diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -1,2723 +1,2723 @@
----
-tags:
-- mteb
-- Sentence Transformers
-- sentence-similarity
-- sentence-transformers
-model-index:
-- name: e5-large-v2
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 79.22388059701493
-    - type: ap
-      value: 43.20816505595132
-    - type: f1
-      value: 73.27811303522058
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 93.748325
-    - type: ap
-      value: 90.72534979701297
-    - type: f1
-      value: 93.73895874282185
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 48.612
-    - type: f1
-      value: 47.61157345898393
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.541999999999998
-    - type: map_at_10
-      value: 38.208
-    - type: map_at_100
-      value: 39.417
-    - type: map_at_1000
-      value: 39.428999999999995
-    - type: map_at_3
-      value: 33.95
-    - type: map_at_5
-      value: 36.329
-    - type: mrr_at_1
-      value: 23.755000000000003
-    - type: mrr_at_10
-      value: 38.288
-    - type: mrr_at_100
-      value: 39.511
-    - type: mrr_at_1000
-      value: 39.523
-    - type: mrr_at_3
-      value: 34.009
-    - type: mrr_at_5
-      value: 36.434
-    - type: ndcg_at_1
-      value: 23.541999999999998
-    - type: ndcg_at_10
-      value: 46.417
-    - type: ndcg_at_100
-      value: 51.812000000000005
-    - type: ndcg_at_1000
-      value: 52.137
-    - type: ndcg_at_3
-      value: 37.528
-    - type: ndcg_at_5
-      value: 41.81
-    - type: precision_at_1
-      value: 23.541999999999998
-    - type: precision_at_10
-      value: 7.269
-    - type: precision_at_100
-      value: 0.9690000000000001
-    - type: precision_at_1000
-      value: 0.099
-    - type: precision_at_3
-      value: 15.979
-    - type: precision_at_5
-      value: 11.664
-    - type: recall_at_1
-      value: 23.541999999999998
-    - type: recall_at_10
-      value: 72.688
-    - type: recall_at_100
-      value: 96.871
-    - type: recall_at_1000
-      value: 99.431
-    - type: recall_at_3
-      value: 47.937000000000005
-    - type: recall_at_5
-      value: 58.321
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 45.546499570522094
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 41.01607489943561
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 59.616107510107774
-    - type: mrr
-      value: 72.75106626214661
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.33018094733868
-    - type: cos_sim_spearman
-      value: 83.60190492611737
-    - type: euclidean_pearson
-      value: 82.1492450218961
-    - type: euclidean_spearman
-      value: 82.70308926526991
-    - type: manhattan_pearson
-      value: 81.93959600076842
-    - type: manhattan_spearman
-      value: 82.73260801016369
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 84.54545454545455
-    - type: f1
-      value: 84.49582530928923
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 37.362725540120096
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 34.849509608178145
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 31.502999999999997
-    - type: map_at_10
-      value: 43.323
-    - type: map_at_100
-      value: 44.708999999999996
-    - type: map_at_1000
-      value: 44.838
-    - type: map_at_3
-      value: 38.987
-    - type: map_at_5
-      value: 41.516999999999996
-    - type: mrr_at_1
-      value: 38.769999999999996
-    - type: mrr_at_10
-      value: 49.13
-    - type: mrr_at_100
-      value: 49.697
-    - type: mrr_at_1000
-      value: 49.741
-    - type: mrr_at_3
-      value: 45.804
-    - type: mrr_at_5
-      value: 47.842
-    - type: ndcg_at_1
-      value: 38.769999999999996
-    - type: ndcg_at_10
-      value: 50.266999999999996
-    - type: ndcg_at_100
-      value: 54.967
-    - type: ndcg_at_1000
-      value: 56.976000000000006
-    - type: ndcg_at_3
-      value: 43.823
-    - type: ndcg_at_5
-      value: 47.12
-    - type: precision_at_1
-      value: 38.769999999999996
-    - type: precision_at_10
-      value: 10.057
-    - type: precision_at_100
-      value: 1.554
-    - type: precision_at_1000
-      value: 0.202
-    - type: precision_at_3
-      value: 21.125
-    - type: precision_at_5
-      value: 15.851
-    - type: recall_at_1
-      value: 31.502999999999997
-    - type: recall_at_10
-      value: 63.715999999999994
-    - type: recall_at_100
-      value: 83.61800000000001
-    - type: recall_at_1000
-      value: 96.63199999999999
-    - type: recall_at_3
-      value: 45.403
-    - type: recall_at_5
-      value: 54.481
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 27.833000000000002
-    - type: map_at_10
-      value: 37.330999999999996
-    - type: map_at_100
-      value: 38.580999999999996
-    - type: map_at_1000
-      value: 38.708
-    - type: map_at_3
-      value: 34.713
-    - type: map_at_5
-      value: 36.104
-    - type: mrr_at_1
-      value: 35.223
-    - type: mrr_at_10
-      value: 43.419000000000004
-    - type: mrr_at_100
-      value: 44.198
-    - type: mrr_at_1000
-      value: 44.249
-    - type: mrr_at_3
-      value: 41.614000000000004
-    - type: mrr_at_5
-      value: 42.553000000000004
-    - type: ndcg_at_1
-      value: 35.223
-    - type: ndcg_at_10
-      value: 42.687999999999995
-    - type: ndcg_at_100
-      value: 47.447
-    - type: ndcg_at_1000
-      value: 49.701
-    - type: ndcg_at_3
-      value: 39.162
-    - type: ndcg_at_5
-      value: 40.557
-    - type: precision_at_1
-      value: 35.223
-    - type: precision_at_10
-      value: 7.962
-    - type: precision_at_100
-      value: 1.304
-    - type: precision_at_1000
-      value: 0.18
-    - type: precision_at_3
-      value: 19.023
-    - type: precision_at_5
-      value: 13.184999999999999
-    - type: recall_at_1
-      value: 27.833000000000002
-    - type: recall_at_10
-      value: 51.881
-    - type: recall_at_100
-      value: 72.04
-    - type: recall_at_1000
-      value: 86.644
-    - type: recall_at_3
-      value: 40.778
-    - type: recall_at_5
-      value: 45.176
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 38.175
-    - type: map_at_10
-      value: 51.174
-    - type: map_at_100
-      value: 52.26499999999999
-    - type: map_at_1000
-      value: 52.315999999999995
-    - type: map_at_3
-      value: 47.897
-    - type: map_at_5
-      value: 49.703
-    - type: mrr_at_1
-      value: 43.448
-    - type: mrr_at_10
-      value: 54.505
-    - type: mrr_at_100
-      value: 55.216
-    - type: mrr_at_1000
-      value: 55.242000000000004
-    - type: mrr_at_3
-      value: 51.98500000000001
-    - type: mrr_at_5
-      value: 53.434000000000005
-    - type: ndcg_at_1
-      value: 43.448
-    - type: ndcg_at_10
-      value: 57.282
-    - type: ndcg_at_100
-      value: 61.537
-    - type: ndcg_at_1000
-      value: 62.546
-    - type: ndcg_at_3
-      value: 51.73799999999999
-    - type: ndcg_at_5
-      value: 54.324
-    - type: precision_at_1
-      value: 43.448
-    - type: precision_at_10
-      value: 9.292
-    - type: precision_at_100
-      value: 1.233
-    - type: precision_at_1000
-      value: 0.136
-    - type: precision_at_3
-      value: 23.218
-    - type: precision_at_5
-      value: 15.887
-    - type: recall_at_1
-      value: 38.175
-    - type: recall_at_10
-      value: 72.00999999999999
-    - type: recall_at_100
-      value: 90.155
-    - type: recall_at_1000
-      value: 97.257
-    - type: recall_at_3
-      value: 57.133
-    - type: recall_at_5
-      value: 63.424
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.405
-    - type: map_at_10
-      value: 30.043
-    - type: map_at_100
-      value: 31.191000000000003
-    - type: map_at_1000
-      value: 31.275
-    - type: map_at_3
-      value: 27.034000000000002
-    - type: map_at_5
-      value: 28.688000000000002
-    - type: mrr_at_1
-      value: 24.068
-    - type: mrr_at_10
-      value: 31.993
-    - type: mrr_at_100
-      value: 32.992
-    - type: mrr_at_1000
-      value: 33.050000000000004
-    - type: mrr_at_3
-      value: 28.964000000000002
-    - type: mrr_at_5
-      value: 30.653000000000002
-    - type: ndcg_at_1
-      value: 24.068
-    - type: ndcg_at_10
-      value: 35.198
-    - type: ndcg_at_100
-      value: 40.709
-    - type: ndcg_at_1000
-      value: 42.855
-    - type: ndcg_at_3
-      value: 29.139
-    - type: ndcg_at_5
-      value: 32.045
-    - type: precision_at_1
-      value: 24.068
-    - type: precision_at_10
-      value: 5.65
-    - type: precision_at_100
-      value: 0.885
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_3
-      value: 12.279
-    - type: precision_at_5
-      value: 8.994
-    - type: recall_at_1
-      value: 22.405
-    - type: recall_at_10
-      value: 49.391
-    - type: recall_at_100
-      value: 74.53699999999999
-    - type: recall_at_1000
-      value: 90.605
-    - type: recall_at_3
-      value: 33.126
-    - type: recall_at_5
-      value: 40.073
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 13.309999999999999
-    - type: map_at_10
-      value: 20.688000000000002
-    - type: map_at_100
-      value: 22.022
-    - type: map_at_1000
-      value: 22.152
-    - type: map_at_3
-      value: 17.954
-    - type: map_at_5
-      value: 19.439
-    - type: mrr_at_1
-      value: 16.294
-    - type: mrr_at_10
-      value: 24.479
-    - type: mrr_at_100
-      value: 25.515
-    - type: mrr_at_1000
-      value: 25.593
-    - type: mrr_at_3
-      value: 21.642
-    - type: mrr_at_5
-      value: 23.189999999999998
-    - type: ndcg_at_1
-      value: 16.294
-    - type: ndcg_at_10
-      value: 25.833000000000002
-    - type: ndcg_at_100
-      value: 32.074999999999996
-    - type: ndcg_at_1000
-      value: 35.083
-    - type: ndcg_at_3
-      value: 20.493
-    - type: ndcg_at_5
-      value: 22.949
-    - type: precision_at_1
-      value: 16.294
-    - type: precision_at_10
-      value: 5.112
-    - type: precision_at_100
-      value: 0.96
-    - type: precision_at_1000
-      value: 0.134
-    - type: precision_at_3
-      value: 9.908999999999999
-    - type: precision_at_5
-      value: 7.587000000000001
-    - type: recall_at_1
-      value: 13.309999999999999
-    - type: recall_at_10
-      value: 37.851
-    - type: recall_at_100
-      value: 64.835
-    - type: recall_at_1000
-      value: 86.334
-    - type: recall_at_3
-      value: 23.493
-    - type: recall_at_5
-      value: 29.528
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.857999999999997
-    - type: map_at_10
-      value: 35.503
-    - type: map_at_100
-      value: 36.957
-    - type: map_at_1000
-      value: 37.065
-    - type: map_at_3
-      value: 32.275999999999996
-    - type: map_at_5
-      value: 34.119
-    - type: mrr_at_1
-      value: 31.954
-    - type: mrr_at_10
-      value: 40.851
-    - type: mrr_at_100
-      value: 41.863
-    - type: mrr_at_1000
-      value: 41.900999999999996
-    - type: mrr_at_3
-      value: 38.129999999999995
-    - type: mrr_at_5
-      value: 39.737
-    - type: ndcg_at_1
-      value: 31.954
-    - type: ndcg_at_10
-      value: 41.343999999999994
-    - type: ndcg_at_100
-      value: 47.397
-    - type: ndcg_at_1000
-      value: 49.501
-    - type: ndcg_at_3
-      value: 36.047000000000004
-    - type: ndcg_at_5
-      value: 38.639
-    - type: precision_at_1
-      value: 31.954
-    - type: precision_at_10
-      value: 7.68
-    - type: precision_at_100
-      value: 1.247
-    - type: precision_at_1000
-      value: 0.16199999999999998
-    - type: precision_at_3
-      value: 17.132
-    - type: precision_at_5
-      value: 12.589
-    - type: recall_at_1
-      value: 25.857999999999997
-    - type: recall_at_10
-      value: 53.43599999999999
-    - type: recall_at_100
-      value: 78.82400000000001
-    - type: recall_at_1000
-      value: 92.78999999999999
-    - type: recall_at_3
-      value: 38.655
-    - type: recall_at_5
-      value: 45.216
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.709
-    - type: map_at_10
-      value: 34.318
-    - type: map_at_100
-      value: 35.657
-    - type: map_at_1000
-      value: 35.783
-    - type: map_at_3
-      value: 31.326999999999998
-    - type: map_at_5
-      value: 33.021
-    - type: mrr_at_1
-      value: 30.137000000000004
-    - type: mrr_at_10
-      value: 39.093
-    - type: mrr_at_100
-      value: 39.992
-    - type: mrr_at_1000
-      value: 40.056999999999995
-    - type: mrr_at_3
-      value: 36.606
-    - type: mrr_at_5
-      value: 37.861
-    - type: ndcg_at_1
-      value: 30.137000000000004
-    - type: ndcg_at_10
-      value: 39.974
-    - type: ndcg_at_100
-      value: 45.647999999999996
-    - type: ndcg_at_1000
-      value: 48.259
-    - type: ndcg_at_3
-      value: 35.028
-    - type: ndcg_at_5
-      value: 37.175999999999995
-    - type: precision_at_1
-      value: 30.137000000000004
-    - type: precision_at_10
-      value: 7.363
-    - type: precision_at_100
-      value: 1.184
-    - type: precision_at_1000
-      value: 0.161
-    - type: precision_at_3
-      value: 16.857
-    - type: precision_at_5
-      value: 11.963
-    - type: recall_at_1
-      value: 24.709
-    - type: recall_at_10
-      value: 52.087
-    - type: recall_at_100
-      value: 76.125
-    - type: recall_at_1000
-      value: 93.82300000000001
-    - type: recall_at_3
-      value: 38.149
-    - type: recall_at_5
-      value: 43.984
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.40791666666667
-    - type: map_at_10
-      value: 32.458083333333335
-    - type: map_at_100
-      value: 33.691916666666664
-    - type: map_at_1000
-      value: 33.81191666666666
-    - type: map_at_3
-      value: 29.51625
-    - type: map_at_5
-      value: 31.168083333333335
-    - type: mrr_at_1
-      value: 27.96591666666666
-    - type: mrr_at_10
-      value: 36.528583333333344
-    - type: mrr_at_100
-      value: 37.404
-    - type: mrr_at_1000
-      value: 37.464333333333336
-    - type: mrr_at_3
-      value: 33.92883333333333
-    - type: mrr_at_5
-      value: 35.41933333333333
-    - type: ndcg_at_1
-      value: 27.96591666666666
-    - type: ndcg_at_10
-      value: 37.89141666666666
-    - type: ndcg_at_100
-      value: 43.23066666666666
-    - type: ndcg_at_1000
-      value: 45.63258333333333
-    - type: ndcg_at_3
-      value: 32.811249999999994
-    - type: ndcg_at_5
-      value: 35.22566666666667
-    - type: precision_at_1
-      value: 27.96591666666666
-    - type: precision_at_10
-      value: 6.834083333333332
-    - type: precision_at_100
-      value: 1.12225
-    - type: precision_at_1000
-      value: 0.15241666666666667
-    - type: precision_at_3
-      value: 15.264333333333335
-    - type: precision_at_5
-      value: 11.039416666666666
-    - type: recall_at_1
-      value: 23.40791666666667
-    - type: recall_at_10
-      value: 49.927083333333336
-    - type: recall_at_100
-      value: 73.44641666666668
-    - type: recall_at_1000
-      value: 90.19950000000001
-    - type: recall_at_3
-      value: 35.88341666666667
-    - type: recall_at_5
-      value: 42.061249999999994
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 19.592000000000002
-    - type: map_at_10
-      value: 26.895999999999997
-    - type: map_at_100
-      value: 27.921000000000003
-    - type: map_at_1000
-      value: 28.02
-    - type: map_at_3
-      value: 24.883
-    - type: map_at_5
-      value: 25.812
-    - type: mrr_at_1
-      value: 22.698999999999998
-    - type: mrr_at_10
-      value: 29.520999999999997
-    - type: mrr_at_100
-      value: 30.458000000000002
-    - type: mrr_at_1000
-      value: 30.526999999999997
-    - type: mrr_at_3
-      value: 27.633000000000003
-    - type: mrr_at_5
-      value: 28.483999999999998
-    - type: ndcg_at_1
-      value: 22.698999999999998
-    - type: ndcg_at_10
-      value: 31.061
-    - type: ndcg_at_100
-      value: 36.398
-    - type: ndcg_at_1000
-      value: 38.89
-    - type: ndcg_at_3
-      value: 27.149
-    - type: ndcg_at_5
-      value: 28.627000000000002
-    - type: precision_at_1
-      value: 22.698999999999998
-    - type: precision_at_10
-      value: 5.106999999999999
-    - type: precision_at_100
-      value: 0.857
-    - type: precision_at_1000
-      value: 0.11499999999999999
-    - type: precision_at_3
-      value: 11.963
-    - type: precision_at_5
-      value: 8.221
-    - type: recall_at_1
-      value: 19.592000000000002
-    - type: recall_at_10
-      value: 41.329
-    - type: recall_at_100
-      value: 66.094
-    - type: recall_at_1000
-      value: 84.511
-    - type: recall_at_3
-      value: 30.61
-    - type: recall_at_5
-      value: 34.213
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 14.71
-    - type: map_at_10
-      value: 20.965
-    - type: map_at_100
-      value: 21.994
-    - type: map_at_1000
-      value: 22.133
-    - type: map_at_3
-      value: 18.741
-    - type: map_at_5
-      value: 19.951
-    - type: mrr_at_1
-      value: 18.307000000000002
-    - type: mrr_at_10
-      value: 24.66
-    - type: mrr_at_100
-      value: 25.540000000000003
-    - type: mrr_at_1000
-      value: 25.629
-    - type: mrr_at_3
-      value: 22.511
-    - type: mrr_at_5
-      value: 23.72
-    - type: ndcg_at_1
-      value: 18.307000000000002
-    - type: ndcg_at_10
-      value: 25.153
-    - type: ndcg_at_100
-      value: 30.229
-    - type: ndcg_at_1000
-      value: 33.623
-    - type: ndcg_at_3
-      value: 21.203
-    - type: ndcg_at_5
-      value: 23.006999999999998
-    - type: precision_at_1
-      value: 18.307000000000002
-    - type: precision_at_10
-      value: 4.725
-    - type: precision_at_100
-      value: 0.8659999999999999
-    - type: precision_at_1000
-      value: 0.133
-    - type: precision_at_3
-      value: 10.14
-    - type: precision_at_5
-      value: 7.481
-    - type: recall_at_1
-      value: 14.71
-    - type: recall_at_10
-      value: 34.087
-    - type: recall_at_100
-      value: 57.147999999999996
-    - type: recall_at_1000
-      value: 81.777
-    - type: recall_at_3
-      value: 22.996
-    - type: recall_at_5
-      value: 27.73
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.472
-    - type: map_at_10
-      value: 32.699
-    - type: map_at_100
-      value: 33.867000000000004
-    - type: map_at_1000
-      value: 33.967000000000006
-    - type: map_at_3
-      value: 29.718
-    - type: map_at_5
-      value: 31.345
-    - type: mrr_at_1
-      value: 28.265
-    - type: mrr_at_10
-      value: 36.945
-    - type: mrr_at_100
-      value: 37.794
-    - type: mrr_at_1000
-      value: 37.857
-    - type: mrr_at_3
-      value: 34.266000000000005
-    - type: mrr_at_5
-      value: 35.768
-    - type: ndcg_at_1
-      value: 28.265
-    - type: ndcg_at_10
-      value: 38.35
-    - type: ndcg_at_100
-      value: 43.739
-    - type: ndcg_at_1000
-      value: 46.087
-    - type: ndcg_at_3
-      value: 33.004
-    - type: ndcg_at_5
-      value: 35.411
-    - type: precision_at_1
-      value: 28.265
-    - type: precision_at_10
-      value: 6.715999999999999
-    - type: precision_at_100
-      value: 1.059
-    - type: precision_at_1000
-      value: 0.13799999999999998
-    - type: precision_at_3
-      value: 15.299
-    - type: precision_at_5
-      value: 10.951
-    - type: recall_at_1
-      value: 23.472
-    - type: recall_at_10
-      value: 51.413
-    - type: recall_at_100
-      value: 75.17
-    - type: recall_at_1000
-      value: 91.577
-    - type: recall_at_3
-      value: 36.651
-    - type: recall_at_5
-      value: 42.814
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.666
-    - type: map_at_10
-      value: 32.963
-    - type: map_at_100
-      value: 34.544999999999995
-    - type: map_at_1000
-      value: 34.792
-    - type: map_at_3
-      value: 29.74
-    - type: map_at_5
-      value: 31.5
-    - type: mrr_at_1
-      value: 29.051
-    - type: mrr_at_10
-      value: 38.013000000000005
-    - type: mrr_at_100
-      value: 38.997
-    - type: mrr_at_1000
-      value: 39.055
-    - type: mrr_at_3
-      value: 34.947
-    - type: mrr_at_5
-      value: 36.815
-    - type: ndcg_at_1
-      value: 29.051
-    - type: ndcg_at_10
-      value: 39.361000000000004
-    - type: ndcg_at_100
-      value: 45.186
-    - type: ndcg_at_1000
-      value: 47.867
-    - type: ndcg_at_3
-      value: 33.797
-    - type: ndcg_at_5
-      value: 36.456
-    - type: precision_at_1
-      value: 29.051
-    - type: precision_at_10
-      value: 7.668
-    - type: precision_at_100
-      value: 1.532
-    - type: precision_at_1000
-      value: 0.247
-    - type: precision_at_3
-      value: 15.876000000000001
-    - type: precision_at_5
-      value: 11.779
-    - type: recall_at_1
-      value: 23.666
-    - type: recall_at_10
-      value: 51.858000000000004
-    - type: recall_at_100
-      value: 77.805
-    - type: recall_at_1000
-      value: 94.504
-    - type: recall_at_3
-      value: 36.207
-    - type: recall_at_5
-      value: 43.094
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 15.662
-    - type: map_at_10
-      value: 23.594
-    - type: map_at_100
-      value: 24.593999999999998
-    - type: map_at_1000
-      value: 24.694
-    - type: map_at_3
-      value: 20.925
-    - type: map_at_5
-      value: 22.817999999999998
-    - type: mrr_at_1
-      value: 17.375
-    - type: mrr_at_10
-      value: 25.734
-    - type: mrr_at_100
-      value: 26.586
-    - type: mrr_at_1000
-      value: 26.671
-    - type: mrr_at_3
-      value: 23.044
-    - type: mrr_at_5
-      value: 24.975
-    - type: ndcg_at_1
-      value: 17.375
-    - type: ndcg_at_10
-      value: 28.186
-    - type: ndcg_at_100
-      value: 33.436
-    - type: ndcg_at_1000
-      value: 36.203
-    - type: ndcg_at_3
-      value: 23.152
-    - type: ndcg_at_5
-      value: 26.397
-    - type: precision_at_1
-      value: 17.375
-    - type: precision_at_10
-      value: 4.677
-    - type: precision_at_100
-      value: 0.786
-    - type: precision_at_1000
-      value: 0.109
-    - type: precision_at_3
-      value: 10.351
-    - type: precision_at_5
-      value: 7.985
-    - type: recall_at_1
-      value: 15.662
-    - type: recall_at_10
-      value: 40.066
-    - type: recall_at_100
-      value: 65.006
-    - type: recall_at_1000
-      value: 85.94000000000001
-    - type: recall_at_3
-      value: 27.400000000000002
-    - type: recall_at_5
-      value: 35.002
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 8.853
-    - type: map_at_10
-      value: 15.568000000000001
-    - type: map_at_100
-      value: 17.383000000000003
-    - type: map_at_1000
-      value: 17.584
-    - type: map_at_3
-      value: 12.561
-    - type: map_at_5
-      value: 14.056
-    - type: mrr_at_1
-      value: 18.958
-    - type: mrr_at_10
-      value: 28.288000000000004
-    - type: mrr_at_100
-      value: 29.432000000000002
-    - type: mrr_at_1000
-      value: 29.498
-    - type: mrr_at_3
-      value: 25.049
-    - type: mrr_at_5
-      value: 26.857
-    - type: ndcg_at_1
-      value: 18.958
-    - type: ndcg_at_10
-      value: 22.21
-    - type: ndcg_at_100
-      value: 29.596
-    - type: ndcg_at_1000
-      value: 33.583
-    - type: ndcg_at_3
-      value: 16.994999999999997
-    - type: ndcg_at_5
-      value: 18.95
-    - type: precision_at_1
-      value: 18.958
-    - type: precision_at_10
-      value: 7.192
-    - type: precision_at_100
-      value: 1.5
-    - type: precision_at_1000
-      value: 0.22399999999999998
-    - type: precision_at_3
-      value: 12.573
-    - type: precision_at_5
-      value: 10.202
-    - type: recall_at_1
-      value: 8.853
-    - type: recall_at_10
-      value: 28.087
-    - type: recall_at_100
-      value: 53.701
-    - type: recall_at_1000
-      value: 76.29899999999999
-    - type: recall_at_3
-      value: 15.913
-    - type: recall_at_5
-      value: 20.658
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 9.077
-    - type: map_at_10
-      value: 20.788999999999998
-    - type: map_at_100
-      value: 30.429000000000002
-    - type: map_at_1000
-      value: 32.143
-    - type: map_at_3
-      value: 14.692
-    - type: map_at_5
-      value: 17.139
-    - type: mrr_at_1
-      value: 70.75
-    - type: mrr_at_10
-      value: 78.036
-    - type: mrr_at_100
-      value: 78.401
-    - type: mrr_at_1000
-      value: 78.404
-    - type: mrr_at_3
-      value: 76.75
-    - type: mrr_at_5
-      value: 77.47500000000001
-    - type: ndcg_at_1
-      value: 58.12500000000001
-    - type: ndcg_at_10
-      value: 44.015
-    - type: ndcg_at_100
-      value: 49.247
-    - type: ndcg_at_1000
-      value: 56.211999999999996
-    - type: ndcg_at_3
-      value: 49.151
-    - type: ndcg_at_5
-      value: 46.195
-    - type: precision_at_1
-      value: 70.75
-    - type: precision_at_10
-      value: 35.5
-    - type: precision_at_100
-      value: 11.355
-    - type: precision_at_1000
-      value: 2.1950000000000003
-    - type: precision_at_3
-      value: 53.083000000000006
-    - type: precision_at_5
-      value: 44.800000000000004
-    - type: recall_at_1
-      value: 9.077
-    - type: recall_at_10
-      value: 26.259
-    - type: recall_at_100
-      value: 56.547000000000004
-    - type: recall_at_1000
-      value: 78.551
-    - type: recall_at_3
-      value: 16.162000000000003
-    - type: recall_at_5
-      value: 19.753999999999998
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 49.44500000000001
-    - type: f1
-      value: 44.67067691783401
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 68.182
-    - type: map_at_10
-      value: 78.223
-    - type: map_at_100
-      value: 78.498
-    - type: map_at_1000
-      value: 78.512
-    - type: map_at_3
-      value: 76.71
-    - type: map_at_5
-      value: 77.725
-    - type: mrr_at_1
-      value: 73.177
-    - type: mrr_at_10
-      value: 82.513
-    - type: mrr_at_100
-      value: 82.633
-    - type: mrr_at_1000
-      value: 82.635
-    - type: mrr_at_3
-      value: 81.376
-    - type: mrr_at_5
-      value: 82.182
-    - type: ndcg_at_1
-      value: 73.177
-    - type: ndcg_at_10
-      value: 82.829
-    - type: ndcg_at_100
-      value: 83.84
-    - type: ndcg_at_1000
-      value: 84.07900000000001
-    - type: ndcg_at_3
-      value: 80.303
-    - type: ndcg_at_5
-      value: 81.846
-    - type: precision_at_1
-      value: 73.177
-    - type: precision_at_10
-      value: 10.241999999999999
-    - type: precision_at_100
-      value: 1.099
-    - type: precision_at_1000
-      value: 0.11399999999999999
-    - type: precision_at_3
-      value: 31.247999999999998
-    - type: precision_at_5
-      value: 19.697
-    - type: recall_at_1
-      value: 68.182
-    - type: recall_at_10
-      value: 92.657
-    - type: recall_at_100
-      value: 96.709
-    - type: recall_at_1000
-      value: 98.184
-    - type: recall_at_3
-      value: 85.9
-    - type: recall_at_5
-      value: 89.755
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 21.108
-    - type: map_at_10
-      value: 33.342
-    - type: map_at_100
-      value: 35.281
-    - type: map_at_1000
-      value: 35.478
-    - type: map_at_3
-      value: 29.067
-    - type: map_at_5
-      value: 31.563000000000002
-    - type: mrr_at_1
-      value: 41.667
-    - type: mrr_at_10
-      value: 49.913000000000004
-    - type: mrr_at_100
-      value: 50.724000000000004
-    - type: mrr_at_1000
-      value: 50.766
-    - type: mrr_at_3
-      value: 47.504999999999995
-    - type: mrr_at_5
-      value: 49.033
-    - type: ndcg_at_1
-      value: 41.667
-    - type: ndcg_at_10
-      value: 41.144
-    - type: ndcg_at_100
-      value: 48.326
-    - type: ndcg_at_1000
-      value: 51.486
-    - type: ndcg_at_3
-      value: 37.486999999999995
-    - type: ndcg_at_5
-      value: 38.78
-    - type: precision_at_1
-      value: 41.667
-    - type: precision_at_10
-      value: 11.358
-    - type: precision_at_100
-      value: 1.873
-    - type: precision_at_1000
-      value: 0.244
-    - type: precision_at_3
-      value: 25
-    - type: precision_at_5
-      value: 18.519
-    - type: recall_at_1
-      value: 21.108
-    - type: recall_at_10
-      value: 47.249
-    - type: recall_at_100
-      value: 74.52
-    - type: recall_at_1000
-      value: 93.31
-    - type: recall_at_3
-      value: 33.271
-    - type: recall_at_5
-      value: 39.723000000000006
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 40.317
-    - type: map_at_10
-      value: 64.861
-    - type: map_at_100
-      value: 65.697
-    - type: map_at_1000
-      value: 65.755
-    - type: map_at_3
-      value: 61.258
-    - type: map_at_5
-      value: 63.590999999999994
-    - type: mrr_at_1
-      value: 80.635
-    - type: mrr_at_10
-      value: 86.528
-    - type: mrr_at_100
-      value: 86.66199999999999
-    - type: mrr_at_1000
-      value: 86.666
-    - type: mrr_at_3
-      value: 85.744
-    - type: mrr_at_5
-      value: 86.24300000000001
-    - type: ndcg_at_1
-      value: 80.635
-    - type: ndcg_at_10
-      value: 73.13199999999999
-    - type: ndcg_at_100
-      value: 75.927
-    - type: ndcg_at_1000
-      value: 76.976
-    - type: ndcg_at_3
-      value: 68.241
-    - type: ndcg_at_5
-      value: 71.071
-    - type: precision_at_1
-      value: 80.635
-    - type: precision_at_10
-      value: 15.326
-    - type: precision_at_100
-      value: 1.7500000000000002
-    - type: precision_at_1000
-      value: 0.189
-    - type: precision_at_3
-      value: 43.961
-    - type: precision_at_5
-      value: 28.599999999999998
-    - type: recall_at_1
-      value: 40.317
-    - type: recall_at_10
-      value: 76.631
-    - type: recall_at_100
-      value: 87.495
-    - type: recall_at_1000
-      value: 94.362
-    - type: recall_at_3
-      value: 65.94200000000001
-    - type: recall_at_5
-      value: 71.499
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 91.686
-    - type: ap
-      value: 87.5577120393173
-    - type: f1
-      value: 91.6629447355139
-  - task:
-      type: Retrieval
-    dataset:
-      type: msmarco
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.702
-    - type: map_at_10
-      value: 36.414
-    - type: map_at_100
-      value: 37.561
-    - type: map_at_1000
-      value: 37.605
-    - type: map_at_3
-      value: 32.456
-    - type: map_at_5
-      value: 34.827000000000005
-    - type: mrr_at_1
-      value: 24.355
-    - type: mrr_at_10
-      value: 37.01
-    - type: mrr_at_100
-      value: 38.085
-    - type: mrr_at_1000
-      value: 38.123000000000005
-    - type: mrr_at_3
-      value: 33.117999999999995
-    - type: mrr_at_5
-      value: 35.452
-    - type: ndcg_at_1
-      value: 24.384
-    - type: ndcg_at_10
-      value: 43.456
-    - type: ndcg_at_100
-      value: 48.892
-    - type: ndcg_at_1000
-      value: 49.964
-    - type: ndcg_at_3
-      value: 35.475
-    - type: ndcg_at_5
-      value: 39.711
-    - type: precision_at_1
-      value: 24.384
-    - type: precision_at_10
-      value: 6.7940000000000005
-    - type: precision_at_100
-      value: 0.951
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 15.052999999999999
-    - type: precision_at_5
-      value: 11.189
-    - type: recall_at_1
-      value: 23.702
-    - type: recall_at_10
-      value: 65.057
-    - type: recall_at_100
-      value: 90.021
-    - type: recall_at_1000
-      value: 98.142
-    - type: recall_at_3
-      value: 43.551
-    - type: recall_at_5
-      value: 53.738
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 94.62380300957591
-    - type: f1
-      value: 94.49871222100734
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 77.14090287277702
-    - type: f1
-      value: 60.32101258220515
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 73.84330867518494
-    - type: f1
-      value: 71.92248688515255
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 78.10692669804976
-    - type: f1
-      value: 77.9904839122866
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-p2p
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 31.822988923078444
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-s2s
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 30.38394880253403
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/mind_small
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 31.82504612539082
-    - type: mrr
-      value: 32.84462298174977
-  - task:
-      type: Retrieval
-    dataset:
-      type: nfcorpus
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 6.029
-    - type: map_at_10
-      value: 14.088999999999999
-    - type: map_at_100
-      value: 17.601
-    - type: map_at_1000
-      value: 19.144
-    - type: map_at_3
-      value: 10.156
-    - type: map_at_5
-      value: 11.892
-    - type: mrr_at_1
-      value: 46.44
-    - type: mrr_at_10
-      value: 56.596999999999994
-    - type: mrr_at_100
-      value: 57.11000000000001
-    - type: mrr_at_1000
-      value: 57.14
-    - type: mrr_at_3
-      value: 54.334
-    - type: mrr_at_5
-      value: 55.774
-    - type: ndcg_at_1
-      value: 44.891999999999996
-    - type: ndcg_at_10
-      value: 37.134
-    - type: ndcg_at_100
-      value: 33.652
-    - type: ndcg_at_1000
-      value: 42.548
-    - type: ndcg_at_3
-      value: 41.851
-    - type: ndcg_at_5
-      value: 39.842
-    - type: precision_at_1
-      value: 46.44
-    - type: precision_at_10
-      value: 27.647
-    - type: precision_at_100
-      value: 8.309999999999999
-    - type: precision_at_1000
-      value: 2.146
-    - type: precision_at_3
-      value: 39.422000000000004
-    - type: precision_at_5
-      value: 34.675
-    - type: recall_at_1
-      value: 6.029
-    - type: recall_at_10
-      value: 18.907
-    - type: recall_at_100
-      value: 33.76
-    - type: recall_at_1000
-      value: 65.14999999999999
-    - type: recall_at_3
-      value: 11.584999999999999
-    - type: recall_at_5
-      value: 14.626
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 39.373000000000005
-    - type: map_at_10
-      value: 55.836
-    - type: map_at_100
-      value: 56.611999999999995
-    - type: map_at_1000
-      value: 56.63
-    - type: map_at_3
-      value: 51.747
-    - type: map_at_5
-      value: 54.337999999999994
-    - type: mrr_at_1
-      value: 44.147999999999996
-    - type: mrr_at_10
-      value: 58.42699999999999
-    - type: mrr_at_100
-      value: 58.902
-    - type: mrr_at_1000
-      value: 58.914
-    - type: mrr_at_3
-      value: 55.156000000000006
-    - type: mrr_at_5
-      value: 57.291000000000004
-    - type: ndcg_at_1
-      value: 44.119
-    - type: ndcg_at_10
-      value: 63.444
-    - type: ndcg_at_100
-      value: 66.40599999999999
-    - type: ndcg_at_1000
-      value: 66.822
-    - type: ndcg_at_3
-      value: 55.962
-    - type: ndcg_at_5
-      value: 60.228
-    - type: precision_at_1
-      value: 44.119
-    - type: precision_at_10
-      value: 10.006
-    - type: precision_at_100
-      value: 1.17
-    - type: precision_at_1000
-      value: 0.121
-    - type: precision_at_3
-      value: 25.135
-    - type: precision_at_5
-      value: 17.59
-    - type: recall_at_1
-      value: 39.373000000000005
-    - type: recall_at_10
-      value: 83.78999999999999
-    - type: recall_at_100
-      value: 96.246
-    - type: recall_at_1000
-      value: 99.324
-    - type: recall_at_3
-      value: 64.71900000000001
-    - type: recall_at_5
-      value: 74.508
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 69.199
-    - type: map_at_10
-      value: 82.892
-    - type: map_at_100
-      value: 83.578
-    - type: map_at_1000
-      value: 83.598
-    - type: map_at_3
-      value: 79.948
-    - type: map_at_5
-      value: 81.779
-    - type: mrr_at_1
-      value: 79.67
-    - type: mrr_at_10
-      value: 86.115
-    - type: mrr_at_100
-      value: 86.249
-    - type: mrr_at_1000
-      value: 86.251
-    - type: mrr_at_3
-      value: 85.08200000000001
-    - type: mrr_at_5
-      value: 85.783
-    - type: ndcg_at_1
-      value: 79.67
-    - type: ndcg_at_10
-      value: 86.839
-    - type: ndcg_at_100
-      value: 88.252
-    - type: ndcg_at_1000
-      value: 88.401
-    - type: ndcg_at_3
-      value: 83.86200000000001
-    - type: ndcg_at_5
-      value: 85.473
-    - type: precision_at_1
-      value: 79.67
-    - type: precision_at_10
-      value: 13.19
-    - type: precision_at_100
-      value: 1.521
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 36.677
-    - type: precision_at_5
-      value: 24.118000000000002
-    - type: recall_at_1
-      value: 69.199
-    - type: recall_at_10
-      value: 94.321
-    - type: recall_at_100
-      value: 99.20400000000001
-    - type: recall_at_1000
-      value: 99.947
-    - type: recall_at_3
-      value: 85.787
-    - type: recall_at_5
-      value: 90.365
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 55.82810046856353
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 63.38132611783628
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 5.127000000000001
-    - type: map_at_10
-      value: 12.235
-    - type: map_at_100
-      value: 14.417
-    - type: map_at_1000
-      value: 14.75
-    - type: map_at_3
-      value: 8.906
-    - type: map_at_5
-      value: 10.591000000000001
-    - type: mrr_at_1
-      value: 25.2
-    - type: mrr_at_10
-      value: 35.879
-    - type: mrr_at_100
-      value: 36.935
-    - type: mrr_at_1000
-      value: 36.997
-    - type: mrr_at_3
-      value: 32.783
-    - type: mrr_at_5
-      value: 34.367999999999995
-    - type: ndcg_at_1
-      value: 25.2
-    - type: ndcg_at_10
-      value: 20.509
-    - type: ndcg_at_100
-      value: 28.67
-    - type: ndcg_at_1000
-      value: 34.42
-    - type: ndcg_at_3
-      value: 19.948
-    - type: ndcg_at_5
-      value: 17.166
-    - type: precision_at_1
-      value: 25.2
-    - type: precision_at_10
-      value: 10.440000000000001
-    - type: precision_at_100
-      value: 2.214
-    - type: precision_at_1000
-      value: 0.359
-    - type: precision_at_3
-      value: 18.533
-    - type: precision_at_5
-      value: 14.860000000000001
-    - type: recall_at_1
-      value: 5.127000000000001
-    - type: recall_at_10
-      value: 21.147
-    - type: recall_at_100
-      value: 44.946999999999996
-    - type: recall_at_1000
-      value: 72.89
-    - type: recall_at_3
-      value: 11.277
-    - type: recall_at_5
-      value: 15.042
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.0373011786213
-    - type: cos_sim_spearman
-      value: 79.27889560856613
-    - type: euclidean_pearson
-      value: 80.31186315495655
-    - type: euclidean_spearman
-      value: 79.41630415280811
-    - type: manhattan_pearson
-      value: 80.31755140442013
-    - type: manhattan_spearman
-      value: 79.43069870027611
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.8659751342045
-    - type: cos_sim_spearman
-      value: 76.95377612997667
-    - type: euclidean_pearson
-      value: 81.24552945497848
-    - type: euclidean_spearman
-      value: 77.18236963555253
-    - type: manhattan_pearson
-      value: 81.26477607759037
-    - type: manhattan_spearman
-      value: 77.13821753062756
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.34597139044875
-    - type: cos_sim_spearman
-      value: 84.124169425592
-    - type: euclidean_pearson
-      value: 83.68590721511401
-    - type: euclidean_spearman
-      value: 84.18846190846398
-    - type: manhattan_pearson
-      value: 83.57630235061498
-    - type: manhattan_spearman
-      value: 84.10244043726902
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.67641885599572
-    - type: cos_sim_spearman
-      value: 80.46450725650428
-    - type: euclidean_pearson
-      value: 81.61645042715865
-    - type: euclidean_spearman
-      value: 80.61418394236874
-    - type: manhattan_pearson
-      value: 81.55712034928871
-    - type: manhattan_spearman
-      value: 80.57905670523951
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 88.86650310886782
-    - type: cos_sim_spearman
-      value: 89.76081629222328
-    - type: euclidean_pearson
-      value: 89.1530747029954
-    - type: euclidean_spearman
-      value: 89.80990657280248
-    - type: manhattan_pearson
-      value: 89.10640563278132
-    - type: manhattan_spearman
-      value: 89.76282108434047
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.93864027911118
-    - type: cos_sim_spearman
-      value: 85.47096193999023
-    - type: euclidean_pearson
-      value: 85.03141840870533
-    - type: euclidean_spearman
-      value: 85.43124029598181
-    - type: manhattan_pearson
-      value: 84.99002664393512
-    - type: manhattan_spearman
-      value: 85.39169195120834
-  - 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: 88.7045343749832
-    - type: cos_sim_spearman
-      value: 89.03262221146677
-    - type: euclidean_pearson
-      value: 89.56078218264365
-    - type: euclidean_spearman
-      value: 89.17827006466868
-    - type: manhattan_pearson
-      value: 89.52717595468582
-    - type: manhattan_spearman
-      value: 89.15878115952923
-  - 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: 64.20191302875551
-    - type: cos_sim_spearman
-      value: 64.11446552557646
-    - type: euclidean_pearson
-      value: 64.6918197393619
-    - type: euclidean_spearman
-      value: 63.440182631197764
-    - type: manhattan_pearson
-      value: 64.55692904121835
-    - type: manhattan_spearman
-      value: 63.424877742756266
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 86.37793104662344
-    - type: cos_sim_spearman
-      value: 87.7357802629067
-    - type: euclidean_pearson
-      value: 87.4286301545109
-    - type: euclidean_spearman
-      value: 87.78452920777421
-    - type: manhattan_pearson
-      value: 87.42445169331255
-    - type: manhattan_spearman
-      value: 87.78537677249598
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 84.31465405081792
-    - type: mrr
-      value: 95.7173781193389
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 57.760999999999996
-    - type: map_at_10
-      value: 67.904
-    - type: map_at_100
-      value: 68.539
-    - type: map_at_1000
-      value: 68.562
-    - type: map_at_3
-      value: 65.415
-    - type: map_at_5
-      value: 66.788
-    - type: mrr_at_1
-      value: 60.333000000000006
-    - type: mrr_at_10
-      value: 68.797
-    - type: mrr_at_100
-      value: 69.236
-    - type: mrr_at_1000
-      value: 69.257
-    - type: mrr_at_3
-      value: 66.667
-    - type: mrr_at_5
-      value: 67.967
-    - type: ndcg_at_1
-      value: 60.333000000000006
-    - type: ndcg_at_10
-      value: 72.24199999999999
-    - type: ndcg_at_100
-      value: 74.86
-    - type: ndcg_at_1000
-      value: 75.354
-    - type: ndcg_at_3
-      value: 67.93400000000001
-    - type: ndcg_at_5
-      value: 70.02199999999999
-    - type: precision_at_1
-      value: 60.333000000000006
-    - type: precision_at_10
-      value: 9.533
-    - type: precision_at_100
-      value: 1.09
-    - type: precision_at_1000
-      value: 0.11299999999999999
-    - type: precision_at_3
-      value: 26.778000000000002
-    - type: precision_at_5
-      value: 17.467
-    - type: recall_at_1
-      value: 57.760999999999996
-    - type: recall_at_10
-      value: 84.383
-    - type: recall_at_100
-      value: 96.267
-    - type: recall_at_1000
-      value: 100
-    - type: recall_at_3
-      value: 72.628
-    - type: recall_at_5
-      value: 78.094
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.8029702970297
-    - type: cos_sim_ap
-      value: 94.9210324173411
-    - type: cos_sim_f1
-      value: 89.8521162672106
-    - type: cos_sim_precision
-      value: 91.67533818938605
-    - type: cos_sim_recall
-      value: 88.1
-    - type: dot_accuracy
-      value: 99.69504950495049
-    - type: dot_ap
-      value: 90.4919719146181
-    - type: dot_f1
-      value: 84.72289156626506
-    - type: dot_precision
-      value: 81.76744186046511
-    - type: dot_recall
-      value: 87.9
-    - type: euclidean_accuracy
-      value: 99.79702970297029
-    - type: euclidean_ap
-      value: 94.87827463795753
-    - type: euclidean_f1
-      value: 89.55680081507896
-    - type: euclidean_precision
-      value: 91.27725856697819
-    - type: euclidean_recall
-      value: 87.9
-    - type: manhattan_accuracy
-      value: 99.7990099009901
-    - type: manhattan_ap
-      value: 94.87587025149682
-    - type: manhattan_f1
-      value: 89.76298537569339
-    - type: manhattan_precision
-      value: 90.53916581892166
-    - type: manhattan_recall
-      value: 89
-    - type: max_accuracy
-      value: 99.8029702970297
-    - type: max_ap
-      value: 94.9210324173411
-    - type: max_f1
-      value: 89.8521162672106
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 65.92385753948724
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 33.671756975431144
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 50.677928036739004
-    - type: mrr
-      value: 51.56413133435193
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 30.523589340819683
-    - type: cos_sim_spearman
-      value: 30.187407518823235
-    - type: dot_pearson
-      value: 29.039713969699015
-    - type: dot_spearman
-      value: 29.114740651155508
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.211
-    - type: map_at_10
-      value: 1.6199999999999999
-    - type: map_at_100
-      value: 8.658000000000001
-    - type: map_at_1000
-      value: 21.538
-    - type: map_at_3
-      value: 0.575
-    - type: map_at_5
-      value: 0.919
-    - type: mrr_at_1
-      value: 78
-    - type: mrr_at_10
-      value: 86.18599999999999
-    - type: mrr_at_100
-      value: 86.18599999999999
-    - type: mrr_at_1000
-      value: 86.18599999999999
-    - type: mrr_at_3
-      value: 85
-    - type: mrr_at_5
-      value: 85.9
-    - type: ndcg_at_1
-      value: 74
-    - type: ndcg_at_10
-      value: 66.542
-    - type: ndcg_at_100
-      value: 50.163999999999994
-    - type: ndcg_at_1000
-      value: 45.696999999999996
-    - type: ndcg_at_3
-      value: 71.531
-    - type: ndcg_at_5
-      value: 70.45
-    - type: precision_at_1
-      value: 78
-    - type: precision_at_10
-      value: 69.39999999999999
-    - type: precision_at_100
-      value: 51.06
-    - type: precision_at_1000
-      value: 20.022000000000002
-    - type: precision_at_3
-      value: 76
-    - type: precision_at_5
-      value: 74.8
-    - type: recall_at_1
-      value: 0.211
-    - type: recall_at_10
-      value: 1.813
-    - type: recall_at_100
-      value: 12.098
-    - type: recall_at_1000
-      value: 42.618
-    - type: recall_at_3
-      value: 0.603
-    - type: recall_at_5
-      value: 0.987
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 2.2079999999999997
-    - type: map_at_10
-      value: 7.777000000000001
-    - type: map_at_100
-      value: 12.825000000000001
-    - type: map_at_1000
-      value: 14.196
-    - type: map_at_3
-      value: 4.285
-    - type: map_at_5
-      value: 6.177
-    - type: mrr_at_1
-      value: 30.612000000000002
-    - type: mrr_at_10
-      value: 42.635
-    - type: mrr_at_100
-      value: 43.955
-    - type: mrr_at_1000
-      value: 43.955
-    - type: mrr_at_3
-      value: 38.435
-    - type: mrr_at_5
-      value: 41.088
-    - type: ndcg_at_1
-      value: 28.571
-    - type: ndcg_at_10
-      value: 20.666999999999998
-    - type: ndcg_at_100
-      value: 31.840000000000003
-    - type: ndcg_at_1000
-      value: 43.191
-    - type: ndcg_at_3
-      value: 23.45
-    - type: ndcg_at_5
-      value: 22.994
-    - type: precision_at_1
-      value: 30.612000000000002
-    - type: precision_at_10
-      value: 17.959
-    - type: precision_at_100
-      value: 6.755
-    - type: precision_at_1000
-      value: 1.4200000000000002
-    - type: precision_at_3
-      value: 23.810000000000002
-    - type: precision_at_5
-      value: 23.673
-    - type: recall_at_1
-      value: 2.2079999999999997
-    - type: recall_at_10
-      value: 13.144
-    - type: recall_at_100
-      value: 42.491
-    - type: recall_at_1000
-      value: 77.04299999999999
-    - type: recall_at_3
-      value: 5.3469999999999995
-    - type: recall_at_5
-      value: 9.139
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 70.9044
-    - type: ap
-      value: 14.625783489340755
-    - type: f1
-      value: 54.814936562590546
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 60.94227504244483
-    - type: f1
-      value: 61.22516038508854
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 49.602409155145864
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 86.94641473445789
-    - type: cos_sim_ap
-      value: 76.91572747061197
-    - type: cos_sim_f1
-      value: 70.14348097317529
-    - type: cos_sim_precision
-      value: 66.53254437869822
-    - type: cos_sim_recall
-      value: 74.1688654353562
-    - type: dot_accuracy
-      value: 84.80061989628658
-    - type: dot_ap
-      value: 70.7952548895177
-    - type: dot_f1
-      value: 65.44780728844965
-    - type: dot_precision
-      value: 61.53310104529617
-    - type: dot_recall
-      value: 69.89445910290237
-    - type: euclidean_accuracy
-      value: 86.94641473445789
-    - type: euclidean_ap
-      value: 76.80774009393652
-    - type: euclidean_f1
-      value: 70.30522503879979
-    - type: euclidean_precision
-      value: 68.94977168949772
-    - type: euclidean_recall
-      value: 71.71503957783642
-    - type: manhattan_accuracy
-      value: 86.8629671574179
-    - type: manhattan_ap
-      value: 76.76518632600317
-    - type: manhattan_f1
-      value: 70.16056518946692
-    - type: manhattan_precision
-      value: 68.360450563204
-    - type: manhattan_recall
-      value: 72.0580474934037
-    - type: max_accuracy
-      value: 86.94641473445789
-    - type: max_ap
-      value: 76.91572747061197
-    - type: max_f1
-      value: 70.30522503879979
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 89.10428066907285
-    - type: cos_sim_ap
-      value: 86.25114759921435
-    - type: cos_sim_f1
-      value: 78.37857884586856
-    - type: cos_sim_precision
-      value: 75.60818546078993
-    - type: cos_sim_recall
-      value: 81.35971666153372
-    - type: dot_accuracy
-      value: 87.41995575736406
-    - type: dot_ap
-      value: 81.51838010086782
-    - type: dot_f1
-      value: 74.77398015435503
-    - type: dot_precision
-      value: 71.53002390662354
-    - type: dot_recall
-      value: 78.32614721281182
-    - type: euclidean_accuracy
-      value: 89.12368533395428
-    - type: euclidean_ap
-      value: 86.33456799874504
-    - type: euclidean_f1
-      value: 78.45496750232127
-    - type: euclidean_precision
-      value: 75.78388462366364
-    - type: euclidean_recall
-      value: 81.32121958731136
-    - type: manhattan_accuracy
-      value: 89.10622113556099
-    - type: manhattan_ap
-      value: 86.31215061745333
-    - type: manhattan_f1
-      value: 78.40684906011539
-    - type: manhattan_precision
-      value: 75.89536643366722
-    - type: manhattan_recall
-      value: 81.09023714197721
-    - type: max_accuracy
-      value: 89.12368533395428
-    - type: max_ap
-      value: 86.33456799874504
-    - type: max_f1
-      value: 78.45496750232127
-language:
-- en
-license: mit
----
-
-# E5-large-v2
-
-[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 24 layers and the embedding size is 1024.
-
-## 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-large-v2')
-model = AutoModel.from_pretrained('intfloat/e5-large-v2')
-
-# 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-large-v2')
-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.
+---
+tags:
+- mteb
+- Sentence Transformers
+- sentence-similarity
+- sentence-transformers
+model-index:
+- name: e5-large-v2
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 79.22388059701493
+    - type: ap
+      value: 43.20816505595132
+    - type: f1
+      value: 73.27811303522058
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.748325
+    - type: ap
+      value: 90.72534979701297
+    - type: f1
+      value: 93.73895874282185
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.612
+    - type: f1
+      value: 47.61157345898393
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.541999999999998
+    - type: map_at_10
+      value: 38.208
+    - type: map_at_100
+      value: 39.417
+    - type: map_at_1000
+      value: 39.428999999999995
+    - type: map_at_3
+      value: 33.95
+    - type: map_at_5
+      value: 36.329
+    - type: mrr_at_1
+      value: 23.755000000000003
+    - type: mrr_at_10
+      value: 38.288
+    - type: mrr_at_100
+      value: 39.511
+    - type: mrr_at_1000
+      value: 39.523
+    - type: mrr_at_3
+      value: 34.009
+    - type: mrr_at_5
+      value: 36.434
+    - type: ndcg_at_1
+      value: 23.541999999999998
+    - type: ndcg_at_10
+      value: 46.417
+    - type: ndcg_at_100
+      value: 51.812000000000005
+    - type: ndcg_at_1000
+      value: 52.137
+    - type: ndcg_at_3
+      value: 37.528
+    - type: ndcg_at_5
+      value: 41.81
+    - type: precision_at_1
+      value: 23.541999999999998
+    - type: precision_at_10
+      value: 7.269
+    - type: precision_at_100
+      value: 0.9690000000000001
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 15.979
+    - type: precision_at_5
+      value: 11.664
+    - type: recall_at_1
+      value: 23.541999999999998
+    - type: recall_at_10
+      value: 72.688
+    - type: recall_at_100
+      value: 96.871
+    - type: recall_at_1000
+      value: 99.431
+    - type: recall_at_3
+      value: 47.937000000000005
+    - type: recall_at_5
+      value: 58.321
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 45.546499570522094
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 41.01607489943561
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 59.616107510107774
+    - type: mrr
+      value: 72.75106626214661
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.33018094733868
+    - type: cos_sim_spearman
+      value: 83.60190492611737
+    - type: euclidean_pearson
+      value: 82.1492450218961
+    - type: euclidean_spearman
+      value: 82.70308926526991
+    - type: manhattan_pearson
+      value: 81.93959600076842
+    - type: manhattan_spearman
+      value: 82.73260801016369
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 84.54545454545455
+    - type: f1
+      value: 84.49582530928923
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 37.362725540120096
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 34.849509608178145
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.502999999999997
+    - type: map_at_10
+      value: 43.323
+    - type: map_at_100
+      value: 44.708999999999996
+    - type: map_at_1000
+      value: 44.838
+    - type: map_at_3
+      value: 38.987
+    - type: map_at_5
+      value: 41.516999999999996
+    - type: mrr_at_1
+      value: 38.769999999999996
+    - type: mrr_at_10
+      value: 49.13
+    - type: mrr_at_100
+      value: 49.697
+    - type: mrr_at_1000
+      value: 49.741
+    - type: mrr_at_3
+      value: 45.804
+    - type: mrr_at_5
+      value: 47.842
+    - type: ndcg_at_1
+      value: 38.769999999999996
+    - type: ndcg_at_10
+      value: 50.266999999999996
+    - type: ndcg_at_100
+      value: 54.967
+    - type: ndcg_at_1000
+      value: 56.976000000000006
+    - type: ndcg_at_3
+      value: 43.823
+    - type: ndcg_at_5
+      value: 47.12
+    - type: precision_at_1
+      value: 38.769999999999996
+    - type: precision_at_10
+      value: 10.057
+    - type: precision_at_100
+      value: 1.554
+    - type: precision_at_1000
+      value: 0.202
+    - type: precision_at_3
+      value: 21.125
+    - type: precision_at_5
+      value: 15.851
+    - type: recall_at_1
+      value: 31.502999999999997
+    - type: recall_at_10
+      value: 63.715999999999994
+    - type: recall_at_100
+      value: 83.61800000000001
+    - type: recall_at_1000
+      value: 96.63199999999999
+    - type: recall_at_3
+      value: 45.403
+    - type: recall_at_5
+      value: 54.481
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.833000000000002
+    - type: map_at_10
+      value: 37.330999999999996
+    - type: map_at_100
+      value: 38.580999999999996
+    - type: map_at_1000
+      value: 38.708
+    - type: map_at_3
+      value: 34.713
+    - type: map_at_5
+      value: 36.104
+    - type: mrr_at_1
+      value: 35.223
+    - type: mrr_at_10
+      value: 43.419000000000004
+    - type: mrr_at_100
+      value: 44.198
+    - type: mrr_at_1000
+      value: 44.249
+    - type: mrr_at_3
+      value: 41.614000000000004
+    - type: mrr_at_5
+      value: 42.553000000000004
+    - type: ndcg_at_1
+      value: 35.223
+    - type: ndcg_at_10
+      value: 42.687999999999995
+    - type: ndcg_at_100
+      value: 47.447
+    - type: ndcg_at_1000
+      value: 49.701
+    - type: ndcg_at_3
+      value: 39.162
+    - type: ndcg_at_5
+      value: 40.557
+    - type: precision_at_1
+      value: 35.223
+    - type: precision_at_10
+      value: 7.962
+    - type: precision_at_100
+      value: 1.304
+    - type: precision_at_1000
+      value: 0.18
+    - type: precision_at_3
+      value: 19.023
+    - type: precision_at_5
+      value: 13.184999999999999
+    - type: recall_at_1
+      value: 27.833000000000002
+    - type: recall_at_10
+      value: 51.881
+    - type: recall_at_100
+      value: 72.04
+    - type: recall_at_1000
+      value: 86.644
+    - type: recall_at_3
+      value: 40.778
+    - type: recall_at_5
+      value: 45.176
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.175
+    - type: map_at_10
+      value: 51.174
+    - type: map_at_100
+      value: 52.26499999999999
+    - type: map_at_1000
+      value: 52.315999999999995
+    - type: map_at_3
+      value: 47.897
+    - type: map_at_5
+      value: 49.703
+    - type: mrr_at_1
+      value: 43.448
+    - type: mrr_at_10
+      value: 54.505
+    - type: mrr_at_100
+      value: 55.216
+    - type: mrr_at_1000
+      value: 55.242000000000004
+    - type: mrr_at_3
+      value: 51.98500000000001
+    - type: mrr_at_5
+      value: 53.434000000000005
+    - type: ndcg_at_1
+      value: 43.448
+    - type: ndcg_at_10
+      value: 57.282
+    - type: ndcg_at_100
+      value: 61.537
+    - type: ndcg_at_1000
+      value: 62.546
+    - type: ndcg_at_3
+      value: 51.73799999999999
+    - type: ndcg_at_5
+      value: 54.324
+    - type: precision_at_1
+      value: 43.448
+    - type: precision_at_10
+      value: 9.292
+    - type: precision_at_100
+      value: 1.233
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 23.218
+    - type: precision_at_5
+      value: 15.887
+    - type: recall_at_1
+      value: 38.175
+    - type: recall_at_10
+      value: 72.00999999999999
+    - type: recall_at_100
+      value: 90.155
+    - type: recall_at_1000
+      value: 97.257
+    - type: recall_at_3
+      value: 57.133
+    - type: recall_at_5
+      value: 63.424
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.405
+    - type: map_at_10
+      value: 30.043
+    - type: map_at_100
+      value: 31.191000000000003
+    - type: map_at_1000
+      value: 31.275
+    - type: map_at_3
+      value: 27.034000000000002
+    - type: map_at_5
+      value: 28.688000000000002
+    - type: mrr_at_1
+      value: 24.068
+    - type: mrr_at_10
+      value: 31.993
+    - type: mrr_at_100
+      value: 32.992
+    - type: mrr_at_1000
+      value: 33.050000000000004
+    - type: mrr_at_3
+      value: 28.964000000000002
+    - type: mrr_at_5
+      value: 30.653000000000002
+    - type: ndcg_at_1
+      value: 24.068
+    - type: ndcg_at_10
+      value: 35.198
+    - type: ndcg_at_100
+      value: 40.709
+    - type: ndcg_at_1000
+      value: 42.855
+    - type: ndcg_at_3
+      value: 29.139
+    - type: ndcg_at_5
+      value: 32.045
+    - type: precision_at_1
+      value: 24.068
+    - type: precision_at_10
+      value: 5.65
+    - type: precision_at_100
+      value: 0.885
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 12.279
+    - type: precision_at_5
+      value: 8.994
+    - type: recall_at_1
+      value: 22.405
+    - type: recall_at_10
+      value: 49.391
+    - type: recall_at_100
+      value: 74.53699999999999
+    - type: recall_at_1000
+      value: 90.605
+    - type: recall_at_3
+      value: 33.126
+    - type: recall_at_5
+      value: 40.073
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 13.309999999999999
+    - type: map_at_10
+      value: 20.688000000000002
+    - type: map_at_100
+      value: 22.022
+    - type: map_at_1000
+      value: 22.152
+    - type: map_at_3
+      value: 17.954
+    - type: map_at_5
+      value: 19.439
+    - type: mrr_at_1
+      value: 16.294
+    - type: mrr_at_10
+      value: 24.479
+    - type: mrr_at_100
+      value: 25.515
+    - type: mrr_at_1000
+      value: 25.593
+    - type: mrr_at_3
+      value: 21.642
+    - type: mrr_at_5
+      value: 23.189999999999998
+    - type: ndcg_at_1
+      value: 16.294
+    - type: ndcg_at_10
+      value: 25.833000000000002
+    - type: ndcg_at_100
+      value: 32.074999999999996
+    - type: ndcg_at_1000
+      value: 35.083
+    - type: ndcg_at_3
+      value: 20.493
+    - type: ndcg_at_5
+      value: 22.949
+    - type: precision_at_1
+      value: 16.294
+    - type: precision_at_10
+      value: 5.112
+    - type: precision_at_100
+      value: 0.96
+    - type: precision_at_1000
+      value: 0.134
+    - type: precision_at_3
+      value: 9.908999999999999
+    - type: precision_at_5
+      value: 7.587000000000001
+    - type: recall_at_1
+      value: 13.309999999999999
+    - type: recall_at_10
+      value: 37.851
+    - type: recall_at_100
+      value: 64.835
+    - type: recall_at_1000
+      value: 86.334
+    - type: recall_at_3
+      value: 23.493
+    - type: recall_at_5
+      value: 29.528
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.857999999999997
+    - type: map_at_10
+      value: 35.503
+    - type: map_at_100
+      value: 36.957
+    - type: map_at_1000
+      value: 37.065
+    - type: map_at_3
+      value: 32.275999999999996
+    - type: map_at_5
+      value: 34.119
+    - type: mrr_at_1
+      value: 31.954
+    - type: mrr_at_10
+      value: 40.851
+    - type: mrr_at_100
+      value: 41.863
+    - type: mrr_at_1000
+      value: 41.900999999999996
+    - type: mrr_at_3
+      value: 38.129999999999995
+    - type: mrr_at_5
+      value: 39.737
+    - type: ndcg_at_1
+      value: 31.954
+    - type: ndcg_at_10
+      value: 41.343999999999994
+    - type: ndcg_at_100
+      value: 47.397
+    - type: ndcg_at_1000
+      value: 49.501
+    - type: ndcg_at_3
+      value: 36.047000000000004
+    - type: ndcg_at_5
+      value: 38.639
+    - type: precision_at_1
+      value: 31.954
+    - type: precision_at_10
+      value: 7.68
+    - type: precision_at_100
+      value: 1.247
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 17.132
+    - type: precision_at_5
+      value: 12.589
+    - type: recall_at_1
+      value: 25.857999999999997
+    - type: recall_at_10
+      value: 53.43599999999999
+    - type: recall_at_100
+      value: 78.82400000000001
+    - type: recall_at_1000
+      value: 92.78999999999999
+    - type: recall_at_3
+      value: 38.655
+    - type: recall_at_5
+      value: 45.216
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.709
+    - type: map_at_10
+      value: 34.318
+    - type: map_at_100
+      value: 35.657
+    - type: map_at_1000
+      value: 35.783
+    - type: map_at_3
+      value: 31.326999999999998
+    - type: map_at_5
+      value: 33.021
+    - type: mrr_at_1
+      value: 30.137000000000004
+    - type: mrr_at_10
+      value: 39.093
+    - type: mrr_at_100
+      value: 39.992
+    - type: mrr_at_1000
+      value: 40.056999999999995
+    - type: mrr_at_3
+      value: 36.606
+    - type: mrr_at_5
+      value: 37.861
+    - type: ndcg_at_1
+      value: 30.137000000000004
+    - type: ndcg_at_10
+      value: 39.974
+    - type: ndcg_at_100
+      value: 45.647999999999996
+    - type: ndcg_at_1000
+      value: 48.259
+    - type: ndcg_at_3
+      value: 35.028
+    - type: ndcg_at_5
+      value: 37.175999999999995
+    - type: precision_at_1
+      value: 30.137000000000004
+    - type: precision_at_10
+      value: 7.363
+    - type: precision_at_100
+      value: 1.184
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 16.857
+    - type: precision_at_5
+      value: 11.963
+    - type: recall_at_1
+      value: 24.709
+    - type: recall_at_10
+      value: 52.087
+    - type: recall_at_100
+      value: 76.125
+    - type: recall_at_1000
+      value: 93.82300000000001
+    - type: recall_at_3
+      value: 38.149
+    - type: recall_at_5
+      value: 43.984
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.40791666666667
+    - type: map_at_10
+      value: 32.458083333333335
+    - type: map_at_100
+      value: 33.691916666666664
+    - type: map_at_1000
+      value: 33.81191666666666
+    - type: map_at_3
+      value: 29.51625
+    - type: map_at_5
+      value: 31.168083333333335
+    - type: mrr_at_1
+      value: 27.96591666666666
+    - type: mrr_at_10
+      value: 36.528583333333344
+    - type: mrr_at_100
+      value: 37.404
+    - type: mrr_at_1000
+      value: 37.464333333333336
+    - type: mrr_at_3
+      value: 33.92883333333333
+    - type: mrr_at_5
+      value: 35.41933333333333
+    - type: ndcg_at_1
+      value: 27.96591666666666
+    - type: ndcg_at_10
+      value: 37.89141666666666
+    - type: ndcg_at_100
+      value: 43.23066666666666
+    - type: ndcg_at_1000
+      value: 45.63258333333333
+    - type: ndcg_at_3
+      value: 32.811249999999994
+    - type: ndcg_at_5
+      value: 35.22566666666667
+    - type: precision_at_1
+      value: 27.96591666666666
+    - type: precision_at_10
+      value: 6.834083333333332
+    - type: precision_at_100
+      value: 1.12225
+    - type: precision_at_1000
+      value: 0.15241666666666667
+    - type: precision_at_3
+      value: 15.264333333333335
+    - type: precision_at_5
+      value: 11.039416666666666
+    - type: recall_at_1
+      value: 23.40791666666667
+    - type: recall_at_10
+      value: 49.927083333333336
+    - type: recall_at_100
+      value: 73.44641666666668
+    - type: recall_at_1000
+      value: 90.19950000000001
+    - type: recall_at_3
+      value: 35.88341666666667
+    - type: recall_at_5
+      value: 42.061249999999994
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.592000000000002
+    - type: map_at_10
+      value: 26.895999999999997
+    - type: map_at_100
+      value: 27.921000000000003
+    - type: map_at_1000
+      value: 28.02
+    - type: map_at_3
+      value: 24.883
+    - type: map_at_5
+      value: 25.812
+    - type: mrr_at_1
+      value: 22.698999999999998
+    - type: mrr_at_10
+      value: 29.520999999999997
+    - type: mrr_at_100
+      value: 30.458000000000002
+    - type: mrr_at_1000
+      value: 30.526999999999997
+    - type: mrr_at_3
+      value: 27.633000000000003
+    - type: mrr_at_5
+      value: 28.483999999999998
+    - type: ndcg_at_1
+      value: 22.698999999999998
+    - type: ndcg_at_10
+      value: 31.061
+    - type: ndcg_at_100
+      value: 36.398
+    - type: ndcg_at_1000
+      value: 38.89
+    - type: ndcg_at_3
+      value: 27.149
+    - type: ndcg_at_5
+      value: 28.627000000000002
+    - type: precision_at_1
+      value: 22.698999999999998
+    - type: precision_at_10
+      value: 5.106999999999999
+    - type: precision_at_100
+      value: 0.857
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 11.963
+    - type: precision_at_5
+      value: 8.221
+    - type: recall_at_1
+      value: 19.592000000000002
+    - type: recall_at_10
+      value: 41.329
+    - type: recall_at_100
+      value: 66.094
+    - type: recall_at_1000
+      value: 84.511
+    - type: recall_at_3
+      value: 30.61
+    - type: recall_at_5
+      value: 34.213
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 14.71
+    - type: map_at_10
+      value: 20.965
+    - type: map_at_100
+      value: 21.994
+    - type: map_at_1000
+      value: 22.133
+    - type: map_at_3
+      value: 18.741
+    - type: map_at_5
+      value: 19.951
+    - type: mrr_at_1
+      value: 18.307000000000002
+    - type: mrr_at_10
+      value: 24.66
+    - type: mrr_at_100
+      value: 25.540000000000003
+    - type: mrr_at_1000
+      value: 25.629
+    - type: mrr_at_3
+      value: 22.511
+    - type: mrr_at_5
+      value: 23.72
+    - type: ndcg_at_1
+      value: 18.307000000000002
+    - type: ndcg_at_10
+      value: 25.153
+    - type: ndcg_at_100
+      value: 30.229
+    - type: ndcg_at_1000
+      value: 33.623
+    - type: ndcg_at_3
+      value: 21.203
+    - type: ndcg_at_5
+      value: 23.006999999999998
+    - type: precision_at_1
+      value: 18.307000000000002
+    - type: precision_at_10
+      value: 4.725
+    - type: precision_at_100
+      value: 0.8659999999999999
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 10.14
+    - type: precision_at_5
+      value: 7.481
+    - type: recall_at_1
+      value: 14.71
+    - type: recall_at_10
+      value: 34.087
+    - type: recall_at_100
+      value: 57.147999999999996
+    - type: recall_at_1000
+      value: 81.777
+    - type: recall_at_3
+      value: 22.996
+    - type: recall_at_5
+      value: 27.73
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.472
+    - type: map_at_10
+      value: 32.699
+    - type: map_at_100
+      value: 33.867000000000004
+    - type: map_at_1000
+      value: 33.967000000000006
+    - type: map_at_3
+      value: 29.718
+    - type: map_at_5
+      value: 31.345
+    - type: mrr_at_1
+      value: 28.265
+    - type: mrr_at_10
+      value: 36.945
+    - type: mrr_at_100
+      value: 37.794
+    - type: mrr_at_1000
+      value: 37.857
+    - type: mrr_at_3
+      value: 34.266000000000005
+    - type: mrr_at_5
+      value: 35.768
+    - type: ndcg_at_1
+      value: 28.265
+    - type: ndcg_at_10
+      value: 38.35
+    - type: ndcg_at_100
+      value: 43.739
+    - type: ndcg_at_1000
+      value: 46.087
+    - type: ndcg_at_3
+      value: 33.004
+    - type: ndcg_at_5
+      value: 35.411
+    - type: precision_at_1
+      value: 28.265
+    - type: precision_at_10
+      value: 6.715999999999999
+    - type: precision_at_100
+      value: 1.059
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 15.299
+    - type: precision_at_5
+      value: 10.951
+    - type: recall_at_1
+      value: 23.472
+    - type: recall_at_10
+      value: 51.413
+    - type: recall_at_100
+      value: 75.17
+    - type: recall_at_1000
+      value: 91.577
+    - type: recall_at_3
+      value: 36.651
+    - type: recall_at_5
+      value: 42.814
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.666
+    - type: map_at_10
+      value: 32.963
+    - type: map_at_100
+      value: 34.544999999999995
+    - type: map_at_1000
+      value: 34.792
+    - type: map_at_3
+      value: 29.74
+    - type: map_at_5
+      value: 31.5
+    - type: mrr_at_1
+      value: 29.051
+    - type: mrr_at_10
+      value: 38.013000000000005
+    - type: mrr_at_100
+      value: 38.997
+    - type: mrr_at_1000
+      value: 39.055
+    - type: mrr_at_3
+      value: 34.947
+    - type: mrr_at_5
+      value: 36.815
+    - type: ndcg_at_1
+      value: 29.051
+    - type: ndcg_at_10
+      value: 39.361000000000004
+    - type: ndcg_at_100
+      value: 45.186
+    - type: ndcg_at_1000
+      value: 47.867
+    - type: ndcg_at_3
+      value: 33.797
+    - type: ndcg_at_5
+      value: 36.456
+    - type: precision_at_1
+      value: 29.051
+    - type: precision_at_10
+      value: 7.668
+    - type: precision_at_100
+      value: 1.532
+    - type: precision_at_1000
+      value: 0.247
+    - type: precision_at_3
+      value: 15.876000000000001
+    - type: precision_at_5
+      value: 11.779
+    - type: recall_at_1
+      value: 23.666
+    - type: recall_at_10
+      value: 51.858000000000004
+    - type: recall_at_100
+      value: 77.805
+    - type: recall_at_1000
+      value: 94.504
+    - type: recall_at_3
+      value: 36.207
+    - type: recall_at_5
+      value: 43.094
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.662
+    - type: map_at_10
+      value: 23.594
+    - type: map_at_100
+      value: 24.593999999999998
+    - type: map_at_1000
+      value: 24.694
+    - type: map_at_3
+      value: 20.925
+    - type: map_at_5
+      value: 22.817999999999998
+    - type: mrr_at_1
+      value: 17.375
+    - type: mrr_at_10
+      value: 25.734
+    - type: mrr_at_100
+      value: 26.586
+    - type: mrr_at_1000
+      value: 26.671
+    - type: mrr_at_3
+      value: 23.044
+    - type: mrr_at_5
+      value: 24.975
+    - type: ndcg_at_1
+      value: 17.375
+    - type: ndcg_at_10
+      value: 28.186
+    - type: ndcg_at_100
+      value: 33.436
+    - type: ndcg_at_1000
+      value: 36.203
+    - type: ndcg_at_3
+      value: 23.152
+    - type: ndcg_at_5
+      value: 26.397
+    - type: precision_at_1
+      value: 17.375
+    - type: precision_at_10
+      value: 4.677
+    - type: precision_at_100
+      value: 0.786
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 10.351
+    - type: precision_at_5
+      value: 7.985
+    - type: recall_at_1
+      value: 15.662
+    - type: recall_at_10
+      value: 40.066
+    - type: recall_at_100
+      value: 65.006
+    - type: recall_at_1000
+      value: 85.94000000000001
+    - type: recall_at_3
+      value: 27.400000000000002
+    - type: recall_at_5
+      value: 35.002
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.853
+    - type: map_at_10
+      value: 15.568000000000001
+    - type: map_at_100
+      value: 17.383000000000003
+    - type: map_at_1000
+      value: 17.584
+    - type: map_at_3
+      value: 12.561
+    - type: map_at_5
+      value: 14.056
+    - type: mrr_at_1
+      value: 18.958
+    - type: mrr_at_10
+      value: 28.288000000000004
+    - type: mrr_at_100
+      value: 29.432000000000002
+    - type: mrr_at_1000
+      value: 29.498
+    - type: mrr_at_3
+      value: 25.049
+    - type: mrr_at_5
+      value: 26.857
+    - type: ndcg_at_1
+      value: 18.958
+    - type: ndcg_at_10
+      value: 22.21
+    - type: ndcg_at_100
+      value: 29.596
+    - type: ndcg_at_1000
+      value: 33.583
+    - type: ndcg_at_3
+      value: 16.994999999999997
+    - type: ndcg_at_5
+      value: 18.95
+    - type: precision_at_1
+      value: 18.958
+    - type: precision_at_10
+      value: 7.192
+    - type: precision_at_100
+      value: 1.5
+    - type: precision_at_1000
+      value: 0.22399999999999998
+    - type: precision_at_3
+      value: 12.573
+    - type: precision_at_5
+      value: 10.202
+    - type: recall_at_1
+      value: 8.853
+    - type: recall_at_10
+      value: 28.087
+    - type: recall_at_100
+      value: 53.701
+    - type: recall_at_1000
+      value: 76.29899999999999
+    - type: recall_at_3
+      value: 15.913
+    - type: recall_at_5
+      value: 20.658
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.077
+    - type: map_at_10
+      value: 20.788999999999998
+    - type: map_at_100
+      value: 30.429000000000002
+    - type: map_at_1000
+      value: 32.143
+    - type: map_at_3
+      value: 14.692
+    - type: map_at_5
+      value: 17.139
+    - type: mrr_at_1
+      value: 70.75
+    - type: mrr_at_10
+      value: 78.036
+    - type: mrr_at_100
+      value: 78.401
+    - type: mrr_at_1000
+      value: 78.404
+    - type: mrr_at_3
+      value: 76.75
+    - type: mrr_at_5
+      value: 77.47500000000001
+    - type: ndcg_at_1
+      value: 58.12500000000001
+    - type: ndcg_at_10
+      value: 44.015
+    - type: ndcg_at_100
+      value: 49.247
+    - type: ndcg_at_1000
+      value: 56.211999999999996
+    - type: ndcg_at_3
+      value: 49.151
+    - type: ndcg_at_5
+      value: 46.195
+    - type: precision_at_1
+      value: 70.75
+    - type: precision_at_10
+      value: 35.5
+    - type: precision_at_100
+      value: 11.355
+    - type: precision_at_1000
+      value: 2.1950000000000003
+    - type: precision_at_3
+      value: 53.083000000000006
+    - type: precision_at_5
+      value: 44.800000000000004
+    - type: recall_at_1
+      value: 9.077
+    - type: recall_at_10
+      value: 26.259
+    - type: recall_at_100
+      value: 56.547000000000004
+    - type: recall_at_1000
+      value: 78.551
+    - type: recall_at_3
+      value: 16.162000000000003
+    - type: recall_at_5
+      value: 19.753999999999998
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 49.44500000000001
+    - type: f1
+      value: 44.67067691783401
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 68.182
+    - type: map_at_10
+      value: 78.223
+    - type: map_at_100
+      value: 78.498
+    - type: map_at_1000
+      value: 78.512
+    - type: map_at_3
+      value: 76.71
+    - type: map_at_5
+      value: 77.725
+    - type: mrr_at_1
+      value: 73.177
+    - type: mrr_at_10
+      value: 82.513
+    - type: mrr_at_100
+      value: 82.633
+    - type: mrr_at_1000
+      value: 82.635
+    - type: mrr_at_3
+      value: 81.376
+    - type: mrr_at_5
+      value: 82.182
+    - type: ndcg_at_1
+      value: 73.177
+    - type: ndcg_at_10
+      value: 82.829
+    - type: ndcg_at_100
+      value: 83.84
+    - type: ndcg_at_1000
+      value: 84.07900000000001
+    - type: ndcg_at_3
+      value: 80.303
+    - type: ndcg_at_5
+      value: 81.846
+    - type: precision_at_1
+      value: 73.177
+    - type: precision_at_10
+      value: 10.241999999999999
+    - type: precision_at_100
+      value: 1.099
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 31.247999999999998
+    - type: precision_at_5
+      value: 19.697
+    - type: recall_at_1
+      value: 68.182
+    - type: recall_at_10
+      value: 92.657
+    - type: recall_at_100
+      value: 96.709
+    - type: recall_at_1000
+      value: 98.184
+    - type: recall_at_3
+      value: 85.9
+    - type: recall_at_5
+      value: 89.755
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.108
+    - type: map_at_10
+      value: 33.342
+    - type: map_at_100
+      value: 35.281
+    - type: map_at_1000
+      value: 35.478
+    - type: map_at_3
+      value: 29.067
+    - type: map_at_5
+      value: 31.563000000000002
+    - type: mrr_at_1
+      value: 41.667
+    - type: mrr_at_10
+      value: 49.913000000000004
+    - type: mrr_at_100
+      value: 50.724000000000004
+    - type: mrr_at_1000
+      value: 50.766
+    - type: mrr_at_3
+      value: 47.504999999999995
+    - type: mrr_at_5
+      value: 49.033
+    - type: ndcg_at_1
+      value: 41.667
+    - type: ndcg_at_10
+      value: 41.144
+    - type: ndcg_at_100
+      value: 48.326
+    - type: ndcg_at_1000
+      value: 51.486
+    - type: ndcg_at_3
+      value: 37.486999999999995
+    - type: ndcg_at_5
+      value: 38.78
+    - type: precision_at_1
+      value: 41.667
+    - type: precision_at_10
+      value: 11.358
+    - type: precision_at_100
+      value: 1.873
+    - type: precision_at_1000
+      value: 0.244
+    - type: precision_at_3
+      value: 25
+    - type: precision_at_5
+      value: 18.519
+    - type: recall_at_1
+      value: 21.108
+    - type: recall_at_10
+      value: 47.249
+    - type: recall_at_100
+      value: 74.52
+    - type: recall_at_1000
+      value: 93.31
+    - type: recall_at_3
+      value: 33.271
+    - type: recall_at_5
+      value: 39.723000000000006
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.317
+    - type: map_at_10
+      value: 64.861
+    - type: map_at_100
+      value: 65.697
+    - type: map_at_1000
+      value: 65.755
+    - type: map_at_3
+      value: 61.258
+    - type: map_at_5
+      value: 63.590999999999994
+    - type: mrr_at_1
+      value: 80.635
+    - type: mrr_at_10
+      value: 86.528
+    - type: mrr_at_100
+      value: 86.66199999999999
+    - type: mrr_at_1000
+      value: 86.666
+    - type: mrr_at_3
+      value: 85.744
+    - type: mrr_at_5
+      value: 86.24300000000001
+    - type: ndcg_at_1
+      value: 80.635
+    - type: ndcg_at_10
+      value: 73.13199999999999
+    - type: ndcg_at_100
+      value: 75.927
+    - type: ndcg_at_1000
+      value: 76.976
+    - type: ndcg_at_3
+      value: 68.241
+    - type: ndcg_at_5
+      value: 71.071
+    - type: precision_at_1
+      value: 80.635
+    - type: precision_at_10
+      value: 15.326
+    - type: precision_at_100
+      value: 1.7500000000000002
+    - type: precision_at_1000
+      value: 0.189
+    - type: precision_at_3
+      value: 43.961
+    - type: precision_at_5
+      value: 28.599999999999998
+    - type: recall_at_1
+      value: 40.317
+    - type: recall_at_10
+      value: 76.631
+    - type: recall_at_100
+      value: 87.495
+    - type: recall_at_1000
+      value: 94.362
+    - type: recall_at_3
+      value: 65.94200000000001
+    - type: recall_at_5
+      value: 71.499
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 91.686
+    - type: ap
+      value: 87.5577120393173
+    - type: f1
+      value: 91.6629447355139
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.702
+    - type: map_at_10
+      value: 36.414
+    - type: map_at_100
+      value: 37.561
+    - type: map_at_1000
+      value: 37.605
+    - type: map_at_3
+      value: 32.456
+    - type: map_at_5
+      value: 34.827000000000005
+    - type: mrr_at_1
+      value: 24.355
+    - type: mrr_at_10
+      value: 37.01
+    - type: mrr_at_100
+      value: 38.085
+    - type: mrr_at_1000
+      value: 38.123000000000005
+    - type: mrr_at_3
+      value: 33.117999999999995
+    - type: mrr_at_5
+      value: 35.452
+    - type: ndcg_at_1
+      value: 24.384
+    - type: ndcg_at_10
+      value: 43.456
+    - type: ndcg_at_100
+      value: 48.892
+    - type: ndcg_at_1000
+      value: 49.964
+    - type: ndcg_at_3
+      value: 35.475
+    - type: ndcg_at_5
+      value: 39.711
+    - type: precision_at_1
+      value: 24.384
+    - type: precision_at_10
+      value: 6.7940000000000005
+    - type: precision_at_100
+      value: 0.951
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 15.052999999999999
+    - type: precision_at_5
+      value: 11.189
+    - type: recall_at_1
+      value: 23.702
+    - type: recall_at_10
+      value: 65.057
+    - type: recall_at_100
+      value: 90.021
+    - type: recall_at_1000
+      value: 98.142
+    - type: recall_at_3
+      value: 43.551
+    - type: recall_at_5
+      value: 53.738
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 94.62380300957591
+    - type: f1
+      value: 94.49871222100734
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 77.14090287277702
+    - type: f1
+      value: 60.32101258220515
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 73.84330867518494
+    - type: f1
+      value: 71.92248688515255
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.10692669804976
+    - type: f1
+      value: 77.9904839122866
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 31.822988923078444
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 30.38394880253403
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.82504612539082
+    - type: mrr
+      value: 32.84462298174977
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.029
+    - type: map_at_10
+      value: 14.088999999999999
+    - type: map_at_100
+      value: 17.601
+    - type: map_at_1000
+      value: 19.144
+    - type: map_at_3
+      value: 10.156
+    - type: map_at_5
+      value: 11.892
+    - type: mrr_at_1
+      value: 46.44
+    - type: mrr_at_10
+      value: 56.596999999999994
+    - type: mrr_at_100
+      value: 57.11000000000001
+    - type: mrr_at_1000
+      value: 57.14
+    - type: mrr_at_3
+      value: 54.334
+    - type: mrr_at_5
+      value: 55.774
+    - type: ndcg_at_1
+      value: 44.891999999999996
+    - type: ndcg_at_10
+      value: 37.134
+    - type: ndcg_at_100
+      value: 33.652
+    - type: ndcg_at_1000
+      value: 42.548
+    - type: ndcg_at_3
+      value: 41.851
+    - type: ndcg_at_5
+      value: 39.842
+    - type: precision_at_1
+      value: 46.44
+    - type: precision_at_10
+      value: 27.647
+    - type: precision_at_100
+      value: 8.309999999999999
+    - type: precision_at_1000
+      value: 2.146
+    - type: precision_at_3
+      value: 39.422000000000004
+    - type: precision_at_5
+      value: 34.675
+    - type: recall_at_1
+      value: 6.029
+    - type: recall_at_10
+      value: 18.907
+    - type: recall_at_100
+      value: 33.76
+    - type: recall_at_1000
+      value: 65.14999999999999
+    - type: recall_at_3
+      value: 11.584999999999999
+    - type: recall_at_5
+      value: 14.626
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.373000000000005
+    - type: map_at_10
+      value: 55.836
+    - type: map_at_100
+      value: 56.611999999999995
+    - type: map_at_1000
+      value: 56.63
+    - type: map_at_3
+      value: 51.747
+    - type: map_at_5
+      value: 54.337999999999994
+    - type: mrr_at_1
+      value: 44.147999999999996
+    - type: mrr_at_10
+      value: 58.42699999999999
+    - type: mrr_at_100
+      value: 58.902
+    - type: mrr_at_1000
+      value: 58.914
+    - type: mrr_at_3
+      value: 55.156000000000006
+    - type: mrr_at_5
+      value: 57.291000000000004
+    - type: ndcg_at_1
+      value: 44.119
+    - type: ndcg_at_10
+      value: 63.444
+    - type: ndcg_at_100
+      value: 66.40599999999999
+    - type: ndcg_at_1000
+      value: 66.822
+    - type: ndcg_at_3
+      value: 55.962
+    - type: ndcg_at_5
+      value: 60.228
+    - type: precision_at_1
+      value: 44.119
+    - type: precision_at_10
+      value: 10.006
+    - type: precision_at_100
+      value: 1.17
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 25.135
+    - type: precision_at_5
+      value: 17.59
+    - type: recall_at_1
+      value: 39.373000000000005
+    - type: recall_at_10
+      value: 83.78999999999999
+    - type: recall_at_100
+      value: 96.246
+    - type: recall_at_1000
+      value: 99.324
+    - type: recall_at_3
+      value: 64.71900000000001
+    - type: recall_at_5
+      value: 74.508
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 69.199
+    - type: map_at_10
+      value: 82.892
+    - type: map_at_100
+      value: 83.578
+    - type: map_at_1000
+      value: 83.598
+    - type: map_at_3
+      value: 79.948
+    - type: map_at_5
+      value: 81.779
+    - type: mrr_at_1
+      value: 79.67
+    - type: mrr_at_10
+      value: 86.115
+    - type: mrr_at_100
+      value: 86.249
+    - type: mrr_at_1000
+      value: 86.251
+    - type: mrr_at_3
+      value: 85.08200000000001
+    - type: mrr_at_5
+      value: 85.783
+    - type: ndcg_at_1
+      value: 79.67
+    - type: ndcg_at_10
+      value: 86.839
+    - type: ndcg_at_100
+      value: 88.252
+    - type: ndcg_at_1000
+      value: 88.401
+    - type: ndcg_at_3
+      value: 83.86200000000001
+    - type: ndcg_at_5
+      value: 85.473
+    - type: precision_at_1
+      value: 79.67
+    - type: precision_at_10
+      value: 13.19
+    - type: precision_at_100
+      value: 1.521
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 36.677
+    - type: precision_at_5
+      value: 24.118000000000002
+    - type: recall_at_1
+      value: 69.199
+    - type: recall_at_10
+      value: 94.321
+    - type: recall_at_100
+      value: 99.20400000000001
+    - type: recall_at_1000
+      value: 99.947
+    - type: recall_at_3
+      value: 85.787
+    - type: recall_at_5
+      value: 90.365
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 55.82810046856353
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 63.38132611783628
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.127000000000001
+    - type: map_at_10
+      value: 12.235
+    - type: map_at_100
+      value: 14.417
+    - type: map_at_1000
+      value: 14.75
+    - type: map_at_3
+      value: 8.906
+    - type: map_at_5
+      value: 10.591000000000001
+    - type: mrr_at_1
+      value: 25.2
+    - type: mrr_at_10
+      value: 35.879
+    - type: mrr_at_100
+      value: 36.935
+    - type: mrr_at_1000
+      value: 36.997
+    - type: mrr_at_3
+      value: 32.783
+    - type: mrr_at_5
+      value: 34.367999999999995
+    - type: ndcg_at_1
+      value: 25.2
+    - type: ndcg_at_10
+      value: 20.509
+    - type: ndcg_at_100
+      value: 28.67
+    - type: ndcg_at_1000
+      value: 34.42
+    - type: ndcg_at_3
+      value: 19.948
+    - type: ndcg_at_5
+      value: 17.166
+    - type: precision_at_1
+      value: 25.2
+    - type: precision_at_10
+      value: 10.440000000000001
+    - type: precision_at_100
+      value: 2.214
+    - type: precision_at_1000
+      value: 0.359
+    - type: precision_at_3
+      value: 18.533
+    - type: precision_at_5
+      value: 14.860000000000001
+    - type: recall_at_1
+      value: 5.127000000000001
+    - type: recall_at_10
+      value: 21.147
+    - type: recall_at_100
+      value: 44.946999999999996
+    - type: recall_at_1000
+      value: 72.89
+    - type: recall_at_3
+      value: 11.277
+    - type: recall_at_5
+      value: 15.042
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.0373011786213
+    - type: cos_sim_spearman
+      value: 79.27889560856613
+    - type: euclidean_pearson
+      value: 80.31186315495655
+    - type: euclidean_spearman
+      value: 79.41630415280811
+    - type: manhattan_pearson
+      value: 80.31755140442013
+    - type: manhattan_spearman
+      value: 79.43069870027611
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.8659751342045
+    - type: cos_sim_spearman
+      value: 76.95377612997667
+    - type: euclidean_pearson
+      value: 81.24552945497848
+    - type: euclidean_spearman
+      value: 77.18236963555253
+    - type: manhattan_pearson
+      value: 81.26477607759037
+    - type: manhattan_spearman
+      value: 77.13821753062756
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.34597139044875
+    - type: cos_sim_spearman
+      value: 84.124169425592
+    - type: euclidean_pearson
+      value: 83.68590721511401
+    - type: euclidean_spearman
+      value: 84.18846190846398
+    - type: manhattan_pearson
+      value: 83.57630235061498
+    - type: manhattan_spearman
+      value: 84.10244043726902
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.67641885599572
+    - type: cos_sim_spearman
+      value: 80.46450725650428
+    - type: euclidean_pearson
+      value: 81.61645042715865
+    - type: euclidean_spearman
+      value: 80.61418394236874
+    - type: manhattan_pearson
+      value: 81.55712034928871
+    - type: manhattan_spearman
+      value: 80.57905670523951
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.86650310886782
+    - type: cos_sim_spearman
+      value: 89.76081629222328
+    - type: euclidean_pearson
+      value: 89.1530747029954
+    - type: euclidean_spearman
+      value: 89.80990657280248
+    - type: manhattan_pearson
+      value: 89.10640563278132
+    - type: manhattan_spearman
+      value: 89.76282108434047
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.93864027911118
+    - type: cos_sim_spearman
+      value: 85.47096193999023
+    - type: euclidean_pearson
+      value: 85.03141840870533
+    - type: euclidean_spearman
+      value: 85.43124029598181
+    - type: manhattan_pearson
+      value: 84.99002664393512
+    - type: manhattan_spearman
+      value: 85.39169195120834
+  - 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: 88.7045343749832
+    - type: cos_sim_spearman
+      value: 89.03262221146677
+    - type: euclidean_pearson
+      value: 89.56078218264365
+    - type: euclidean_spearman
+      value: 89.17827006466868
+    - type: manhattan_pearson
+      value: 89.52717595468582
+    - type: manhattan_spearman
+      value: 89.15878115952923
+  - 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: 64.20191302875551
+    - type: cos_sim_spearman
+      value: 64.11446552557646
+    - type: euclidean_pearson
+      value: 64.6918197393619
+    - type: euclidean_spearman
+      value: 63.440182631197764
+    - type: manhattan_pearson
+      value: 64.55692904121835
+    - type: manhattan_spearman
+      value: 63.424877742756266
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.37793104662344
+    - type: cos_sim_spearman
+      value: 87.7357802629067
+    - type: euclidean_pearson
+      value: 87.4286301545109
+    - type: euclidean_spearman
+      value: 87.78452920777421
+    - type: manhattan_pearson
+      value: 87.42445169331255
+    - type: manhattan_spearman
+      value: 87.78537677249598
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 84.31465405081792
+    - type: mrr
+      value: 95.7173781193389
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 57.760999999999996
+    - type: map_at_10
+      value: 67.904
+    - type: map_at_100
+      value: 68.539
+    - type: map_at_1000
+      value: 68.562
+    - type: map_at_3
+      value: 65.415
+    - type: map_at_5
+      value: 66.788
+    - type: mrr_at_1
+      value: 60.333000000000006
+    - type: mrr_at_10
+      value: 68.797
+    - type: mrr_at_100
+      value: 69.236
+    - type: mrr_at_1000
+      value: 69.257
+    - type: mrr_at_3
+      value: 66.667
+    - type: mrr_at_5
+      value: 67.967
+    - type: ndcg_at_1
+      value: 60.333000000000006
+    - type: ndcg_at_10
+      value: 72.24199999999999
+    - type: ndcg_at_100
+      value: 74.86
+    - type: ndcg_at_1000
+      value: 75.354
+    - type: ndcg_at_3
+      value: 67.93400000000001
+    - type: ndcg_at_5
+      value: 70.02199999999999
+    - type: precision_at_1
+      value: 60.333000000000006
+    - type: precision_at_10
+      value: 9.533
+    - type: precision_at_100
+      value: 1.09
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 26.778000000000002
+    - type: precision_at_5
+      value: 17.467
+    - type: recall_at_1
+      value: 57.760999999999996
+    - type: recall_at_10
+      value: 84.383
+    - type: recall_at_100
+      value: 96.267
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 72.628
+    - type: recall_at_5
+      value: 78.094
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.8029702970297
+    - type: cos_sim_ap
+      value: 94.9210324173411
+    - type: cos_sim_f1
+      value: 89.8521162672106
+    - type: cos_sim_precision
+      value: 91.67533818938605
+    - type: cos_sim_recall
+      value: 88.1
+    - type: dot_accuracy
+      value: 99.69504950495049
+    - type: dot_ap
+      value: 90.4919719146181
+    - type: dot_f1
+      value: 84.72289156626506
+    - type: dot_precision
+      value: 81.76744186046511
+    - type: dot_recall
+      value: 87.9
+    - type: euclidean_accuracy
+      value: 99.79702970297029
+    - type: euclidean_ap
+      value: 94.87827463795753
+    - type: euclidean_f1
+      value: 89.55680081507896
+    - type: euclidean_precision
+      value: 91.27725856697819
+    - type: euclidean_recall
+      value: 87.9
+    - type: manhattan_accuracy
+      value: 99.7990099009901
+    - type: manhattan_ap
+      value: 94.87587025149682
+    - type: manhattan_f1
+      value: 89.76298537569339
+    - type: manhattan_precision
+      value: 90.53916581892166
+    - type: manhattan_recall
+      value: 89
+    - type: max_accuracy
+      value: 99.8029702970297
+    - type: max_ap
+      value: 94.9210324173411
+    - type: max_f1
+      value: 89.8521162672106
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 65.92385753948724
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 33.671756975431144
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.677928036739004
+    - type: mrr
+      value: 51.56413133435193
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.523589340819683
+    - type: cos_sim_spearman
+      value: 30.187407518823235
+    - type: dot_pearson
+      value: 29.039713969699015
+    - type: dot_spearman
+      value: 29.114740651155508
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.211
+    - type: map_at_10
+      value: 1.6199999999999999
+    - type: map_at_100
+      value: 8.658000000000001
+    - type: map_at_1000
+      value: 21.538
+    - type: map_at_3
+      value: 0.575
+    - type: map_at_5
+      value: 0.919
+    - type: mrr_at_1
+      value: 78
+    - type: mrr_at_10
+      value: 86.18599999999999
+    - type: mrr_at_100
+      value: 86.18599999999999
+    - type: mrr_at_1000
+      value: 86.18599999999999
+    - type: mrr_at_3
+      value: 85
+    - type: mrr_at_5
+      value: 85.9
+    - type: ndcg_at_1
+      value: 74
+    - type: ndcg_at_10
+      value: 66.542
+    - type: ndcg_at_100
+      value: 50.163999999999994
+    - type: ndcg_at_1000
+      value: 45.696999999999996
+    - type: ndcg_at_3
+      value: 71.531
+    - type: ndcg_at_5
+      value: 70.45
+    - type: precision_at_1
+      value: 78
+    - type: precision_at_10
+      value: 69.39999999999999
+    - type: precision_at_100
+      value: 51.06
+    - type: precision_at_1000
+      value: 20.022000000000002
+    - type: precision_at_3
+      value: 76
+    - type: precision_at_5
+      value: 74.8
+    - type: recall_at_1
+      value: 0.211
+    - type: recall_at_10
+      value: 1.813
+    - type: recall_at_100
+      value: 12.098
+    - type: recall_at_1000
+      value: 42.618
+    - type: recall_at_3
+      value: 0.603
+    - type: recall_at_5
+      value: 0.987
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.2079999999999997
+    - type: map_at_10
+      value: 7.777000000000001
+    - type: map_at_100
+      value: 12.825000000000001
+    - type: map_at_1000
+      value: 14.196
+    - type: map_at_3
+      value: 4.285
+    - type: map_at_5
+      value: 6.177
+    - type: mrr_at_1
+      value: 30.612000000000002
+    - type: mrr_at_10
+      value: 42.635
+    - type: mrr_at_100
+      value: 43.955
+    - type: mrr_at_1000
+      value: 43.955
+    - type: mrr_at_3
+      value: 38.435
+    - type: mrr_at_5
+      value: 41.088
+    - type: ndcg_at_1
+      value: 28.571
+    - type: ndcg_at_10
+      value: 20.666999999999998
+    - type: ndcg_at_100
+      value: 31.840000000000003
+    - type: ndcg_at_1000
+      value: 43.191
+    - type: ndcg_at_3
+      value: 23.45
+    - type: ndcg_at_5
+      value: 22.994
+    - type: precision_at_1
+      value: 30.612000000000002
+    - type: precision_at_10
+      value: 17.959
+    - type: precision_at_100
+      value: 6.755
+    - type: precision_at_1000
+      value: 1.4200000000000002
+    - type: precision_at_3
+      value: 23.810000000000002
+    - type: precision_at_5
+      value: 23.673
+    - type: recall_at_1
+      value: 2.2079999999999997
+    - type: recall_at_10
+      value: 13.144
+    - type: recall_at_100
+      value: 42.491
+    - type: recall_at_1000
+      value: 77.04299999999999
+    - type: recall_at_3
+      value: 5.3469999999999995
+    - type: recall_at_5
+      value: 9.139
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.9044
+    - type: ap
+      value: 14.625783489340755
+    - type: f1
+      value: 54.814936562590546
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 60.94227504244483
+    - type: f1
+      value: 61.22516038508854
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.602409155145864
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.94641473445789
+    - type: cos_sim_ap
+      value: 76.91572747061197
+    - type: cos_sim_f1
+      value: 70.14348097317529
+    - type: cos_sim_precision
+      value: 66.53254437869822
+    - type: cos_sim_recall
+      value: 74.1688654353562
+    - type: dot_accuracy
+      value: 84.80061989628658
+    - type: dot_ap
+      value: 70.7952548895177
+    - type: dot_f1
+      value: 65.44780728844965
+    - type: dot_precision
+      value: 61.53310104529617
+    - type: dot_recall
+      value: 69.89445910290237
+    - type: euclidean_accuracy
+      value: 86.94641473445789
+    - type: euclidean_ap
+      value: 76.80774009393652
+    - type: euclidean_f1
+      value: 70.30522503879979
+    - type: euclidean_precision
+      value: 68.94977168949772
+    - type: euclidean_recall
+      value: 71.71503957783642
+    - type: manhattan_accuracy
+      value: 86.8629671574179
+    - type: manhattan_ap
+      value: 76.76518632600317
+    - type: manhattan_f1
+      value: 70.16056518946692
+    - type: manhattan_precision
+      value: 68.360450563204
+    - type: manhattan_recall
+      value: 72.0580474934037
+    - type: max_accuracy
+      value: 86.94641473445789
+    - type: max_ap
+      value: 76.91572747061197
+    - type: max_f1
+      value: 70.30522503879979
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.10428066907285
+    - type: cos_sim_ap
+      value: 86.25114759921435
+    - type: cos_sim_f1
+      value: 78.37857884586856
+    - type: cos_sim_precision
+      value: 75.60818546078993
+    - type: cos_sim_recall
+      value: 81.35971666153372
+    - type: dot_accuracy
+      value: 87.41995575736406
+    - type: dot_ap
+      value: 81.51838010086782
+    - type: dot_f1
+      value: 74.77398015435503
+    - type: dot_precision
+      value: 71.53002390662354
+    - type: dot_recall
+      value: 78.32614721281182
+    - type: euclidean_accuracy
+      value: 89.12368533395428
+    - type: euclidean_ap
+      value: 86.33456799874504
+    - type: euclidean_f1
+      value: 78.45496750232127
+    - type: euclidean_precision
+      value: 75.78388462366364
+    - type: euclidean_recall
+      value: 81.32121958731136
+    - type: manhattan_accuracy
+      value: 89.10622113556099
+    - type: manhattan_ap
+      value: 86.31215061745333
+    - type: manhattan_f1
+      value: 78.40684906011539
+    - type: manhattan_precision
+      value: 75.89536643366722
+    - type: manhattan_recall
+      value: 81.09023714197721
+    - type: max_accuracy
+      value: 89.12368533395428
+    - type: max_ap
+      value: 86.33456799874504
+    - type: max_f1
+      value: 78.45496750232127
+language:
+- en
+license: mit
+---
+
+# E5-large-v2
+
+[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 24 layers and the embedding size is 1024.
+
+## 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-large-v2')
+model = AutoModel.from_pretrained('intfloat/e5-large-v2')
+
+# 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-large-v2')
+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.