+      value: 41.02
+    - type: ndcg_at_100
+      value: 45.407
+    - type: ndcg_at_1000
+      value: 52.68000000000001
+    - type: ndcg_at_3
+      value: 46.088
+    - type: ndcg_at_5
+      value: 43.236000000000004
+    - type: precision_at_1
+      value: 63.74999999999999
+    - type: precision_at_10
+      value: 32.35
+    - type: precision_at_100
+      value: 10.363
+    - type: precision_at_1000
+      value: 2.18
+    - type: precision_at_3
+      value: 49.667
+    - type: precision_at_5
+      value: 41.5
+    - type: recall_at_1
+      value: 8.511000000000001
+    - type: recall_at_10
+      value: 24.851
+    - type: recall_at_100
+      value: 50.745
+    - type: recall_at_1000
+      value: 73.265
+    - type: recall_at_3
+      value: 15.716
+    - type: recall_at_5
+      value: 19.256
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 49.43500000000001
+    - type: f1
+      value: 44.56288273966374
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.858
+    - type: map_at_10
+      value: 52.276
+    - type: map_at_100
+      value: 52.928
+    - type: map_at_1000
+      value: 52.966
+    - type: map_at_3
+      value: 49.729
+    - type: map_at_5
+      value: 51.27
+    - type: mrr_at_1
+      value: 43.624
+    - type: mrr_at_10
+      value: 55.22899999999999
+    - type: mrr_at_100
+      value: 55.823
+    - type: mrr_at_1000
+      value: 55.85
+    - type: mrr_at_3
+      value: 52.739999999999995
+    - type: mrr_at_5
+      value: 54.251000000000005
+    - type: ndcg_at_1
+      value: 43.624
+    - type: ndcg_at_10
+      value: 58.23500000000001
+    - type: ndcg_at_100
+      value: 61.315
+    - type: ndcg_at_1000
+      value: 62.20099999999999
+    - type: ndcg_at_3
+      value: 53.22
+    - type: ndcg_at_5
+      value: 55.88999999999999
+    - type: precision_at_1
+      value: 43.624
+    - type: precision_at_10
+      value: 8.068999999999999
+    - type: precision_at_100
+      value: 0.975
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 21.752
+    - type: precision_at_5
+      value: 14.515
+    - type: recall_at_1
+      value: 40.858
+    - type: recall_at_10
+      value: 73.744
+    - type: recall_at_100
+      value: 87.667
+    - type: recall_at_1000
+      value: 94.15599999999999
+    - type: recall_at_3
+      value: 60.287
+    - type: recall_at_5
+      value: 66.703
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.864
+    - type: map_at_10
+      value: 28.592000000000002
+    - type: map_at_100
+      value: 30.165
+    - type: map_at_1000
+      value: 30.364
+    - type: map_at_3
+      value: 24.586
+    - type: map_at_5
+      value: 26.717000000000002
+    - type: mrr_at_1
+      value: 35.031
+    - type: mrr_at_10
+      value: 43.876
+    - type: mrr_at_100
+      value: 44.683
+    - type: mrr_at_1000
+      value: 44.736
+    - type: mrr_at_3
+      value: 40.998000000000005
+    - type: mrr_at_5
+      value: 42.595
+    - type: ndcg_at_1
+      value: 35.031
+    - type: ndcg_at_10
+      value: 36.368
+    - type: ndcg_at_100
+      value: 42.472
+    - type: ndcg_at_1000
+      value: 45.973000000000006
+    - type: ndcg_at_3
+      value: 31.915
+    - type: ndcg_at_5
+      value: 33.394
+    - type: precision_at_1
+      value: 35.031
+    - type: precision_at_10
+      value: 10.139
+    - type: precision_at_100
+      value: 1.6420000000000001
+    - type: precision_at_1000
+      value: 0.22699999999999998
+    - type: precision_at_3
+      value: 21.142
+    - type: precision_at_5
+      value: 15.772
+    - type: recall_at_1
+      value: 17.864
+    - type: recall_at_10
+      value: 43.991
+    - type: recall_at_100
+      value: 66.796
+    - type: recall_at_1000
+      value: 87.64
+    - type: recall_at_3
+      value: 28.915999999999997
+    - type: recall_at_5
+      value: 35.185
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.556
+    - type: map_at_10
+      value: 53.056000000000004
+    - type: map_at_100
+      value: 53.909
+    - type: map_at_1000
+      value: 53.98
+    - type: map_at_3
+      value: 49.982
+    - type: map_at_5
+      value: 51.9
+    - type: mrr_at_1
+      value: 73.113
+    - type: mrr_at_10
+      value: 79.381
+    - type: mrr_at_100
+      value: 79.60300000000001
+    - type: mrr_at_1000
+      value: 79.617
+    - type: mrr_at_3
+      value: 78.298
+    - type: mrr_at_5
+      value: 78.995
+    - type: ndcg_at_1
+      value: 73.113
+    - type: ndcg_at_10
+      value: 62.21
+    - type: ndcg_at_100
+      value: 65.242
+    - type: ndcg_at_1000
+      value: 66.667
+    - type: ndcg_at_3
+      value: 57.717
+    - type: ndcg_at_5
+      value: 60.224
+    - type: precision_at_1
+      value: 73.113
+    - type: precision_at_10
+      value: 12.842999999999998
+    - type: precision_at_100
+      value: 1.522
+    - type: precision_at_1000
+      value: 0.17099999999999999
+    - type: precision_at_3
+      value: 36.178
+    - type: precision_at_5
+      value: 23.695
+    - type: recall_at_1
+      value: 36.556
+    - type: recall_at_10
+      value: 64.213
+    - type: recall_at_100
+      value: 76.077
+    - type: recall_at_1000
+      value: 85.53699999999999
+    - type: recall_at_3
+      value: 54.266999999999996
+    - type: recall_at_5
+      value: 59.236999999999995
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 75.958
+    - type: ap
+      value: 69.82869527654348
+    - type: f1
+      value: 75.89120903005633
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.608
+    - type: map_at_10
+      value: 36.144
+    - type: map_at_100
+      value: 37.244
+    - type: map_at_1000
+      value: 37.291999999999994
+    - type: map_at_3
+      value: 32.287
+    - type: map_at_5
+      value: 34.473
+    - type: mrr_at_1
+      value: 24.226
+    - type: mrr_at_10
+      value: 36.711
+    - type: mrr_at_100
+      value: 37.758
+    - type: mrr_at_1000
+      value: 37.8
+    - type: mrr_at_3
+      value: 32.92
+    - type: mrr_at_5
+      value: 35.104
+    - type: ndcg_at_1
+      value: 24.269
+    - type: ndcg_at_10
+      value: 43.138
+    - type: ndcg_at_100
+      value: 48.421
+    - type: ndcg_at_1000
+      value: 49.592000000000006
+    - type: ndcg_at_3
+      value: 35.269
+    - type: ndcg_at_5
+      value: 39.175
+    - type: precision_at_1
+      value: 24.269
+    - type: precision_at_10
+      value: 6.755999999999999
+    - type: precision_at_100
+      value: 0.941
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.938
+    - type: precision_at_5
+      value: 10.934000000000001
+    - type: recall_at_1
+      value: 23.608
+    - type: recall_at_10
+      value: 64.679
+    - type: recall_at_100
+      value: 89.027
+    - type: recall_at_1000
+      value: 97.91
+    - type: recall_at_3
+      value: 43.25
+    - type: recall_at_5
+      value: 52.617000000000004
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.21477428180576
+    - type: f1
+      value: 92.92502305092152
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 74.76744186046511
+    - type: f1
+      value: 59.19855520057899
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 72.24613315400134
+    - type: f1
+      value: 70.19950395651232
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 76.75857431069268
+    - type: f1
+      value: 76.5433450230191
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 31.525463791623604
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.28695907385136
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.068174046665224
+    - type: mrr
+      value: 30.827586642840803
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.322
+    - type: map_at_10
+      value: 13.919999999999998
+    - type: map_at_100
+      value: 17.416
+    - type: map_at_1000
+      value: 18.836
+    - type: map_at_3
+      value: 10.111
+    - type: map_at_5
+      value: 11.991999999999999
+    - type: mrr_at_1
+      value: 48.297000000000004
+    - type: mrr_at_10
+      value: 57.114
+    - type: mrr_at_100
+      value: 57.713
+    - type: mrr_at_1000
+      value: 57.751
+    - type: mrr_at_3
+      value: 55.108000000000004
+    - type: mrr_at_5
+      value: 56.533
+    - type: ndcg_at_1
+      value: 46.44
+    - type: ndcg_at_10
+      value: 36.589
+    - type: ndcg_at_100
+      value: 33.202
+    - type: ndcg_at_1000
+      value: 41.668
+    - type: ndcg_at_3
+      value: 41.302
+    - type: ndcg_at_5
+      value: 39.829
+    - type: precision_at_1
+      value: 47.988
+    - type: precision_at_10
+      value: 27.059
+    - type: precision_at_100
+      value: 8.235000000000001
+    - type: precision_at_1000
+      value: 2.091
+    - type: precision_at_3
+      value: 38.184000000000005
+    - type: precision_at_5
+      value: 34.365
+    - type: recall_at_1
+      value: 6.322
+    - type: recall_at_10
+      value: 18.288
+    - type: recall_at_100
+      value: 32.580999999999996
+    - type: recall_at_1000
+      value: 63.605999999999995
+    - type: recall_at_3
+      value: 11.266
+    - type: recall_at_5
+      value: 14.69
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.586999999999996
+    - type: map_at_10
+      value: 52.464
+    - type: map_at_100
+      value: 53.384
+    - type: map_at_1000
+      value: 53.405
+    - type: map_at_3
+      value: 48.408
+    - type: map_at_5
+      value: 50.788999999999994
+    - type: mrr_at_1
+      value: 40.904
+    - type: mrr_at_10
+      value: 54.974000000000004
+    - type: mrr_at_100
+      value: 55.60699999999999
+    - type: mrr_at_1000
+      value: 55.623
+    - type: mrr_at_3
+      value: 51.73799999999999
+    - type: mrr_at_5
+      value: 53.638
+    - type: ndcg_at_1
+      value: 40.904
+    - type: ndcg_at_10
+      value: 59.965999999999994
+    - type: ndcg_at_100
+      value: 63.613
+    - type: ndcg_at_1000
+      value: 64.064
+    - type: ndcg_at_3
+      value: 52.486
+    - type: ndcg_at_5
+      value: 56.377
+    - type: precision_at_1
+      value: 40.904
+    - type: precision_at_10
+      value: 9.551
+    - type: precision_at_100
+      value: 1.162
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 23.552
+    - type: precision_at_5
+      value: 16.436999999999998
+    - type: recall_at_1
+      value: 36.586999999999996
+    - type: recall_at_10
+      value: 80.094
+    - type: recall_at_100
+      value: 95.515
+    - type: recall_at_1000
+      value: 98.803
+    - type: recall_at_3
+      value: 60.907
+    - type: recall_at_5
+      value: 69.817
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.422
+    - type: map_at_10
+      value: 84.113
+    - type: map_at_100
+      value: 84.744
+    - type: map_at_1000
+      value: 84.762
+    - type: map_at_3
+      value: 81.171
+    - type: map_at_5
+      value: 83.039
+    - type: mrr_at_1
+      value: 81.12
+    - type: mrr_at_10
+      value: 87.277
+    - type: mrr_at_100
+      value: 87.384
+    - type: mrr_at_1000
+      value: 87.385
+    - type: mrr_at_3
+      value: 86.315
+    - type: mrr_at_5
+      value: 86.981
+    - type: ndcg_at_1
+      value: 81.12
+    - type: ndcg_at_10
+      value: 87.92
+    - type: ndcg_at_100
+      value: 89.178
+    - type: ndcg_at_1000
+      value: 89.29899999999999
+    - type: ndcg_at_3
+      value: 85.076
+    - type: ndcg_at_5
+      value: 86.67099999999999
+    - type: precision_at_1
+      value: 81.12
+    - type: precision_at_10
+      value: 13.325999999999999
+    - type: precision_at_100
+      value: 1.524
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.16
+    - type: precision_at_5
+      value: 24.456
+    - type: recall_at_1
+      value: 70.422
+    - type: recall_at_10
+      value: 95.00800000000001
+    - type: recall_at_100
+      value: 99.38
+    - type: recall_at_1000
+      value: 99.94800000000001
+    - type: recall_at_3
+      value: 86.809
+    - type: recall_at_5
+      value: 91.334
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 48.18491891699636
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 62.190639679711914
+  - 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: 11.268
+    - type: map_at_100
+      value: 13.129
+    - type: map_at_1000
+      value: 13.41
+    - type: map_at_3
+      value: 8.103
+    - type: map_at_5
+      value: 9.609
+    - type: mrr_at_1
+      value: 22
+    - type: mrr_at_10
+      value: 32.248
+    - type: mrr_at_100
+      value: 33.355000000000004
+    - type: mrr_at_1000
+      value: 33.42
+    - type: mrr_at_3
+      value: 29.15
+    - type: mrr_at_5
+      value: 30.785
+    - type: ndcg_at_1
+      value: 22
+    - type: ndcg_at_10
+      value: 18.990000000000002
+    - type: ndcg_at_100
+      value: 26.302999999999997
+    - type: ndcg_at_1000
+      value: 31.537
+    - type: ndcg_at_3
+      value: 18.034
+    - type: ndcg_at_5
+      value: 15.655
+    - type: precision_at_1
+      value: 22
+    - type: precision_at_10
+      value: 9.91
+    - type: precision_at_100
+      value: 2.0420000000000003
+    - type: precision_at_1000
+      value: 0.33
+    - type: precision_at_3
+      value: 16.933
+    - type: precision_at_5
+      value: 13.719999999999999
+    - type: recall_at_1
+      value: 4.478
+    - type: recall_at_10
+      value: 20.087
+    - type: recall_at_100
+      value: 41.457
+    - type: recall_at_1000
+      value: 67.10199999999999
+    - type: recall_at_3
+      value: 10.313
+    - type: recall_at_5
+      value: 13.927999999999999
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.27341574565806
+    - type: cos_sim_spearman
+      value: 79.66419880841734
+    - type: euclidean_pearson
+      value: 81.32473321838208
+    - type: euclidean_spearman
+      value: 79.29828832085133
+    - type: manhattan_pearson
+      value: 81.25554065883132
+    - type: manhattan_spearman
+      value: 79.23275543279853
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.40468875905418
+    - type: cos_sim_spearman
+      value: 74.2189990321174
+    - type: euclidean_pearson
+      value: 80.74376966290956
+    - type: euclidean_spearman
+      value: 74.97663839079335
+    - type: manhattan_pearson
+      value: 80.69779331646207
+    - type: manhattan_spearman
+      value: 75.00225252917613
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.5745290053095
+    - type: cos_sim_spearman
+      value: 83.31401180333397
+    - type: euclidean_pearson
+      value: 82.96500607325534
+    - type: euclidean_spearman
+      value: 83.8534967935793
+    - type: manhattan_pearson
+      value: 82.83112050632508
+    - type: manhattan_spearman
+      value: 83.70877296557838
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.67833656607704
+    - type: cos_sim_spearman
+      value: 78.52252410630707
+    - type: euclidean_pearson
+      value: 80.071189514343
+    - type: euclidean_spearman
+      value: 78.95143545742796
+    - type: manhattan_pearson
+      value: 80.0128926165121
+    - type: manhattan_spearman
+      value: 78.91236678732628
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.48437639980746
+    - type: cos_sim_spearman
+      value: 88.34876527774259
+    - type: euclidean_pearson
+      value: 87.64898081823888
+    - type: euclidean_spearman
+      value: 88.58937180804213
+    - type: manhattan_pearson
+      value: 87.5942417815288
+    - type: manhattan_spearman
+      value: 88.53013922267687
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.69189187164781
+    - type: cos_sim_spearman
+      value: 84.15327883572112
+    - type: euclidean_pearson
+      value: 83.64202266685898
+    - type: euclidean_spearman
+      value: 84.6219602318862
+    - type: manhattan_pearson
+      value: 83.53256698709998
+    - type: manhattan_spearman
+      value: 84.49260712904946
+  - 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: 87.09508017611589
+    - type: cos_sim_spearman
+      value: 87.23010990417097
+    - type: euclidean_pearson
+      value: 87.62545569077133
+    - type: euclidean_spearman
+      value: 86.71152051711714
+    - type: manhattan_pearson
+      value: 87.5057154278377
+    - type: manhattan_spearman
+      value: 86.60611898281267
+  - 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: 61.72129893941176
+    - type: cos_sim_spearman
+      value: 62.87871412069194
+    - type: euclidean_pearson
+      value: 63.21077648290454
+    - type: euclidean_spearman
+      value: 63.03263080805978
+    - type: manhattan_pearson
+      value: 63.20740860135976
+    - type: manhattan_spearman
+      value: 62.89930471802817
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.039118236799
+    - type: cos_sim_spearman
+      value: 86.18102563389962
+    - type: euclidean_pearson
+      value: 85.62977041471879
+    - type: euclidean_spearman
+      value: 86.02478990544347
+    - type: manhattan_pearson
+      value: 85.60786740521806
+    - type: manhattan_spearman
+      value: 85.99546210442547
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 82.89875069737266
+    - type: mrr
+      value: 95.42621322033087
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 58.660999999999994
+    - type: map_at_10
+      value: 68.738
+    - type: map_at_100
+      value: 69.33200000000001
+    - type: map_at_1000
+      value: 69.352
+    - type: map_at_3
+      value: 66.502
+    - type: map_at_5
+      value: 67.686
+    - type: mrr_at_1
+      value: 61.667
+    - type: mrr_at_10
+      value: 70.003
+    - type: mrr_at_100
+      value: 70.441
+    - type: mrr_at_1000
+      value: 70.46
+    - type: mrr_at_3
+      value: 68.278
+    - type: mrr_at_5
+      value: 69.194
+    - type: ndcg_at_1
+      value: 61.667
+    - type: ndcg_at_10
+      value: 73.083
+    - type: ndcg_at_100
+      value: 75.56
+    - type: ndcg_at_1000
+      value: 76.01400000000001
+    - type: ndcg_at_3
+      value: 69.28699999999999
+    - type: ndcg_at_5
+      value: 70.85000000000001
+    - type: precision_at_1
+      value: 61.667
+    - type: precision_at_10
+      value: 9.6
+    - type: precision_at_100
+      value: 1.087
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 27.111
+    - type: precision_at_5
+      value: 17.467
+    - type: recall_at_1
+      value: 58.660999999999994
+    - type: recall_at_10
+      value: 85.02199999999999
+    - type: recall_at_100
+      value: 95.933
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 74.506
+    - type: recall_at_5
+      value: 78.583
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.8029702970297
+    - type: cos_sim_ap
+      value: 94.87673936635738
+    - type: cos_sim_f1
+      value: 90.00502260170768
+    - type: cos_sim_precision
+      value: 90.41372351160445
+    - type: cos_sim_recall
+      value: 89.60000000000001
+    - type: dot_accuracy
+      value: 99.57524752475247
+    - type: dot_ap
+      value: 84.81717934496321
+    - type: dot_f1
+      value: 78.23026646556059
+    - type: dot_precision
+      value: 78.66531850353893
+    - type: dot_recall
+      value: 77.8
+    - type: euclidean_accuracy
+      value: 99.8029702970297
+    - type: euclidean_ap
+      value: 94.74658253135284
+    - type: euclidean_f1
+      value: 90.08470353761834
+    - type: euclidean_precision
+      value: 89.77159880834161
+    - type: euclidean_recall
+      value: 90.4
+    - type: manhattan_accuracy
+      value: 99.8
+    - type: manhattan_ap
+      value: 94.69224030742787
+    - type: manhattan_f1
+      value: 89.9502487562189
+    - type: manhattan_precision
+      value: 89.50495049504951
+    - type: manhattan_recall
+      value: 90.4
+    - type: max_accuracy
+      value: 99.8029702970297
+    - type: max_ap
+      value: 94.87673936635738
+    - type: max_f1
+      value: 90.08470353761834
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 63.906039623153035
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 32.56053830923281
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.15326538775145
+    - type: mrr
+      value: 50.99279295051355
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.44030762047337
+    - type: cos_sim_spearman
+      value: 31.00910300264562
+    - type: dot_pearson
+      value: 26.88257194766013
+    - type: dot_spearman
+      value: 27.646202679013577
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.247
+    - type: map_at_10
+      value: 1.9429999999999998
+    - type: map_at_100
+      value: 10.82
+    - type: map_at_1000
+      value: 25.972
+    - type: map_at_3
+      value: 0.653
+    - type: map_at_5
+      value: 1.057
+    - type: mrr_at_1
+      value: 94
+    - type: mrr_at_10
+      value: 96.333
+    - type: mrr_at_100
+      value: 96.333
+    - type: mrr_at_1000
+      value: 96.333
+    - type: mrr_at_3
+      value: 96.333
+    - type: mrr_at_5
+      value: 96.333
+    - type: ndcg_at_1
+      value: 89
+    - type: ndcg_at_10
+      value: 79.63799999999999
+    - type: ndcg_at_100
+      value: 57.961
+    - type: ndcg_at_1000
+      value: 50.733
+    - type: ndcg_at_3
+      value: 84.224
+    - type: ndcg_at_5
+      value: 82.528
+    - type: precision_at_1
+      value: 94
+    - type: precision_at_10
+      value: 84.2
+    - type: precision_at_100
+      value: 59.36
+    - type: precision_at_1000
+      value: 22.738
+    - type: precision_at_3
+      value: 88
+    - type: precision_at_5
+      value: 86.8
+    - type: recall_at_1
+      value: 0.247
+    - type: recall_at_10
+      value: 2.131
+    - type: recall_at_100
+      value: 14.035
+    - type: recall_at_1000
+      value: 47.457
+    - type: recall_at_3
+      value: 0.6779999999999999
+    - type: recall_at_5
+      value: 1.124
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.603
+    - type: map_at_10
+      value: 11.667
+    - type: map_at_100
+      value: 16.474
+    - type: map_at_1000
+      value: 18.074
+    - type: map_at_3
+      value: 6.03
+    - type: map_at_5
+      value: 8.067
+    - type: mrr_at_1
+      value: 34.694
+    - type: mrr_at_10
+      value: 51.063
+    - type: mrr_at_100
+      value: 51.908
+    - type: mrr_at_1000
+      value: 51.908
+    - type: mrr_at_3
+      value: 47.959
+    - type: mrr_at_5
+      value: 49.694
+    - type: ndcg_at_1
+      value: 32.653
+    - type: ndcg_at_10
+      value: 28.305000000000003
+    - type: ndcg_at_100
+      value: 35.311
+    - type: ndcg_at_1000
+      value: 47.644999999999996
+    - type: ndcg_at_3
+      value: 32.187
+    - type: ndcg_at_5
+      value: 29.134999999999998
+    - type: precision_at_1
+      value: 34.694
+    - type: precision_at_10
+      value: 26.122
+    - type: precision_at_100
+      value: 6.755
+    - type: precision_at_1000
+      value: 1.467
+    - type: precision_at_3
+      value: 34.694
+    - type: precision_at_5
+      value: 30.203999999999997
+    - type: recall_at_1
+      value: 2.603
+    - type: recall_at_10
+      value: 18.716
+    - type: recall_at_100
+      value: 42.512
+    - type: recall_at_1000
+      value: 79.32000000000001
+    - type: recall_at_3
+      value: 7.59
+    - type: recall_at_5
+      value: 10.949
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 74.117
+    - type: ap
+      value: 15.89357321699319
+    - type: f1
+      value: 57.14385866369257
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 61.38370118845502
+    - type: f1
+      value: 61.67038693866553
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 42.57754941537969
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.1775049174465
+    - type: cos_sim_ap
+      value: 74.3994879581554
+    - type: cos_sim_f1
+      value: 69.32903671308551
+    - type: cos_sim_precision
+      value: 61.48193508879363
+    - type: cos_sim_recall
+      value: 79.47229551451187
+    - type: dot_accuracy
+      value: 81.65345413363534
+    - type: dot_ap
+      value: 59.690898346685096
+    - type: dot_f1
+      value: 57.27622826467499
+    - type: dot_precision
+      value: 51.34965473948525
+    - type: dot_recall
+      value: 64.74934036939314
+    - type: euclidean_accuracy
+      value: 86.04637301066937
+    - type: euclidean_ap
+      value: 74.33009001775268
+    - type: euclidean_f1
+      value: 69.2458374142997
+    - type: euclidean_precision
+      value: 64.59570580173595
+    - type: euclidean_recall
+      value: 74.6174142480211
+    - type: manhattan_accuracy
+      value: 86.11193896405793
+    - type: manhattan_ap
+      value: 74.2964140130421
+    - type: manhattan_f1
+      value: 69.11601528788066
+    - type: manhattan_precision
+      value: 64.86924323073363
+    - type: manhattan_recall
+      value: 73.95778364116094
+    - type: max_accuracy
+      value: 86.1775049174465
+    - type: max_ap
+      value: 74.3994879581554
+    - type: max_f1
+      value: 69.32903671308551
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.01501921061823
+    - type: cos_sim_ap
+      value: 85.97819287477351
+    - type: cos_sim_f1
+      value: 78.33882858518875
+    - type: cos_sim_precision
+      value: 75.49446626204926
+    - type: cos_sim_recall
+      value: 81.40591315060055
+    - type: dot_accuracy
+      value: 86.47494857763806
+    - type: dot_ap
+      value: 78.77420360340282
+    - type: dot_f1
+      value: 73.06433247936238
+    - type: dot_precision
+      value: 67.92140777983595
+    - type: dot_recall
+      value: 79.04989220819218
+    - type: euclidean_accuracy
+      value: 88.7297706368611
+    - type: euclidean_ap
+      value: 85.61550568529317
+    - type: euclidean_f1
+      value: 77.84805525263539
+    - type: euclidean_precision
+      value: 73.73639994491117
+    - type: euclidean_recall
+      value: 82.44533415460425
+    - type: manhattan_accuracy
+      value: 88.75111576823068
+    - type: manhattan_ap
+      value: 85.58701671476263
+    - type: manhattan_f1
+      value: 77.70169909067856
+    - type: manhattan_precision
+      value: 73.37666780704755
+    - type: manhattan_recall
+      value: 82.5685247921158
+    - type: max_accuracy
+      value: 89.01501921061823
+    - type: max_ap
+      value: 85.97819287477351
+    - type: max_f1
+      value: 78.33882858518875
+language:
+- en
 license: mit
 ---
+
+## E5-base
+
+**News (May 2023): please switch to [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2), which has better performance and same method of usage.**
+
+[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
+Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
+
+This model has 12 layers and the embedding size is 768.
+
+## Usage
+
+Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
+
+```python
+import torch.nn.functional as F
+
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+
+# Each input text should start with "query: " or "passage: ".
+# For tasks other than retrieval, you can simply use the "query: " prefix.
+input_texts = ['query: how much protein should a female eat',
+               'query: summit define',
+               "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+               "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."]
+
+tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base')
+model = AutoModel.from_pretrained('intfloat/e5-base')
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+outputs = model(**batch_dict)
+embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+```
+
+## Training Details
+
+Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
+
+## Benchmark Evaluation
+
+Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results 
+on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
+
+## Support for Sentence Transformers
+
+Below is an example for usage with sentence_transformers.
+```python
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('intfloat/e5-base')
+input_texts = [
+    'query: how much protein should a female eat',
+    'query: summit define',
+    "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
+    "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
+]
+embeddings = model.encode(input_texts, normalize_embeddings=True)
+```
+
+Package requirements
+
+`pip install sentence_transformers~=2.2.2`
+
+Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
+
+## FAQ
+
+**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
+
+Yes, this is how the model is trained, otherwise you will see a performance degradation.
+
+Here are some rules of thumb:
+- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
+
+- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
+
+- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
+
+**2. Why are my reproduced results slightly different from reported in the model card?**
+
+Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
+
+**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
+
+This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. 
+
+For text embedding tasks like text retrieval or semantic similarity, 
+what matters is the relative order of the scores instead of the absolute values, 
+so this should not be an issue.
+
+## Citation
+
+If you find our paper or models helpful, please consider cite as follows:
+
+```
+@article{wang2022text,
+  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
+  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
+  journal={arXiv preprint arXiv:2212.03533},
+  year={2022}
+}
+```
+
+## Limitations
+
+This model only works for English texts. Long texts will be truncated to at most 512 tokens.

config.json

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+    "per_model_config": {
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