Training in progress, step 1000

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snapshot/best/1_Pooling/config.json

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snapshot/best/2_MixtureEmbeddingsModel/MixSentenceTransformer_config.json

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snapshot/best/2_MixtureEmbeddingsModel/expert_00_TaylorAI_bge-micro-v2/README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- mteb
+model-index:
+- name: bge_micro
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 67.76119402985074
+    - type: ap
+      value: 29.637849284211114
+    - type: f1
+      value: 61.31181187111905
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 79.7547
+    - type: ap
+      value: 74.21401629809145
+    - type: f1
+      value: 79.65319615433783
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 37.452000000000005
+    - type: f1
+      value: 37.0245198854966
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.152
+    - type: map_at_10
+      value: 46.702
+    - type: map_at_100
+      value: 47.563
+    - type: map_at_1000
+      value: 47.567
+    - type: map_at_3
+      value: 42.058
+    - type: map_at_5
+      value: 44.608
+    - type: mrr_at_1
+      value: 32.006
+    - type: mrr_at_10
+      value: 47.064
+    - type: mrr_at_100
+      value: 47.910000000000004
+    - type: mrr_at_1000
+      value: 47.915
+    - type: mrr_at_3
+      value: 42.283
+    - type: mrr_at_5
+      value: 44.968
+    - type: ndcg_at_1
+      value: 31.152
+    - type: ndcg_at_10
+      value: 55.308
+    - type: ndcg_at_100
+      value: 58.965
+    - type: ndcg_at_1000
+      value: 59.067
+    - type: ndcg_at_3
+      value: 45.698
+    - type: ndcg_at_5
+      value: 50.296
+    - type: precision_at_1
+      value: 31.152
+    - type: precision_at_10
+      value: 8.279
+    - type: precision_at_100
+      value: 0.987
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 18.753
+    - type: precision_at_5
+      value: 13.485
+    - type: recall_at_1
+      value: 31.152
+    - type: recall_at_10
+      value: 82.788
+    - type: recall_at_100
+      value: 98.72
+    - type: recall_at_1000
+      value: 99.502
+    - type: recall_at_3
+      value: 56.259
+    - type: recall_at_5
+      value: 67.425
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 44.52692241938116
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 33.245710292773595
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 58.08493637155168
+    - type: mrr
+      value: 71.94378490084861
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.1602804378326
+    - type: cos_sim_spearman
+      value: 82.92478106365587
+    - type: euclidean_pearson
+      value: 82.27930167277077
+    - type: euclidean_spearman
+      value: 82.18560759458093
+    - type: manhattan_pearson
+      value: 82.34277425888187
+    - type: manhattan_spearman
+      value: 81.72776583704467
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 81.17207792207792
+    - type: f1
+      value: 81.09893836310513
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 36.109308463095516
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 28.06048212317168
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.233999999999998
+    - type: map_at_10
+      value: 38.092999999999996
+    - type: map_at_100
+      value: 39.473
+    - type: map_at_1000
+      value: 39.614
+    - type: map_at_3
+      value: 34.839
+    - type: map_at_5
+      value: 36.523
+    - type: mrr_at_1
+      value: 35.193000000000005
+    - type: mrr_at_10
+      value: 44.089
+    - type: mrr_at_100
+      value: 44.927
+    - type: mrr_at_1000
+      value: 44.988
+    - type: mrr_at_3
+      value: 41.559000000000005
+    - type: mrr_at_5
+      value: 43.162
+    - type: ndcg_at_1
+      value: 35.193000000000005
+    - type: ndcg_at_10
+      value: 44.04
+    - type: ndcg_at_100
+      value: 49.262
+    - type: ndcg_at_1000
+      value: 51.847
+    - type: ndcg_at_3
+      value: 39.248
+    - type: ndcg_at_5
+      value: 41.298
+    - type: precision_at_1
+      value: 35.193000000000005
+    - type: precision_at_10
+      value: 8.555
+    - type: precision_at_100
+      value: 1.3820000000000001
+    - type: precision_at_1000
+      value: 0.189
+    - type: precision_at_3
+      value: 19.123
+    - type: precision_at_5
+      value: 13.648
+    - type: recall_at_1
+      value: 28.233999999999998
+    - type: recall_at_10
+      value: 55.094
+    - type: recall_at_100
+      value: 76.85300000000001
+    - type: recall_at_1000
+      value: 94.163
+    - type: recall_at_3
+      value: 40.782000000000004
+    - type: recall_at_5
+      value: 46.796
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.538
+    - type: map_at_10
+      value: 28.449
+    - type: map_at_100
+      value: 29.471000000000004
+    - type: map_at_1000
+      value: 29.599999999999998
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+      value: 26.371
+    - type: map_at_5
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+    - type: mrr_at_1
+      value: 26.815
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+      value: 31.561
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+    - type: ndcg_at_1
+      value: 26.815
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+      value: 32.67
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+      value: 37.039
+    - type: ndcg_at_1000
+      value: 39.769
+    - type: ndcg_at_3
+      value: 29.523
+    - type: ndcg_at_5
+      value: 31.048
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+    - type: precision_at_10
+      value: 5.955
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+    - type: precision_at_3
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+    - type: recall_at_1
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+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
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+  - task:
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+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
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+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
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+    - type: recall_at_10
+      value: 38.706
+    - type: recall_at_100
+      value: 61.242
+    - type: recall_at_1000
+      value: 84.84
+    - type: recall_at_3
+      value: 24.973
+    - type: recall_at_5
+      value: 30.603
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.918000000000003
+    - type: map_at_10
+      value: 34.296
+    - type: map_at_100
+      value: 35.632000000000005
+    - type: map_at_1000
+      value: 35.748999999999995
+    - type: map_at_3
+      value: 31.304
+    - type: map_at_5
+      value: 33.166000000000004
+    - type: mrr_at_1
+      value: 30.703000000000003
+    - type: mrr_at_10
+      value: 39.655
+    - type: mrr_at_100
+      value: 40.569
+    - type: mrr_at_1000
+      value: 40.621
+    - type: mrr_at_3
+      value: 37.023
+    - type: mrr_at_5
+      value: 38.664
+    - type: ndcg_at_1
+      value: 30.703000000000003
+    - type: ndcg_at_10
+      value: 39.897
+    - type: ndcg_at_100
+      value: 45.777
+    - type: ndcg_at_1000
+      value: 48.082
+    - type: ndcg_at_3
+      value: 35.122
+    - type: ndcg_at_5
+      value: 37.691
+    - type: precision_at_1
+      value: 30.703000000000003
+    - type: precision_at_10
+      value: 7.305000000000001
+    - type: precision_at_100
+      value: 1.208
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 16.811
+    - type: precision_at_5
+      value: 12.203999999999999
+    - type: recall_at_1
+      value: 24.918000000000003
+    - type: recall_at_10
+      value: 51.31
+    - type: recall_at_100
+      value: 76.534
+    - type: recall_at_1000
+      value: 91.911
+    - type: recall_at_3
+      value: 37.855
+    - type: recall_at_5
+      value: 44.493
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.416
+    - type: map_at_10
+      value: 30.474
+    - type: map_at_100
+      value: 31.759999999999998
+    - type: map_at_1000
+      value: 31.891000000000002
+    - type: map_at_3
+      value: 27.728
+    - type: map_at_5
+      value: 29.247
+    - type: mrr_at_1
+      value: 28.881
+    - type: mrr_at_10
+      value: 36.418
+    - type: mrr_at_100
+      value: 37.347
+    - type: mrr_at_1000
+      value: 37.415
+    - type: mrr_at_3
+      value: 33.942
+    - type: mrr_at_5
+      value: 35.386
+    - type: ndcg_at_1
+      value: 28.881
+    - type: ndcg_at_10
+      value: 35.812
+    - type: ndcg_at_100
+      value: 41.574
+    - type: ndcg_at_1000
+      value: 44.289
+    - type: ndcg_at_3
+      value: 31.239
+    - type: ndcg_at_5
+      value: 33.302
+    - type: precision_at_1
+      value: 28.881
+    - type: precision_at_10
+      value: 6.598
+    - type: precision_at_100
+      value: 1.1079999999999999
+    - type: precision_at_1000
+      value: 0.151
+    - type: precision_at_3
+      value: 14.954
+    - type: precision_at_5
+      value: 10.776
+    - type: recall_at_1
+      value: 22.416
+    - type: recall_at_10
+      value: 46.243
+    - type: recall_at_100
+      value: 71.352
+    - type: recall_at_1000
+      value: 90.034
+    - type: recall_at_3
+      value: 32.873000000000005
+    - type: recall_at_5
+      value: 38.632
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.528166666666667
+    - type: map_at_10
+      value: 30.317833333333333
+    - type: map_at_100
+      value: 31.44108333333333
+    - type: map_at_1000
+      value: 31.566666666666666
+    - type: map_at_3
+      value: 27.84425
+    - type: map_at_5
+      value: 29.233333333333334
+    - type: mrr_at_1
+      value: 26.75733333333333
+    - type: mrr_at_10
+      value: 34.24425
+    - type: mrr_at_100
+      value: 35.11375
+    - type: mrr_at_1000
+      value: 35.184333333333335
+    - type: mrr_at_3
+      value: 32.01225
+    - type: mrr_at_5
+      value: 33.31225
+    - type: ndcg_at_1
+      value: 26.75733333333333
+    - type: ndcg_at_10
+      value: 35.072583333333334
+    - type: ndcg_at_100
+      value: 40.13358333333334
+    - type: ndcg_at_1000
+      value: 42.81825
+    - type: ndcg_at_3
+      value: 30.79275000000001
+    - type: ndcg_at_5
+      value: 32.822
+    - type: precision_at_1
+      value: 26.75733333333333
+    - type: precision_at_10
+      value: 6.128083333333334
+    - type: precision_at_100
+      value: 1.019
+    - type: precision_at_1000
+      value: 0.14391666666666664
+    - type: precision_at_3
+      value: 14.129916666666665
+    - type: precision_at_5
+      value: 10.087416666666668
+    - type: recall_at_1
+      value: 22.528166666666667
+    - type: recall_at_10
+      value: 45.38341666666667
+    - type: recall_at_100
+      value: 67.81791666666668
+    - type: recall_at_1000
+      value: 86.71716666666666
+    - type: recall_at_3
+      value: 33.38741666666667
+    - type: recall_at_5
+      value: 38.62041666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.975
+    - type: map_at_10
+      value: 28.144999999999996
+    - type: map_at_100
+      value: 28.994999999999997
+    - type: map_at_1000
+      value: 29.086000000000002
+    - type: map_at_3
+      value: 25.968999999999998
+    - type: map_at_5
+      value: 27.321
+    - type: mrr_at_1
+      value: 25.0
+    - type: mrr_at_10
+      value: 30.822
+    - type: mrr_at_100
+      value: 31.647
+    - type: mrr_at_1000
+      value: 31.712
+    - type: mrr_at_3
+      value: 28.860000000000003
+    - type: mrr_at_5
+      value: 30.041
+    - type: ndcg_at_1
+      value: 25.0
+    - type: ndcg_at_10
+      value: 31.929999999999996
+    - type: ndcg_at_100
+      value: 36.258
+    - type: ndcg_at_1000
+      value: 38.682
+    - type: ndcg_at_3
+      value: 27.972
+    - type: ndcg_at_5
+      value: 30.089
+    - type: precision_at_1
+      value: 25.0
+    - type: precision_at_10
+      value: 4.923
+    - type: precision_at_100
+      value: 0.767
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 11.860999999999999
+    - type: precision_at_5
+      value: 8.466
+    - type: recall_at_1
+      value: 21.975
+    - type: recall_at_10
+      value: 41.102
+    - type: recall_at_100
+      value: 60.866
+    - type: recall_at_1000
+      value: 78.781
+    - type: recall_at_3
+      value: 30.268
+    - type: recall_at_5
+      value: 35.552
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.845999999999998
+    - type: map_at_10
+      value: 21.861
+    - type: map_at_100
+      value: 22.798
+    - type: map_at_1000
+      value: 22.925
+    - type: map_at_3
+      value: 19.922
+    - type: map_at_5
+      value: 21.054000000000002
+    - type: mrr_at_1
+      value: 19.098000000000003
+    - type: mrr_at_10
+      value: 25.397
+    - type: mrr_at_100
+      value: 26.246000000000002
+    - type: mrr_at_1000
+      value: 26.33
+    - type: mrr_at_3
+      value: 23.469
+    - type: mrr_at_5
+      value: 24.646
+    - type: ndcg_at_1
+      value: 19.098000000000003
+    - type: ndcg_at_10
+      value: 25.807999999999996
+    - type: ndcg_at_100
+      value: 30.445
+    - type: ndcg_at_1000
+      value: 33.666000000000004
+    - type: ndcg_at_3
+      value: 22.292
+    - type: ndcg_at_5
+      value: 24.075
+    - type: precision_at_1
+      value: 19.098000000000003
+    - type: precision_at_10
+      value: 4.58
+    - type: precision_at_100
+      value: 0.8099999999999999
+    - type: precision_at_1000
+      value: 0.126
+    - type: precision_at_3
+      value: 10.346
+    - type: precision_at_5
+      value: 7.542999999999999
+    - type: recall_at_1
+      value: 15.845999999999998
+    - type: recall_at_10
+      value: 34.172999999999995
+    - type: recall_at_100
+      value: 55.24099999999999
+    - type: recall_at_1000
+      value: 78.644
+    - type: recall_at_3
+      value: 24.401
+    - type: recall_at_5
+      value: 28.938000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.974
+    - type: map_at_10
+      value: 30.108
+    - type: map_at_100
+      value: 31.208000000000002
+    - type: map_at_1000
+      value: 31.330999999999996
+    - type: map_at_3
+      value: 27.889999999999997
+    - type: map_at_5
+      value: 29.023
+    - type: mrr_at_1
+      value: 26.493
+    - type: mrr_at_10
+      value: 33.726
+    - type: mrr_at_100
+      value: 34.622
+    - type: mrr_at_1000
+      value: 34.703
+    - type: mrr_at_3
+      value: 31.575999999999997
+    - type: mrr_at_5
+      value: 32.690999999999995
+    - type: ndcg_at_1
+      value: 26.493
+    - type: ndcg_at_10
+      value: 34.664
+    - type: ndcg_at_100
+      value: 39.725
+    - type: ndcg_at_1000
+      value: 42.648
+    - type: ndcg_at_3
+      value: 30.447999999999997
+    - type: ndcg_at_5
+      value: 32.145
+    - type: precision_at_1
+      value: 26.493
+    - type: precision_at_10
+      value: 5.7090000000000005
+    - type: precision_at_100
+      value: 0.9199999999999999
+    - type: precision_at_1000
+      value: 0.129
+    - type: precision_at_3
+      value: 13.464
+    - type: precision_at_5
+      value: 9.384
+    - type: recall_at_1
+      value: 22.974
+    - type: recall_at_10
+      value: 45.097
+    - type: recall_at_100
+      value: 66.908
+    - type: recall_at_1000
+      value: 87.495
+    - type: recall_at_3
+      value: 33.338
+    - type: recall_at_5
+      value: 37.499
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.408
+    - type: map_at_10
+      value: 29.580000000000002
+    - type: map_at_100
+      value: 31.145
+    - type: map_at_1000
+      value: 31.369000000000003
+    - type: map_at_3
+      value: 27.634999999999998
+    - type: map_at_5
+      value: 28.766000000000002
+    - type: mrr_at_1
+      value: 27.272999999999996
+    - type: mrr_at_10
+      value: 33.93
+    - type: mrr_at_100
+      value: 34.963
+    - type: mrr_at_1000
+      value: 35.031
+    - type: mrr_at_3
+      value: 32.016
+    - type: mrr_at_5
+      value: 33.221000000000004
+    - type: ndcg_at_1
+      value: 27.272999999999996
+    - type: ndcg_at_10
+      value: 33.993
+    - type: ndcg_at_100
+      value: 40.333999999999996
+    - type: ndcg_at_1000
+      value: 43.361
+    - type: ndcg_at_3
+      value: 30.918
+    - type: ndcg_at_5
+      value: 32.552
+    - type: precision_at_1
+      value: 27.272999999999996
+    - type: precision_at_10
+      value: 6.285
+    - type: precision_at_100
+      value: 1.389
+    - type: precision_at_1000
+      value: 0.232
+    - type: precision_at_3
+      value: 14.427000000000001
+    - type: precision_at_5
+      value: 10.356
+    - type: recall_at_1
+      value: 22.408
+    - type: recall_at_10
+      value: 41.318
+    - type: recall_at_100
+      value: 70.539
+    - type: recall_at_1000
+      value: 90.197
+    - type: recall_at_3
+      value: 32.513
+    - type: recall_at_5
+      value: 37.0
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.258000000000003
+    - type: map_at_10
+      value: 24.294
+    - type: map_at_100
+      value: 25.305
+    - type: map_at_1000
+      value: 25.419999999999998
+    - type: map_at_3
+      value: 22.326999999999998
+    - type: map_at_5
+      value: 23.31
+    - type: mrr_at_1
+      value: 18.484
+    - type: mrr_at_10
+      value: 25.863999999999997
+    - type: mrr_at_100
+      value: 26.766000000000002
+    - type: mrr_at_1000
+      value: 26.855
+    - type: mrr_at_3
+      value: 23.968
+    - type: mrr_at_5
+      value: 24.911
+    - type: ndcg_at_1
+      value: 18.484
+    - type: ndcg_at_10
+      value: 28.433000000000003
+    - type: ndcg_at_100
+      value: 33.405
+    - type: ndcg_at_1000
+      value: 36.375
+    - type: ndcg_at_3
+      value: 24.455
+    - type: ndcg_at_5
+      value: 26.031
+    - type: precision_at_1
+      value: 18.484
+    - type: precision_at_10
+      value: 4.603
+    - type: precision_at_100
+      value: 0.773
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 10.659
+    - type: precision_at_5
+      value: 7.505000000000001
+    - type: recall_at_1
+      value: 17.258000000000003
+    - type: recall_at_10
+      value: 39.589999999999996
+    - type: recall_at_100
+      value: 62.592000000000006
+    - type: recall_at_1000
+      value: 84.917
+    - type: recall_at_3
+      value: 28.706
+    - type: recall_at_5
+      value: 32.224000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.578999999999999
+    - type: map_at_10
+      value: 17.642
+    - type: map_at_100
+      value: 19.451
+    - type: map_at_1000
+      value: 19.647000000000002
+    - type: map_at_3
+      value: 14.618
+    - type: map_at_5
+      value: 16.145
+    - type: mrr_at_1
+      value: 23.322000000000003
+    - type: mrr_at_10
+      value: 34.204
+    - type: mrr_at_100
+      value: 35.185
+    - type: mrr_at_1000
+      value: 35.235
+    - type: mrr_at_3
+      value: 30.847
+    - type: mrr_at_5
+      value: 32.824
+    - type: ndcg_at_1
+      value: 23.322000000000003
+    - type: ndcg_at_10
+      value: 25.352999999999998
+    - type: ndcg_at_100
+      value: 32.574
+    - type: ndcg_at_1000
+      value: 36.073
+    - type: ndcg_at_3
+      value: 20.318
+    - type: ndcg_at_5
+      value: 22.111
+    - type: precision_at_1
+      value: 23.322000000000003
+    - type: precision_at_10
+      value: 8.02
+    - type: precision_at_100
+      value: 1.5730000000000002
+    - type: precision_at_1000
+      value: 0.22200000000000003
+    - type: precision_at_3
+      value: 15.049000000000001
+    - type: precision_at_5
+      value: 11.87
+    - type: recall_at_1
+      value: 10.578999999999999
+    - type: recall_at_10
+      value: 30.964999999999996
+    - type: recall_at_100
+      value: 55.986000000000004
+    - type: recall_at_1000
+      value: 75.565
+    - type: recall_at_3
+      value: 18.686
+    - type: recall_at_5
+      value: 23.629
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 7.327
+    - type: map_at_10
+      value: 14.904
+    - type: map_at_100
+      value: 20.29
+    - type: map_at_1000
+      value: 21.42
+    - type: map_at_3
+      value: 10.911
+    - type: map_at_5
+      value: 12.791
+    - type: mrr_at_1
+      value: 57.25
+    - type: mrr_at_10
+      value: 66.62700000000001
+    - type: mrr_at_100
+      value: 67.035
+    - type: mrr_at_1000
+      value: 67.052
+    - type: mrr_at_3
+      value: 64.833
+    - type: mrr_at_5
+      value: 65.908
+    - type: ndcg_at_1
+      value: 43.75
+    - type: ndcg_at_10
+      value: 32.246
+    - type: ndcg_at_100
+      value: 35.774
+    - type: ndcg_at_1000
+      value: 42.872
+    - type: ndcg_at_3
+      value: 36.64
+    - type: ndcg_at_5
+      value: 34.487
+    - type: precision_at_1
+      value: 57.25
+    - type: precision_at_10
+      value: 25.924999999999997
+    - type: precision_at_100
+      value: 7.670000000000001
+    - type: precision_at_1000
+      value: 1.599
+    - type: precision_at_3
+      value: 41.167
+    - type: precision_at_5
+      value: 34.65
+    - type: recall_at_1
+      value: 7.327
+    - type: recall_at_10
+      value: 19.625
+    - type: recall_at_100
+      value: 41.601
+    - type: recall_at_1000
+      value: 65.117
+    - type: recall_at_3
+      value: 12.308
+    - type: recall_at_5
+      value: 15.437999999999999
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 44.53
+    - type: f1
+      value: 39.39884255816736
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 58.913000000000004
+    - type: map_at_10
+      value: 69.592
+    - type: map_at_100
+      value: 69.95599999999999
+    - type: map_at_1000
+      value: 69.973
+    - type: map_at_3
+      value: 67.716
+    - type: map_at_5
+      value: 68.899
+    - type: mrr_at_1
+      value: 63.561
+    - type: mrr_at_10
+      value: 74.2
+    - type: mrr_at_100
+      value: 74.468
+    - type: mrr_at_1000
+      value: 74.47500000000001
+    - type: mrr_at_3
+      value: 72.442
+    - type: mrr_at_5
+      value: 73.58
+    - type: ndcg_at_1
+      value: 63.561
+    - type: ndcg_at_10
+      value: 74.988
+    - type: ndcg_at_100
+      value: 76.52799999999999
+    - type: ndcg_at_1000
+      value: 76.88000000000001
+    - type: ndcg_at_3
+      value: 71.455
+    - type: ndcg_at_5
+      value: 73.42699999999999
+    - type: precision_at_1
+      value: 63.561
+    - type: precision_at_10
+      value: 9.547
+    - type: precision_at_100
+      value: 1.044
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 28.143
+    - type: precision_at_5
+      value: 18.008
+    - type: recall_at_1
+      value: 58.913000000000004
+    - type: recall_at_10
+      value: 87.18
+    - type: recall_at_100
+      value: 93.852
+    - type: recall_at_1000
+      value: 96.256
+    - type: recall_at_3
+      value: 77.55199999999999
+    - type: recall_at_5
+      value: 82.42399999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 11.761000000000001
+    - type: map_at_10
+      value: 19.564999999999998
+    - type: map_at_100
+      value: 21.099
+    - type: map_at_1000
+      value: 21.288999999999998
+    - type: map_at_3
+      value: 16.683999999999997
+    - type: map_at_5
+      value: 18.307000000000002
+    - type: mrr_at_1
+      value: 23.302
+    - type: mrr_at_10
+      value: 30.979
+    - type: mrr_at_100
+      value: 32.121
+    - type: mrr_at_1000
+      value: 32.186
+    - type: mrr_at_3
+      value: 28.549000000000003
+    - type: mrr_at_5
+      value: 30.038999999999998
+    - type: ndcg_at_1
+      value: 23.302
+    - type: ndcg_at_10
+      value: 25.592
+    - type: ndcg_at_100
+      value: 32.416
+    - type: ndcg_at_1000
+      value: 36.277
+    - type: ndcg_at_3
+      value: 22.151
+    - type: ndcg_at_5
+      value: 23.483999999999998
+    - type: precision_at_1
+      value: 23.302
+    - type: precision_at_10
+      value: 7.377000000000001
+    - type: precision_at_100
+      value: 1.415
+    - type: precision_at_1000
+      value: 0.212
+    - type: precision_at_3
+      value: 14.712
+    - type: precision_at_5
+      value: 11.358
+    - type: recall_at_1
+      value: 11.761000000000001
+    - type: recall_at_10
+      value: 31.696
+    - type: recall_at_100
+      value: 58.01500000000001
+    - type: recall_at_1000
+      value: 81.572
+    - type: recall_at_3
+      value: 20.742
+    - type: recall_at_5
+      value: 25.707
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.275
+    - type: map_at_10
+      value: 44.712
+    - type: map_at_100
+      value: 45.621
+    - type: map_at_1000
+      value: 45.698
+    - type: map_at_3
+      value: 42.016999999999996
+    - type: map_at_5
+      value: 43.659
+    - type: mrr_at_1
+      value: 64.551
+    - type: mrr_at_10
+      value: 71.58099999999999
+    - type: mrr_at_100
+      value: 71.952
+    - type: mrr_at_1000
+      value: 71.96900000000001
+    - type: mrr_at_3
+      value: 70.236
+    - type: mrr_at_5
+      value: 71.051
+    - type: ndcg_at_1
+      value: 64.551
+    - type: ndcg_at_10
+      value: 53.913999999999994
+    - type: ndcg_at_100
+      value: 57.421
+    - type: ndcg_at_1000
+      value: 59.06
+    - type: ndcg_at_3
+      value: 49.716
+    - type: ndcg_at_5
+      value: 51.971999999999994
+    - type: precision_at_1
+      value: 64.551
+    - type: precision_at_10
+      value: 11.110000000000001
+    - type: precision_at_100
+      value: 1.388
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 30.822
+    - type: precision_at_5
+      value: 20.273
+    - type: recall_at_1
+      value: 32.275
+    - type: recall_at_10
+      value: 55.55
+    - type: recall_at_100
+      value: 69.38600000000001
+    - type: recall_at_1000
+      value: 80.35799999999999
+    - type: recall_at_3
+      value: 46.232
+    - type: recall_at_5
+      value: 50.682
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 76.4604
+    - type: ap
+      value: 70.40498168422701
+    - type: f1
+      value: 76.38572688476046
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.065999999999999
+    - type: map_at_10
+      value: 25.058000000000003
+    - type: map_at_100
+      value: 26.268
+    - type: map_at_1000
+      value: 26.344
+    - type: map_at_3
+      value: 21.626
+    - type: map_at_5
+      value: 23.513
+    - type: mrr_at_1
+      value: 15.501000000000001
+    - type: mrr_at_10
+      value: 25.548
+    - type: mrr_at_100
+      value: 26.723000000000003
+    - type: mrr_at_1000
+      value: 26.793
+    - type: mrr_at_3
+      value: 22.142
+    - type: mrr_at_5
+      value: 24.024
+    - type: ndcg_at_1
+      value: 15.501000000000001
+    - type: ndcg_at_10
+      value: 31.008000000000003
+    - type: ndcg_at_100
+      value: 37.08
+    - type: ndcg_at_1000
+      value: 39.102
+    - type: ndcg_at_3
+      value: 23.921999999999997
+    - type: ndcg_at_5
+      value: 27.307
+    - type: precision_at_1
+      value: 15.501000000000001
+    - type: precision_at_10
+      value: 5.155
+    - type: precision_at_100
+      value: 0.822
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 10.363
+    - type: precision_at_5
+      value: 7.917000000000001
+    - type: recall_at_1
+      value: 15.065999999999999
+    - type: recall_at_10
+      value: 49.507
+    - type: recall_at_100
+      value: 78.118
+    - type: recall_at_1000
+      value: 93.881
+    - type: recall_at_3
+      value: 30.075000000000003
+    - type: recall_at_5
+      value: 38.222
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 90.6703146374829
+    - type: f1
+      value: 90.1258004293966
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 68.29229366165072
+    - type: f1
+      value: 50.016194478997875
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 68.57767316745124
+    - type: f1
+      value: 67.16194062146954
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 73.92064559515804
+    - type: f1
+      value: 73.6680729569968
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 31.56335607367883
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.131807833734268
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.07390328719844
+    - type: mrr
+      value: 32.117370992867905
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.274
+    - type: map_at_10
+      value: 11.489
+    - type: map_at_100
+      value: 14.518
+    - type: map_at_1000
+      value: 15.914
+    - type: map_at_3
+      value: 8.399
+    - type: map_at_5
+      value: 9.889000000000001
+    - type: mrr_at_1
+      value: 42.724000000000004
+    - type: mrr_at_10
+      value: 51.486
+    - type: mrr_at_100
+      value: 51.941
+    - type: mrr_at_1000
+      value: 51.99
+    - type: mrr_at_3
+      value: 49.278
+    - type: mrr_at_5
+      value: 50.485
+    - type: ndcg_at_1
+      value: 39.938
+    - type: ndcg_at_10
+      value: 31.862000000000002
+    - type: ndcg_at_100
+      value: 29.235
+    - type: ndcg_at_1000
+      value: 37.802
+    - type: ndcg_at_3
+      value: 35.754999999999995
+    - type: ndcg_at_5
+      value: 34.447
+    - type: precision_at_1
+      value: 42.105
+    - type: precision_at_10
+      value: 23.901
+    - type: precision_at_100
+      value: 7.715
+    - type: precision_at_1000
+      value: 2.045
+    - type: precision_at_3
+      value: 33.437
+    - type: precision_at_5
+      value: 29.782999999999998
+    - type: recall_at_1
+      value: 5.274
+    - type: recall_at_10
+      value: 15.351
+    - type: recall_at_100
+      value: 29.791
+    - type: recall_at_1000
+      value: 60.722
+    - type: recall_at_3
+      value: 9.411
+    - type: recall_at_5
+      value: 12.171999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.099
+    - type: map_at_10
+      value: 27.913
+    - type: map_at_100
+      value: 29.281000000000002
+    - type: map_at_1000
+      value: 29.343999999999998
+    - type: map_at_3
+      value: 23.791
+    - type: map_at_5
+      value: 26.049
+    - type: mrr_at_1
+      value: 18.337
+    - type: mrr_at_10
+      value: 29.953999999999997
+    - type: mrr_at_100
+      value: 31.080999999999996
+    - type: mrr_at_1000
+      value: 31.130000000000003
+    - type: mrr_at_3
+      value: 26.168000000000003
+    - type: mrr_at_5
+      value: 28.277
+    - type: ndcg_at_1
+      value: 18.308
+    - type: ndcg_at_10
+      value: 34.938
+    - type: ndcg_at_100
+      value: 41.125
+    - type: ndcg_at_1000
+      value: 42.708
+    - type: ndcg_at_3
+      value: 26.805
+    - type: ndcg_at_5
+      value: 30.686999999999998
+    - type: precision_at_1
+      value: 18.308
+    - type: precision_at_10
+      value: 6.476999999999999
+    - type: precision_at_100
+      value: 0.9939999999999999
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 12.784999999999998
+    - type: precision_at_5
+      value: 9.878
+    - type: recall_at_1
+      value: 16.099
+    - type: recall_at_10
+      value: 54.63
+    - type: recall_at_100
+      value: 82.24900000000001
+    - type: recall_at_1000
+      value: 94.242
+    - type: recall_at_3
+      value: 33.174
+    - type: recall_at_5
+      value: 42.164
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 67.947
+    - type: map_at_10
+      value: 81.499
+    - type: map_at_100
+      value: 82.17
+    - type: map_at_1000
+      value: 82.194
+    - type: map_at_3
+      value: 78.567
+    - type: map_at_5
+      value: 80.34400000000001
+    - type: mrr_at_1
+      value: 78.18
+    - type: mrr_at_10
+      value: 85.05
+    - type: mrr_at_100
+      value: 85.179
+    - type: mrr_at_1000
+      value: 85.181
+    - type: mrr_at_3
+      value: 83.91
+    - type: mrr_at_5
+      value: 84.638
+    - type: ndcg_at_1
+      value: 78.2
+    - type: ndcg_at_10
+      value: 85.715
+    - type: ndcg_at_100
+      value: 87.2
+    - type: ndcg_at_1000
+      value: 87.39
+    - type: ndcg_at_3
+      value: 82.572
+    - type: ndcg_at_5
+      value: 84.176
+    - type: precision_at_1
+      value: 78.2
+    - type: precision_at_10
+      value: 12.973
+    - type: precision_at_100
+      value: 1.5010000000000001
+    - type: precision_at_1000
+      value: 0.156
+    - type: precision_at_3
+      value: 35.949999999999996
+    - type: precision_at_5
+      value: 23.62
+    - type: recall_at_1
+      value: 67.947
+    - type: recall_at_10
+      value: 93.804
+    - type: recall_at_100
+      value: 98.971
+    - type: recall_at_1000
+      value: 99.91600000000001
+    - type: recall_at_3
+      value: 84.75399999999999
+    - type: recall_at_5
+      value: 89.32
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 45.457201684255104
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 55.162226937477875
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.173
+    - type: map_at_10
+      value: 10.463000000000001
+    - type: map_at_100
+      value: 12.278
+    - type: map_at_1000
+      value: 12.572
+    - type: map_at_3
+      value: 7.528
+    - type: map_at_5
+      value: 8.863
+    - type: mrr_at_1
+      value: 20.599999999999998
+    - type: mrr_at_10
+      value: 30.422
+    - type: mrr_at_100
+      value: 31.6
+    - type: mrr_at_1000
+      value: 31.663000000000004
+    - type: mrr_at_3
+      value: 27.400000000000002
+    - type: mrr_at_5
+      value: 29.065
+    - type: ndcg_at_1
+      value: 20.599999999999998
+    - type: ndcg_at_10
+      value: 17.687
+    - type: ndcg_at_100
+      value: 25.172
+    - type: ndcg_at_1000
+      value: 30.617
+    - type: ndcg_at_3
+      value: 16.81
+    - type: ndcg_at_5
+      value: 14.499
+    - type: precision_at_1
+      value: 20.599999999999998
+    - type: precision_at_10
+      value: 9.17
+    - type: precision_at_100
+      value: 2.004
+    - type: precision_at_1000
+      value: 0.332
+    - type: precision_at_3
+      value: 15.6
+    - type: precision_at_5
+      value: 12.58
+    - type: recall_at_1
+      value: 4.173
+    - type: recall_at_10
+      value: 18.575
+    - type: recall_at_100
+      value: 40.692
+    - type: recall_at_1000
+      value: 67.467
+    - type: recall_at_3
+      value: 9.488000000000001
+    - type: recall_at_5
+      value: 12.738
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.12603499315416
+    - type: cos_sim_spearman
+      value: 73.62060290948378
+    - type: euclidean_pearson
+      value: 78.14083565781135
+    - type: euclidean_spearman
+      value: 73.16840437541543
+    - type: manhattan_pearson
+      value: 77.92017261109734
+    - type: manhattan_spearman
+      value: 72.8805059949965
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.75955377133172
+    - type: cos_sim_spearman
+      value: 71.8872633964069
+    - type: euclidean_pearson
+      value: 76.31922068538256
+    - type: euclidean_spearman
+      value: 70.86449661855376
+    - type: manhattan_pearson
+      value: 76.47852229730407
+    - type: manhattan_spearman
+      value: 70.99367421984789
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 78.80762722908158
+    - type: cos_sim_spearman
+      value: 79.84588978756372
+    - type: euclidean_pearson
+      value: 79.8216849781164
+    - type: euclidean_spearman
+      value: 80.22647061695481
+    - type: manhattan_pearson
+      value: 79.56604194112572
+    - type: manhattan_spearman
+      value: 79.96495189862462
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.1012718092742
+    - type: cos_sim_spearman
+      value: 76.86011381793661
+    - type: euclidean_pearson
+      value: 79.94426039862019
+    - type: euclidean_spearman
+      value: 77.36751135465131
+    - type: manhattan_pearson
+      value: 79.87959373304288
+    - type: manhattan_spearman
+      value: 77.37717129004746
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.90618420346104
+    - type: cos_sim_spearman
+      value: 84.77290791243722
+    - type: euclidean_pearson
+      value: 84.64732258073293
+    - type: euclidean_spearman
+      value: 85.21053649543357
+    - type: manhattan_pearson
+      value: 84.61616883522647
+    - type: manhattan_spearman
+      value: 85.19803126766931
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.52192114059063
+    - type: cos_sim_spearman
+      value: 81.9103244827937
+    - type: euclidean_pearson
+      value: 80.99375176138985
+    - type: euclidean_spearman
+      value: 81.540250641079
+    - type: manhattan_pearson
+      value: 80.84979573396426
+    - type: manhattan_spearman
+      value: 81.3742591621492
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.82166001234197
+    - type: cos_sim_spearman
+      value: 86.81857495659123
+    - type: euclidean_pearson
+      value: 85.72798403202849
+    - type: euclidean_spearman
+      value: 85.70482438950965
+    - type: manhattan_pearson
+      value: 85.51579093130357
+    - type: manhattan_spearman
+      value: 85.41233705379751
+  - 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: 64.48071151079803
+    - type: cos_sim_spearman
+      value: 65.37838108084044
+    - type: euclidean_pearson
+      value: 64.67378947096257
+    - type: euclidean_spearman
+      value: 65.39187147219869
+    - type: manhattan_pearson
+      value: 65.35487466133208
+    - type: manhattan_spearman
+      value: 65.51328499442272
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.64702367823314
+    - type: cos_sim_spearman
+      value: 82.49732953181818
+    - type: euclidean_pearson
+      value: 83.05996062475664
+    - type: euclidean_spearman
+      value: 82.28159546751176
+    - type: manhattan_pearson
+      value: 82.98305503664952
+    - type: manhattan_spearman
+      value: 82.18405771943928
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 78.5744649318696
+    - type: mrr
+      value: 93.35386291268645
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 52.093999999999994
+    - type: map_at_10
+      value: 61.646
+    - type: map_at_100
+      value: 62.197
+    - type: map_at_1000
+      value: 62.22800000000001
+    - type: map_at_3
+      value: 58.411
+    - type: map_at_5
+      value: 60.585
+    - type: mrr_at_1
+      value: 55.00000000000001
+    - type: mrr_at_10
+      value: 62.690999999999995
+    - type: mrr_at_100
+      value: 63.139
+    - type: mrr_at_1000
+      value: 63.166999999999994
+    - type: mrr_at_3
+      value: 60.111000000000004
+    - type: mrr_at_5
+      value: 61.778
+    - type: ndcg_at_1
+      value: 55.00000000000001
+    - type: ndcg_at_10
+      value: 66.271
+    - type: ndcg_at_100
+      value: 68.879
+    - type: ndcg_at_1000
+      value: 69.722
+    - type: ndcg_at_3
+      value: 60.672000000000004
+    - type: ndcg_at_5
+      value: 63.929
+    - type: precision_at_1
+      value: 55.00000000000001
+    - type: precision_at_10
+      value: 9.0
+    - type: precision_at_100
+      value: 1.043
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 23.555999999999997
+    - type: precision_at_5
+      value: 16.2
+    - type: recall_at_1
+      value: 52.093999999999994
+    - type: recall_at_10
+      value: 79.567
+    - type: recall_at_100
+      value: 91.60000000000001
+    - type: recall_at_1000
+      value: 98.333
+    - type: recall_at_3
+      value: 64.633
+    - type: recall_at_5
+      value: 72.68299999999999
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.83267326732673
+    - type: cos_sim_ap
+      value: 95.77995366495178
+    - type: cos_sim_f1
+      value: 91.51180311401306
+    - type: cos_sim_precision
+      value: 91.92734611503532
+    - type: cos_sim_recall
+      value: 91.10000000000001
+    - type: dot_accuracy
+      value: 99.63366336633663
+    - type: dot_ap
+      value: 88.53996286967461
+    - type: dot_f1
+      value: 81.06537530266343
+    - type: dot_precision
+      value: 78.59154929577464
+    - type: dot_recall
+      value: 83.7
+    - type: euclidean_accuracy
+      value: 99.82376237623762
+    - type: euclidean_ap
+      value: 95.53192209281187
+    - type: euclidean_f1
+      value: 91.19683481701286
+    - type: euclidean_precision
+      value: 90.21526418786692
+    - type: euclidean_recall
+      value: 92.2
+    - type: manhattan_accuracy
+      value: 99.82376237623762
+    - type: manhattan_ap
+      value: 95.55642082191741
+    - type: manhattan_f1
+      value: 91.16186693147964
+    - type: manhattan_precision
+      value: 90.53254437869822
+    - type: manhattan_recall
+      value: 91.8
+    - type: max_accuracy
+      value: 99.83267326732673
+    - type: max_ap
+      value: 95.77995366495178
+    - type: max_f1
+      value: 91.51180311401306
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 54.508462134213474
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.06549765184959
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 49.43129549466616
+    - type: mrr
+      value: 50.20613169510227
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.069516173193044
+    - type: cos_sim_spearman
+      value: 29.872498354017353
+    - type: dot_pearson
+      value: 28.80761257516063
+    - type: dot_spearman
+      value: 28.397422678527708
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.169
+    - type: map_at_10
+      value: 1.208
+    - type: map_at_100
+      value: 5.925
+    - type: map_at_1000
+      value: 14.427000000000001
+    - type: map_at_3
+      value: 0.457
+    - type: map_at_5
+      value: 0.716
+    - type: mrr_at_1
+      value: 64.0
+    - type: mrr_at_10
+      value: 74.075
+    - type: mrr_at_100
+      value: 74.303
+    - type: mrr_at_1000
+      value: 74.303
+    - type: mrr_at_3
+      value: 71.0
+    - type: mrr_at_5
+      value: 72.89999999999999
+    - type: ndcg_at_1
+      value: 57.99999999999999
+    - type: ndcg_at_10
+      value: 50.376
+    - type: ndcg_at_100
+      value: 38.582
+    - type: ndcg_at_1000
+      value: 35.663
+    - type: ndcg_at_3
+      value: 55.592
+    - type: ndcg_at_5
+      value: 53.647999999999996
+    - type: precision_at_1
+      value: 64.0
+    - type: precision_at_10
+      value: 53.2
+    - type: precision_at_100
+      value: 39.6
+    - type: precision_at_1000
+      value: 16.218
+    - type: precision_at_3
+      value: 59.333000000000006
+    - type: precision_at_5
+      value: 57.599999999999994
+    - type: recall_at_1
+      value: 0.169
+    - type: recall_at_10
+      value: 1.423
+    - type: recall_at_100
+      value: 9.049999999999999
+    - type: recall_at_1000
+      value: 34.056999999999995
+    - type: recall_at_3
+      value: 0.48700000000000004
+    - type: recall_at_5
+      value: 0.792
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.319
+    - type: map_at_10
+      value: 7.112
+    - type: map_at_100
+      value: 12.588
+    - type: map_at_1000
+      value: 14.056
+    - type: map_at_3
+      value: 2.8049999999999997
+    - type: map_at_5
+      value: 4.68
+    - type: mrr_at_1
+      value: 18.367
+    - type: mrr_at_10
+      value: 33.94
+    - type: mrr_at_100
+      value: 35.193000000000005
+    - type: mrr_at_1000
+      value: 35.193000000000005
+    - type: mrr_at_3
+      value: 29.932
+    - type: mrr_at_5
+      value: 32.279
+    - type: ndcg_at_1
+      value: 15.306000000000001
+    - type: ndcg_at_10
+      value: 18.096
+    - type: ndcg_at_100
+      value: 30.512
+    - type: ndcg_at_1000
+      value: 42.148
+    - type: ndcg_at_3
+      value: 17.034
+    - type: ndcg_at_5
+      value: 18.509
+    - type: precision_at_1
+      value: 18.367
+    - type: precision_at_10
+      value: 18.776
+    - type: precision_at_100
+      value: 7.02
+    - type: precision_at_1000
+      value: 1.467
+    - type: precision_at_3
+      value: 19.048000000000002
+    - type: precision_at_5
+      value: 22.041
+    - type: recall_at_1
+      value: 1.319
+    - type: recall_at_10
+      value: 13.748
+    - type: recall_at_100
+      value: 43.972
+    - type: recall_at_1000
+      value: 79.557
+    - type: recall_at_3
+      value: 4.042
+    - type: recall_at_5
+      value: 7.742
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.2282
+    - type: ap
+      value: 13.995763859570426
+    - type: f1
+      value: 54.08126256731344
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 57.64006791171477
+    - type: f1
+      value: 57.95841320748957
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 40.19267841788564
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 83.96614412588663
+    - type: cos_sim_ap
+      value: 67.75985678572738
+    - type: cos_sim_f1
+      value: 64.04661542276222
+    - type: cos_sim_precision
+      value: 60.406922357343305
+    - type: cos_sim_recall
+      value: 68.15303430079156
+    - type: dot_accuracy
+      value: 79.5732252488526
+    - type: dot_ap
+      value: 51.30562107572645
+    - type: dot_f1
+      value: 53.120759837177744
+    - type: dot_precision
+      value: 46.478037198258804
+    - type: dot_recall
+      value: 61.97889182058047
+    - type: euclidean_accuracy
+      value: 84.00786791440663
+    - type: euclidean_ap
+      value: 67.58930214486998
+    - type: euclidean_f1
+      value: 64.424821579775
+    - type: euclidean_precision
+      value: 59.4817958454322
+    - type: euclidean_recall
+      value: 70.26385224274406
+    - type: manhattan_accuracy
+      value: 83.87673600762949
+    - type: manhattan_ap
+      value: 67.4250981523309
+    - type: manhattan_f1
+      value: 64.10286658015808
+    - type: manhattan_precision
+      value: 57.96885001066781
+    - type: manhattan_recall
+      value: 71.68865435356201
+    - type: max_accuracy
+      value: 84.00786791440663
+    - type: max_ap
+      value: 67.75985678572738
+    - type: max_f1
+      value: 64.424821579775
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.41347459929368
+    - type: cos_sim_ap
+      value: 84.89261930113058
+    - type: cos_sim_f1
+      value: 77.13677607258877
+    - type: cos_sim_precision
+      value: 74.88581164358733
+    - type: cos_sim_recall
+      value: 79.52725592854944
+    - type: dot_accuracy
+      value: 86.32359219156285
+    - type: dot_ap
+      value: 79.29794992131094
+    - type: dot_f1
+      value: 72.84356337679777
+    - type: dot_precision
+      value: 67.31761478675462
+    - type: dot_recall
+      value: 79.35786880197105
+    - type: euclidean_accuracy
+      value: 88.33585593976791
+    - type: euclidean_ap
+      value: 84.73257641312746
+    - type: euclidean_f1
+      value: 76.83529582788195
+    - type: euclidean_precision
+      value: 72.76294052863436
+    - type: euclidean_recall
+      value: 81.3905143209116
+    - type: manhattan_accuracy
+      value: 88.3086894089339
+    - type: manhattan_ap
+      value: 84.66304891729399
+    - type: manhattan_f1
+      value: 76.8181650632165
+    - type: manhattan_precision
+      value: 73.6864436744219
+    - type: manhattan_recall
+      value: 80.22790267939637
+    - type: max_accuracy
+      value: 88.41347459929368
+    - type: max_ap
+      value: 84.89261930113058
+    - type: max_f1
+      value: 77.13677607258877
+---
+
+# bge-micro-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+Distilled in a 2-step training process (bge-micro was step 1) from `BAAI/bge-small-en-v1.5`.
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

snapshot/best/2_MixtureEmbeddingsModel/expert_00_TaylorAI_bge-micro-v2/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "/home/jupyter-wb536061/.cache/torch/sentence_transformers/TaylorAI_bge-micro-v2/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

snapshot/best/2_MixtureEmbeddingsModel/expert_00_TaylorAI_bge-micro-v2/config_sentence_transformers.json

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snapshot/best/2_MixtureEmbeddingsModel/expert_00_TaylorAI_bge-micro-v2/sentence_bert_config.json

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snapshot/best/2_MixtureEmbeddingsModel/expert_00_TaylorAI_bge-micro-v2/tokenizer_config.json

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snapshot/best/2_MixtureEmbeddingsModel/expert_01_khoa-klaytn_bge-small-en-v1.5-angle/1_Pooling/config.json

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snapshot/best/2_MixtureEmbeddingsModel/expert_01_khoa-klaytn_bge-small-en-v1.5-angle/README.md

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+---
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- mteb
+model-index:
+- name: bge-small-en-v1.5-angle
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 73.79104477611939
+    - type: ap
+      value: 37.21923821573361
+    - type: f1
+      value: 68.0914945617093
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 92.75377499999999
+    - type: ap
+      value: 89.46766124546022
+    - type: f1
+      value: 92.73884001331487
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 46.986
+    - type: f1
+      value: 46.55936786727896
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 35.846000000000004
+    - type: map_at_10
+      value: 51.388
+    - type: map_at_100
+      value: 52.132999999999996
+    - type: map_at_1000
+      value: 52.141000000000005
+    - type: map_at_3
+      value: 47.037
+    - type: map_at_5
+      value: 49.579
+    - type: mrr_at_1
+      value: 36.558
+    - type: mrr_at_10
+      value: 51.658
+    - type: mrr_at_100
+      value: 52.402
+    - type: mrr_at_1000
+      value: 52.410000000000004
+    - type: mrr_at_3
+      value: 47.345
+    - type: mrr_at_5
+      value: 49.797999999999995
+    - type: ndcg_at_1
+      value: 35.846000000000004
+    - type: ndcg_at_10
+      value: 59.550000000000004
+    - type: ndcg_at_100
+      value: 62.596
+    - type: ndcg_at_1000
+      value: 62.759
+    - type: ndcg_at_3
+      value: 50.666999999999994
+    - type: ndcg_at_5
+      value: 55.228
+    - type: precision_at_1
+      value: 35.846000000000004
+    - type: precision_at_10
+      value: 8.542
+    - type: precision_at_100
+      value: 0.984
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.389
+    - type: precision_at_5
+      value: 14.438
+    - type: recall_at_1
+      value: 35.846000000000004
+    - type: recall_at_10
+      value: 85.42
+    - type: recall_at_100
+      value: 98.43499999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 61.166
+    - type: recall_at_5
+      value: 72.191
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.402770198163594
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 40.01545436974177
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.586465273207196
+    - type: mrr
+      value: 74.42169019038825
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.1891186537969
+    - type: cos_sim_spearman
+      value: 83.75492046087288
+    - type: euclidean_pearson
+      value: 84.11766204805357
+    - type: euclidean_spearman
+      value: 84.01456493126516
+    - type: manhattan_pearson
+      value: 84.2132950502772
+    - type: manhattan_spearman
+      value: 83.89227298813377
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.74025974025975
+    - type: f1
+      value: 85.71493566466381
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 38.467181385006434
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 34.719496037339056
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.587000000000003
+    - type: map_at_10
+      value: 41.114
+    - type: map_at_100
+      value: 42.532
+    - type: map_at_1000
+      value: 42.661
+    - type: map_at_3
+      value: 37.483
+    - type: map_at_5
+      value: 39.652
+    - type: mrr_at_1
+      value: 36.338
+    - type: mrr_at_10
+      value: 46.763
+    - type: mrr_at_100
+      value: 47.393
+    - type: mrr_at_1000
+      value: 47.445
+    - type: mrr_at_3
+      value: 43.538
+    - type: mrr_at_5
+      value: 45.556000000000004
+    - type: ndcg_at_1
+      value: 36.338
+    - type: ndcg_at_10
+      value: 47.658
+    - type: ndcg_at_100
+      value: 52.824000000000005
+    - type: ndcg_at_1000
+      value: 54.913999999999994
+    - type: ndcg_at_3
+      value: 41.989
+    - type: ndcg_at_5
+      value: 44.944
+    - type: precision_at_1
+      value: 36.338
+    - type: precision_at_10
+      value: 9.156
+    - type: precision_at_100
+      value: 1.4789999999999999
+    - type: precision_at_1000
+      value: 0.196
+    - type: precision_at_3
+      value: 20.076
+    - type: precision_at_5
+      value: 14.85
+    - type: recall_at_1
+      value: 29.587000000000003
+    - type: recall_at_10
+      value: 60.746
+    - type: recall_at_100
+      value: 82.157
+    - type: recall_at_1000
+      value: 95.645
+    - type: recall_at_3
+      value: 44.821
+    - type: recall_at_5
+      value: 52.819
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.239
+    - type: map_at_10
+      value: 39.989000000000004
+    - type: map_at_100
+      value: 41.196
+    - type: map_at_1000
+      value: 41.325
+    - type: map_at_3
+      value: 37.261
+    - type: map_at_5
+      value: 38.833
+    - type: mrr_at_1
+      value: 37.516
+    - type: mrr_at_10
+      value: 46.177
+    - type: mrr_at_100
+      value: 46.806
+    - type: mrr_at_1000
+      value: 46.849000000000004
+    - type: mrr_at_3
+      value: 44.002
+    - type: mrr_at_5
+      value: 45.34
+    - type: ndcg_at_1
+      value: 37.516
+    - type: ndcg_at_10
+      value: 45.586
+    - type: ndcg_at_100
+      value: 49.897000000000006
+    - type: ndcg_at_1000
+      value: 51.955
+    - type: ndcg_at_3
+      value: 41.684
+    - type: ndcg_at_5
+      value: 43.617
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+      value: 37.516
+    - type: precision_at_10
+      value: 8.522
+    - type: precision_at_100
+      value: 1.374
+    - type: precision_at_1000
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+    - type: precision_at_3
+      value: 20.105999999999998
+    - type: precision_at_5
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+    - type: recall_at_1
+      value: 30.239
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+    - type: recall_at_100
+      value: 73.375
+    - type: recall_at_1000
+      value: 86.29599999999999
+    - type: recall_at_3
+      value: 43.269000000000005
+    - type: recall_at_5
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+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.338
+    - type: map_at_10
+      value: 50.468999999999994
+    - type: map_at_100
+      value: 51.553000000000004
+    - type: map_at_1000
+      value: 51.608
+    - type: map_at_3
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+    - type: map_at_5
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+    - type: mrr_at_1
+      value: 44.201
+    - type: mrr_at_10
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+    - type: mrr_at_100
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+    - type: mrr_at_1000
+      value: 54.791000000000004
+    - type: mrr_at_3
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+    - type: mrr_at_5
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+    - type: ndcg_at_1
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+    - type: ndcg_at_100
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+    - type: precision_at_100
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+    - type: precision_at_1000
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+    - type: precision_at_3
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+    - type: precision_at_5
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+    - type: recall_at_1
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+    - type: recall_at_3
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+    - type: recall_at_5
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+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.682
+    - type: map_at_10
+      value: 33.498
+    - type: map_at_100
+      value: 34.461000000000006
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+    - type: mrr_at_1
+      value: 27.683999999999997
+    - type: mrr_at_10
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+    - type: mrr_at_100
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+    - type: mrr_at_3
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+    - type: ndcg_at_3
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+    - type: recall_at_5
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+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.073999999999998
+    - type: map_at_10
+      value: 24.352999999999998
+    - type: map_at_100
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+    - type: mrr_at_10
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+    - type: mrr_at_100
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+      value: 19.776
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+      value: 29.701
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+      value: 35.307
+    - type: ndcg_at_1000
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+      value: 0.133
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+      value: 12.065
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+      value: 8.905000000000001
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+      value: 16.073999999999998
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+      value: 41.647
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+      value: 66.884
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+      value: 85.91499999999999
+    - type: recall_at_3
+      value: 27.916
+    - type: recall_at_5
+      value: 33.729
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.444999999999997
+    - type: map_at_10
+      value: 38.218999999999994
+    - type: map_at_100
+      value: 39.595
+    - type: map_at_1000
+      value: 39.709
+    - type: map_at_3
+      value: 35.586
+    - type: map_at_5
+      value: 36.895
+    - type: mrr_at_1
+      value: 34.841
+    - type: mrr_at_10
+      value: 44.106
+    - type: mrr_at_100
+      value: 44.98
+    - type: mrr_at_1000
+      value: 45.03
+    - type: mrr_at_3
+      value: 41.979
+    - type: mrr_at_5
+      value: 43.047999999999995
+    - type: ndcg_at_1
+      value: 34.841
+    - type: ndcg_at_10
+      value: 43.922
+    - type: ndcg_at_100
+      value: 49.504999999999995
+    - type: ndcg_at_1000
+      value: 51.675000000000004
+    - type: ndcg_at_3
+      value: 39.858
+    - type: ndcg_at_5
+      value: 41.408
+    - type: precision_at_1
+      value: 34.841
+    - type: precision_at_10
+      value: 7.872999999999999
+    - type: precision_at_100
+      value: 1.2449999999999999
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 18.993
+    - type: precision_at_5
+      value: 13.032
+    - type: recall_at_1
+      value: 28.444999999999997
+    - type: recall_at_10
+      value: 54.984
+    - type: recall_at_100
+      value: 78.342
+    - type: recall_at_1000
+      value: 92.77
+    - type: recall_at_3
+      value: 42.842999999999996
+    - type: recall_at_5
+      value: 47.247
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.072
+    - type: map_at_10
+      value: 32.354
+    - type: map_at_100
+      value: 33.800000000000004
+    - type: map_at_1000
+      value: 33.908
+    - type: map_at_3
+      value: 29.232000000000003
+    - type: map_at_5
+      value: 31.049
+    - type: mrr_at_1
+      value: 29.110000000000003
+    - type: mrr_at_10
+      value: 38.03
+    - type: mrr_at_100
+      value: 39.032
+    - type: mrr_at_1000
+      value: 39.086999999999996
+    - type: mrr_at_3
+      value: 35.407
+    - type: mrr_at_5
+      value: 36.76
+    - type: ndcg_at_1
+      value: 29.110000000000003
+    - type: ndcg_at_10
+      value: 38.231
+    - type: ndcg_at_100
+      value: 44.425
+    - type: ndcg_at_1000
+      value: 46.771
+    - type: ndcg_at_3
+      value: 33.095
+    - type: ndcg_at_5
+      value: 35.459
+    - type: precision_at_1
+      value: 29.110000000000003
+    - type: precision_at_10
+      value: 7.215000000000001
+    - type: precision_at_100
+      value: 1.2109999999999999
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 16.058
+    - type: precision_at_5
+      value: 11.644
+    - type: recall_at_1
+      value: 23.072
+    - type: recall_at_10
+      value: 50.285999999999994
+    - type: recall_at_100
+      value: 76.596
+    - type: recall_at_1000
+      value: 92.861
+    - type: recall_at_3
+      value: 35.702
+    - type: recall_at_5
+      value: 42.152
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.937916666666666
+    - type: map_at_10
+      value: 33.755250000000004
+    - type: map_at_100
+      value: 34.955999999999996
+    - type: map_at_1000
+      value: 35.070499999999996
+    - type: map_at_3
+      value: 30.98708333333333
+    - type: map_at_5
+      value: 32.51491666666666
+    - type: mrr_at_1
+      value: 29.48708333333333
+    - type: mrr_at_10
+      value: 37.92183333333334
+    - type: mrr_at_100
+      value: 38.76583333333333
+    - type: mrr_at_1000
+      value: 38.82466666666667
+    - type: mrr_at_3
+      value: 35.45125
+    - type: mrr_at_5
+      value: 36.827000000000005
+    - type: ndcg_at_1
+      value: 29.48708333333333
+    - type: ndcg_at_10
+      value: 39.05225
+    - type: ndcg_at_100
+      value: 44.25983333333334
+    - type: ndcg_at_1000
+      value: 46.568333333333335
+    - type: ndcg_at_3
+      value: 34.271583333333325
+    - type: ndcg_at_5
+      value: 36.483916666666666
+    - type: precision_at_1
+      value: 29.48708333333333
+    - type: precision_at_10
+      value: 6.865749999999999
+    - type: precision_at_100
+      value: 1.1195833333333332
+    - type: precision_at_1000
+      value: 0.15058333333333335
+    - type: precision_at_3
+      value: 15.742083333333333
+    - type: precision_at_5
+      value: 11.221916666666667
+    - type: recall_at_1
+      value: 24.937916666666666
+    - type: recall_at_10
+      value: 50.650416666666665
+    - type: recall_at_100
+      value: 73.55383333333334
+    - type: recall_at_1000
+      value: 89.61691666666667
+    - type: recall_at_3
+      value: 37.27808333333334
+    - type: recall_at_5
+      value: 42.99475
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.947
+    - type: map_at_10
+      value: 30.575000000000003
+    - type: map_at_100
+      value: 31.465
+    - type: map_at_1000
+      value: 31.558000000000003
+    - type: map_at_3
+      value: 28.814
+    - type: map_at_5
+      value: 29.738999999999997
+    - type: mrr_at_1
+      value: 26.994
+    - type: mrr_at_10
+      value: 33.415
+    - type: mrr_at_100
+      value: 34.18
+    - type: mrr_at_1000
+      value: 34.245
+    - type: mrr_at_3
+      value: 31.621
+    - type: mrr_at_5
+      value: 32.549
+    - type: ndcg_at_1
+      value: 26.994
+    - type: ndcg_at_10
+      value: 34.482
+    - type: ndcg_at_100
+      value: 38.915
+    - type: ndcg_at_1000
+      value: 41.355
+    - type: ndcg_at_3
+      value: 31.139
+    - type: ndcg_at_5
+      value: 32.589
+    - type: precision_at_1
+      value: 26.994
+    - type: precision_at_10
+      value: 5.322
+    - type: precision_at_100
+      value: 0.8160000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 13.344000000000001
+    - type: precision_at_5
+      value: 8.988
+    - type: recall_at_1
+      value: 23.947
+    - type: recall_at_10
+      value: 43.647999999999996
+    - type: recall_at_100
+      value: 63.851
+    - type: recall_at_1000
+      value: 82.0
+    - type: recall_at_3
+      value: 34.288000000000004
+    - type: recall_at_5
+      value: 38.117000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.197
+    - type: map_at_10
+      value: 22.968
+    - type: map_at_100
+      value: 24.095
+    - type: map_at_1000
+      value: 24.217
+    - type: map_at_3
+      value: 20.771
+    - type: map_at_5
+      value: 21.995
+    - type: mrr_at_1
+      value: 19.511
+    - type: mrr_at_10
+      value: 26.55
+    - type: mrr_at_100
+      value: 27.500999999999998
+    - type: mrr_at_1000
+      value: 27.578999999999997
+    - type: mrr_at_3
+      value: 24.421
+    - type: mrr_at_5
+      value: 25.604
+    - type: ndcg_at_1
+      value: 19.511
+    - type: ndcg_at_10
+      value: 27.386
+    - type: ndcg_at_100
+      value: 32.828
+    - type: ndcg_at_1000
+      value: 35.739
+    - type: ndcg_at_3
+      value: 23.405
+    - type: ndcg_at_5
+      value: 25.255
+    - type: precision_at_1
+      value: 19.511
+    - type: precision_at_10
+      value: 5.017
+    - type: precision_at_100
+      value: 0.91
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 11.023
+    - type: precision_at_5
+      value: 8.025
+    - type: recall_at_1
+      value: 16.197
+    - type: recall_at_10
+      value: 37.09
+    - type: recall_at_100
+      value: 61.778
+    - type: recall_at_1000
+      value: 82.56599999999999
+    - type: recall_at_3
+      value: 26.034000000000002
+    - type: recall_at_5
+      value: 30.762
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.41
+    - type: map_at_10
+      value: 33.655
+    - type: map_at_100
+      value: 34.892
+    - type: map_at_1000
+      value: 34.995
+    - type: map_at_3
+      value: 30.94
+    - type: map_at_5
+      value: 32.303
+    - type: mrr_at_1
+      value: 29.477999999999998
+    - type: mrr_at_10
+      value: 37.443
+    - type: mrr_at_100
+      value: 38.383
+    - type: mrr_at_1000
+      value: 38.440000000000005
+    - type: mrr_at_3
+      value: 34.949999999999996
+    - type: mrr_at_5
+      value: 36.228
+    - type: ndcg_at_1
+      value: 29.477999999999998
+    - type: ndcg_at_10
+      value: 38.769
+    - type: ndcg_at_100
+      value: 44.245000000000005
+    - type: ndcg_at_1000
+      value: 46.593
+    - type: ndcg_at_3
+      value: 33.623
+    - type: ndcg_at_5
+      value: 35.766
+    - type: precision_at_1
+      value: 29.477999999999998
+    - type: precision_at_10
+      value: 6.455
+    - type: precision_at_100
+      value: 1.032
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 14.893999999999998
+    - type: precision_at_5
+      value: 10.485
+    - type: recall_at_1
+      value: 25.41
+    - type: recall_at_10
+      value: 50.669
+    - type: recall_at_100
+      value: 74.084
+    - type: recall_at_1000
+      value: 90.435
+    - type: recall_at_3
+      value: 36.679
+    - type: recall_at_5
+      value: 41.94
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.339
+    - type: map_at_10
+      value: 31.852000000000004
+    - type: map_at_100
+      value: 33.411
+    - type: map_at_1000
+      value: 33.62
+    - type: map_at_3
+      value: 28.929
+    - type: map_at_5
+      value: 30.542
+    - type: mrr_at_1
+      value: 28.063
+    - type: mrr_at_10
+      value: 36.301
+    - type: mrr_at_100
+      value: 37.288
+    - type: mrr_at_1000
+      value: 37.349
+    - type: mrr_at_3
+      value: 33.663
+    - type: mrr_at_5
+      value: 35.165
+    - type: ndcg_at_1
+      value: 28.063
+    - type: ndcg_at_10
+      value: 37.462
+    - type: ndcg_at_100
+      value: 43.620999999999995
+    - type: ndcg_at_1000
+      value: 46.211
+    - type: ndcg_at_3
+      value: 32.68
+    - type: ndcg_at_5
+      value: 34.981
+    - type: precision_at_1
+      value: 28.063
+    - type: precision_at_10
+      value: 7.1739999999999995
+    - type: precision_at_100
+      value: 1.486
+    - type: precision_at_1000
+      value: 0.23500000000000001
+    - type: precision_at_3
+      value: 15.217
+    - type: precision_at_5
+      value: 11.265
+    - type: recall_at_1
+      value: 23.339
+    - type: recall_at_10
+      value: 48.376999999999995
+    - type: recall_at_100
+      value: 76.053
+    - type: recall_at_1000
+      value: 92.455
+    - type: recall_at_3
+      value: 34.735
+    - type: recall_at_5
+      value: 40.71
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.925
+    - type: map_at_10
+      value: 26.017000000000003
+    - type: map_at_100
+      value: 27.034000000000002
+    - type: map_at_1000
+      value: 27.156000000000002
+    - type: map_at_3
+      value: 23.604
+    - type: map_at_5
+      value: 24.75
+    - type: mrr_at_1
+      value: 20.333000000000002
+    - type: mrr_at_10
+      value: 27.915
+    - type: mrr_at_100
+      value: 28.788000000000004
+    - type: mrr_at_1000
+      value: 28.877999999999997
+    - type: mrr_at_3
+      value: 25.446999999999996
+    - type: mrr_at_5
+      value: 26.648
+    - type: ndcg_at_1
+      value: 20.333000000000002
+    - type: ndcg_at_10
+      value: 30.673000000000002
+    - type: ndcg_at_100
+      value: 35.618
+    - type: ndcg_at_1000
+      value: 38.517
+    - type: ndcg_at_3
+      value: 25.71
+    - type: ndcg_at_5
+      value: 27.679
+    - type: precision_at_1
+      value: 20.333000000000002
+    - type: precision_at_10
+      value: 4.9910000000000005
+    - type: precision_at_100
+      value: 0.8130000000000001
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 11.029
+    - type: precision_at_5
+      value: 7.8740000000000006
+    - type: recall_at_1
+      value: 18.925
+    - type: recall_at_10
+      value: 43.311
+    - type: recall_at_100
+      value: 66.308
+    - type: recall_at_1000
+      value: 87.49
+    - type: recall_at_3
+      value: 29.596
+    - type: recall_at_5
+      value: 34.245
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 13.714
+    - type: map_at_10
+      value: 23.194
+    - type: map_at_100
+      value: 24.976000000000003
+    - type: map_at_1000
+      value: 25.166
+    - type: map_at_3
+      value: 19.709
+    - type: map_at_5
+      value: 21.523999999999997
+    - type: mrr_at_1
+      value: 30.619000000000003
+    - type: mrr_at_10
+      value: 42.563
+    - type: mrr_at_100
+      value: 43.386
+    - type: mrr_at_1000
+      value: 43.423
+    - type: mrr_at_3
+      value: 39.555
+    - type: mrr_at_5
+      value: 41.268
+    - type: ndcg_at_1
+      value: 30.619000000000003
+    - type: ndcg_at_10
+      value: 31.836
+    - type: ndcg_at_100
+      value: 38.652
+    - type: ndcg_at_1000
+      value: 42.088
+    - type: ndcg_at_3
+      value: 26.733
+    - type: ndcg_at_5
+      value: 28.435
+    - type: precision_at_1
+      value: 30.619000000000003
+    - type: precision_at_10
+      value: 9.751999999999999
+    - type: precision_at_100
+      value: 1.71
+    - type: precision_at_1000
+      value: 0.23500000000000001
+    - type: precision_at_3
+      value: 19.935
+    - type: precision_at_5
+      value: 14.984
+    - type: recall_at_1
+      value: 13.714
+    - type: recall_at_10
+      value: 37.26
+    - type: recall_at_100
+      value: 60.546
+    - type: recall_at_1000
+      value: 79.899
+    - type: recall_at_3
+      value: 24.325
+    - type: recall_at_5
+      value: 29.725
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.462
+    - type: map_at_10
+      value: 18.637
+    - type: map_at_100
+      value: 26.131999999999998
+    - type: map_at_1000
+      value: 27.607
+    - type: map_at_3
+      value: 13.333
+    - type: map_at_5
+      value: 15.654000000000002
+    - type: mrr_at_1
+      value: 66.25
+    - type: mrr_at_10
+      value: 74.32600000000001
+    - type: mrr_at_100
+      value: 74.60900000000001
+    - type: mrr_at_1000
+      value: 74.62
+    - type: mrr_at_3
+      value: 72.667
+    - type: mrr_at_5
+      value: 73.817
+    - type: ndcg_at_1
+      value: 53.87499999999999
+    - type: ndcg_at_10
+      value: 40.028999999999996
+    - type: ndcg_at_100
+      value: 44.199
+    - type: ndcg_at_1000
+      value: 51.629999999999995
+    - type: ndcg_at_3
+      value: 44.113
+    - type: ndcg_at_5
+      value: 41.731
+    - type: precision_at_1
+      value: 66.25
+    - type: precision_at_10
+      value: 31.900000000000002
+    - type: precision_at_100
+      value: 10.043000000000001
+    - type: precision_at_1000
+      value: 1.926
+    - type: precision_at_3
+      value: 47.417
+    - type: precision_at_5
+      value: 40.65
+    - type: recall_at_1
+      value: 8.462
+    - type: recall_at_10
+      value: 24.293
+    - type: recall_at_100
+      value: 50.146
+    - type: recall_at_1000
+      value: 74.034
+    - type: recall_at_3
+      value: 14.967
+    - type: recall_at_5
+      value: 18.682000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 47.84499999999999
+    - type: f1
+      value: 42.48106691979349
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 74.034
+    - type: map_at_10
+      value: 82.76
+    - type: map_at_100
+      value: 82.968
+    - type: map_at_1000
+      value: 82.98299999999999
+    - type: map_at_3
+      value: 81.768
+    - type: map_at_5
+      value: 82.418
+    - type: mrr_at_1
+      value: 80.048
+    - type: mrr_at_10
+      value: 87.64999999999999
+    - type: mrr_at_100
+      value: 87.712
+    - type: mrr_at_1000
+      value: 87.713
+    - type: mrr_at_3
+      value: 87.01100000000001
+    - type: mrr_at_5
+      value: 87.466
+    - type: ndcg_at_1
+      value: 80.048
+    - type: ndcg_at_10
+      value: 86.643
+    - type: ndcg_at_100
+      value: 87.361
+    - type: ndcg_at_1000
+      value: 87.606
+    - type: ndcg_at_3
+      value: 85.137
+    - type: ndcg_at_5
+      value: 86.016
+    - type: precision_at_1
+      value: 80.048
+    - type: precision_at_10
+      value: 10.372
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 32.638
+    - type: precision_at_5
+      value: 20.177
+    - type: recall_at_1
+      value: 74.034
+    - type: recall_at_10
+      value: 93.769
+    - type: recall_at_100
+      value: 96.569
+    - type: recall_at_1000
+      value: 98.039
+    - type: recall_at_3
+      value: 89.581
+    - type: recall_at_5
+      value: 91.906
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.5
+    - type: map_at_10
+      value: 32.857
+    - type: map_at_100
+      value: 34.589
+    - type: map_at_1000
+      value: 34.778
+    - type: map_at_3
+      value: 29.160999999999998
+    - type: map_at_5
+      value: 31.033
+    - type: mrr_at_1
+      value: 40.123
+    - type: mrr_at_10
+      value: 48.776
+    - type: mrr_at_100
+      value: 49.495
+    - type: mrr_at_1000
+      value: 49.539
+    - type: mrr_at_3
+      value: 46.605000000000004
+    - type: mrr_at_5
+      value: 47.654
+    - type: ndcg_at_1
+      value: 40.123
+    - type: ndcg_at_10
+      value: 40.343
+    - type: ndcg_at_100
+      value: 46.56
+    - type: ndcg_at_1000
+      value: 49.777
+    - type: ndcg_at_3
+      value: 37.322
+    - type: ndcg_at_5
+      value: 37.791000000000004
+    - type: precision_at_1
+      value: 40.123
+    - type: precision_at_10
+      value: 11.08
+    - type: precision_at_100
+      value: 1.752
+    - type: precision_at_1000
+      value: 0.232
+    - type: precision_at_3
+      value: 24.897
+    - type: precision_at_5
+      value: 17.809
+    - type: recall_at_1
+      value: 20.5
+    - type: recall_at_10
+      value: 46.388
+    - type: recall_at_100
+      value: 69.552
+    - type: recall_at_1000
+      value: 89.011
+    - type: recall_at_3
+      value: 33.617999999999995
+    - type: recall_at_5
+      value: 38.211
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.135999999999996
+    - type: map_at_10
+      value: 61.673
+    - type: map_at_100
+      value: 62.562
+    - type: map_at_1000
+      value: 62.62
+    - type: map_at_3
+      value: 58.467999999999996
+    - type: map_at_5
+      value: 60.463
+    - type: mrr_at_1
+      value: 78.271
+    - type: mrr_at_10
+      value: 84.119
+    - type: mrr_at_100
+      value: 84.29299999999999
+    - type: mrr_at_1000
+      value: 84.299
+    - type: mrr_at_3
+      value: 83.18900000000001
+    - type: mrr_at_5
+      value: 83.786
+    - type: ndcg_at_1
+      value: 78.271
+    - type: ndcg_at_10
+      value: 69.935
+    - type: ndcg_at_100
+      value: 73.01299999999999
+    - type: ndcg_at_1000
+      value: 74.126
+    - type: ndcg_at_3
+      value: 65.388
+    - type: ndcg_at_5
+      value: 67.906
+    - type: precision_at_1
+      value: 78.271
+    - type: precision_at_10
+      value: 14.562
+    - type: precision_at_100
+      value: 1.6969999999999998
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 41.841
+    - type: precision_at_5
+      value: 27.087
+    - type: recall_at_1
+      value: 39.135999999999996
+    - type: recall_at_10
+      value: 72.809
+    - type: recall_at_100
+      value: 84.86200000000001
+    - type: recall_at_1000
+      value: 92.208
+    - type: recall_at_3
+      value: 62.76199999999999
+    - type: recall_at_5
+      value: 67.718
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 90.60600000000001
+    - type: ap
+      value: 86.6579587804335
+    - type: f1
+      value: 90.5938853929307
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.852
+    - type: map_at_10
+      value: 33.982
+    - type: map_at_100
+      value: 35.116
+    - type: map_at_1000
+      value: 35.167
+    - type: map_at_3
+      value: 30.134
+    - type: map_at_5
+      value: 32.340999999999994
+    - type: mrr_at_1
+      value: 22.479
+    - type: mrr_at_10
+      value: 34.594
+    - type: mrr_at_100
+      value: 35.672
+    - type: mrr_at_1000
+      value: 35.716
+    - type: mrr_at_3
+      value: 30.84
+    - type: mrr_at_5
+      value: 32.998
+    - type: ndcg_at_1
+      value: 22.493
+    - type: ndcg_at_10
+      value: 40.833000000000006
+    - type: ndcg_at_100
+      value: 46.357
+    - type: ndcg_at_1000
+      value: 47.637
+    - type: ndcg_at_3
+      value: 32.995999999999995
+    - type: ndcg_at_5
+      value: 36.919000000000004
+    - type: precision_at_1
+      value: 22.493
+    - type: precision_at_10
+      value: 6.465999999999999
+    - type: precision_at_100
+      value: 0.9249999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.030999999999999
+    - type: precision_at_5
+      value: 10.413
+    - type: recall_at_1
+      value: 21.852
+    - type: recall_at_10
+      value: 61.934999999999995
+    - type: recall_at_100
+      value: 87.611
+    - type: recall_at_1000
+      value: 97.441
+    - type: recall_at_3
+      value: 40.583999999999996
+    - type: recall_at_5
+      value: 49.992999999999995
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.36069311445507
+    - type: f1
+      value: 93.16456330371453
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 74.74692202462381
+    - type: f1
+      value: 58.17903579421599
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 74.80833893745796
+    - type: f1
+      value: 72.70786592684664
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.69872225958305
+    - type: f1
+      value: 78.61626934504731
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.058658628717694
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 30.85561739360599
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.290259910144385
+    - type: mrr
+      value: 32.44223046102856
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.288
+    - type: map_at_10
+      value: 12.267999999999999
+    - type: map_at_100
+      value: 15.557000000000002
+    - type: map_at_1000
+      value: 16.98
+    - type: map_at_3
+      value: 8.866
+    - type: map_at_5
+      value: 10.418
+    - type: mrr_at_1
+      value: 43.653
+    - type: mrr_at_10
+      value: 52.681
+    - type: mrr_at_100
+      value: 53.315999999999995
+    - type: mrr_at_1000
+      value: 53.357
+    - type: mrr_at_3
+      value: 51.393
+    - type: mrr_at_5
+      value: 51.903999999999996
+    - type: ndcg_at_1
+      value: 42.415000000000006
+    - type: ndcg_at_10
+      value: 34.305
+    - type: ndcg_at_100
+      value: 30.825999999999997
+    - type: ndcg_at_1000
+      value: 39.393
+    - type: ndcg_at_3
+      value: 39.931
+    - type: ndcg_at_5
+      value: 37.519999999999996
+    - type: precision_at_1
+      value: 43.653
+    - type: precision_at_10
+      value: 25.728
+    - type: precision_at_100
+      value: 7.932
+    - type: precision_at_1000
+      value: 2.07
+    - type: precision_at_3
+      value: 38.184000000000005
+    - type: precision_at_5
+      value: 32.879000000000005
+    - type: recall_at_1
+      value: 5.288
+    - type: recall_at_10
+      value: 16.195
+    - type: recall_at_100
+      value: 31.135
+    - type: recall_at_1000
+      value: 61.531000000000006
+    - type: recall_at_3
+      value: 10.313
+    - type: recall_at_5
+      value: 12.754999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.216
+    - type: map_at_10
+      value: 42.588
+    - type: map_at_100
+      value: 43.702999999999996
+    - type: map_at_1000
+      value: 43.739
+    - type: map_at_3
+      value: 38.177
+    - type: map_at_5
+      value: 40.754000000000005
+    - type: mrr_at_1
+      value: 31.866
+    - type: mrr_at_10
+      value: 45.189
+    - type: mrr_at_100
+      value: 46.056000000000004
+    - type: mrr_at_1000
+      value: 46.081
+    - type: mrr_at_3
+      value: 41.526999999999994
+    - type: mrr_at_5
+      value: 43.704
+    - type: ndcg_at_1
+      value: 31.837
+    - type: ndcg_at_10
+      value: 50.178
+    - type: ndcg_at_100
+      value: 54.98800000000001
+    - type: ndcg_at_1000
+      value: 55.812
+    - type: ndcg_at_3
+      value: 41.853
+    - type: ndcg_at_5
+      value: 46.153
+    - type: precision_at_1
+      value: 31.837
+    - type: precision_at_10
+      value: 8.43
+    - type: precision_at_100
+      value: 1.1119999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 19.023
+    - type: precision_at_5
+      value: 13.911000000000001
+    - type: recall_at_1
+      value: 28.216
+    - type: recall_at_10
+      value: 70.8
+    - type: recall_at_100
+      value: 91.857
+    - type: recall_at_1000
+      value: 97.941
+    - type: recall_at_3
+      value: 49.196
+    - type: recall_at_5
+      value: 59.072
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 71.22800000000001
+    - type: map_at_10
+      value: 85.115
+    - type: map_at_100
+      value: 85.72
+    - type: map_at_1000
+      value: 85.737
+    - type: map_at_3
+      value: 82.149
+    - type: map_at_5
+      value: 84.029
+    - type: mrr_at_1
+      value: 81.96
+    - type: mrr_at_10
+      value: 88.00200000000001
+    - type: mrr_at_100
+      value: 88.088
+    - type: mrr_at_1000
+      value: 88.089
+    - type: mrr_at_3
+      value: 87.055
+    - type: mrr_at_5
+      value: 87.715
+    - type: ndcg_at_1
+      value: 82.01
+    - type: ndcg_at_10
+      value: 88.78
+    - type: ndcg_at_100
+      value: 89.91
+    - type: ndcg_at_1000
+      value: 90.013
+    - type: ndcg_at_3
+      value: 85.957
+    - type: ndcg_at_5
+      value: 87.56
+    - type: precision_at_1
+      value: 82.01
+    - type: precision_at_10
+      value: 13.462
+    - type: precision_at_100
+      value: 1.528
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.553
+    - type: precision_at_5
+      value: 24.732000000000003
+    - type: recall_at_1
+      value: 71.22800000000001
+    - type: recall_at_10
+      value: 95.69
+    - type: recall_at_100
+      value: 99.531
+    - type: recall_at_1000
+      value: 99.98
+    - type: recall_at_3
+      value: 87.632
+    - type: recall_at_5
+      value: 92.117
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 52.31768034366916
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 60.640266772723606
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.7780000000000005
+    - type: map_at_10
+      value: 12.299
+    - type: map_at_100
+      value: 14.363000000000001
+    - type: map_at_1000
+      value: 14.71
+    - type: map_at_3
+      value: 8.738999999999999
+    - type: map_at_5
+      value: 10.397
+    - type: mrr_at_1
+      value: 23.599999999999998
+    - type: mrr_at_10
+      value: 34.845
+    - type: mrr_at_100
+      value: 35.916
+    - type: mrr_at_1000
+      value: 35.973
+    - type: mrr_at_3
+      value: 31.7
+    - type: mrr_at_5
+      value: 33.535
+    - type: ndcg_at_1
+      value: 23.599999999999998
+    - type: ndcg_at_10
+      value: 20.522000000000002
+    - type: ndcg_at_100
+      value: 28.737000000000002
+    - type: ndcg_at_1000
+      value: 34.596
+    - type: ndcg_at_3
+      value: 19.542
+    - type: ndcg_at_5
+      value: 16.958000000000002
+    - type: precision_at_1
+      value: 23.599999999999998
+    - type: precision_at_10
+      value: 10.67
+    - type: precision_at_100
+      value: 2.259
+    - type: precision_at_1000
+      value: 0.367
+    - type: precision_at_3
+      value: 18.333
+    - type: precision_at_5
+      value: 14.879999999999999
+    - type: recall_at_1
+      value: 4.7780000000000005
+    - type: recall_at_10
+      value: 21.617
+    - type: recall_at_100
+      value: 45.905
+    - type: recall_at_1000
+      value: 74.42
+    - type: recall_at_3
+      value: 11.148
+    - type: recall_at_5
+      value: 15.082999999999998
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.22372750297885
+    - type: cos_sim_spearman
+      value: 79.40972617119405
+    - type: euclidean_pearson
+      value: 80.6101072020434
+    - type: euclidean_spearman
+      value: 79.53844217225202
+    - type: manhattan_pearson
+      value: 80.57265975286111
+    - type: manhattan_spearman
+      value: 79.46335611792958
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.43713315520749
+    - type: cos_sim_spearman
+      value: 77.44128693329532
+    - type: euclidean_pearson
+      value: 81.63869928101123
+    - type: euclidean_spearman
+      value: 77.29512977961515
+    - type: manhattan_pearson
+      value: 81.63704185566183
+    - type: manhattan_spearman
+      value: 77.29909412738657
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.59451537860527
+    - type: cos_sim_spearman
+      value: 82.97994638856723
+    - type: euclidean_pearson
+      value: 82.89478688288412
+    - type: euclidean_spearman
+      value: 83.58740751053104
+    - type: manhattan_pearson
+      value: 82.69140840941608
+    - type: manhattan_spearman
+      value: 83.33665956040555
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.00756527711764
+    - type: cos_sim_spearman
+      value: 81.83560996841379
+    - type: euclidean_pearson
+      value: 82.07684151976518
+    - type: euclidean_spearman
+      value: 82.00913052060511
+    - type: manhattan_pearson
+      value: 82.05690778488794
+    - type: manhattan_spearman
+      value: 82.02260252019525
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.13710262895447
+    - type: cos_sim_spearman
+      value: 87.26412811156248
+    - type: euclidean_pearson
+      value: 86.94151453230228
+    - type: euclidean_spearman
+      value: 87.5363796699571
+    - type: manhattan_pearson
+      value: 86.86989424083748
+    - type: manhattan_spearman
+      value: 87.47315940781353
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.0230597603627
+    - type: cos_sim_spearman
+      value: 84.93344499318864
+    - type: euclidean_pearson
+      value: 84.23754743431141
+    - type: euclidean_spearman
+      value: 85.09707376597099
+    - type: manhattan_pearson
+      value: 84.04325160987763
+    - type: manhattan_spearman
+      value: 84.89353071339909
+  - 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: 86.75620824563921
+    - type: cos_sim_spearman
+      value: 87.15065513706398
+    - type: euclidean_pearson
+      value: 88.26281533633521
+    - type: euclidean_spearman
+      value: 87.51963738643983
+    - type: manhattan_pearson
+      value: 88.25599267618065
+    - type: manhattan_spearman
+      value: 87.58048736047483
+  - 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: 64.74645319195137
+    - type: cos_sim_spearman
+      value: 65.29996325037214
+    - type: euclidean_pearson
+      value: 67.04297794086443
+    - type: euclidean_spearman
+      value: 65.43841726694343
+    - type: manhattan_pearson
+      value: 67.39459955690904
+    - type: manhattan_spearman
+      value: 65.92864704413651
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.31291020270801
+    - type: cos_sim_spearman
+      value: 85.86473738688068
+    - type: euclidean_pearson
+      value: 85.65537275064152
+    - type: euclidean_spearman
+      value: 86.13087454209642
+    - type: manhattan_pearson
+      value: 85.43946955047609
+    - type: manhattan_spearman
+      value: 85.91568175344916
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 85.93798118350695
+    - type: mrr
+      value: 95.93536274908824
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 57.594
+    - type: map_at_10
+      value: 66.81899999999999
+    - type: map_at_100
+      value: 67.368
+    - type: map_at_1000
+      value: 67.4
+    - type: map_at_3
+      value: 64.061
+    - type: map_at_5
+      value: 65.47
+    - type: mrr_at_1
+      value: 60.667
+    - type: mrr_at_10
+      value: 68.219
+    - type: mrr_at_100
+      value: 68.655
+    - type: mrr_at_1000
+      value: 68.684
+    - type: mrr_at_3
+      value: 66.22200000000001
+    - type: mrr_at_5
+      value: 67.289
+    - type: ndcg_at_1
+      value: 60.667
+    - type: ndcg_at_10
+      value: 71.275
+    - type: ndcg_at_100
+      value: 73.642
+    - type: ndcg_at_1000
+      value: 74.373
+    - type: ndcg_at_3
+      value: 66.521
+    - type: ndcg_at_5
+      value: 68.581
+    - type: precision_at_1
+      value: 60.667
+    - type: precision_at_10
+      value: 9.433
+    - type: precision_at_100
+      value: 1.0699999999999998
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 25.556
+    - type: precision_at_5
+      value: 16.8
+    - type: recall_at_1
+      value: 57.594
+    - type: recall_at_10
+      value: 83.622
+    - type: recall_at_100
+      value: 94.167
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 70.64399999999999
+    - type: recall_at_5
+      value: 75.983
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.85841584158416
+    - type: cos_sim_ap
+      value: 96.66996142314342
+    - type: cos_sim_f1
+      value: 92.83208020050125
+    - type: cos_sim_precision
+      value: 93.06532663316584
+    - type: cos_sim_recall
+      value: 92.60000000000001
+    - type: dot_accuracy
+      value: 99.85841584158416
+    - type: dot_ap
+      value: 96.6775307676576
+    - type: dot_f1
+      value: 92.69289729177312
+    - type: dot_precision
+      value: 94.77533960292581
+    - type: dot_recall
+      value: 90.7
+    - type: euclidean_accuracy
+      value: 99.86138613861387
+    - type: euclidean_ap
+      value: 96.6338454403108
+    - type: euclidean_f1
+      value: 92.92214357937311
+    - type: euclidean_precision
+      value: 93.96728016359918
+    - type: euclidean_recall
+      value: 91.9
+    - type: manhattan_accuracy
+      value: 99.86237623762376
+    - type: manhattan_ap
+      value: 96.60370449645053
+    - type: manhattan_f1
+      value: 92.91177970423253
+    - type: manhattan_precision
+      value: 94.7970863683663
+    - type: manhattan_recall
+      value: 91.10000000000001
+    - type: max_accuracy
+      value: 99.86237623762376
+    - type: max_ap
+      value: 96.6775307676576
+    - type: max_f1
+      value: 92.92214357937311
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 60.77977058695198
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.2725272535638
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.64052466362125
+    - type: mrr
+      value: 54.533067014684654
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.677624219206578
+    - type: cos_sim_spearman
+      value: 30.121368518123447
+    - type: dot_pearson
+      value: 30.69870088041608
+    - type: dot_spearman
+      value: 29.61284927093751
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.22
+    - type: map_at_10
+      value: 1.855
+    - type: map_at_100
+      value: 9.885
+    - type: map_at_1000
+      value: 23.416999999999998
+    - type: map_at_3
+      value: 0.637
+    - type: map_at_5
+      value: 1.024
+    - type: mrr_at_1
+      value: 88.0
+    - type: mrr_at_10
+      value: 93.067
+    - type: mrr_at_100
+      value: 93.067
+    - type: mrr_at_1000
+      value: 93.067
+    - type: mrr_at_3
+      value: 92.667
+    - type: mrr_at_5
+      value: 93.067
+    - type: ndcg_at_1
+      value: 82.0
+    - type: ndcg_at_10
+      value: 75.899
+    - type: ndcg_at_100
+      value: 55.115
+    - type: ndcg_at_1000
+      value: 48.368
+    - type: ndcg_at_3
+      value: 79.704
+    - type: ndcg_at_5
+      value: 78.39699999999999
+    - type: precision_at_1
+      value: 88.0
+    - type: precision_at_10
+      value: 79.60000000000001
+    - type: precision_at_100
+      value: 56.06
+    - type: precision_at_1000
+      value: 21.206
+    - type: precision_at_3
+      value: 84.667
+    - type: precision_at_5
+      value: 83.2
+    - type: recall_at_1
+      value: 0.22
+    - type: recall_at_10
+      value: 2.078
+    - type: recall_at_100
+      value: 13.297
+    - type: recall_at_1000
+      value: 44.979
+    - type: recall_at_3
+      value: 0.6689999999999999
+    - type: recall_at_5
+      value: 1.106
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.258
+    - type: map_at_10
+      value: 10.439
+    - type: map_at_100
+      value: 16.89
+    - type: map_at_1000
+      value: 18.407999999999998
+    - type: map_at_3
+      value: 5.668
+    - type: map_at_5
+      value: 7.718
+    - type: mrr_at_1
+      value: 32.653
+    - type: mrr_at_10
+      value: 51.159
+    - type: mrr_at_100
+      value: 51.714000000000006
+    - type: mrr_at_1000
+      value: 51.714000000000006
+    - type: mrr_at_3
+      value: 47.959
+    - type: mrr_at_5
+      value: 50.407999999999994
+    - type: ndcg_at_1
+      value: 29.592000000000002
+    - type: ndcg_at_10
+      value: 26.037
+    - type: ndcg_at_100
+      value: 37.924
+    - type: ndcg_at_1000
+      value: 49.126999999999995
+    - type: ndcg_at_3
+      value: 30.631999999999998
+    - type: ndcg_at_5
+      value: 28.571
+    - type: precision_at_1
+      value: 32.653
+    - type: precision_at_10
+      value: 22.857
+    - type: precision_at_100
+      value: 7.754999999999999
+    - type: precision_at_1000
+      value: 1.529
+    - type: precision_at_3
+      value: 34.014
+    - type: precision_at_5
+      value: 29.796
+    - type: recall_at_1
+      value: 2.258
+    - type: recall_at_10
+      value: 16.554
+    - type: recall_at_100
+      value: 48.439
+    - type: recall_at_1000
+      value: 82.80499999999999
+    - type: recall_at_3
+      value: 7.283
+    - type: recall_at_5
+      value: 10.732
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 69.8858
+    - type: ap
+      value: 13.835684144362109
+    - type: f1
+      value: 53.803351693244586
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 60.50650820599886
+    - type: f1
+      value: 60.84357825979259
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 48.52131044852134
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.59337187816654
+    - type: cos_sim_ap
+      value: 73.23925826533437
+    - type: cos_sim_f1
+      value: 67.34693877551021
+    - type: cos_sim_precision
+      value: 62.40432237730752
+    - type: cos_sim_recall
+      value: 73.13984168865434
+    - type: dot_accuracy
+      value: 85.31322644096085
+    - type: dot_ap
+      value: 72.30723963807422
+    - type: dot_f1
+      value: 66.47051612112296
+    - type: dot_precision
+      value: 62.0792305930845
+    - type: dot_recall
+      value: 71.53034300791556
+    - type: euclidean_accuracy
+      value: 85.61125350181797
+    - type: euclidean_ap
+      value: 73.32843720487845
+    - type: euclidean_f1
+      value: 67.36549633745895
+    - type: euclidean_precision
+      value: 64.60755813953489
+    - type: euclidean_recall
+      value: 70.36939313984169
+    - type: manhattan_accuracy
+      value: 85.63509566668654
+    - type: manhattan_ap
+      value: 73.16658488311325
+    - type: manhattan_f1
+      value: 67.20597386434349
+    - type: manhattan_precision
+      value: 63.60424028268551
+    - type: manhattan_recall
+      value: 71.2401055408971
+    - type: max_accuracy
+      value: 85.63509566668654
+    - type: max_ap
+      value: 73.32843720487845
+    - type: max_f1
+      value: 67.36549633745895
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.33779640625606
+    - type: cos_sim_ap
+      value: 84.83868375898157
+    - type: cos_sim_f1
+      value: 77.16506154017773
+    - type: cos_sim_precision
+      value: 74.62064005753327
+    - type: cos_sim_recall
+      value: 79.88912842623961
+    - type: dot_accuracy
+      value: 88.02732176815307
+    - type: dot_ap
+      value: 83.95089283763002
+    - type: dot_f1
+      value: 76.29635101196631
+    - type: dot_precision
+      value: 73.31771720613288
+    - type: dot_recall
+      value: 79.52725592854944
+    - type: euclidean_accuracy
+      value: 88.44452206310397
+    - type: euclidean_ap
+      value: 84.98384576824827
+    - type: euclidean_f1
+      value: 77.29311047696697
+    - type: euclidean_precision
+      value: 74.51232583065381
+    - type: euclidean_recall
+      value: 80.28949799815214
+    - type: manhattan_accuracy
+      value: 88.47362906042613
+    - type: manhattan_ap
+      value: 84.91421462218432
+    - type: manhattan_f1
+      value: 77.05107637204792
+    - type: manhattan_precision
+      value: 74.74484256243214
+    - type: manhattan_recall
+      value: 79.50415768401602
+    - type: max_accuracy
+      value: 88.47362906042613
+    - type: max_ap
+      value: 84.98384576824827
+    - type: max_f1
+      value: 77.29311047696697
+license: mit
+language:
+- en
+---
+
+Finetuned using the same data & library as [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1)
+
+<h1 align="center">FlagEmbedding</h1>
+
+
+<h4 align="center">
+    <p>
+        <a href=#model-list>Model List</a> | 
+        <a href=#frequently-asked-questions>FAQ</a> |
+        <a href=#usage>Usage</a>  |
+        <a href="#evaluation">Evaluation</a> |
+        <a href="#train">Train</a> |
+        <a href="#contact">Contact</a> |
+        <a href="#citation">Citation</a> |
+        <a href="#license">License</a> 
+    <p>
+</h4>
+
+More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
+
+
+[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
+
+FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification,  clustering, or semantic search.
+And it also can be used in vector databases for LLMs.
+
+************* 🌟**Updates**🌟 *************
+- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf)  :fire:  
+- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released 
+- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released 
+- 09/12/2023: New models: 
+    - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. 
+    - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
+ 
+
+<details>
+  <summary>More</summary>
+<!-- ### More -->
+    
+- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. 
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).  
+- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**  
+- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:   
+- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.  
+  
+</details>
+
+
+## Model List
+
+`bge` is short for `BAAI general embedding`.
+
+|              Model              | Language | | Description | query instruction for retrieval [1] |
+|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
+|  [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder)  |   English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
+|  [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)  |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] |   |
+|  [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) |   Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] |   |
+|  [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+
+
+[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
+
+[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. 
+For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
+
+All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. 
+If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
+
+
+## Frequently asked questions
+
+<details>
+  <summary>1. How to fine-tune bge embedding model?</summary>
+
+  <!-- ### How to fine-tune bge embedding model? -->
+Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. 
+Some suggestions:
+- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
+- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
+- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
+
+  
+</details>
+
+<details>
+  <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
+
+  <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
+**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** 
+
+Since we finetune the models by contrastive learning with a temperature of 0.01, 
+the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
+So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
+
+For downstream tasks, such as passage retrieval or semantic similarity, 
+**what matters is the relative order of the scores, not the absolute value.**
+If you need to filter similar sentences based on a similarity threshold, 
+please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
+
+</details>
+
+<details>
+  <summary>3. When does the query instruction need to be used</summary>
+
+  <!-- ### When does the query instruction need to be used -->
+
+For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. 
+No instruction only has a slight degradation in retrieval performance compared with using instruction. 
+So you can generate embedding without instruction in all cases for convenience.
+ 
+For a retrieval task that uses short queries to find long related documents, 
+it is recommended to add instructions for these short queries.
+**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
+In all cases, the documents/passages do not need to add the instruction. 
+
+</details>
+
+
+## Usage 
+
+### Usage for Embedding Model
+
+Here are some examples for using `bge` models with 
+[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
+
+```python
+from FlagEmbedding import FlagModel
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
+model = FlagModel('BAAI/bge-large-zh-v1.5', 
+                  query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
+                  use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+embeddings_1 = model.encode(sentences_1)
+embeddings_2 = model.encode(sentences_2)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+
+# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
+# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+q_embeddings = model.encode_queries(queries)
+p_embeddings = model.encode(passages)
+scores = q_embeddings @ p_embeddings.T
+```
+For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). 
+
+By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
+You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
+
+
+#### Using Sentence-Transformers
+
+You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
+
+```
+pip install -U sentence-transformers
+```
+```python
+from sentence_transformers import SentenceTransformer
+sentences_1 = ["样例数据-1", "样例数据-2"]
+sentences_2 = ["样例数据-3", "样例数据-4"]
+model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
+embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+For s2p(short query to long passage) retrieval task, 
+each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
+But the instruction is not needed for passages.
+```python
+from sentence_transformers import SentenceTransformer
+queries = ['query_1', 'query_2']
+passages = ["样例文档-1", "样例文档-2"]
+instruction = "为这个句子生成表示以用于检索相关文章："
+
+model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
+q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
+p_embeddings = model.encode(passages, normalize_embeddings=True)
+scores = q_embeddings @ p_embeddings.T
+```
+
+#### Using Langchain 
+
+You can use `bge` in langchain like this:
+```python
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+model_name = "BAAI/bge-large-en-v1.5"
+model_kwargs = {'device': 'cuda'}
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+model = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs,
+    query_instruction="为这个句子生成表示以用于检索相关文章："
+)
+model.query_instruction = "为这个句子生成表示以用于检索相关文章："
+```
+
+
+#### Using HuggingFace Transformers
+
+With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Sentences we want sentence embeddings for
+sentences = ["样例数据-1", "样例数据-2"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
+model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
+model.eval()
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
+# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+    # Perform pooling. In this case, cls pooling.
+    sentence_embeddings = model_output[0][:, 0]
+# normalize embeddings
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+print("Sentence embeddings:", sentence_embeddings)
+```
+
+### Usage for Reranker
+
+Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. 
+You can get a relevance score by inputting query and passage to the reranker. 
+The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
+
+
+#### Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+
+Get relevance scores (higher scores indicate more relevance):
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+
+
+#### Using Huggingface transformers
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+    print(scores)
+```
+
+## Evaluation  
+
+`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
+For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). 
+
+- **MTEB**:   
+
+| Model Name |  Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) |  STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 |  **64.23** | **54.29** |  46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |  
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |  768 | 512 | 63.55 | 53.25 |   45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |  
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |  384 | 512 | 62.17 |51.68 | 43.82 |  84.92 | 58.36 | 81.59 | 30.12 | 74.14 |  
+| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) |  1024 | 512 | 63.98 |  53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | 
+| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) |  768 | 512 |  63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | 
+| [gte-large](https://huggingface.co/thenlper/gte-large) |  1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
+| [gte-base](https://huggingface.co/thenlper/gte-base) 	|  768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) |  1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
+| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) |  384 | 512 | 62.11 |  51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |  
+| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) |  768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) |  768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
+| [gte-small](https://huggingface.co/thenlper/gte-small) |  384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) |  768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	|  768 | 514 	| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	|  4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
+
+
+
+- **C-MTEB**:  
+We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. 
+Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
+ 
+| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 |  **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |  
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 |  63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |  
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |   
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
+| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
+| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 |  63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
+| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
+| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
+| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
+| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
+| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
+| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
+| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
+| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
+
+
+- **Reranking**:
+See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
+
+| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
+|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|  
+| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |  
+| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |  
+| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |  
+| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |  
+| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |  
+| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |  
+| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |  
+| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |  
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |  
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |  
+
+\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
+
+## Train
+
+### BAAI Embedding 
+
+We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. 
+**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
+We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
+Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
+More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
+
+
+
+### BGE Reranker
+
+Cross-encoder will perform full-attention over the input pair, 
+which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
+Therefore, it can be used to re-rank the top-k documents returned by embedding model.
+We train the cross-encoder on a multilingual pair data, 
+The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). 
+More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
+
+## Contact
+If you have any question or suggestion related to this project, feel free to open an issue or pull request.
+You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). 
+
+
+## Citation
+
+If you find this repository useful, please consider giving a star :star: and citation
+
+```
+@misc{bge_embedding,
+      title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, 
+      author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
+      year={2023},
+      eprint={2309.07597},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+## License
+FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

snapshot/best/2_MixtureEmbeddingsModel/expert_01_khoa-klaytn_bge-small-en-v1.5-angle/config.json

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snapshot/best/2_MixtureEmbeddingsModel/expert_02_thenlper_gte-small/README.md

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+---
+tags:
+- mteb
+- sentence-similarity
+- sentence-transformers
+- Sentence Transformers
+model-index:
+- name: gte-small 
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 73.22388059701493
+    - type: ap
+      value: 36.09895941426988
+    - type: f1
+      value: 67.3205651539195
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 91.81894999999999
+    - type: ap
+      value: 88.5240138417305
+    - type: f1
+      value: 91.80367382706962
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.032
+    - type: f1
+      value: 47.4490665674719
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.725
+    - type: map_at_10
+      value: 46.604
+    - type: map_at_100
+      value: 47.535
+    - type: map_at_1000
+      value: 47.538000000000004
+    - type: map_at_3
+      value: 41.833
+    - type: map_at_5
+      value: 44.61
+    - type: mrr_at_1
+      value: 31.223
+    - type: mrr_at_10
+      value: 46.794000000000004
+    - type: mrr_at_100
+      value: 47.725
+    - type: mrr_at_1000
+      value: 47.727000000000004
+    - type: mrr_at_3
+      value: 42.07
+    - type: mrr_at_5
+      value: 44.812000000000005
+    - type: ndcg_at_1
+      value: 30.725
+    - type: ndcg_at_10
+      value: 55.440999999999995
+    - type: ndcg_at_100
+      value: 59.134
+    - type: ndcg_at_1000
+      value: 59.199
+    - type: ndcg_at_3
+      value: 45.599000000000004
+    - type: ndcg_at_5
+      value: 50.637
+    - type: precision_at_1
+      value: 30.725
+    - type: precision_at_10
+      value: 8.364
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 18.848000000000003
+    - type: precision_at_5
+      value: 13.77
+    - type: recall_at_1
+      value: 30.725
+    - type: recall_at_10
+      value: 83.64200000000001
+    - type: recall_at_100
+      value: 99.14699999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 56.543
+    - type: recall_at_5
+      value: 68.848
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.90178078197678
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 40.25728393431922
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 61.720297062897764
+    - type: mrr
+      value: 75.24139295607439
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.43527309184616
+    - type: cos_sim_spearman
+      value: 88.17128615100206
+    - type: euclidean_pearson
+      value: 87.89922623089282
+    - type: euclidean_spearman
+      value: 87.96104039655451
+    - type: manhattan_pearson
+      value: 87.9818290932077
+    - type: manhattan_spearman
+      value: 88.00923426576885
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 84.0844155844156
+    - type: f1
+      value: 84.01485017302213
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 38.36574769259432
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 35.4857033165287
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.261
+    - type: map_at_10
+      value: 42.419000000000004
+    - type: map_at_100
+      value: 43.927
+    - type: map_at_1000
+      value: 44.055
+    - type: map_at_3
+      value: 38.597
+    - type: map_at_5
+      value: 40.701
+    - type: mrr_at_1
+      value: 36.91
+    - type: mrr_at_10
+      value: 48.02
+    - type: mrr_at_100
+      value: 48.658
+    - type: mrr_at_1000
+      value: 48.708
+    - type: mrr_at_3
+      value: 44.945
+    - type: mrr_at_5
+      value: 46.705000000000005
+    - type: ndcg_at_1
+      value: 36.91
+    - type: ndcg_at_10
+      value: 49.353
+    - type: ndcg_at_100
+      value: 54.456
+    - type: ndcg_at_1000
+      value: 56.363
+    - type: ndcg_at_3
+      value: 43.483
+    - type: ndcg_at_5
+      value: 46.150999999999996
+    - type: precision_at_1
+      value: 36.91
+    - type: precision_at_10
+      value: 9.700000000000001
+    - type: precision_at_100
+      value: 1.557
+    - type: precision_at_1000
+      value: 0.202
+    - type: precision_at_3
+      value: 21.078
+    - type: precision_at_5
+      value: 15.421999999999999
+    - type: recall_at_1
+      value: 30.261
+    - type: recall_at_10
+      value: 63.242
+    - type: recall_at_100
+      value: 84.09100000000001
+    - type: recall_at_1000
+      value: 96.143
+    - type: recall_at_3
+      value: 46.478
+    - type: recall_at_5
+      value: 53.708
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.145
+    - type: map_at_10
+      value: 40.996
+    - type: map_at_100
+      value: 42.266999999999996
+    - type: map_at_1000
+      value: 42.397
+    - type: map_at_3
+      value: 38.005
+    - type: map_at_5
+      value: 39.628
+    - type: mrr_at_1
+      value: 38.344
+    - type: mrr_at_10
+      value: 46.827000000000005
+    - type: mrr_at_100
+      value: 47.446
+    - type: mrr_at_1000
+      value: 47.489
+    - type: mrr_at_3
+      value: 44.448
+    - type: mrr_at_5
+      value: 45.747
+    - type: ndcg_at_1
+      value: 38.344
+    - type: ndcg_at_10
+      value: 46.733000000000004
+    - type: ndcg_at_100
+      value: 51.103
+    - type: ndcg_at_1000
+      value: 53.075
+    - type: ndcg_at_3
+      value: 42.366
+    - type: ndcg_at_5
+      value: 44.242
+    - type: precision_at_1
+      value: 38.344
+    - type: precision_at_10
+      value: 8.822000000000001
+    - type: precision_at_100
+      value: 1.417
+    - type: precision_at_1000
+      value: 0.187
+    - type: precision_at_3
+      value: 20.403
+    - type: precision_at_5
+      value: 14.306
+    - type: recall_at_1
+      value: 31.145
+    - type: recall_at_10
+      value: 56.909
+    - type: recall_at_100
+      value: 75.274
+    - type: recall_at_1000
+      value: 87.629
+    - type: recall_at_3
+      value: 43.784
+    - type: recall_at_5
+      value: 49.338
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.83
+    - type: map_at_10
+      value: 51.553000000000004
+    - type: map_at_100
+      value: 52.581
+    - type: map_at_1000
+      value: 52.638
+    - type: map_at_3
+      value: 48.112
+    - type: map_at_5
+      value: 50.095
+    - type: mrr_at_1
+      value: 44.513999999999996
+    - type: mrr_at_10
+      value: 54.998000000000005
+    - type: mrr_at_100
+      value: 55.650999999999996
+    - type: mrr_at_1000
+      value: 55.679
+    - type: mrr_at_3
+      value: 52.602000000000004
+    - type: mrr_at_5
+      value: 53.931
+    - type: ndcg_at_1
+      value: 44.513999999999996
+    - type: ndcg_at_10
+      value: 57.67400000000001
+    - type: ndcg_at_100
+      value: 61.663999999999994
+    - type: ndcg_at_1000
+      value: 62.743
+    - type: ndcg_at_3
+      value: 51.964
+    - type: ndcg_at_5
+      value: 54.773
+    - type: precision_at_1
+      value: 44.513999999999996
+    - type: precision_at_10
+      value: 9.423
+    - type: precision_at_100
+      value: 1.2309999999999999
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 23.323
+    - type: precision_at_5
+      value: 16.163
+    - type: recall_at_1
+      value: 38.83
+    - type: recall_at_10
+      value: 72.327
+    - type: recall_at_100
+      value: 89.519
+    - type: recall_at_1000
+      value: 97.041
+    - type: recall_at_3
+      value: 57.206
+    - type: recall_at_5
+      value: 63.88399999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.484
+    - type: map_at_10
+      value: 34.527
+    - type: map_at_100
+      value: 35.661
+    - type: map_at_1000
+      value: 35.739
+    - type: map_at_3
+      value: 32.199
+    - type: map_at_5
+      value: 33.632
+    - type: mrr_at_1
+      value: 27.458
+    - type: mrr_at_10
+      value: 36.543
+    - type: mrr_at_100
+      value: 37.482
+    - type: mrr_at_1000
+      value: 37.543
+    - type: mrr_at_3
+      value: 34.256
+    - type: mrr_at_5
+      value: 35.618
+    - type: ndcg_at_1
+      value: 27.458
+    - type: ndcg_at_10
+      value: 39.396
+    - type: ndcg_at_100
+      value: 44.742
+    - type: ndcg_at_1000
+      value: 46.708
+    - type: ndcg_at_3
+      value: 34.817
+    - type: ndcg_at_5
+      value: 37.247
+    - type: precision_at_1
+      value: 27.458
+    - type: precision_at_10
+      value: 5.976999999999999
+    - type: precision_at_100
+      value: 0.907
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 14.878
+    - type: precision_at_5
+      value: 10.35
+    - type: recall_at_1
+      value: 25.484
+    - type: recall_at_10
+      value: 52.317
+    - type: recall_at_100
+      value: 76.701
+    - type: recall_at_1000
+      value: 91.408
+    - type: recall_at_3
+      value: 40.043
+    - type: recall_at_5
+      value: 45.879
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.719
+    - type: map_at_10
+      value: 25.269000000000002
+    - type: map_at_100
+      value: 26.442
+    - type: map_at_1000
+      value: 26.557
+    - type: map_at_3
+      value: 22.56
+    - type: map_at_5
+      value: 24.082
+    - type: mrr_at_1
+      value: 20.896
+    - type: mrr_at_10
+      value: 29.982999999999997
+    - type: mrr_at_100
+      value: 30.895
+    - type: mrr_at_1000
+      value: 30.961
+    - type: mrr_at_3
+      value: 27.239
+    - type: mrr_at_5
+      value: 28.787000000000003
+    - type: ndcg_at_1
+      value: 20.896
+    - type: ndcg_at_10
+      value: 30.814000000000004
+    - type: ndcg_at_100
+      value: 36.418
+    - type: ndcg_at_1000
+      value: 39.182
+    - type: ndcg_at_3
+      value: 25.807999999999996
+    - type: ndcg_at_5
+      value: 28.143
+    - type: precision_at_1
+      value: 20.896
+    - type: precision_at_10
+      value: 5.821
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 12.562000000000001
+    - type: precision_at_5
+      value: 9.254
+    - type: recall_at_1
+      value: 16.719
+    - type: recall_at_10
+      value: 43.155
+    - type: recall_at_100
+      value: 67.831
+    - type: recall_at_1000
+      value: 87.617
+    - type: recall_at_3
+      value: 29.259
+    - type: recall_at_5
+      value: 35.260999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.398999999999997
+    - type: map_at_10
+      value: 39.876
+    - type: map_at_100
+      value: 41.205999999999996
+    - type: map_at_1000
+      value: 41.321999999999996
+    - type: map_at_3
+      value: 36.588
+    - type: map_at_5
+      value: 38.538
+    - type: mrr_at_1
+      value: 35.9
+    - type: mrr_at_10
+      value: 45.528
+    - type: mrr_at_100
+      value: 46.343
+    - type: mrr_at_1000
+      value: 46.388
+    - type: mrr_at_3
+      value: 42.862
+    - type: mrr_at_5
+      value: 44.440000000000005
+    - type: ndcg_at_1
+      value: 35.9
+    - type: ndcg_at_10
+      value: 45.987
+    - type: ndcg_at_100
+      value: 51.370000000000005
+    - type: ndcg_at_1000
+      value: 53.400000000000006
+    - type: ndcg_at_3
+      value: 40.841
+    - type: ndcg_at_5
+      value: 43.447
+    - type: precision_at_1
+      value: 35.9
+    - type: precision_at_10
+      value: 8.393
+    - type: precision_at_100
+      value: 1.283
+    - type: precision_at_1000
+      value: 0.166
+    - type: precision_at_3
+      value: 19.538
+    - type: precision_at_5
+      value: 13.975000000000001
+    - type: recall_at_1
+      value: 29.398999999999997
+    - type: recall_at_10
+      value: 58.361
+    - type: recall_at_100
+      value: 81.081
+    - type: recall_at_1000
+      value: 94.004
+    - type: recall_at_3
+      value: 43.657000000000004
+    - type: recall_at_5
+      value: 50.519999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.589
+    - type: map_at_10
+      value: 31.608999999999998
+    - type: map_at_100
+      value: 33.128
+    - type: map_at_1000
+      value: 33.247
+    - type: map_at_3
+      value: 28.671999999999997
+    - type: map_at_5
+      value: 30.233999999999998
+    - type: mrr_at_1
+      value: 26.712000000000003
+    - type: mrr_at_10
+      value: 36.713
+    - type: mrr_at_100
+      value: 37.713
+    - type: mrr_at_1000
+      value: 37.771
+    - type: mrr_at_3
+      value: 34.075
+    - type: mrr_at_5
+      value: 35.451
+    - type: ndcg_at_1
+      value: 26.712000000000003
+    - type: ndcg_at_10
+      value: 37.519999999999996
+    - type: ndcg_at_100
+      value: 43.946000000000005
+    - type: ndcg_at_1000
+      value: 46.297
+    - type: ndcg_at_3
+      value: 32.551
+    - type: ndcg_at_5
+      value: 34.660999999999994
+    - type: precision_at_1
+      value: 26.712000000000003
+    - type: precision_at_10
+      value: 7.066
+    - type: precision_at_100
+      value: 1.216
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 15.906
+    - type: precision_at_5
+      value: 11.437999999999999
+    - type: recall_at_1
+      value: 21.589
+    - type: recall_at_10
+      value: 50.090999999999994
+    - type: recall_at_100
+      value: 77.43900000000001
+    - type: recall_at_1000
+      value: 93.35900000000001
+    - type: recall_at_3
+      value: 36.028999999999996
+    - type: recall_at_5
+      value: 41.698
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.121666666666663
+    - type: map_at_10
+      value: 34.46258333333334
+    - type: map_at_100
+      value: 35.710499999999996
+    - type: map_at_1000
+      value: 35.82691666666666
+    - type: map_at_3
+      value: 31.563249999999996
+    - type: map_at_5
+      value: 33.189750000000004
+    - type: mrr_at_1
+      value: 29.66441666666667
+    - type: mrr_at_10
+      value: 38.5455
+    - type: mrr_at_100
+      value: 39.39566666666667
+    - type: mrr_at_1000
+      value: 39.45325
+    - type: mrr_at_3
+      value: 36.003333333333345
+    - type: mrr_at_5
+      value: 37.440916666666666
+    - type: ndcg_at_1
+      value: 29.66441666666667
+    - type: ndcg_at_10
+      value: 39.978416666666675
+    - type: ndcg_at_100
+      value: 45.278666666666666
+    - type: ndcg_at_1000
+      value: 47.52275
+    - type: ndcg_at_3
+      value: 35.00058333333334
+    - type: ndcg_at_5
+      value: 37.34908333333333
+    - type: precision_at_1
+      value: 29.66441666666667
+    - type: precision_at_10
+      value: 7.094500000000001
+    - type: precision_at_100
+      value: 1.1523333333333332
+    - type: precision_at_1000
+      value: 0.15358333333333332
+    - type: precision_at_3
+      value: 16.184166666666663
+    - type: precision_at_5
+      value: 11.6005
+    - type: recall_at_1
+      value: 25.121666666666663
+    - type: recall_at_10
+      value: 52.23975000000001
+    - type: recall_at_100
+      value: 75.48408333333333
+    - type: recall_at_1000
+      value: 90.95316666666668
+    - type: recall_at_3
+      value: 38.38458333333333
+    - type: recall_at_5
+      value: 44.39933333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.569000000000003
+    - type: map_at_10
+      value: 30.389
+    - type: map_at_100
+      value: 31.396
+    - type: map_at_1000
+      value: 31.493
+    - type: map_at_3
+      value: 28.276
+    - type: map_at_5
+      value: 29.459000000000003
+    - type: mrr_at_1
+      value: 26.534000000000002
+    - type: mrr_at_10
+      value: 33.217999999999996
+    - type: mrr_at_100
+      value: 34.054
+    - type: mrr_at_1000
+      value: 34.12
+    - type: mrr_at_3
+      value: 31.058000000000003
+    - type: mrr_at_5
+      value: 32.330999999999996
+    - type: ndcg_at_1
+      value: 26.534000000000002
+    - type: ndcg_at_10
+      value: 34.608
+    - type: ndcg_at_100
+      value: 39.391999999999996
+    - type: ndcg_at_1000
+      value: 41.837999999999994
+    - type: ndcg_at_3
+      value: 30.564999999999998
+    - type: ndcg_at_5
+      value: 32.509
+    - type: precision_at_1
+      value: 26.534000000000002
+    - type: precision_at_10
+      value: 5.414
+    - type: precision_at_100
+      value: 0.847
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 12.986
+    - type: precision_at_5
+      value: 9.202
+    - type: recall_at_1
+      value: 23.569000000000003
+    - type: recall_at_10
+      value: 44.896
+    - type: recall_at_100
+      value: 66.476
+    - type: recall_at_1000
+      value: 84.548
+    - type: recall_at_3
+      value: 33.79
+    - type: recall_at_5
+      value: 38.512
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.36
+    - type: map_at_10
+      value: 23.57
+    - type: map_at_100
+      value: 24.698999999999998
+    - type: map_at_1000
+      value: 24.834999999999997
+    - type: map_at_3
+      value: 21.093
+    - type: map_at_5
+      value: 22.418
+    - type: mrr_at_1
+      value: 19.718
+    - type: mrr_at_10
+      value: 27.139999999999997
+    - type: mrr_at_100
+      value: 28.097
+    - type: mrr_at_1000
+      value: 28.177999999999997
+    - type: mrr_at_3
+      value: 24.805
+    - type: mrr_at_5
+      value: 26.121
+    - type: ndcg_at_1
+      value: 19.718
+    - type: ndcg_at_10
+      value: 28.238999999999997
+    - type: ndcg_at_100
+      value: 33.663
+    - type: ndcg_at_1000
+      value: 36.763
+    - type: ndcg_at_3
+      value: 23.747
+    - type: ndcg_at_5
+      value: 25.796000000000003
+    - type: precision_at_1
+      value: 19.718
+    - type: precision_at_10
+      value: 5.282
+    - type: precision_at_100
+      value: 0.9390000000000001
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 11.264000000000001
+    - type: precision_at_5
+      value: 8.341
+    - type: recall_at_1
+      value: 16.36
+    - type: recall_at_10
+      value: 38.669
+    - type: recall_at_100
+      value: 63.184
+    - type: recall_at_1000
+      value: 85.33800000000001
+    - type: recall_at_3
+      value: 26.214
+    - type: recall_at_5
+      value: 31.423000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.618999999999996
+    - type: map_at_10
+      value: 34.361999999999995
+    - type: map_at_100
+      value: 35.534
+    - type: map_at_1000
+      value: 35.634
+    - type: map_at_3
+      value: 31.402
+    - type: map_at_5
+      value: 32.815
+    - type: mrr_at_1
+      value: 30.037000000000003
+    - type: mrr_at_10
+      value: 38.284
+    - type: mrr_at_100
+      value: 39.141999999999996
+    - type: mrr_at_1000
+      value: 39.2
+    - type: mrr_at_3
+      value: 35.603
+    - type: mrr_at_5
+      value: 36.867
+    - type: ndcg_at_1
+      value: 30.037000000000003
+    - type: ndcg_at_10
+      value: 39.87
+    - type: ndcg_at_100
+      value: 45.243
+    - type: ndcg_at_1000
+      value: 47.507
+    - type: ndcg_at_3
+      value: 34.371
+    - type: ndcg_at_5
+      value: 36.521
+    - type: precision_at_1
+      value: 30.037000000000003
+    - type: precision_at_10
+      value: 6.819
+    - type: precision_at_100
+      value: 1.0699999999999998
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 15.392
+    - type: precision_at_5
+      value: 10.821
+    - type: recall_at_1
+      value: 25.618999999999996
+    - type: recall_at_10
+      value: 52.869
+    - type: recall_at_100
+      value: 76.395
+    - type: recall_at_1000
+      value: 92.19500000000001
+    - type: recall_at_3
+      value: 37.943
+    - type: recall_at_5
+      value: 43.342999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.283
+    - type: map_at_10
+      value: 32.155
+    - type: map_at_100
+      value: 33.724
+    - type: map_at_1000
+      value: 33.939
+    - type: map_at_3
+      value: 29.018
+    - type: map_at_5
+      value: 30.864000000000004
+    - type: mrr_at_1
+      value: 28.063
+    - type: mrr_at_10
+      value: 36.632
+    - type: mrr_at_100
+      value: 37.606
+    - type: mrr_at_1000
+      value: 37.671
+    - type: mrr_at_3
+      value: 33.992
+    - type: mrr_at_5
+      value: 35.613
+    - type: ndcg_at_1
+      value: 28.063
+    - type: ndcg_at_10
+      value: 38.024
+    - type: ndcg_at_100
+      value: 44.292
+    - type: ndcg_at_1000
+      value: 46.818
+    - type: ndcg_at_3
+      value: 32.965
+    - type: ndcg_at_5
+      value: 35.562
+    - type: precision_at_1
+      value: 28.063
+    - type: precision_at_10
+      value: 7.352
+    - type: precision_at_100
+      value: 1.514
+    - type: precision_at_1000
+      value: 0.23800000000000002
+    - type: precision_at_3
+      value: 15.481
+    - type: precision_at_5
+      value: 11.542
+    - type: recall_at_1
+      value: 23.283
+    - type: recall_at_10
+      value: 49.756
+    - type: recall_at_100
+      value: 78.05
+    - type: recall_at_1000
+      value: 93.854
+    - type: recall_at_3
+      value: 35.408
+    - type: recall_at_5
+      value: 42.187000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.201999999999998
+    - type: map_at_10
+      value: 26.826
+    - type: map_at_100
+      value: 27.961000000000002
+    - type: map_at_1000
+      value: 28.066999999999997
+    - type: map_at_3
+      value: 24.237000000000002
+    - type: map_at_5
+      value: 25.811
+    - type: mrr_at_1
+      value: 20.887
+    - type: mrr_at_10
+      value: 28.660000000000004
+    - type: mrr_at_100
+      value: 29.660999999999998
+    - type: mrr_at_1000
+      value: 29.731
+    - type: mrr_at_3
+      value: 26.155
+    - type: mrr_at_5
+      value: 27.68
+    - type: ndcg_at_1
+      value: 20.887
+    - type: ndcg_at_10
+      value: 31.523
+    - type: ndcg_at_100
+      value: 37.055
+    - type: ndcg_at_1000
+      value: 39.579
+    - type: ndcg_at_3
+      value: 26.529000000000003
+    - type: ndcg_at_5
+      value: 29.137
+    - type: precision_at_1
+      value: 20.887
+    - type: precision_at_10
+      value: 5.065
+    - type: precision_at_100
+      value: 0.856
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 11.399
+    - type: precision_at_5
+      value: 8.392
+    - type: recall_at_1
+      value: 19.201999999999998
+    - type: recall_at_10
+      value: 44.285000000000004
+    - type: recall_at_100
+      value: 69.768
+    - type: recall_at_1000
+      value: 88.302
+    - type: recall_at_3
+      value: 30.804
+    - type: recall_at_5
+      value: 37.039
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 11.244
+    - type: map_at_10
+      value: 18.956
+    - type: map_at_100
+      value: 20.674
+    - type: map_at_1000
+      value: 20.863
+    - type: map_at_3
+      value: 15.923000000000002
+    - type: map_at_5
+      value: 17.518
+    - type: mrr_at_1
+      value: 25.080999999999996
+    - type: mrr_at_10
+      value: 35.94
+    - type: mrr_at_100
+      value: 36.969
+    - type: mrr_at_1000
+      value: 37.013
+    - type: mrr_at_3
+      value: 32.617000000000004
+    - type: mrr_at_5
+      value: 34.682
+    - type: ndcg_at_1
+      value: 25.080999999999996
+    - type: ndcg_at_10
+      value: 26.539
+    - type: ndcg_at_100
+      value: 33.601
+    - type: ndcg_at_1000
+      value: 37.203
+    - type: ndcg_at_3
+      value: 21.695999999999998
+    - type: ndcg_at_5
+      value: 23.567
+    - type: precision_at_1
+      value: 25.080999999999996
+    - type: precision_at_10
+      value: 8.143
+    - type: precision_at_100
+      value: 1.5650000000000002
+    - type: precision_at_1000
+      value: 0.22300000000000003
+    - type: precision_at_3
+      value: 15.983
+    - type: precision_at_5
+      value: 12.417
+    - type: recall_at_1
+      value: 11.244
+    - type: recall_at_10
+      value: 31.457
+    - type: recall_at_100
+      value: 55.92
+    - type: recall_at_1000
+      value: 76.372
+    - type: recall_at_3
+      value: 19.784
+    - type: recall_at_5
+      value: 24.857000000000003
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.595
+    - type: map_at_10
+      value: 18.75
+    - type: map_at_100
+      value: 26.354
+    - type: map_at_1000
+      value: 27.912
+    - type: map_at_3
+      value: 13.794
+    - type: map_at_5
+      value: 16.021
+    - type: mrr_at_1
+      value: 65.75
+    - type: mrr_at_10
+      value: 73.837
+    - type: mrr_at_100
+      value: 74.22800000000001
+    - type: mrr_at_1000
+      value: 74.234
+    - type: mrr_at_3
+      value: 72.5
+    - type: mrr_at_5
+      value: 73.387
+    - type: ndcg_at_1
+      value: 52.625
+    - type: ndcg_at_10
+      value: 39.101
+    - type: ndcg_at_100
+      value: 43.836000000000006
+    - type: ndcg_at_1000
+      value: 51.086
+    - type: ndcg_at_3
+      value: 44.229
+    - type: ndcg_at_5
+      value: 41.555
+    - type: precision_at_1
+      value: 65.75
+    - type: precision_at_10
+      value: 30.45
+    - type: precision_at_100
+      value: 9.81
+    - type: precision_at_1000
+      value: 2.045
+    - type: precision_at_3
+      value: 48.667
+    - type: precision_at_5
+      value: 40.8
+    - type: recall_at_1
+      value: 8.595
+    - type: recall_at_10
+      value: 24.201
+    - type: recall_at_100
+      value: 50.096
+    - type: recall_at_1000
+      value: 72.677
+    - type: recall_at_3
+      value: 15.212
+    - type: recall_at_5
+      value: 18.745
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 46.565
+    - type: f1
+      value: 41.49914329345582
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 66.60000000000001
+    - type: map_at_10
+      value: 76.838
+    - type: map_at_100
+      value: 77.076
+    - type: map_at_1000
+      value: 77.09
+    - type: map_at_3
+      value: 75.545
+    - type: map_at_5
+      value: 76.39
+    - type: mrr_at_1
+      value: 71.707
+    - type: mrr_at_10
+      value: 81.514
+    - type: mrr_at_100
+      value: 81.64099999999999
+    - type: mrr_at_1000
+      value: 81.645
+    - type: mrr_at_3
+      value: 80.428
+    - type: mrr_at_5
+      value: 81.159
+    - type: ndcg_at_1
+      value: 71.707
+    - type: ndcg_at_10
+      value: 81.545
+    - type: ndcg_at_100
+      value: 82.477
+    - type: ndcg_at_1000
+      value: 82.73899999999999
+    - type: ndcg_at_3
+      value: 79.292
+    - type: ndcg_at_5
+      value: 80.599
+    - type: precision_at_1
+      value: 71.707
+    - type: precision_at_10
+      value: 10.035
+    - type: precision_at_100
+      value: 1.068
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 30.918
+    - type: precision_at_5
+      value: 19.328
+    - type: recall_at_1
+      value: 66.60000000000001
+    - type: recall_at_10
+      value: 91.353
+    - type: recall_at_100
+      value: 95.21
+    - type: recall_at_1000
+      value: 96.89999999999999
+    - type: recall_at_3
+      value: 85.188
+    - type: recall_at_5
+      value: 88.52
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.338
+    - type: map_at_10
+      value: 31.752000000000002
+    - type: map_at_100
+      value: 33.516
+    - type: map_at_1000
+      value: 33.694
+    - type: map_at_3
+      value: 27.716
+    - type: map_at_5
+      value: 29.67
+    - type: mrr_at_1
+      value: 38.117000000000004
+    - type: mrr_at_10
+      value: 47.323
+    - type: mrr_at_100
+      value: 48.13
+    - type: mrr_at_1000
+      value: 48.161
+    - type: mrr_at_3
+      value: 45.062000000000005
+    - type: mrr_at_5
+      value: 46.358
+    - type: ndcg_at_1
+      value: 38.117000000000004
+    - type: ndcg_at_10
+      value: 39.353
+    - type: ndcg_at_100
+      value: 46.044000000000004
+    - type: ndcg_at_1000
+      value: 49.083
+    - type: ndcg_at_3
+      value: 35.891
+    - type: ndcg_at_5
+      value: 36.661
+    - type: precision_at_1
+      value: 38.117000000000004
+    - type: precision_at_10
+      value: 11.187999999999999
+    - type: precision_at_100
+      value: 1.802
+    - type: precision_at_1000
+      value: 0.234
+    - type: precision_at_3
+      value: 24.126
+    - type: precision_at_5
+      value: 17.562
+    - type: recall_at_1
+      value: 19.338
+    - type: recall_at_10
+      value: 45.735
+    - type: recall_at_100
+      value: 71.281
+    - type: recall_at_1000
+      value: 89.537
+    - type: recall_at_3
+      value: 32.525
+    - type: recall_at_5
+      value: 37.671
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.995
+    - type: map_at_10
+      value: 55.032000000000004
+    - type: map_at_100
+      value: 55.86
+    - type: map_at_1000
+      value: 55.932
+    - type: map_at_3
+      value: 52.125
+    - type: map_at_5
+      value: 53.884
+    - type: mrr_at_1
+      value: 73.991
+    - type: mrr_at_10
+      value: 80.096
+    - type: mrr_at_100
+      value: 80.32000000000001
+    - type: mrr_at_1000
+      value: 80.331
+    - type: mrr_at_3
+      value: 79.037
+    - type: mrr_at_5
+      value: 79.719
+    - type: ndcg_at_1
+      value: 73.991
+    - type: ndcg_at_10
+      value: 63.786
+    - type: ndcg_at_100
+      value: 66.78
+    - type: ndcg_at_1000
+      value: 68.255
+    - type: ndcg_at_3
+      value: 59.501000000000005
+    - type: ndcg_at_5
+      value: 61.82299999999999
+    - type: precision_at_1
+      value: 73.991
+    - type: precision_at_10
+      value: 13.157
+    - type: precision_at_100
+      value: 1.552
+    - type: precision_at_1000
+      value: 0.17500000000000002
+    - type: precision_at_3
+      value: 37.519999999999996
+    - type: precision_at_5
+      value: 24.351
+    - type: recall_at_1
+      value: 36.995
+    - type: recall_at_10
+      value: 65.78699999999999
+    - type: recall_at_100
+      value: 77.583
+    - type: recall_at_1000
+      value: 87.421
+    - type: recall_at_3
+      value: 56.279999999999994
+    - type: recall_at_5
+      value: 60.878
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 86.80239999999999
+    - type: ap
+      value: 81.97305141128378
+    - type: f1
+      value: 86.76976305549273
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.166
+    - type: map_at_10
+      value: 33.396
+    - type: map_at_100
+      value: 34.588
+    - type: map_at_1000
+      value: 34.637
+    - type: map_at_3
+      value: 29.509999999999998
+    - type: map_at_5
+      value: 31.719
+    - type: mrr_at_1
+      value: 21.762
+    - type: mrr_at_10
+      value: 33.969
+    - type: mrr_at_100
+      value: 35.099000000000004
+    - type: mrr_at_1000
+      value: 35.141
+    - type: mrr_at_3
+      value: 30.148000000000003
+    - type: mrr_at_5
+      value: 32.324000000000005
+    - type: ndcg_at_1
+      value: 21.776999999999997
+    - type: ndcg_at_10
+      value: 40.306999999999995
+    - type: ndcg_at_100
+      value: 46.068
+    - type: ndcg_at_1000
+      value: 47.3
+    - type: ndcg_at_3
+      value: 32.416
+    - type: ndcg_at_5
+      value: 36.345
+    - type: precision_at_1
+      value: 21.776999999999997
+    - type: precision_at_10
+      value: 6.433
+    - type: precision_at_100
+      value: 0.932
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 13.897
+    - type: precision_at_5
+      value: 10.324
+    - type: recall_at_1
+      value: 21.166
+    - type: recall_at_10
+      value: 61.587
+    - type: recall_at_100
+      value: 88.251
+    - type: recall_at_1000
+      value: 97.727
+    - type: recall_at_3
+      value: 40.196
+    - type: recall_at_5
+      value: 49.611
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.04605563155496
+    - type: f1
+      value: 92.78007303978372
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 69.65116279069767
+    - type: f1
+      value: 52.75775172527262
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 70.34633490248822
+    - type: f1
+      value: 68.15345065392562
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 75.63887020847343
+    - type: f1
+      value: 76.08074680233685
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.77933406071333
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 32.06504927238196
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.20682480490871
+    - type: mrr
+      value: 33.41462721527003
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.548
+    - type: map_at_10
+      value: 13.086999999999998
+    - type: map_at_100
+      value: 16.698
+    - type: map_at_1000
+      value: 18.151999999999997
+    - type: map_at_3
+      value: 9.576
+    - type: map_at_5
+      value: 11.175
+    - type: mrr_at_1
+      value: 44.272
+    - type: mrr_at_10
+      value: 53.635999999999996
+    - type: mrr_at_100
+      value: 54.228
+    - type: mrr_at_1000
+      value: 54.26499999999999
+    - type: mrr_at_3
+      value: 51.754
+    - type: mrr_at_5
+      value: 53.086
+    - type: ndcg_at_1
+      value: 42.724000000000004
+    - type: ndcg_at_10
+      value: 34.769
+    - type: ndcg_at_100
+      value: 32.283
+    - type: ndcg_at_1000
+      value: 40.843
+    - type: ndcg_at_3
+      value: 39.852
+    - type: ndcg_at_5
+      value: 37.858999999999995
+    - type: precision_at_1
+      value: 44.272
+    - type: precision_at_10
+      value: 26.068
+    - type: precision_at_100
+      value: 8.328000000000001
+    - type: precision_at_1000
+      value: 2.1
+    - type: precision_at_3
+      value: 37.874
+    - type: precision_at_5
+      value: 33.065
+    - type: recall_at_1
+      value: 5.548
+    - type: recall_at_10
+      value: 16.936999999999998
+    - type: recall_at_100
+      value: 33.72
+    - type: recall_at_1000
+      value: 64.348
+    - type: recall_at_3
+      value: 10.764999999999999
+    - type: recall_at_5
+      value: 13.361
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.008
+    - type: map_at_10
+      value: 42.675000000000004
+    - type: map_at_100
+      value: 43.85
+    - type: map_at_1000
+      value: 43.884
+    - type: map_at_3
+      value: 38.286
+    - type: map_at_5
+      value: 40.78
+    - type: mrr_at_1
+      value: 31.518
+    - type: mrr_at_10
+      value: 45.015
+    - type: mrr_at_100
+      value: 45.924
+    - type: mrr_at_1000
+      value: 45.946999999999996
+    - type: mrr_at_3
+      value: 41.348
+    - type: mrr_at_5
+      value: 43.428
+    - type: ndcg_at_1
+      value: 31.489
+    - type: ndcg_at_10
+      value: 50.285999999999994
+    - type: ndcg_at_100
+      value: 55.291999999999994
+    - type: ndcg_at_1000
+      value: 56.05
+    - type: ndcg_at_3
+      value: 41.976
+    - type: ndcg_at_5
+      value: 46.103
+    - type: precision_at_1
+      value: 31.489
+    - type: precision_at_10
+      value: 8.456
+    - type: precision_at_100
+      value: 1.125
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 19.09
+    - type: precision_at_5
+      value: 13.841000000000001
+    - type: recall_at_1
+      value: 28.008
+    - type: recall_at_10
+      value: 71.21499999999999
+    - type: recall_at_100
+      value: 92.99
+    - type: recall_at_1000
+      value: 98.578
+    - type: recall_at_3
+      value: 49.604
+    - type: recall_at_5
+      value: 59.094
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.351
+    - type: map_at_10
+      value: 84.163
+    - type: map_at_100
+      value: 84.785
+    - type: map_at_1000
+      value: 84.801
+    - type: map_at_3
+      value: 81.16
+    - type: map_at_5
+      value: 83.031
+    - type: mrr_at_1
+      value: 80.96
+    - type: mrr_at_10
+      value: 87.241
+    - type: mrr_at_100
+      value: 87.346
+    - type: mrr_at_1000
+      value: 87.347
+    - type: mrr_at_3
+      value: 86.25699999999999
+    - type: mrr_at_5
+      value: 86.907
+    - type: ndcg_at_1
+      value: 80.97
+    - type: ndcg_at_10
+      value: 88.017
+    - type: ndcg_at_100
+      value: 89.241
+    - type: ndcg_at_1000
+      value: 89.34299999999999
+    - type: ndcg_at_3
+      value: 85.053
+    - type: ndcg_at_5
+      value: 86.663
+    - type: precision_at_1
+      value: 80.97
+    - type: precision_at_10
+      value: 13.358
+    - type: precision_at_100
+      value: 1.525
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.143
+    - type: precision_at_5
+      value: 24.451999999999998
+    - type: recall_at_1
+      value: 70.351
+    - type: recall_at_10
+      value: 95.39800000000001
+    - type: recall_at_100
+      value: 99.55199999999999
+    - type: recall_at_1000
+      value: 99.978
+    - type: recall_at_3
+      value: 86.913
+    - type: recall_at_5
+      value: 91.448
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 55.62406719814139
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 61.386700035141736
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.618
+    - type: map_at_10
+      value: 12.920000000000002
+    - type: map_at_100
+      value: 15.304
+    - type: map_at_1000
+      value: 15.656999999999998
+    - type: map_at_3
+      value: 9.187
+    - type: map_at_5
+      value: 10.937
+    - type: mrr_at_1
+      value: 22.8
+    - type: mrr_at_10
+      value: 35.13
+    - type: mrr_at_100
+      value: 36.239
+    - type: mrr_at_1000
+      value: 36.291000000000004
+    - type: mrr_at_3
+      value: 31.917
+    - type: mrr_at_5
+      value: 33.787
+    - type: ndcg_at_1
+      value: 22.8
+    - type: ndcg_at_10
+      value: 21.382
+    - type: ndcg_at_100
+      value: 30.257
+    - type: ndcg_at_1000
+      value: 36.001
+    - type: ndcg_at_3
+      value: 20.43
+    - type: ndcg_at_5
+      value: 17.622
+    - type: precision_at_1
+      value: 22.8
+    - type: precision_at_10
+      value: 11.26
+    - type: precision_at_100
+      value: 2.405
+    - type: precision_at_1000
+      value: 0.377
+    - type: precision_at_3
+      value: 19.633
+    - type: precision_at_5
+      value: 15.68
+    - type: recall_at_1
+      value: 4.618
+    - type: recall_at_10
+      value: 22.811999999999998
+    - type: recall_at_100
+      value: 48.787000000000006
+    - type: recall_at_1000
+      value: 76.63799999999999
+    - type: recall_at_3
+      value: 11.952
+    - type: recall_at_5
+      value: 15.892000000000001
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.01529458252244
+    - type: cos_sim_spearman
+      value: 77.92985224770254
+    - type: euclidean_pearson
+      value: 81.04251429422487
+    - type: euclidean_spearman
+      value: 77.92838490549133
+    - type: manhattan_pearson
+      value: 80.95892251458979
+    - type: manhattan_spearman
+      value: 77.81028089705941
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.97885282534388
+    - type: cos_sim_spearman
+      value: 75.1221970851712
+    - type: euclidean_pearson
+      value: 80.34455956720097
+    - type: euclidean_spearman
+      value: 74.5894274239938
+    - type: manhattan_pearson
+      value: 80.38999766325465
+    - type: manhattan_spearman
+      value: 74.68524557166975
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.95746064915672
+    - type: cos_sim_spearman
+      value: 85.08683458043946
+    - type: euclidean_pearson
+      value: 84.56699492836385
+    - type: euclidean_spearman
+      value: 85.66089116133713
+    - type: manhattan_pearson
+      value: 84.47553323458541
+    - type: manhattan_spearman
+      value: 85.56142206781472
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.71377893595067
+    - type: cos_sim_spearman
+      value: 81.03453291428589
+    - type: euclidean_pearson
+      value: 82.57136298308613
+    - type: euclidean_spearman
+      value: 81.15839961890875
+    - type: manhattan_pearson
+      value: 82.55157879373837
+    - type: manhattan_spearman
+      value: 81.1540163767054
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.64197832372373
+    - type: cos_sim_spearman
+      value: 88.31966852492485
+    - type: euclidean_pearson
+      value: 87.98692129976983
+    - type: euclidean_spearman
+      value: 88.6247340837856
+    - type: manhattan_pearson
+      value: 87.90437827826412
+    - type: manhattan_spearman
+      value: 88.56278787131457
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.84159950146693
+    - type: cos_sim_spearman
+      value: 83.90678384140168
+    - type: euclidean_pearson
+      value: 83.19005018860221
+    - type: euclidean_spearman
+      value: 84.16260415876295
+    - type: manhattan_pearson
+      value: 83.05030612994494
+    - type: manhattan_spearman
+      value: 83.99605629718336
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.49935350176666
+    - type: cos_sim_spearman
+      value: 87.59086606735383
+    - type: euclidean_pearson
+      value: 88.06537181129983
+    - type: euclidean_spearman
+      value: 87.6687448086014
+    - type: manhattan_pearson
+      value: 87.96599131972935
+    - type: manhattan_spearman
+      value: 87.63295748969642
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 67.68232799482763
+    - type: cos_sim_spearman
+      value: 67.99930378085793
+    - type: euclidean_pearson
+      value: 68.50275360001696
+    - type: euclidean_spearman
+      value: 67.81588179309259
+    - type: manhattan_pearson
+      value: 68.5892154749763
+    - type: manhattan_spearman
+      value: 67.84357259640682
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.37049618406554
+    - type: cos_sim_spearman
+      value: 85.57014313159492
+    - type: euclidean_pearson
+      value: 85.57469513908282
+    - type: euclidean_spearman
+      value: 85.661948135258
+    - type: manhattan_pearson
+      value: 85.36866831229028
+    - type: manhattan_spearman
+      value: 85.5043455368843
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 84.83259065376154
+    - type: mrr
+      value: 95.58455433455433
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 58.817
+    - type: map_at_10
+      value: 68.459
+    - type: map_at_100
+      value: 68.951
+    - type: map_at_1000
+      value: 68.979
+    - type: map_at_3
+      value: 65.791
+    - type: map_at_5
+      value: 67.583
+    - type: mrr_at_1
+      value: 61.667
+    - type: mrr_at_10
+      value: 69.368
+    - type: mrr_at_100
+      value: 69.721
+    - type: mrr_at_1000
+      value: 69.744
+    - type: mrr_at_3
+      value: 67.278
+    - type: mrr_at_5
+      value: 68.611
+    - type: ndcg_at_1
+      value: 61.667
+    - type: ndcg_at_10
+      value: 72.70100000000001
+    - type: ndcg_at_100
+      value: 74.928
+    - type: ndcg_at_1000
+      value: 75.553
+    - type: ndcg_at_3
+      value: 68.203
+    - type: ndcg_at_5
+      value: 70.804
+    - type: precision_at_1
+      value: 61.667
+    - type: precision_at_10
+      value: 9.533
+    - type: precision_at_100
+      value: 1.077
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 26.444000000000003
+    - type: precision_at_5
+      value: 17.599999999999998
+    - type: recall_at_1
+      value: 58.817
+    - type: recall_at_10
+      value: 84.789
+    - type: recall_at_100
+      value: 95.0
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 72.8
+    - type: recall_at_5
+      value: 79.294
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.8108910891089
+    - type: cos_sim_ap
+      value: 95.5743678558349
+    - type: cos_sim_f1
+      value: 90.43133366385722
+    - type: cos_sim_precision
+      value: 89.67551622418878
+    - type: cos_sim_recall
+      value: 91.2
+    - type: dot_accuracy
+      value: 99.75841584158415
+    - type: dot_ap
+      value: 94.00786363627253
+    - type: dot_f1
+      value: 87.51910341314316
+    - type: dot_precision
+      value: 89.20041536863967
+    - type: dot_recall
+      value: 85.9
+    - type: euclidean_accuracy
+      value: 99.81485148514851
+    - type: euclidean_ap
+      value: 95.4752113136905
+    - type: euclidean_f1
+      value: 90.44334975369456
+    - type: euclidean_precision
+      value: 89.126213592233
+    - type: euclidean_recall
+      value: 91.8
+    - type: manhattan_accuracy
+      value: 99.81584158415842
+    - type: manhattan_ap
+      value: 95.5163172682464
+    - type: manhattan_f1
+      value: 90.51987767584097
+    - type: manhattan_precision
+      value: 92.3076923076923
+    - type: manhattan_recall
+      value: 88.8
+    - type: max_accuracy
+      value: 99.81584158415842
+    - type: max_ap
+      value: 95.5743678558349
+    - type: max_f1
+      value: 90.51987767584097
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 62.63235986949449
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 36.334795589585575
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.02955214518782
+    - type: mrr
+      value: 52.8004838298956
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.63769566275453
+    - type: cos_sim_spearman
+      value: 30.422379185989335
+    - type: dot_pearson
+      value: 26.88493071882256
+    - type: dot_spearman
+      value: 26.505249740971305
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.21
+    - type: map_at_10
+      value: 1.654
+    - type: map_at_100
+      value: 10.095
+    - type: map_at_1000
+      value: 25.808999999999997
+    - type: map_at_3
+      value: 0.594
+    - type: map_at_5
+      value: 0.9289999999999999
+    - type: mrr_at_1
+      value: 78.0
+    - type: mrr_at_10
+      value: 87.019
+    - type: mrr_at_100
+      value: 87.019
+    - type: mrr_at_1000
+      value: 87.019
+    - type: mrr_at_3
+      value: 86.333
+    - type: mrr_at_5
+      value: 86.733
+    - type: ndcg_at_1
+      value: 73.0
+    - type: ndcg_at_10
+      value: 66.52900000000001
+    - type: ndcg_at_100
+      value: 53.433
+    - type: ndcg_at_1000
+      value: 51.324000000000005
+    - type: ndcg_at_3
+      value: 72.02199999999999
+    - type: ndcg_at_5
+      value: 69.696
+    - type: precision_at_1
+      value: 78.0
+    - type: precision_at_10
+      value: 70.39999999999999
+    - type: precision_at_100
+      value: 55.46
+    - type: precision_at_1000
+      value: 22.758
+    - type: precision_at_3
+      value: 76.667
+    - type: precision_at_5
+      value: 74.0
+    - type: recall_at_1
+      value: 0.21
+    - type: recall_at_10
+      value: 1.8849999999999998
+    - type: recall_at_100
+      value: 13.801
+    - type: recall_at_1000
+      value: 49.649
+    - type: recall_at_3
+      value: 0.632
+    - type: recall_at_5
+      value: 1.009
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.797
+    - type: map_at_10
+      value: 9.01
+    - type: map_at_100
+      value: 14.682
+    - type: map_at_1000
+      value: 16.336000000000002
+    - type: map_at_3
+      value: 4.546
+    - type: map_at_5
+      value: 5.9270000000000005
+    - type: mrr_at_1
+      value: 24.490000000000002
+    - type: mrr_at_10
+      value: 41.156
+    - type: mrr_at_100
+      value: 42.392
+    - type: mrr_at_1000
+      value: 42.408
+    - type: mrr_at_3
+      value: 38.775999999999996
+    - type: mrr_at_5
+      value: 40.102
+    - type: ndcg_at_1
+      value: 21.429000000000002
+    - type: ndcg_at_10
+      value: 22.222
+    - type: ndcg_at_100
+      value: 34.405
+    - type: ndcg_at_1000
+      value: 46.599000000000004
+    - type: ndcg_at_3
+      value: 25.261
+    - type: ndcg_at_5
+      value: 22.695999999999998
+    - type: precision_at_1
+      value: 24.490000000000002
+    - type: precision_at_10
+      value: 19.796
+    - type: precision_at_100
+      value: 7.306
+    - type: precision_at_1000
+      value: 1.5350000000000001
+    - type: precision_at_3
+      value: 27.211000000000002
+    - type: precision_at_5
+      value: 22.857
+    - type: recall_at_1
+      value: 1.797
+    - type: recall_at_10
+      value: 15.706000000000001
+    - type: recall_at_100
+      value: 46.412
+    - type: recall_at_1000
+      value: 83.159
+    - type: recall_at_3
+      value: 6.1370000000000005
+    - type: recall_at_5
+      value: 8.599
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.3302
+    - type: ap
+      value: 14.169121204575601
+    - type: f1
+      value: 54.229345975274235
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 58.22297679683077
+    - type: f1
+      value: 58.62984908377875
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.952922428464255
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 84.68140907194373
+    - type: cos_sim_ap
+      value: 70.12180123666836
+    - type: cos_sim_f1
+      value: 65.77501791258658
+    - type: cos_sim_precision
+      value: 60.07853403141361
+    - type: cos_sim_recall
+      value: 72.66490765171504
+    - type: dot_accuracy
+      value: 81.92167848840674
+    - type: dot_ap
+      value: 60.49837581423469
+    - type: dot_f1
+      value: 58.44186046511628
+    - type: dot_precision
+      value: 52.24532224532224
+    - type: dot_recall
+      value: 66.3060686015831
+    - type: euclidean_accuracy
+      value: 84.73505394289802
+    - type: euclidean_ap
+      value: 70.3278904593286
+    - type: euclidean_f1
+      value: 65.98851124940161
+    - type: euclidean_precision
+      value: 60.38107752956636
+    - type: euclidean_recall
+      value: 72.74406332453826
+    - type: manhattan_accuracy
+      value: 84.73505394289802
+    - type: manhattan_ap
+      value: 70.00737738537337
+    - type: manhattan_f1
+      value: 65.80150784822642
+    - type: manhattan_precision
+      value: 61.892583120204606
+    - type: manhattan_recall
+      value: 70.23746701846966
+    - type: max_accuracy
+      value: 84.73505394289802
+    - type: max_ap
+      value: 70.3278904593286
+    - type: max_f1
+      value: 65.98851124940161
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.44258159661582
+    - type: cos_sim_ap
+      value: 84.91926704880888
+    - type: cos_sim_f1
+      value: 77.07651086632926
+    - type: cos_sim_precision
+      value: 74.5894554883319
+    - type: cos_sim_recall
+      value: 79.73514012935017
+    - type: dot_accuracy
+      value: 85.88116583226608
+    - type: dot_ap
+      value: 78.9753854779923
+    - type: dot_f1
+      value: 72.17757637979255
+    - type: dot_precision
+      value: 66.80647486729143
+    - type: dot_recall
+      value: 78.48783492454572
+    - type: euclidean_accuracy
+      value: 88.5299025885823
+    - type: euclidean_ap
+      value: 85.08006075642194
+    - type: euclidean_f1
+      value: 77.29637336504163
+    - type: euclidean_precision
+      value: 74.69836253950014
+    - type: euclidean_recall
+      value: 80.08161379735141
+    - type: manhattan_accuracy
+      value: 88.55124771995187
+    - type: manhattan_ap
+      value: 85.00941529932851
+    - type: manhattan_f1
+      value: 77.33100233100232
+    - type: manhattan_precision
+      value: 73.37572573956317
+    - type: manhattan_recall
+      value: 81.73698798891284
+    - type: max_accuracy
+      value: 88.55124771995187
+    - type: max_ap
+      value: 85.08006075642194
+    - type: max_f1
+      value: 77.33100233100232
+language:
+- en
+license: mit
+---
+
+# gte-small
+
+General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
+
+The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
+
+## Metrics
+
+We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+
+
+
+| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
+| [**gte-base**](https://huggingface.co/thenlper/gte-base) 	| 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
+| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	| 0.44 | 768 | 514 	| 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	| 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
+| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 	| 0.13 | 384 | 512 	| 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
+| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 	| 0.09 | 384 | 512 	| 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
+| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) 	| 0.44 | 768 | 512 	| 56.00 | 41.1 	| 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
+| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	| 0.22 | 768 | 512 	| 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
+
+
+## Usage
+
+Code example
+
+```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]
+
+input_texts = [
+    "what is the capital of China?",
+    "how to implement quick sort in python?",
+    "Beijing",
+    "sorting algorithms"
+]
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
+model = AutoModel.from_pretrained("thenlper/gte-small")
+
+# 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'])
+
+# (Optionally) normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+```
+
+Use with sentence-transformers:
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+sentences = ['That is a happy person', 'That is a very happy person']
+
+model = SentenceTransformer('thenlper/gte-large')
+embeddings = model.encode(sentences)
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+### Limitation
+
+This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{li2023general,
+      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
+      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
+      year={2023},
+      eprint={2308.03281},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

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+  }
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snapshot/best/2_MixtureEmbeddingsModel/expert_03_sentence-transformers_all-MiniLM-L6-v2/README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+language: en
+license: apache-2.0
+datasets:
+- s2orc
+- flax-sentence-embeddings/stackexchange_xml
+- ms_marco
+- gooaq
+- yahoo_answers_topics
+- code_search_net
+- search_qa
+- eli5
+- snli
+- multi_nli
+- wikihow
+- natural_questions
+- trivia_qa
+- embedding-data/sentence-compression
+- embedding-data/flickr30k-captions
+- embedding-data/altlex
+- embedding-data/simple-wiki
+- embedding-data/QQP
+- embedding-data/SPECTER
+- embedding-data/PAQ_pairs
+- embedding-data/WikiAnswers
+
+---
+
+
+# all-MiniLM-L6-v2
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
+
+------
+
+## Background
+
+The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
+contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 
+1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
+
+We developped this model during the 
+[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
+organized by Hugging Face. We developped this model as part of the project:
+[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
+We then apply the cross entropy loss by comparing with true pairs.
+
+#### Hyper parameters
+
+We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
+We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
+a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
+
+#### Training data
+
+We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
+We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
+
+
+| Dataset                                                  | Paper                                    | Number of training tuples  |
+|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
+| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
+| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
+| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
+| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
+| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
+| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
+| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
+| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
+| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
+| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
+| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
+| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
+| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
+| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
+| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
+| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
+| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
+| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
+| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
+| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
+| **Total** | | **1,170,060,424** |

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snapshot/best/2_MixtureEmbeddingsModel/expert_03_sentence-transformers_all-MiniLM-L6-v2/config_sentence_transformers.json

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