diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -1,3156 +1,3156 @@
----
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- mteb
-- transformers.js
-model-index:
-- name: binarize_False
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 78.13432835820896
-    - type: ap
-      value: 42.190424731303246
-    - type: f1
-      value: 72.34446401534811
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 93.093825
-    - type: ap
-      value: 90.03727505544286
-    - type: f1
-      value: 93.0874055138833
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 48.428000000000004
-    - type: f1
-      value: 47.74311520203536
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
-    metrics:
-    - type: map_at_1
-      value: 23.898
-    - type: map_at_10
-      value: 39.775
-    - type: map_at_100
-      value: 40.827000000000005
-    - type: map_at_1000
-      value: 40.837
-    - type: map_at_20
-      value: 40.604
-    - type: map_at_3
-      value: 34.519
-    - type: map_at_5
-      value: 37.307
-    - type: mrr_at_1
-      value: 24.395
-    - type: mrr_at_10
-      value: 39.963
-    - type: mrr_at_100
-      value: 41.014
-    - type: mrr_at_1000
-      value: 41.024
-    - type: mrr_at_20
-      value: 40.791
-    - type: mrr_at_3
-      value: 34.732
-    - type: mrr_at_5
-      value: 37.480999999999995
-    - type: ndcg_at_1
-      value: 23.898
-    - type: ndcg_at_10
-      value: 48.962
-    - type: ndcg_at_100
-      value: 53.386
-    - type: ndcg_at_1000
-      value: 53.634
-    - type: ndcg_at_20
-      value: 51.898999999999994
-    - type: ndcg_at_3
-      value: 38.034
-    - type: ndcg_at_5
-      value: 43.036
-    - type: precision_at_1
-      value: 23.898
-    - type: precision_at_10
-      value: 7.852
-    - type: precision_at_100
-      value: 0.9769999999999999
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_20
-      value: 4.4990000000000006
-    - type: precision_at_3
-      value: 16.073999999999998
-    - type: precision_at_5
-      value: 12.063
-    - type: recall_at_1
-      value: 23.898
-    - type: recall_at_10
-      value: 78.521
-    - type: recall_at_100
-      value: 97.724
-    - type: recall_at_1000
-      value: 99.644
-    - type: recall_at_20
-      value: 89.972
-    - type: recall_at_3
-      value: 48.222
-    - type: recall_at_5
-      value: 60.313
-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 47.69067314293749
-    - type: v_measures
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-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 38.0916537995626
-    - type: v_measures
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-  - task:
-      type: Reranking
-    dataset:
-      type: None
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 62.33195643912506
-    - type: mrr
-      value: 76.43978366970057
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 81.20285894915236
-    - type: cos_sim_spearman
-      value: 78.16322678527897
-    - type: euclidean_pearson
-      value: 80.6118408638417
-    - type: euclidean_spearman
-      value: 78.19033583671204
-    - type: manhattan_pearson
-      value: 80.41282660275819
-    - type: manhattan_spearman
-      value: 77.98611431591628
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 85.25324675324676
-    - type: f1
-      value: 85.19854235582687
-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 39.65216461057432
-    - type: v_measures
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-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 33.28787287895752
-    - type: v_measures
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-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: f46a197baaae43b4f621051089b82a364682dfeb
-    metrics:
-    - type: map_at_1
-      value: 32.677
-    - type: map_at_10
-      value: 43.739
-    - type: map_at_100
-      value: 45.152
-    - type: map_at_1000
-      value: 45.279
-    - type: map_at_20
-      value: 44.553
-    - type: map_at_3
-      value: 40.321
-    - type: map_at_5
-      value: 42.201
-    - type: mrr_at_1
-      value: 40.2
-    - type: mrr_at_10
-      value: 49.755
-    - type: mrr_at_100
-      value: 50.468
-    - type: mrr_at_1000
-      value: 50.513
-    - type: mrr_at_20
-      value: 50.192
-    - type: mrr_at_3
-      value: 47.163
-    - type: mrr_at_5
-      value: 48.686
-    - type: ndcg_at_1
-      value: 40.2
-    - type: ndcg_at_10
-      value: 49.963
-    - type: ndcg_at_100
-      value: 54.978
-    - type: ndcg_at_1000
-      value: 56.979
-    - type: ndcg_at_20
-      value: 51.983000000000004
-    - type: ndcg_at_3
-      value: 45.086999999999996
-    - type: ndcg_at_5
-      value: 47.309
-    - type: precision_at_1
-      value: 40.2
-    - type: precision_at_10
-      value: 9.328
-    - type: precision_at_100
-      value: 1.443
-    - type: precision_at_1000
-      value: 0.19
-    - type: precision_at_20
-      value: 5.558
-    - type: precision_at_3
-      value: 21.364
-    - type: precision_at_5
-      value: 15.222
-    - type: recall_at_1
-      value: 32.677
-    - type: recall_at_10
-      value: 61.71
-    - type: recall_at_100
-      value: 82.431
-    - type: recall_at_1000
-      value: 94.896
-    - type: recall_at_20
-      value: 68.73700000000001
-    - type: recall_at_3
-      value: 47.431
-    - type: recall_at_5
-      value: 53.739000000000004
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
-    metrics:
-    - type: map_at_1
-      value: 32.71
-    - type: map_at_10
-      value: 43.297000000000004
-    - type: map_at_100
-      value: 44.607
-    - type: map_at_1000
-      value: 44.729
-    - type: map_at_20
-      value: 44.013999999999996
-    - type: map_at_3
-      value: 40.213
-    - type: map_at_5
-      value: 42.004000000000005
-    - type: mrr_at_1
-      value: 40.892
-    - type: mrr_at_10
-      value: 49.394
-    - type: mrr_at_100
-      value: 50.005
-    - type: mrr_at_1000
-      value: 50.043000000000006
-    - type: mrr_at_20
-      value: 49.764
-    - type: mrr_at_3
-      value: 47.134
-    - type: mrr_at_5
-      value: 48.522
-    - type: ndcg_at_1
-      value: 40.892
-    - type: ndcg_at_10
-      value: 49.047000000000004
-    - type: ndcg_at_100
-      value: 53.266999999999996
-    - type: ndcg_at_1000
-      value: 55.096999999999994
-    - type: ndcg_at_20
-      value: 50.707
-    - type: ndcg_at_3
-      value: 44.896
-    - type: ndcg_at_5
-      value: 46.983000000000004
-    - type: precision_at_1
-      value: 40.892
-    - type: precision_at_10
-      value: 9.293
-    - type: precision_at_100
-      value: 1.473
-    - type: precision_at_1000
-      value: 0.192
-    - type: precision_at_20
-      value: 5.446
-    - type: precision_at_3
-      value: 21.592
-    - type: precision_at_5
-      value: 15.540999999999999
-    - type: recall_at_1
-      value: 32.71
-    - type: recall_at_10
-      value: 58.592999999999996
-    - type: recall_at_100
-      value: 76.242
-    - type: recall_at_1000
-      value: 87.717
-    - type: recall_at_20
-      value: 64.646
-    - type: recall_at_3
-      value: 46.253
-    - type: recall_at_5
-      value: 51.946999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
-    metrics:
-    - type: map_at_1
-      value: 41.644999999999996
-    - type: map_at_10
-      value: 53.825
-    - type: map_at_100
-      value: 54.82
-    - type: map_at_1000
-      value: 54.87499999999999
-    - type: map_at_20
-      value: 54.43
-    - type: map_at_3
-      value: 50.705
-    - type: map_at_5
-      value: 52.501
-    - type: mrr_at_1
-      value: 47.524
-    - type: mrr_at_10
-      value: 57.260999999999996
-    - type: mrr_at_100
-      value: 57.902
-    - type: mrr_at_1000
-      value: 57.931999999999995
-    - type: mrr_at_20
-      value: 57.689
-    - type: mrr_at_3
-      value: 55.089
-    - type: mrr_at_5
-      value: 56.38999999999999
-    - type: ndcg_at_1
-      value: 47.524
-    - type: ndcg_at_10
-      value: 59.41499999999999
-    - type: ndcg_at_100
-      value: 63.258
-    - type: ndcg_at_1000
-      value: 64.376
-    - type: ndcg_at_20
-      value: 61.149
-    - type: ndcg_at_3
-      value: 54.381
-    - type: ndcg_at_5
-      value: 56.89999999999999
-    - type: precision_at_1
-      value: 47.524
-    - type: precision_at_10
-      value: 9.386
-    - type: precision_at_100
-      value: 1.221
-    - type: precision_at_1000
-      value: 0.136
-    - type: precision_at_20
-      value: 5.223
-    - type: precision_at_3
-      value: 24.096
-    - type: precision_at_5
-      value: 16.364
-    - type: recall_at_1
-      value: 41.644999999999996
-    - type: recall_at_10
-      value: 72.386
-    - type: recall_at_100
-      value: 88.794
-    - type: recall_at_1000
-      value: 96.75399999999999
-    - type: recall_at_20
-      value: 78.74
-    - type: recall_at_3
-      value: 59.028000000000006
-    - type: recall_at_5
-      value: 65.197
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: 5003b3064772da1887988e05400cf3806fe491f2
-    metrics:
-    - type: map_at_1
-      value: 28.648
-    - type: map_at_10
-      value: 36.388999999999996
-    - type: map_at_100
-      value: 37.372
-    - type: map_at_1000
-      value: 37.457
-    - type: map_at_20
-      value: 36.912
-    - type: map_at_3
-      value: 34.076
-    - type: map_at_5
-      value: 35.415
-    - type: mrr_at_1
-      value: 30.508000000000003
-    - type: mrr_at_10
-      value: 38.132
-    - type: mrr_at_100
-      value: 39.04
-    - type: mrr_at_1000
-      value: 39.106
-    - type: mrr_at_20
-      value: 38.643
-    - type: mrr_at_3
-      value: 35.876000000000005
-    - type: mrr_at_5
-      value: 37.208999999999996
-    - type: ndcg_at_1
-      value: 30.508000000000003
-    - type: ndcg_at_10
-      value: 40.762
-    - type: ndcg_at_100
-      value: 45.732
-    - type: ndcg_at_1000
-      value: 47.799
-    - type: ndcg_at_20
-      value: 42.591
-    - type: ndcg_at_3
-      value: 36.266999999999996
-    - type: ndcg_at_5
-      value: 38.58
-    - type: precision_at_1
-      value: 30.508000000000003
-    - type: precision_at_10
-      value: 6.010999999999999
-    - type: precision_at_100
-      value: 0.897
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_20
-      value: 3.412
-    - type: precision_at_3
-      value: 14.991
-    - type: precision_at_5
-      value: 10.328
-    - type: recall_at_1
-      value: 28.648
-    - type: recall_at_10
-      value: 52.342999999999996
-    - type: recall_at_100
-      value: 75.268
-    - type: recall_at_1000
-      value: 90.641
-    - type: recall_at_20
-      value: 59.303
-    - type: recall_at_3
-      value: 40.447
-    - type: recall_at_5
-      value: 46.117000000000004
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
-    metrics:
-    - type: map_at_1
-      value: 18.476
-    - type: map_at_10
-      value: 27.148
-    - type: map_at_100
-      value: 28.317999999999998
-    - type: map_at_1000
-      value: 28.427999999999997
-    - type: map_at_20
-      value: 27.764
-    - type: map_at_3
-      value: 24.801000000000002
-    - type: map_at_5
-      value: 26.133
-    - type: mrr_at_1
-      value: 22.886
-    - type: mrr_at_10
-      value: 31.741000000000003
-    - type: mrr_at_100
-      value: 32.708
-    - type: mrr_at_1000
-      value: 32.769
-    - type: mrr_at_20
-      value: 32.296
-    - type: mrr_at_3
-      value: 29.498
-    - type: mrr_at_5
-      value: 30.773
-    - type: ndcg_at_1
-      value: 22.886
-    - type: ndcg_at_10
-      value: 32.265
-    - type: ndcg_at_100
-      value: 37.829
-    - type: ndcg_at_1000
-      value: 40.558
-    - type: ndcg_at_20
-      value: 34.372
-    - type: ndcg_at_3
-      value: 28.105000000000004
-    - type: ndcg_at_5
-      value: 30.04
-    - type: precision_at_1
-      value: 22.886
-    - type: precision_at_10
-      value: 5.808
-    - type: precision_at_100
-      value: 0.985
-    - type: precision_at_1000
-      value: 0.13699999999999998
-    - type: precision_at_20
-      value: 3.495
-    - type: precision_at_3
-      value: 13.639999999999999
-    - type: precision_at_5
-      value: 9.577
-    - type: recall_at_1
-      value: 18.476
-    - type: recall_at_10
-      value: 43.442
-    - type: recall_at_100
-      value: 67.376
-    - type: recall_at_1000
-      value: 86.874
-    - type: recall_at_20
-      value: 51.038
-    - type: recall_at_3
-      value: 31.785999999999998
-    - type: recall_at_5
-      value: 36.858999999999995
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
-    metrics:
-    - type: map_at_1
-      value: 29.098000000000003
-    - type: map_at_10
-      value: 38.97
-    - type: map_at_100
-      value: 40.293
-    - type: map_at_1000
-      value: 40.397
-    - type: map_at_20
-      value: 39.778999999999996
-    - type: map_at_3
-      value: 35.723
-    - type: map_at_5
-      value: 37.519999999999996
-    - type: mrr_at_1
-      value: 35.515
-    - type: mrr_at_10
-      value: 44.55
-    - type: mrr_at_100
-      value: 45.37
-    - type: mrr_at_1000
-      value: 45.412
-    - type: mrr_at_20
-      value: 45.054
-    - type: mrr_at_3
-      value: 41.835
-    - type: mrr_at_5
-      value: 43.356
-    - type: ndcg_at_1
-      value: 35.515
-    - type: ndcg_at_10
-      value: 44.91
-    - type: ndcg_at_100
-      value: 50.27700000000001
-    - type: ndcg_at_1000
-      value: 52.215
-    - type: ndcg_at_20
-      value: 47.235
-    - type: ndcg_at_3
-      value: 39.505
-    - type: ndcg_at_5
-      value: 42.016
-    - type: precision_at_1
-      value: 35.515
-    - type: precision_at_10
-      value: 8.152
-    - type: precision_at_100
-      value: 1.262
-    - type: precision_at_1000
-      value: 0.16
-    - type: precision_at_20
-      value: 4.851
-    - type: precision_at_3
-      value: 18.447
-    - type: precision_at_5
-      value: 13.321
-    - type: recall_at_1
-      value: 29.098000000000003
-    - type: recall_at_10
-      value: 57.115
-    - type: recall_at_100
-      value: 79.467
-    - type: recall_at_1000
-      value: 92.162
-    - type: recall_at_20
-      value: 65.161
-    - type: recall_at_3
-      value: 42.254000000000005
-    - type: recall_at_5
-      value: 48.415
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
-    metrics:
-    - type: map_at_1
-      value: 27.372000000000003
-    - type: map_at_10
-      value: 37.781
-    - type: map_at_100
-      value: 39.128
-    - type: map_at_1000
-      value: 39.238
-    - type: map_at_20
-      value: 38.592
-    - type: map_at_3
-      value: 34.782999999999994
-    - type: map_at_5
-      value: 36.466
-    - type: mrr_at_1
-      value: 33.904
-    - type: mrr_at_10
-      value: 43.15
-    - type: mrr_at_100
-      value: 44.049
-    - type: mrr_at_1000
-      value: 44.107
-    - type: mrr_at_20
-      value: 43.721
-    - type: mrr_at_3
-      value: 40.677
-    - type: mrr_at_5
-      value: 42.19
-    - type: ndcg_at_1
-      value: 33.904
-    - type: ndcg_at_10
-      value: 43.527
-    - type: ndcg_at_100
-      value: 49.004999999999995
-    - type: ndcg_at_1000
-      value: 51.276999999999994
-    - type: ndcg_at_20
-      value: 45.988
-    - type: ndcg_at_3
-      value: 38.824999999999996
-    - type: ndcg_at_5
-      value: 41.04
-    - type: precision_at_1
-      value: 33.904
-    - type: precision_at_10
-      value: 7.854
-    - type: precision_at_100
-      value: 1.2309999999999999
-    - type: precision_at_1000
-      value: 0.16
-    - type: precision_at_20
-      value: 4.692
-    - type: precision_at_3
-      value: 18.531
-    - type: precision_at_5
-      value: 13.150999999999998
-    - type: recall_at_1
-      value: 27.372000000000003
-    - type: recall_at_10
-      value: 55.245999999999995
-    - type: recall_at_100
-      value: 78.278
-    - type: recall_at_1000
-      value: 93.718
-    - type: recall_at_20
-      value: 64.095
-    - type: recall_at_3
-      value: 41.665
-    - type: recall_at_5
-      value: 47.632000000000005
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: f46a197baaae43b4f621051089b82a364682dfeb
-    metrics:
-    - type: map_at_1
-      value: 27.734166666666667
-    - type: map_at_10
-      value: 36.858
-    - type: map_at_100
-      value: 38.043833333333325
-    - type: map_at_1000
-      value: 38.15541666666667
-    - type: map_at_20
-      value: 37.521249999999995
-    - type: map_at_3
-      value: 34.07658333333333
-    - type: map_at_5
-      value: 35.62683333333333
-    - type: mrr_at_1
-      value: 32.676249999999996
-    - type: mrr_at_10
-      value: 40.999
-    - type: mrr_at_100
-      value: 41.835
-    - type: mrr_at_1000
-      value: 41.8895
-    - type: mrr_at_20
-      value: 41.4865
-    - type: mrr_at_3
-      value: 38.645
-    - type: mrr_at_5
-      value: 39.99725000000001
-    - type: ndcg_at_1
-      value: 32.676249999999996
-    - type: ndcg_at_10
-      value: 42.08016666666666
-    - type: ndcg_at_100
-      value: 47.082750000000004
-    - type: ndcg_at_1000
-      value: 49.276583333333335
-    - type: ndcg_at_20
-      value: 44.04808333333334
-    - type: ndcg_at_3
-      value: 37.43375
-    - type: ndcg_at_5
-      value: 39.623000000000005
-    - type: precision_at_1
-      value: 32.676249999999996
-    - type: precision_at_10
-      value: 7.271
-    - type: precision_at_100
-      value: 1.1458333333333333
-    - type: precision_at_1000
-      value: 0.152
-    - type: precision_at_20
-      value: 4.282916666666667
-    - type: precision_at_3
-      value: 17.061416666666666
-    - type: precision_at_5
-      value: 12.05466666666667
-    - type: recall_at_1
-      value: 27.734166666666667
-    - type: recall_at_10
-      value: 53.33574999999999
-    - type: recall_at_100
-      value: 75.16275
-    - type: recall_at_1000
-      value: 90.34891666666665
-    - type: recall_at_20
-      value: 60.4935
-    - type: recall_at_3
-      value: 40.377916666666664
-    - type: recall_at_5
-      value: 46.0195
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
-    metrics:
-    - type: map_at_1
-      value: 25.653
-    - type: map_at_10
-      value: 32.151
-    - type: map_at_100
-      value: 33.152
-    - type: map_at_1000
-      value: 33.243
-    - type: map_at_20
-      value: 32.717
-    - type: map_at_3
-      value: 30.287
-    - type: map_at_5
-      value: 31.25
-    - type: mrr_at_1
-      value: 28.988000000000003
-    - type: mrr_at_10
-      value: 35.131
-    - type: mrr_at_100
-      value: 36.002
-    - type: mrr_at_1000
-      value: 36.069
-    - type: mrr_at_20
-      value: 35.61
-    - type: mrr_at_3
-      value: 33.308
-    - type: mrr_at_5
-      value: 34.259
-    - type: ndcg_at_1
-      value: 28.988000000000003
-    - type: ndcg_at_10
-      value: 35.988
-    - type: ndcg_at_100
-      value: 40.764
-    - type: ndcg_at_1000
-      value: 43.112
-    - type: ndcg_at_20
-      value: 37.852999999999994
-    - type: ndcg_at_3
-      value: 32.562000000000005
-    - type: ndcg_at_5
-      value: 33.983000000000004
-    - type: precision_at_1
-      value: 28.988000000000003
-    - type: precision_at_10
-      value: 5.475
-    - type: precision_at_100
-      value: 0.8500000000000001
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_20
-      value: 3.229
-    - type: precision_at_3
-      value: 13.905999999999999
-    - type: precision_at_5
-      value: 9.386999999999999
-    - type: recall_at_1
-      value: 25.653
-    - type: recall_at_10
-      value: 44.962
-    - type: recall_at_100
-      value: 66.405
-    - type: recall_at_1000
-      value: 83.88799999999999
-    - type: recall_at_20
-      value: 51.79899999999999
-    - type: recall_at_3
-      value: 35.144999999999996
-    - type: recall_at_5
-      value: 38.814
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: 46989137a86843e03a6195de44b09deda022eec7
-    metrics:
-    - type: map_at_1
-      value: 17.825
-    - type: map_at_10
-      value: 25.592
-    - type: map_at_100
-      value: 26.613999999999997
-    - type: map_at_1000
-      value: 26.734
-    - type: map_at_20
-      value: 26.115
-    - type: map_at_3
-      value: 23.119
-    - type: map_at_5
-      value: 24.54
-    - type: mrr_at_1
-      value: 21.335
-    - type: mrr_at_10
-      value: 29.165000000000003
-    - type: mrr_at_100
-      value: 30.049
-    - type: mrr_at_1000
-      value: 30.121
-    - type: mrr_at_20
-      value: 29.639
-    - type: mrr_at_3
-      value: 26.863999999999997
-    - type: mrr_at_5
-      value: 28.185
-    - type: ndcg_at_1
-      value: 21.335
-    - type: ndcg_at_10
-      value: 30.357
-    - type: ndcg_at_100
-      value: 35.410000000000004
-    - type: ndcg_at_1000
-      value: 38.24
-    - type: ndcg_at_20
-      value: 32.08
-    - type: ndcg_at_3
-      value: 25.95
-    - type: ndcg_at_5
-      value: 28.081
-    - type: precision_at_1
-      value: 21.335
-    - type: precision_at_10
-      value: 5.506
-    - type: precision_at_100
-      value: 0.928
-    - type: precision_at_1000
-      value: 0.135
-    - type: precision_at_20
-      value: 3.2550000000000003
-    - type: precision_at_3
-      value: 12.239
-    - type: precision_at_5
-      value: 8.885
-    - type: recall_at_1
-      value: 17.825
-    - type: recall_at_10
-      value: 41.105999999999995
-    - type: recall_at_100
-      value: 64.17
-    - type: recall_at_1000
-      value: 84.19200000000001
-    - type: recall_at_20
-      value: 47.497
-    - type: recall_at_3
-      value: 28.862
-    - type: recall_at_5
-      value: 34.348
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
-    metrics:
-    - type: map_at_1
-      value: 29.435
-    - type: map_at_10
-      value: 38.261
-    - type: map_at_100
-      value: 39.242
-    - type: map_at_1000
-      value: 39.347
-    - type: map_at_20
-      value: 38.742
-    - type: map_at_3
-      value: 35.457
-    - type: map_at_5
-      value: 37.043
-    - type: mrr_at_1
-      value: 34.235
-    - type: mrr_at_10
-      value: 42.24
-    - type: mrr_at_100
-      value: 42.988
-    - type: mrr_at_1000
-      value: 43.043
-    - type: mrr_at_20
-      value: 42.613
-    - type: mrr_at_3
-      value: 39.832
-    - type: mrr_at_5
-      value: 41.227000000000004
-    - type: ndcg_at_1
-      value: 34.235
-    - type: ndcg_at_10
-      value: 43.384
-    - type: ndcg_at_100
-      value: 48.14
-    - type: ndcg_at_1000
-      value: 50.414
-    - type: ndcg_at_20
-      value: 44.913
-    - type: ndcg_at_3
-      value: 38.454
-    - type: ndcg_at_5
-      value: 40.776
-    - type: precision_at_1
-      value: 34.235
-    - type: precision_at_10
-      value: 7.164
-    - type: precision_at_100
-      value: 1.065
-    - type: precision_at_1000
-      value: 0.13699999999999998
-    - type: precision_at_20
-      value: 4.021
-    - type: precision_at_3
-      value: 17.226
-    - type: precision_at_5
-      value: 12.071
-    - type: recall_at_1
-      value: 29.435
-    - type: recall_at_10
-      value: 54.93900000000001
-    - type: recall_at_100
-      value: 76.176
-    - type: recall_at_1000
-      value: 91.989
-    - type: recall_at_20
-      value: 60.451
-    - type: recall_at_3
-      value: 41.332
-    - type: recall_at_5
-      value: 47.316
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
-    metrics:
-    - type: map_at_1
-      value: 25.605
-    - type: map_at_10
-      value: 34.162
-    - type: map_at_100
-      value: 35.827999999999996
-    - type: map_at_1000
-      value: 36.04
-    - type: map_at_20
-      value: 35.016000000000005
-    - type: map_at_3
-      value: 30.984
-    - type: map_at_5
-      value: 32.717
-    - type: mrr_at_1
-      value: 30.435000000000002
-    - type: mrr_at_10
-      value: 38.681
-    - type: mrr_at_100
-      value: 39.656000000000006
-    - type: mrr_at_1000
-      value: 39.71
-    - type: mrr_at_20
-      value: 39.208999999999996
-    - type: mrr_at_3
-      value: 35.903
-    - type: mrr_at_5
-      value: 37.454
-    - type: ndcg_at_1
-      value: 30.435000000000002
-    - type: ndcg_at_10
-      value: 39.916000000000004
-    - type: ndcg_at_100
-      value: 45.958
-    - type: ndcg_at_1000
-      value: 48.449999999999996
-    - type: ndcg_at_20
-      value: 42.085
-    - type: ndcg_at_3
-      value: 34.696
-    - type: ndcg_at_5
-      value: 37.147000000000006
-    - type: precision_at_1
-      value: 30.435000000000002
-    - type: precision_at_10
-      value: 7.767
-    - type: precision_at_100
-      value: 1.547
-    - type: precision_at_1000
-      value: 0.23800000000000002
-    - type: precision_at_20
-      value: 4.941
-    - type: precision_at_3
-      value: 16.073999999999998
-    - type: precision_at_5
-      value: 11.937000000000001
-    - type: recall_at_1
-      value: 25.605
-    - type: recall_at_10
-      value: 50.654999999999994
-    - type: recall_at_100
-      value: 77.609
-    - type: recall_at_1000
-      value: 93.518
-    - type: recall_at_20
-      value: 58.845000000000006
-    - type: recall_at_3
-      value: 36.272
-    - type: recall_at_5
-      value: 42.596000000000004
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
-    metrics:
-    - type: map_at_1
-      value: 23.666
-    - type: map_at_10
-      value: 30.980999999999998
-    - type: map_at_100
-      value: 32.0
-    - type: map_at_1000
-      value: 32.098
-    - type: map_at_20
-      value: 31.621
-    - type: map_at_3
-      value: 28.449999999999996
-    - type: map_at_5
-      value: 29.731999999999996
-    - type: mrr_at_1
-      value: 25.692999999999998
-    - type: mrr_at_10
-      value: 32.788000000000004
-    - type: mrr_at_100
-      value: 33.783
-    - type: mrr_at_1000
-      value: 33.849000000000004
-    - type: mrr_at_20
-      value: 33.408
-    - type: mrr_at_3
-      value: 30.561
-    - type: mrr_at_5
-      value: 31.716
-    - type: ndcg_at_1
-      value: 25.692999999999998
-    - type: ndcg_at_10
-      value: 35.428
-    - type: ndcg_at_100
-      value: 40.375
-    - type: ndcg_at_1000
-      value: 42.802
-    - type: ndcg_at_20
-      value: 37.621
-    - type: ndcg_at_3
-      value: 30.476999999999997
-    - type: ndcg_at_5
-      value: 32.621
-    - type: precision_at_1
-      value: 25.692999999999998
-    - type: precision_at_10
-      value: 5.508
-    - type: precision_at_100
-      value: 0.848
-    - type: precision_at_1000
-      value: 0.116
-    - type: precision_at_20
-      value: 3.272
-    - type: precision_at_3
-      value: 12.631
-    - type: precision_at_5
-      value: 8.872
-    - type: recall_at_1
-      value: 23.666
-    - type: recall_at_10
-      value: 47.532000000000004
-    - type: recall_at_100
-      value: 69.73700000000001
-    - type: recall_at_1000
-      value: 87.83800000000001
-    - type: recall_at_20
-      value: 55.61000000000001
-    - type: recall_at_3
-      value: 34.06
-    - type: recall_at_5
-      value: 39.254
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
-    metrics:
-    - type: map_at_1
-      value: 16.337
-    - type: map_at_10
-      value: 26.488
-    - type: map_at_100
-      value: 28.415000000000003
-    - type: map_at_1000
-      value: 28.584
-    - type: map_at_20
-      value: 27.557
-    - type: map_at_3
-      value: 22.665
-    - type: map_at_5
-      value: 24.542
-    - type: mrr_at_1
-      value: 36.417
-    - type: mrr_at_10
-      value: 48.001
-    - type: mrr_at_100
-      value: 48.784
-    - type: mrr_at_1000
-      value: 48.809000000000005
-    - type: mrr_at_20
-      value: 48.507
-    - type: mrr_at_3
-      value: 45.103
-    - type: mrr_at_5
-      value: 46.843
-    - type: ndcg_at_1
-      value: 36.417
-    - type: ndcg_at_10
-      value: 35.67
-    - type: ndcg_at_100
-      value: 42.716
-    - type: ndcg_at_1000
-      value: 45.639
-    - type: ndcg_at_20
-      value: 38.471
-    - type: ndcg_at_3
-      value: 30.444
-    - type: ndcg_at_5
-      value: 32.004
-    - type: precision_at_1
-      value: 36.417
-    - type: precision_at_10
-      value: 10.73
-    - type: precision_at_100
-      value: 1.833
-    - type: precision_at_1000
-      value: 0.23800000000000002
-    - type: precision_at_20
-      value: 6.596
-    - type: precision_at_3
-      value: 22.302
-    - type: precision_at_5
-      value: 16.521
-    - type: recall_at_1
-      value: 16.337
-    - type: recall_at_10
-      value: 40.671
-    - type: recall_at_100
-      value: 64.55300000000001
-    - type: recall_at_1000
-      value: 80.934
-    - type: recall_at_20
-      value: 48.381
-    - type: recall_at_3
-      value: 27.279999999999998
-    - type: recall_at_5
-      value: 32.621
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
-    metrics:
-    - type: map_at_1
-      value: 9.056000000000001
-    - type: map_at_10
-      value: 19.419
-    - type: map_at_100
-      value: 27.069
-    - type: map_at_1000
-      value: 28.666000000000004
-    - type: map_at_20
-      value: 22.434
-    - type: map_at_3
-      value: 13.895
-    - type: map_at_5
-      value: 16.121
-    - type: mrr_at_1
-      value: 69.0
-    - type: mrr_at_10
-      value: 75.804
-    - type: mrr_at_100
-      value: 76.117
-    - type: mrr_at_1000
-      value: 76.125
-    - type: mrr_at_20
-      value: 76.009
-    - type: mrr_at_3
-      value: 74.375
-    - type: mrr_at_5
-      value: 75.4
-    - type: ndcg_at_1
-      value: 57.49999999999999
-    - type: ndcg_at_10
-      value: 41.495
-    - type: ndcg_at_100
-      value: 45.208
-    - type: ndcg_at_1000
-      value: 52.221
-    - type: ndcg_at_20
-      value: 40.617999999999995
-    - type: ndcg_at_3
-      value: 46.592
-    - type: ndcg_at_5
-      value: 43.559
-    - type: precision_at_1
-      value: 69.0
-    - type: precision_at_10
-      value: 32.574999999999996
-    - type: precision_at_100
-      value: 10.205
-    - type: precision_at_1000
-      value: 2.036
-    - type: precision_at_20
-      value: 24.687
-    - type: precision_at_3
-      value: 49.75
-    - type: precision_at_5
-      value: 42.0
-    - type: recall_at_1
-      value: 9.056000000000001
-    - type: recall_at_10
-      value: 24.866
-    - type: recall_at_100
-      value: 50.097
-    - type: recall_at_1000
-      value: 72.038
-    - type: recall_at_20
-      value: 31.858999999999998
-    - type: recall_at_3
-      value: 15.096000000000002
-    - type: recall_at_5
-      value: 18.548000000000002
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 48.259999999999984
-    - type: f1
-      value: 43.1498589523159
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
-    metrics:
-    - type: map_at_1
-      value: 74.798
-    - type: map_at_10
-      value: 83.454
-    - type: map_at_100
-      value: 83.623
-    - type: map_at_1000
-      value: 83.635
-    - type: map_at_20
-      value: 83.55
-    - type: map_at_3
-      value: 82.392
-    - type: map_at_5
-      value: 83.167
-    - type: mrr_at_1
-      value: 80.708
-    - type: mrr_at_10
-      value: 88.377
-    - type: mrr_at_100
-      value: 88.411
-    - type: mrr_at_1000
-      value: 88.411
-    - type: mrr_at_20
-      value: 88.402
-    - type: mrr_at_3
-      value: 87.646
-    - type: mrr_at_5
-      value: 88.232
-    - type: ndcg_at_1
-      value: 80.708
-    - type: ndcg_at_10
-      value: 87.35199999999999
-    - type: ndcg_at_100
-      value: 87.91600000000001
-    - type: ndcg_at_1000
-      value: 88.12299999999999
-    - type: ndcg_at_20
-      value: 87.593
-    - type: ndcg_at_3
-      value: 85.738
-    - type: ndcg_at_5
-      value: 86.845
-    - type: precision_at_1
-      value: 80.708
-    - type: precision_at_10
-      value: 10.432
-    - type: precision_at_100
-      value: 1.091
-    - type: precision_at_1000
-      value: 0.11299999999999999
-    - type: precision_at_20
-      value: 5.296
-    - type: precision_at_3
-      value: 32.778
-    - type: precision_at_5
-      value: 20.399
-    - type: recall_at_1
-      value: 74.798
-    - type: recall_at_10
-      value: 94.459
-    - type: recall_at_100
-      value: 96.614
-    - type: recall_at_1000
-      value: 97.868
-    - type: recall_at_20
-      value: 95.254
-    - type: recall_at_3
-      value: 90.144
-    - type: recall_at_5
-      value: 92.965
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
-    metrics:
-    - type: map_at_1
-      value: 20.008
-    - type: map_at_10
-      value: 32.731
-    - type: map_at_100
-      value: 34.467999999999996
-    - type: map_at_1000
-      value: 34.643
-    - type: map_at_20
-      value: 33.717000000000006
-    - type: map_at_3
-      value: 28.427999999999997
-    - type: map_at_5
-      value: 30.788
-    - type: mrr_at_1
-      value: 40.586
-    - type: mrr_at_10
-      value: 49.056
-    - type: mrr_at_100
-      value: 49.887
-    - type: mrr_at_1000
-      value: 49.929
-    - type: mrr_at_20
-      value: 49.552
-    - type: mrr_at_3
-      value: 46.785
-    - type: mrr_at_5
-      value: 48.004000000000005
-    - type: ndcg_at_1
-      value: 40.586
-    - type: ndcg_at_10
-      value: 40.589999999999996
-    - type: ndcg_at_100
-      value: 47.03
-    - type: ndcg_at_1000
-      value: 49.994
-    - type: ndcg_at_20
-      value: 43.229
-    - type: ndcg_at_3
-      value: 37.061
-    - type: ndcg_at_5
-      value: 37.992
-    - type: precision_at_1
-      value: 40.586
-    - type: precision_at_10
-      value: 11.219
-    - type: precision_at_100
-      value: 1.781
-    - type: precision_at_1000
-      value: 0.232
-    - type: precision_at_20
-      value: 6.705
-    - type: precision_at_3
-      value: 24.743000000000002
-    - type: precision_at_5
-      value: 18.086
-    - type: recall_at_1
-      value: 20.008
-    - type: recall_at_10
-      value: 47.412
-    - type: recall_at_100
-      value: 71.274
-    - type: recall_at_1000
-      value: 88.898
-    - type: recall_at_20
-      value: 55.706999999999994
-    - type: recall_at_3
-      value: 33.346
-    - type: recall_at_5
-      value: 39.112
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: ab518f4d6fcca38d87c25209f94beba119d02014
-    metrics:
-    - type: map_at_1
-      value: 41.789
-    - type: map_at_10
-      value: 57.898
-    - type: map_at_100
-      value: 58.632
-    - type: map_at_1000
-      value: 58.693
-    - type: map_at_20
-      value: 58.314
-    - type: map_at_3
-      value: 55.236
-    - type: map_at_5
-      value: 56.852999999999994
-    - type: mrr_at_1
-      value: 83.57900000000001
-    - type: mrr_at_10
-      value: 87.631
-    - type: mrr_at_100
-      value: 87.764
-    - type: mrr_at_1000
-      value: 87.77000000000001
-    - type: mrr_at_20
-      value: 87.70700000000001
-    - type: mrr_at_3
-      value: 87.02499999999999
-    - type: mrr_at_5
-      value: 87.34100000000001
-    - type: ndcg_at_1
-      value: 83.57900000000001
-    - type: ndcg_at_10
-      value: 67.11399999999999
-    - type: ndcg_at_100
-      value: 69.686
-    - type: ndcg_at_1000
-      value: 70.926
-    - type: ndcg_at_20
-      value: 68.119
-    - type: ndcg_at_3
-      value: 63.402
-    - type: ndcg_at_5
-      value: 65.354
-    - type: precision_at_1
-      value: 83.57900000000001
-    - type: precision_at_10
-      value: 13.333
-    - type: precision_at_100
-      value: 1.537
-    - type: precision_at_1000
-      value: 0.16999999999999998
-    - type: precision_at_20
-      value: 6.988999999999999
-    - type: precision_at_3
-      value: 38.929
-    - type: precision_at_5
-      value: 24.897
-    - type: recall_at_1
-      value: 41.789
-    - type: recall_at_10
-      value: 66.664
-    - type: recall_at_100
-      value: 76.833
-    - type: recall_at_1000
-      value: 85.14500000000001
-    - type: recall_at_20
-      value: 69.892
-    - type: recall_at_3
-      value: 58.392999999999994
-    - type: recall_at_5
-      value: 62.242
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 86.6108
-    - type: ap
-      value: 81.63890253106925
-    - type: f1
-      value: 86.54585789538082
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: c5a29a104738b98a9e76336939199e264163d4a0
-    metrics:
-    - type: map_at_1
-      value: 22.407
-    - type: map_at_10
-      value: 34.603
-    - type: map_at_100
-      value: 35.808
-    - type: map_at_1000
-      value: 35.855
-    - type: map_at_20
-      value: 35.368
-    - type: map_at_3
-      value: 30.764000000000003
-    - type: map_at_5
-      value: 32.964
-    - type: mrr_at_1
-      value: 23.009
-    - type: mrr_at_10
-      value: 35.136
-    - type: mrr_at_100
-      value: 36.284
-    - type: mrr_at_1000
-      value: 36.325
-    - type: mrr_at_20
-      value: 35.869
-    - type: mrr_at_3
-      value: 31.351000000000003
-    - type: mrr_at_5
-      value: 33.54
-    - type: ndcg_at_1
-      value: 23.009
-    - type: ndcg_at_10
-      value: 41.471999999999994
-    - type: ndcg_at_100
-      value: 47.211999999999996
-    - type: ndcg_at_1000
-      value: 48.361
-    - type: ndcg_at_20
-      value: 44.169000000000004
-    - type: ndcg_at_3
-      value: 33.646
-    - type: ndcg_at_5
-      value: 37.580000000000005
-    - type: precision_at_1
-      value: 23.009
-    - type: precision_at_10
-      value: 6.54
-    - type: precision_at_100
-      value: 0.941
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_20
-      value: 3.832
-    - type: precision_at_3
-      value: 14.283999999999999
-    - type: precision_at_5
-      value: 10.564
-    - type: recall_at_1
-      value: 22.407
-    - type: recall_at_10
-      value: 62.678999999999995
-    - type: recall_at_100
-      value: 89.09700000000001
-    - type: recall_at_1000
-      value: 97.822
-    - type: recall_at_20
-      value: 73.116
-    - type: recall_at_3
-      value: 41.4
-    - type: recall_at_5
-      value: 50.855
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 92.94573643410853
-    - type: f1
-      value: 92.73148878666994
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 77.86137710898313
-    - type: f1
-      value: 60.360562463738724
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 73.83322125084062
-    - type: f1
-      value: 71.61864304680206
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 77.50504371217215
-    - type: f1
-      value: 77.52039268347185
-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 34.346952648910225
-    - type: v_measures
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-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 32.19992734583148
-    - type: v_measures
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-  - task:
-      type: Reranking
-    dataset:
-      type: None
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 30.62309561205373
-    - type: mrr
-      value: 31.707879717902554
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
-    metrics:
-    - type: map_at_1
-      value: 5.668
-    - type: map_at_10
-      value: 12.225999999999999
-    - type: map_at_100
-      value: 15.122
-    - type: map_at_1000
-      value: 16.422
-    - type: map_at_20
-      value: 13.361999999999998
-    - type: map_at_3
-      value: 9.083
-    - type: map_at_5
-      value: 10.5
-    - type: mrr_at_1
-      value: 46.44
-    - type: mrr_at_10
-      value: 53.553
-    - type: mrr_at_100
-      value: 54.15
-    - type: mrr_at_1000
-      value: 54.193000000000005
-    - type: mrr_at_20
-      value: 53.837
-    - type: mrr_at_3
-      value: 51.702999999999996
-    - type: mrr_at_5
-      value: 52.647
-    - type: ndcg_at_1
-      value: 44.272
-    - type: ndcg_at_10
-      value: 33.395
-    - type: ndcg_at_100
-      value: 29.976999999999997
-    - type: ndcg_at_1000
-      value: 38.388
-    - type: ndcg_at_20
-      value: 30.606
-    - type: ndcg_at_3
-      value: 39.212
-    - type: ndcg_at_5
-      value: 36.611
-    - type: precision_at_1
-      value: 46.129999999999995
-    - type: precision_at_10
-      value: 24.334
-    - type: precision_at_100
-      value: 7.553999999999999
-    - type: precision_at_1000
-      value: 1.994
-    - type: precision_at_20
-      value: 17.678
-    - type: precision_at_3
-      value: 36.326
-    - type: precision_at_5
-      value: 31.330999999999996
-    - type: recall_at_1
-      value: 5.668
-    - type: recall_at_10
-      value: 15.837000000000002
-    - type: recall_at_100
-      value: 29.845
-    - type: recall_at_1000
-      value: 60.563
-    - type: recall_at_20
-      value: 18.587999999999997
-    - type: recall_at_3
-      value: 10.096
-    - type: recall_at_5
-      value: 12.261
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
-    metrics:
-    - type: map_at_1
-      value: 39.335
-    - type: map_at_10
-      value: 54.932
-    - type: map_at_100
-      value: 55.742000000000004
-    - type: map_at_1000
-      value: 55.766000000000005
-    - type: map_at_20
-      value: 55.504
-    - type: map_at_3
-      value: 50.904
-    - type: map_at_5
-      value: 53.388999999999996
-    - type: mrr_at_1
-      value: 44.003
-    - type: mrr_at_10
-      value: 57.419
-    - type: mrr_at_100
-      value: 57.963
-    - type: mrr_at_1000
-      value: 57.981
-    - type: mrr_at_20
-      value: 57.80499999999999
-    - type: mrr_at_3
-      value: 54.30199999999999
-    - type: mrr_at_5
-      value: 56.257000000000005
-    - type: ndcg_at_1
-      value: 43.974999999999994
-    - type: ndcg_at_10
-      value: 62.153999999999996
-    - type: ndcg_at_100
-      value: 65.326
-    - type: ndcg_at_1000
-      value: 65.862
-    - type: ndcg_at_20
-      value: 63.922999999999995
-    - type: ndcg_at_3
-      value: 54.834
-    - type: ndcg_at_5
-      value: 58.857000000000006
-    - type: precision_at_1
-      value: 43.974999999999994
-    - type: precision_at_10
-      value: 9.722
-    - type: precision_at_100
-      value: 1.153
-    - type: precision_at_1000
-      value: 0.12
-    - type: precision_at_20
-      value: 5.3
-    - type: precision_at_3
-      value: 24.392
-    - type: precision_at_5
-      value: 16.993
-    - type: recall_at_1
-      value: 39.335
-    - type: recall_at_10
-      value: 81.501
-    - type: recall_at_100
-      value: 94.851
-    - type: recall_at_1000
-      value: 98.817
-    - type: recall_at_20
-      value: 87.968
-    - type: recall_at_3
-      value: 62.795
-    - type: recall_at_5
-      value: 71.985
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
-    metrics:
-    - type: map_at_1
-      value: 71.222
-    - type: map_at_10
-      value: 85.193
-    - type: map_at_100
-      value: 85.802
-    - type: map_at_1000
-      value: 85.81800000000001
-    - type: map_at_20
-      value: 85.587
-    - type: map_at_3
-      value: 82.253
-    - type: map_at_5
-      value: 84.142
-    - type: mrr_at_1
-      value: 82.04
-    - type: mrr_at_10
-      value: 88.101
-    - type: mrr_at_100
-      value: 88.196
-    - type: mrr_at_1000
-      value: 88.196
-    - type: mrr_at_20
-      value: 88.175
-    - type: mrr_at_3
-      value: 87.145
-    - type: mrr_at_5
-      value: 87.825
-    - type: ndcg_at_1
-      value: 82.04
-    - type: ndcg_at_10
-      value: 88.849
-    - type: ndcg_at_100
-      value: 89.992
-    - type: ndcg_at_1000
-      value: 90.089
-    - type: ndcg_at_20
-      value: 89.468
-    - type: ndcg_at_3
-      value: 86.06899999999999
-    - type: ndcg_at_5
-      value: 87.669
-    - type: precision_at_1
-      value: 82.04
-    - type: precision_at_10
-      value: 13.447000000000001
-    - type: precision_at_100
-      value: 1.528
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_20
-      value: 7.116
-    - type: precision_at_3
-      value: 37.617
-    - type: precision_at_5
-      value: 24.776
-    - type: recall_at_1
-      value: 71.222
-    - type: recall_at_10
-      value: 95.73899999999999
-    - type: recall_at_100
-      value: 99.572
-    - type: recall_at_1000
-      value: 99.988
-    - type: recall_at_20
-      value: 97.725
-    - type: recall_at_3
-      value: 87.742
-    - type: recall_at_5
-      value: 92.23400000000001
-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 56.502005725283524
-    - type: v_measures
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-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
-    metrics:
-    - type: v_measure
-      value: 63.14989421688691
-    - type: v_measures
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-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
-    metrics:
-    - type: map_at_1
-      value: 4.4830000000000005
-    - type: map_at_10
-      value: 11.04
-    - type: map_at_100
-      value: 12.764000000000001
-    - type: map_at_1000
-      value: 13.04
-    - type: map_at_20
-      value: 11.953
-    - type: map_at_3
-      value: 8.125
-    - type: map_at_5
-      value: 9.565999999999999
-    - type: mrr_at_1
-      value: 22.1
-    - type: mrr_at_10
-      value: 32.494
-    - type: mrr_at_100
-      value: 33.525
-    - type: mrr_at_1000
-      value: 33.596
-    - type: mrr_at_20
-      value: 33.089
-    - type: mrr_at_3
-      value: 29.416999999999998
-    - type: mrr_at_5
-      value: 31.267
-    - type: ndcg_at_1
-      value: 22.1
-    - type: ndcg_at_10
-      value: 18.587
-    - type: ndcg_at_100
-      value: 25.482
-    - type: ndcg_at_1000
-      value: 30.581999999999997
-    - type: ndcg_at_20
-      value: 21.077
-    - type: ndcg_at_3
-      value: 18.165
-    - type: ndcg_at_5
-      value: 15.676000000000002
-    - type: precision_at_1
-      value: 22.1
-    - type: precision_at_10
-      value: 9.48
-    - type: precision_at_100
-      value: 1.942
-    - type: precision_at_1000
-      value: 0.316
-    - type: precision_at_20
-      value: 6.175
-    - type: precision_at_3
-      value: 17.033
-    - type: precision_at_5
-      value: 13.719999999999999
-    - type: recall_at_1
-      value: 4.4830000000000005
-    - type: recall_at_10
-      value: 19.208
-    - type: recall_at_100
-      value: 39.417
-    - type: recall_at_1000
-      value: 64.235
-    - type: recall_at_20
-      value: 25.057000000000002
-    - type: recall_at_3
-      value: 10.348
-    - type: recall_at_5
-      value: 13.893
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.50181312649208
-    - type: cos_sim_spearman
-      value: 79.92900705478993
-    - type: euclidean_pearson
-      value: 81.13482128094503
-    - type: euclidean_spearman
-      value: 79.92732266864367
-    - type: manhattan_pearson
-      value: 81.06702121654993
-    - type: manhattan_spearman
-      value: 79.86983106619135
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.85431681906961
-    - type: cos_sim_spearman
-      value: 77.61671419416626
-    - type: euclidean_pearson
-      value: 81.30538320520961
-    - type: euclidean_spearman
-      value: 77.62096481461272
-    - type: manhattan_pearson
-      value: 81.2306021173407
-    - type: manhattan_spearman
-      value: 77.58386300715222
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.98057702322754
-    - type: cos_sim_spearman
-      value: 86.13305071688859
-    - type: euclidean_pearson
-      value: 85.70903555966376
-    - type: euclidean_spearman
-      value: 86.13150222328171
-    - type: manhattan_pearson
-      value: 85.69380834788831
-    - type: manhattan_spearman
-      value: 86.10784739081191
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.43368314724589
-    - type: cos_sim_spearman
-      value: 81.26767916144169
-    - type: euclidean_pearson
-      value: 83.23234690932492
-    - type: euclidean_spearman
-      value: 81.2671726214706
-    - type: manhattan_pearson
-      value: 83.2381239261109
-    - type: manhattan_spearman
-      value: 81.27674961470714
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 86.8637546411748
-    - type: cos_sim_spearman
-      value: 88.25330888676139
-    - type: euclidean_pearson
-      value: 87.81194589390417
-    - type: euclidean_spearman
-      value: 88.25258669625579
-    - type: manhattan_pearson
-      value: 87.8131866998459
-    - type: manhattan_spearman
-      value: 88.26523268929576
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.83129743147286
-    - type: cos_sim_spearman
-      value: 85.73732687732624
-    - type: euclidean_pearson
-      value: 85.18051277328075
-    - type: euclidean_spearman
-      value: 85.73565846174445
-    - type: manhattan_pearson
-      value: 85.179029651079
-    - type: manhattan_spearman
-      value: 85.75709685404729
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS17 (en-en)
-      config: en-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.04715794253148
-    - type: cos_sim_spearman
-      value: 87.61577496386343
-    - type: euclidean_pearson
-      value: 88.34713614361046
-    - type: euclidean_spearman
-      value: 87.56541901567275
-    - type: manhattan_pearson
-      value: 88.26010824585985
-    - type: manhattan_spearman
-      value: 87.35211736948182
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STS22 (en)
-      config: en
-      split: test
-      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
-    metrics:
-    - type: cos_sim_pearson
-      value: 62.36160793264433
-    - type: cos_sim_spearman
-      value: 66.07767480051893
-    - type: euclidean_pearson
-      value: 66.4716471304865
-    - type: euclidean_spearman
-      value: 66.03999286501872
-    - type: manhattan_pearson
-      value: 66.46197824372902
-    - type: manhattan_spearman
-      value: 65.82936468127227
-  - task:
-      type: STS
-    dataset:
-      type: None
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 85.27768996785856
-    - type: cos_sim_spearman
-      value: 86.96704639052885
-    - type: euclidean_pearson
-      value: 86.48753189555983
-    - type: euclidean_spearman
-      value: 86.96981285751171
-    - type: manhattan_pearson
-      value: 86.49262465015401
-    - type: manhattan_spearman
-      value: 86.95378609580054
-  - task:
-      type: Reranking
-    dataset:
-      type: None
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 81.52012853393428
-    - type: mrr
-      value: 94.70817671798063
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: 0228b52cf27578f30900b9e5271d331663a030d7
-    metrics:
-    - type: map_at_1
-      value: 55.344
-    - type: map_at_10
-      value: 64.82900000000001
-    - type: map_at_100
-      value: 65.42
-    - type: map_at_1000
-      value: 65.443
-    - type: map_at_20
-      value: 65.2
-    - type: map_at_3
-      value: 61.8
-    - type: map_at_5
-      value: 63.510999999999996
-    - type: mrr_at_1
-      value: 58.333
-    - type: mrr_at_10
-      value: 66.24600000000001
-    - type: mrr_at_100
-      value: 66.742
-    - type: mrr_at_1000
-      value: 66.762
-    - type: mrr_at_20
-      value: 66.549
-    - type: mrr_at_3
-      value: 64.056
-    - type: mrr_at_5
-      value: 65.372
-    - type: ndcg_at_1
-      value: 58.333
-    - type: ndcg_at_10
-      value: 69.626
-    - type: ndcg_at_100
-      value: 72.236
-    - type: ndcg_at_1000
-      value: 72.872
-    - type: ndcg_at_20
-      value: 70.864
-    - type: ndcg_at_3
-      value: 64.50399999999999
-    - type: ndcg_at_5
-      value: 67.07600000000001
-    - type: precision_at_1
-      value: 58.333
-    - type: precision_at_10
-      value: 9.4
-    - type: precision_at_100
-      value: 1.073
-    - type: precision_at_1000
-      value: 0.11299999999999999
-    - type: precision_at_20
-      value: 4.983
-    - type: precision_at_3
-      value: 25.222
-    - type: precision_at_5
-      value: 16.8
-    - type: recall_at_1
-      value: 55.344
-    - type: recall_at_10
-      value: 82.789
-    - type: recall_at_100
-      value: 94.6
-    - type: recall_at_1000
-      value: 99.667
-    - type: recall_at_20
-      value: 87.533
-    - type: recall_at_3
-      value: 69.18299999999999
-    - type: recall_at_5
-      value: 75.622
-  - task:
-      type: PairClassification
-    dataset:
-      type: None
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.69405940594059
-    - type: cos_sim_ap
-      value: 92.03642221694545
-    - type: cos_sim_f1
-      value: 84.06395048994327
-    - type: cos_sim_precision
-      value: 86.79446219382322
-    - type: cos_sim_recall
-      value: 81.5
-    - type: dot_accuracy
-      value: 99.6930693069307
-    - type: dot_ap
-      value: 91.9971441434875
-    - type: dot_f1
-      value: 83.8006230529595
-    - type: dot_precision
-      value: 87.14902807775377
-    - type: dot_recall
-      value: 80.7
-    - type: euclidean_accuracy
-      value: 99.69504950495049
-    - type: euclidean_ap
-      value: 92.03626548389335
-    - type: euclidean_f1
-      value: 84.10732714138285
-    - type: euclidean_precision
-      value: 86.88699360341151
-    - type: euclidean_recall
-      value: 81.5
-    - type: manhattan_accuracy
-      value: 99.69504950495049
-    - type: manhattan_ap
-      value: 92.02049659660081
-    - type: manhattan_f1
-      value: 84.34959349593495
-    - type: manhattan_precision
-      value: 85.74380165289256
-    - type: manhattan_recall
-      value: 83.0
-    - type: max_accuracy
-      value: 99.69504950495049
-    - type: max_ap
-      value: 92.03642221694545
-    - type: max_f1
-      value: 84.34959349593495
-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 67.04916654680977
-    - type: v_measures
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-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 33.36641413495258
-    - type: v_measures
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-  - task:
-      type: Reranking
-    dataset:
-      type: None
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 51.19282080158746
-    - type: mrr
-      value: 51.871100713012474
-  - task:
-      type: Summarization
-    dataset:
-      type: None
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 31.437664703708485
-    - type: cos_sim_spearman
-      value: 31.391119208581575
-    - type: dot_pearson
-      value: 31.19925970504054
-    - type: dot_spearman
-      value: 31.38087224016694
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
-    metrics:
-    - type: map_at_1
-      value: 0.249
-    - type: map_at_10
-      value: 2.163
-    - type: map_at_100
-      value: 13.242999999999999
-    - type: map_at_1000
-      value: 30.866
-    - type: map_at_20
-      value: 3.9539999999999997
-    - type: map_at_3
-      value: 0.718
-    - type: map_at_5
-      value: 1.169
-    - type: mrr_at_1
-      value: 96.0
-    - type: mrr_at_10
-      value: 98.0
-    - type: mrr_at_100
-      value: 98.0
-    - type: mrr_at_1000
-      value: 98.0
-    - type: mrr_at_20
-      value: 98.0
-    - type: mrr_at_3
-      value: 98.0
-    - type: mrr_at_5
-      value: 98.0
-    - type: ndcg_at_1
-      value: 92.0
-    - type: ndcg_at_10
-      value: 84.147
-    - type: ndcg_at_100
-      value: 65.143
-    - type: ndcg_at_1000
-      value: 56.038
-    - type: ndcg_at_20
-      value: 80.869
-    - type: ndcg_at_3
-      value: 89.11200000000001
-    - type: ndcg_at_5
-      value: 87.199
-    - type: precision_at_1
-      value: 96.0
-    - type: precision_at_10
-      value: 87.8
-    - type: precision_at_100
-      value: 66.72
-    - type: precision_at_1000
-      value: 24.684
-    - type: precision_at_20
-      value: 84.3
-    - type: precision_at_3
-      value: 94.0
-    - type: precision_at_5
-      value: 91.2
-    - type: recall_at_1
-      value: 0.249
-    - type: recall_at_10
-      value: 2.284
-    - type: recall_at_100
-      value: 16.025
-    - type: recall_at_1000
-      value: 52.068999999999996
-    - type: recall_at_20
-      value: 4.3180000000000005
-    - type: recall_at_3
-      value: 0.738
-    - type: recall_at_5
-      value: 1.212
-  - task:
-      type: Retrieval
-    dataset:
-      type: None
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
-    metrics:
-    - type: map_at_1
-      value: 3.4520000000000004
-    - type: map_at_10
-      value: 13.045000000000002
-    - type: map_at_100
-      value: 19.442
-    - type: map_at_1000
-      value: 21.09
-    - type: map_at_20
-      value: 15.667
-    - type: map_at_3
-      value: 7.409000000000001
-    - type: map_at_5
-      value: 9.73
-    - type: mrr_at_1
-      value: 46.939
-    - type: mrr_at_10
-      value: 60.295
-    - type: mrr_at_100
-      value: 60.904
-    - type: mrr_at_1000
-      value: 60.919000000000004
-    - type: mrr_at_20
-      value: 60.77
-    - type: mrr_at_3
-      value: 58.50300000000001
-    - type: mrr_at_5
-      value: 59.014
-    - type: ndcg_at_1
-      value: 44.897999999999996
-    - type: ndcg_at_10
-      value: 31.911
-    - type: ndcg_at_100
-      value: 41.945
-    - type: ndcg_at_1000
-      value: 53.181999999999995
-    - type: ndcg_at_20
-      value: 31.505
-    - type: ndcg_at_3
-      value: 39.745000000000005
-    - type: ndcg_at_5
-      value: 35.528999999999996
-    - type: precision_at_1
-      value: 46.939
-    - type: precision_at_10
-      value: 26.531
-    - type: precision_at_100
-      value: 8.163
-    - type: precision_at_1000
-      value: 1.559
-    - type: precision_at_20
-      value: 19.387999999999998
-    - type: precision_at_3
-      value: 40.136
-    - type: precision_at_5
-      value: 33.878
-    - type: recall_at_1
-      value: 3.4520000000000004
-    - type: recall_at_10
-      value: 18.899
-    - type: recall_at_100
-      value: 50.207
-    - type: recall_at_1000
-      value: 83.871
-    - type: recall_at_20
-      value: 26.756999999999998
-    - type: recall_at_3
-      value: 8.729000000000001
-    - type: recall_at_5
-      value: 12.084999999999999
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
-    metrics:
-    - type: accuracy
-      value: 67.4560546875
-    - type: ap
-      value: 12.720403845355294
-    - type: f1
-      value: 51.76062666567839
-  - task:
-      type: Classification
-    dataset:
-      type: None
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 62.36276174306734
-    - type: f1
-      value: 62.69956906934332
-  - task:
-      type: Clustering
-    dataset:
-      type: None
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 49.473492910233965
-    - type: v_measures
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0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281]
-  - task:
-      type: PairClassification
-    dataset:
-      type: None
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 85.75430649102938
-    - type: cos_sim_ap
-      value: 73.62842656477649
-    - type: cos_sim_f1
-      value: 67.76023680315738
-    - type: cos_sim_precision
-      value: 63.61741547012506
-    - type: cos_sim_recall
-      value: 72.4802110817942
-    - type: dot_accuracy
-      value: 85.7423854085951
-    - type: dot_ap
-      value: 73.59147637253723
-    - type: dot_f1
-      value: 67.69498693867396
-    - type: dot_precision
-      value: 64.03859731701577
-    - type: dot_recall
-      value: 71.79419525065963
-    - type: euclidean_accuracy
-      value: 85.7423854085951
-    - type: euclidean_ap
-      value: 73.6288990409654
-    - type: euclidean_f1
-      value: 67.80415430267064
-    - type: euclidean_precision
-      value: 63.79711493718009
-    - type: euclidean_recall
-      value: 72.34828496042216
-    - type: manhattan_accuracy
-      value: 85.69470107885796
-    - type: manhattan_ap
-      value: 73.49219614602531
-    - type: manhattan_f1
-      value: 67.60809797550613
-    - type: manhattan_precision
-      value: 64.22127255460589
-    - type: manhattan_recall
-      value: 71.37203166226914
-    - type: max_accuracy
-      value: 85.75430649102938
-    - type: max_ap
-      value: 73.6288990409654
-    - type: max_f1
-      value: 67.80415430267064
-  - task:
-      type: PairClassification
-    dataset:
-      type: None
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 89.08293553770326
-    - type: cos_sim_ap
-      value: 86.21246419992926
-    - type: cos_sim_f1
-      value: 78.49922526377924
-    - type: cos_sim_precision
-      value: 75.35769939084857
-    - type: cos_sim_recall
-      value: 81.9140745303357
-    - type: dot_accuracy
-      value: 89.08681647067955
-    - type: dot_ap
-      value: 86.19733517196862
-    - type: dot_f1
-      value: 78.51132446157838
-    - type: dot_precision
-      value: 75.70233755093287
-    - type: dot_recall
-      value: 81.53680320295658
-    - type: euclidean_accuracy
-      value: 89.07517367175069
-    - type: euclidean_ap
-      value: 86.21198725320203
-    - type: euclidean_f1
-      value: 78.49867139061116
-    - type: euclidean_precision
-      value: 75.38276155372839
-    - type: euclidean_recall
-      value: 81.88327687095781
-    - type: manhattan_accuracy
-      value: 89.0538285403811
-    - type: manhattan_ap
-      value: 86.17785515765131
-    - type: manhattan_f1
-      value: 78.48184098593084
-    - type: manhattan_precision
-      value: 74.34396308285694
-    - type: manhattan_recall
-      value: 83.10748383122882
-    - type: max_accuracy
-      value: 89.08681647067955
-    - type: max_ap
-      value: 86.21246419992926
-    - type: max_f1
-      value: 78.51132446157838
-license: apache-2.0
-language:
-- en
-base_model:
-- answerdotai/ModernBERT-base
-- nomic-ai/modernbert-embed-unsupervised
----
-
-# ModernBERT Embed
-
-[![image/png](modernbertembed.png)](https://huggingface.co/nomic-ai/modernbert-embed-base)
-
-ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), bringing the new advances of ModernBERT to embeddings!
-
-Trained on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets, `modernbert-embed` also supports Matryoshka Representation Learning dimensions of 256, reducing memory by 3x with minimal performance loss.
-
-## Performance
-
-| Model                 | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10)  | Summarization (1) |
-|-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------|
-| nomic-embed-text-v1   | 768        | 62.4         | 74.1                | 43.9            | **85.2**                | 55.7          | 52.8           | 82.1      | 30.1             |
-| nomic-embed-text-v1.5 | 768        | 62.28        | 73.55               | 43.93           | 84.61                   | 55.78         | **53.01**      | **81.94** | 30.4             |
-| modernbert-embed-base      | 768        | **62.62**    | **74.31**           | **44.98**       | 83.96                   | **56.42**     | 52.89          | 81.78     | **31.39**        |
-| nomic-embed-text-v1.5 | 256        | 61.04        | 72.1                | 43.16           | 84.09                   | 55.18         | 50.81          | 81.34     |   30.05               |
-| modernbert-embed-base      | 256        | 61.17        | 72.40               | 43.82           | 83.45                   | 55.69         | 50.62          | 81.12     | 31.27            |
-
-
-
-## Usage
-
-You can use these models directly with the transformers library. Until the next transformers release, doing so requires installing `transformers` from `main`:
-
-```bash
-pip install git+https://github.com/huggingface/transformers.git
-```
-
-Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
-
-Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
-
-### Sentence Transformers
-
-```python
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer("nomic-ai/modernbert-embed-base")
-
-query_embeddings = model.encode([
-    "search_query: What is TSNE?",
-    "search_query: Who is Laurens van der Maaten?",
-])
-doc_embeddings = model.encode([
-    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
-])
-print(query_embeddings.shape, doc_embeddings.shape)
-# (2, 768) (1, 768)
-
-similarities = model.similarity(query_embeddings, doc_embeddings)
-print(similarities)
-# tensor([[0.7214],
-#         [0.3260]])
-```
-
-<details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
-
-In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
-
-```python
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer("nomic-ai/modernbert-embed-base", truncate_dim=256)
-
-query_embeddings = model.encode([
-    "search_query: What is TSNE?",
-    "search_query: Who is Laurens van der Maaten?",
-])
-doc_embeddings = model.encode([
-    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
-])
-print(query_embeddings.shape, doc_embeddings.shape)
-# (2, 256) (1, 256)
-
-similarities = model.similarity(query_embeddings, doc_embeddings)
-print(similarities)
-# tensor([[0.7759],
-#         [0.3419]])
-```
-
-Note the small differences compared to the full 768-dimensional similarities.
-
-</details>
-
-### Transformers
-
-```python
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-
-
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    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
-    )
-
-
-queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
-documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
-
-tokenizer = AutoTokenizer.from_pretrained("nomic-ai/modernbert-embed-base")
-model = AutoModel.from_pretrained("nomic-ai/modernbert-embed-base")
-
-encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
-encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
-
-with torch.no_grad():
-    queries_outputs = model(**encoded_queries)
-    documents_outputs = model(**encoded_documents)
-
-query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
-query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
-doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
-doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
-print(query_embeddings.shape, doc_embeddings.shape)
-# torch.Size([2, 768]) torch.Size([1, 768])
-
-similarities = query_embeddings @ doc_embeddings.T
-print(similarities)
-# tensor([[0.7214],
-#         [0.3260]])
-```
-
-<details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
-
-In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.
-
-```python
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-
-
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    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
-    )
-
-
-queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
-documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
-
-tokenizer = AutoTokenizer.from_pretrained(".")
-model = AutoModel.from_pretrained(".")
-truncate_dim = 256
-
-encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
-encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
-
-with torch.no_grad():
-    queries_outputs = model(**encoded_queries)
-    documents_outputs = model(**encoded_documents)
-
-query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
-query_embeddings = query_embeddings[:, :truncate_dim]
-query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
-doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
-doc_embeddings = doc_embeddings[:, :truncate_dim]
-doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
-print(query_embeddings.shape, doc_embeddings.shape)
-# torch.Size([2, 256]) torch.Size([1, 256])
-
-similarities = query_embeddings @ doc_embeddings.T
-print(similarities)
-# tensor([[0.7759],
-#         [0.3419]])
-```
-
-Note the small differences compared to the full 768-dimensional similarities.
-
-</details>
-
-### Transformers.js
-
-If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
-```bash
-npm i @huggingface/transformers
-```
-
-Then, you can compute embeddings as follows:
-
-```javascript
-import { pipeline, matmul } from '@huggingface/transformers';
-
-// Create a feature extraction pipeline
-const extractor = await pipeline(
-  "feature-extraction",
-  "nomic-ai/modernbert-embed-base",
-  { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
-);
-
-// Embed queries and documents
-const query_embeddings = await extractor([
-    "search_query: What is TSNE?",
-    "search_query: Who is Laurens van der Maaten?",
-  ], { pooling: "mean", normalize: true },
-);
-const doc_embeddings = await extractor([
-    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
-  ], { pooling: "mean", normalize: true },
-);
-
-// Compute similarity scores
-const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
-console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]
-```
-
-
-## Training
-
-Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
-
-[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
-
-We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
-the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
-
-In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
-
-For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
-
-Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
-
-
-## Join the Nomic Community
-
-- Nomic: [https://nomic.ai](https://nomic.ai)
-- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
-- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
-
-## Citation
-
-If you find the model, dataset, or training code useful, please cite our work
-
-```bibtex
-@misc{nussbaum2024nomic,
-      title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, 
-      author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
-      year={2024},
-      eprint={2402.01613},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+- transformers.js
+model-index:
+- name: binarize_False
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 78.13432835820896
+    - type: ap
+      value: 42.190424731303246
+    - type: f1
+      value: 72.34446401534811
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.093825
+    - type: ap
+      value: 90.03727505544286
+    - type: f1
+      value: 93.0874055138833
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.428000000000004
+    - type: f1
+      value: 47.74311520203536
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: map_at_1
+      value: 23.898
+    - type: map_at_10
+      value: 39.775
+    - type: map_at_100
+      value: 40.827000000000005
+    - type: map_at_1000
+      value: 40.837
+    - type: map_at_20
+      value: 40.604
+    - type: map_at_3
+      value: 34.519
+    - type: map_at_5
+      value: 37.307
+    - type: mrr_at_1
+      value: 24.395
+    - type: mrr_at_10
+      value: 39.963
+    - type: mrr_at_100
+      value: 41.014
+    - type: mrr_at_1000
+      value: 41.024
+    - type: mrr_at_20
+      value: 40.791
+    - type: mrr_at_3
+      value: 34.732
+    - type: mrr_at_5
+      value: 37.480999999999995
+    - type: ndcg_at_1
+      value: 23.898
+    - type: ndcg_at_10
+      value: 48.962
+    - type: ndcg_at_100
+      value: 53.386
+    - type: ndcg_at_1000
+      value: 53.634
+    - type: ndcg_at_20
+      value: 51.898999999999994
+    - type: ndcg_at_3
+      value: 38.034
+    - type: ndcg_at_5
+      value: 43.036
+    - type: precision_at_1
+      value: 23.898
+    - type: precision_at_10
+      value: 7.852
+    - type: precision_at_100
+      value: 0.9769999999999999
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_20
+      value: 4.4990000000000006
+    - type: precision_at_3
+      value: 16.073999999999998
+    - type: precision_at_5
+      value: 12.063
+    - type: recall_at_1
+      value: 23.898
+    - type: recall_at_10
+      value: 78.521
+    - type: recall_at_100
+      value: 97.724
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_20
+      value: 89.972
+    - type: recall_at_3
+      value: 48.222
+    - type: recall_at_5
+      value: 60.313
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.69067314293749
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 38.0916537995626
+    - type: v_measures
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+  - task:
+      type: Reranking
+    dataset:
+      type: None
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.33195643912506
+    - type: mrr
+      value: 76.43978366970057
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.20285894915236
+    - type: cos_sim_spearman
+      value: 78.16322678527897
+    - type: euclidean_pearson
+      value: 80.6118408638417
+    - type: euclidean_spearman
+      value: 78.19033583671204
+    - type: manhattan_pearson
+      value: 80.41282660275819
+    - type: manhattan_spearman
+      value: 77.98611431591628
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 85.25324675324676
+    - type: f1
+      value: 85.19854235582687
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.65216461057432
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 33.28787287895752
+    - type: v_measures
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+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+    metrics:
+    - type: map_at_1
+      value: 32.677
+    - type: map_at_10
+      value: 43.739
+    - type: map_at_100
+      value: 45.152
+    - type: map_at_1000
+      value: 45.279
+    - type: map_at_20
+      value: 44.553
+    - type: map_at_3
+      value: 40.321
+    - type: map_at_5
+      value: 42.201
+    - type: mrr_at_1
+      value: 40.2
+    - type: mrr_at_10
+      value: 49.755
+    - type: mrr_at_100
+      value: 50.468
+    - type: mrr_at_1000
+      value: 50.513
+    - type: mrr_at_20
+      value: 50.192
+    - type: mrr_at_3
+      value: 47.163
+    - type: mrr_at_5
+      value: 48.686
+    - type: ndcg_at_1
+      value: 40.2
+    - type: ndcg_at_10
+      value: 49.963
+    - type: ndcg_at_100
+      value: 54.978
+    - type: ndcg_at_1000
+      value: 56.979
+    - type: ndcg_at_20
+      value: 51.983000000000004
+    - type: ndcg_at_3
+      value: 45.086999999999996
+    - type: ndcg_at_5
+      value: 47.309
+    - type: precision_at_1
+      value: 40.2
+    - type: precision_at_10
+      value: 9.328
+    - type: precision_at_100
+      value: 1.443
+    - type: precision_at_1000
+      value: 0.19
+    - type: precision_at_20
+      value: 5.558
+    - type: precision_at_3
+      value: 21.364
+    - type: precision_at_5
+      value: 15.222
+    - type: recall_at_1
+      value: 32.677
+    - type: recall_at_10
+      value: 61.71
+    - type: recall_at_100
+      value: 82.431
+    - type: recall_at_1000
+      value: 94.896
+    - type: recall_at_20
+      value: 68.73700000000001
+    - type: recall_at_3
+      value: 47.431
+    - type: recall_at_5
+      value: 53.739000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+    metrics:
+    - type: map_at_1
+      value: 32.71
+    - type: map_at_10
+      value: 43.297000000000004
+    - type: map_at_100
+      value: 44.607
+    - type: map_at_1000
+      value: 44.729
+    - type: map_at_20
+      value: 44.013999999999996
+    - type: map_at_3
+      value: 40.213
+    - type: map_at_5
+      value: 42.004000000000005
+    - type: mrr_at_1
+      value: 40.892
+    - type: mrr_at_10
+      value: 49.394
+    - type: mrr_at_100
+      value: 50.005
+    - type: mrr_at_1000
+      value: 50.043000000000006
+    - type: mrr_at_20
+      value: 49.764
+    - type: mrr_at_3
+      value: 47.134
+    - type: mrr_at_5
+      value: 48.522
+    - type: ndcg_at_1
+      value: 40.892
+    - type: ndcg_at_10
+      value: 49.047000000000004
+    - type: ndcg_at_100
+      value: 53.266999999999996
+    - type: ndcg_at_1000
+      value: 55.096999999999994
+    - type: ndcg_at_20
+      value: 50.707
+    - type: ndcg_at_3
+      value: 44.896
+    - type: ndcg_at_5
+      value: 46.983000000000004
+    - type: precision_at_1
+      value: 40.892
+    - type: precision_at_10
+      value: 9.293
+    - type: precision_at_100
+      value: 1.473
+    - type: precision_at_1000
+      value: 0.192
+    - type: precision_at_20
+      value: 5.446
+    - type: precision_at_3
+      value: 21.592
+    - type: precision_at_5
+      value: 15.540999999999999
+    - type: recall_at_1
+      value: 32.71
+    - type: recall_at_10
+      value: 58.592999999999996
+    - type: recall_at_100
+      value: 76.242
+    - type: recall_at_1000
+      value: 87.717
+    - type: recall_at_20
+      value: 64.646
+    - type: recall_at_3
+      value: 46.253
+    - type: recall_at_5
+      value: 51.946999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+    metrics:
+    - type: map_at_1
+      value: 41.644999999999996
+    - type: map_at_10
+      value: 53.825
+    - type: map_at_100
+      value: 54.82
+    - type: map_at_1000
+      value: 54.87499999999999
+    - type: map_at_20
+      value: 54.43
+    - type: map_at_3
+      value: 50.705
+    - type: map_at_5
+      value: 52.501
+    - type: mrr_at_1
+      value: 47.524
+    - type: mrr_at_10
+      value: 57.260999999999996
+    - type: mrr_at_100
+      value: 57.902
+    - type: mrr_at_1000
+      value: 57.931999999999995
+    - type: mrr_at_20
+      value: 57.689
+    - type: mrr_at_3
+      value: 55.089
+    - type: mrr_at_5
+      value: 56.38999999999999
+    - type: ndcg_at_1
+      value: 47.524
+    - type: ndcg_at_10
+      value: 59.41499999999999
+    - type: ndcg_at_100
+      value: 63.258
+    - type: ndcg_at_1000
+      value: 64.376
+    - type: ndcg_at_20
+      value: 61.149
+    - type: ndcg_at_3
+      value: 54.381
+    - type: ndcg_at_5
+      value: 56.89999999999999
+    - type: precision_at_1
+      value: 47.524
+    - type: precision_at_10
+      value: 9.386
+    - type: precision_at_100
+      value: 1.221
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_20
+      value: 5.223
+    - type: precision_at_3
+      value: 24.096
+    - type: precision_at_5
+      value: 16.364
+    - type: recall_at_1
+      value: 41.644999999999996
+    - type: recall_at_10
+      value: 72.386
+    - type: recall_at_100
+      value: 88.794
+    - type: recall_at_1000
+      value: 96.75399999999999
+    - type: recall_at_20
+      value: 78.74
+    - type: recall_at_3
+      value: 59.028000000000006
+    - type: recall_at_5
+      value: 65.197
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+    metrics:
+    - type: map_at_1
+      value: 28.648
+    - type: map_at_10
+      value: 36.388999999999996
+    - type: map_at_100
+      value: 37.372
+    - type: map_at_1000
+      value: 37.457
+    - type: map_at_20
+      value: 36.912
+    - type: map_at_3
+      value: 34.076
+    - type: map_at_5
+      value: 35.415
+    - type: mrr_at_1
+      value: 30.508000000000003
+    - type: mrr_at_10
+      value: 38.132
+    - type: mrr_at_100
+      value: 39.04
+    - type: mrr_at_1000
+      value: 39.106
+    - type: mrr_at_20
+      value: 38.643
+    - type: mrr_at_3
+      value: 35.876000000000005
+    - type: mrr_at_5
+      value: 37.208999999999996
+    - type: ndcg_at_1
+      value: 30.508000000000003
+    - type: ndcg_at_10
+      value: 40.762
+    - type: ndcg_at_100
+      value: 45.732
+    - type: ndcg_at_1000
+      value: 47.799
+    - type: ndcg_at_20
+      value: 42.591
+    - type: ndcg_at_3
+      value: 36.266999999999996
+    - type: ndcg_at_5
+      value: 38.58
+    - type: precision_at_1
+      value: 30.508000000000003
+    - type: precision_at_10
+      value: 6.010999999999999
+    - type: precision_at_100
+      value: 0.897
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_20
+      value: 3.412
+    - type: precision_at_3
+      value: 14.991
+    - type: precision_at_5
+      value: 10.328
+    - type: recall_at_1
+      value: 28.648
+    - type: recall_at_10
+      value: 52.342999999999996
+    - type: recall_at_100
+      value: 75.268
+    - type: recall_at_1000
+      value: 90.641
+    - type: recall_at_20
+      value: 59.303
+    - type: recall_at_3
+      value: 40.447
+    - type: recall_at_5
+      value: 46.117000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: map_at_1
+      value: 18.476
+    - type: map_at_10
+      value: 27.148
+    - type: map_at_100
+      value: 28.317999999999998
+    - type: map_at_1000
+      value: 28.427999999999997
+    - type: map_at_20
+      value: 27.764
+    - type: map_at_3
+      value: 24.801000000000002
+    - type: map_at_5
+      value: 26.133
+    - type: mrr_at_1
+      value: 22.886
+    - type: mrr_at_10
+      value: 31.741000000000003
+    - type: mrr_at_100
+      value: 32.708
+    - type: mrr_at_1000
+      value: 32.769
+    - type: mrr_at_20
+      value: 32.296
+    - type: mrr_at_3
+      value: 29.498
+    - type: mrr_at_5
+      value: 30.773
+    - type: ndcg_at_1
+      value: 22.886
+    - type: ndcg_at_10
+      value: 32.265
+    - type: ndcg_at_100
+      value: 37.829
+    - type: ndcg_at_1000
+      value: 40.558
+    - type: ndcg_at_20
+      value: 34.372
+    - type: ndcg_at_3
+      value: 28.105000000000004
+    - type: ndcg_at_5
+      value: 30.04
+    - type: precision_at_1
+      value: 22.886
+    - type: precision_at_10
+      value: 5.808
+    - type: precision_at_100
+      value: 0.985
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_20
+      value: 3.495
+    - type: precision_at_3
+      value: 13.639999999999999
+    - type: precision_at_5
+      value: 9.577
+    - type: recall_at_1
+      value: 18.476
+    - type: recall_at_10
+      value: 43.442
+    - type: recall_at_100
+      value: 67.376
+    - type: recall_at_1000
+      value: 86.874
+    - type: recall_at_20
+      value: 51.038
+    - type: recall_at_3
+      value: 31.785999999999998
+    - type: recall_at_5
+      value: 36.858999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+    metrics:
+    - type: map_at_1
+      value: 29.098000000000003
+    - type: map_at_10
+      value: 38.97
+    - type: map_at_100
+      value: 40.293
+    - type: map_at_1000
+      value: 40.397
+    - type: map_at_20
+      value: 39.778999999999996
+    - type: map_at_3
+      value: 35.723
+    - type: map_at_5
+      value: 37.519999999999996
+    - type: mrr_at_1
+      value: 35.515
+    - type: mrr_at_10
+      value: 44.55
+    - type: mrr_at_100
+      value: 45.37
+    - type: mrr_at_1000
+      value: 45.412
+    - type: mrr_at_20
+      value: 45.054
+    - type: mrr_at_3
+      value: 41.835
+    - type: mrr_at_5
+      value: 43.356
+    - type: ndcg_at_1
+      value: 35.515
+    - type: ndcg_at_10
+      value: 44.91
+    - type: ndcg_at_100
+      value: 50.27700000000001
+    - type: ndcg_at_1000
+      value: 52.215
+    - type: ndcg_at_20
+      value: 47.235
+    - type: ndcg_at_3
+      value: 39.505
+    - type: ndcg_at_5
+      value: 42.016
+    - type: precision_at_1
+      value: 35.515
+    - type: precision_at_10
+      value: 8.152
+    - type: precision_at_100
+      value: 1.262
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_20
+      value: 4.851
+    - type: precision_at_3
+      value: 18.447
+    - type: precision_at_5
+      value: 13.321
+    - type: recall_at_1
+      value: 29.098000000000003
+    - type: recall_at_10
+      value: 57.115
+    - type: recall_at_100
+      value: 79.467
+    - type: recall_at_1000
+      value: 92.162
+    - type: recall_at_20
+      value: 65.161
+    - type: recall_at_3
+      value: 42.254000000000005
+    - type: recall_at_5
+      value: 48.415
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+    metrics:
+    - type: map_at_1
+      value: 27.372000000000003
+    - type: map_at_10
+      value: 37.781
+    - type: map_at_100
+      value: 39.128
+    - type: map_at_1000
+      value: 39.238
+    - type: map_at_20
+      value: 38.592
+    - type: map_at_3
+      value: 34.782999999999994
+    - type: map_at_5
+      value: 36.466
+    - type: mrr_at_1
+      value: 33.904
+    - type: mrr_at_10
+      value: 43.15
+    - type: mrr_at_100
+      value: 44.049
+    - type: mrr_at_1000
+      value: 44.107
+    - type: mrr_at_20
+      value: 43.721
+    - type: mrr_at_3
+      value: 40.677
+    - type: mrr_at_5
+      value: 42.19
+    - type: ndcg_at_1
+      value: 33.904
+    - type: ndcg_at_10
+      value: 43.527
+    - type: ndcg_at_100
+      value: 49.004999999999995
+    - type: ndcg_at_1000
+      value: 51.276999999999994
+    - type: ndcg_at_20
+      value: 45.988
+    - type: ndcg_at_3
+      value: 38.824999999999996
+    - type: ndcg_at_5
+      value: 41.04
+    - type: precision_at_1
+      value: 33.904
+    - type: precision_at_10
+      value: 7.854
+    - type: precision_at_100
+      value: 1.2309999999999999
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_20
+      value: 4.692
+    - type: precision_at_3
+      value: 18.531
+    - type: precision_at_5
+      value: 13.150999999999998
+    - type: recall_at_1
+      value: 27.372000000000003
+    - type: recall_at_10
+      value: 55.245999999999995
+    - type: recall_at_100
+      value: 78.278
+    - type: recall_at_1000
+      value: 93.718
+    - type: recall_at_20
+      value: 64.095
+    - type: recall_at_3
+      value: 41.665
+    - type: recall_at_5
+      value: 47.632000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+    metrics:
+    - type: map_at_1
+      value: 27.734166666666667
+    - type: map_at_10
+      value: 36.858
+    - type: map_at_100
+      value: 38.043833333333325
+    - type: map_at_1000
+      value: 38.15541666666667
+    - type: map_at_20
+      value: 37.521249999999995
+    - type: map_at_3
+      value: 34.07658333333333
+    - type: map_at_5
+      value: 35.62683333333333
+    - type: mrr_at_1
+      value: 32.676249999999996
+    - type: mrr_at_10
+      value: 40.999
+    - type: mrr_at_100
+      value: 41.835
+    - type: mrr_at_1000
+      value: 41.8895
+    - type: mrr_at_20
+      value: 41.4865
+    - type: mrr_at_3
+      value: 38.645
+    - type: mrr_at_5
+      value: 39.99725000000001
+    - type: ndcg_at_1
+      value: 32.676249999999996
+    - type: ndcg_at_10
+      value: 42.08016666666666
+    - type: ndcg_at_100
+      value: 47.082750000000004
+    - type: ndcg_at_1000
+      value: 49.276583333333335
+    - type: ndcg_at_20
+      value: 44.04808333333334
+    - type: ndcg_at_3
+      value: 37.43375
+    - type: ndcg_at_5
+      value: 39.623000000000005
+    - type: precision_at_1
+      value: 32.676249999999996
+    - type: precision_at_10
+      value: 7.271
+    - type: precision_at_100
+      value: 1.1458333333333333
+    - type: precision_at_1000
+      value: 0.152
+    - type: precision_at_20
+      value: 4.282916666666667
+    - type: precision_at_3
+      value: 17.061416666666666
+    - type: precision_at_5
+      value: 12.05466666666667
+    - type: recall_at_1
+      value: 27.734166666666667
+    - type: recall_at_10
+      value: 53.33574999999999
+    - type: recall_at_100
+      value: 75.16275
+    - type: recall_at_1000
+      value: 90.34891666666665
+    - type: recall_at_20
+      value: 60.4935
+    - type: recall_at_3
+      value: 40.377916666666664
+    - type: recall_at_5
+      value: 46.0195
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+    metrics:
+    - type: map_at_1
+      value: 25.653
+    - type: map_at_10
+      value: 32.151
+    - type: map_at_100
+      value: 33.152
+    - type: map_at_1000
+      value: 33.243
+    - type: map_at_20
+      value: 32.717
+    - type: map_at_3
+      value: 30.287
+    - type: map_at_5
+      value: 31.25
+    - type: mrr_at_1
+      value: 28.988000000000003
+    - type: mrr_at_10
+      value: 35.131
+    - type: mrr_at_100
+      value: 36.002
+    - type: mrr_at_1000
+      value: 36.069
+    - type: mrr_at_20
+      value: 35.61
+    - type: mrr_at_3
+      value: 33.308
+    - type: mrr_at_5
+      value: 34.259
+    - type: ndcg_at_1
+      value: 28.988000000000003
+    - type: ndcg_at_10
+      value: 35.988
+    - type: ndcg_at_100
+      value: 40.764
+    - type: ndcg_at_1000
+      value: 43.112
+    - type: ndcg_at_20
+      value: 37.852999999999994
+    - type: ndcg_at_3
+      value: 32.562000000000005
+    - type: ndcg_at_5
+      value: 33.983000000000004
+    - type: precision_at_1
+      value: 28.988000000000003
+    - type: precision_at_10
+      value: 5.475
+    - type: precision_at_100
+      value: 0.8500000000000001
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_20
+      value: 3.229
+    - type: precision_at_3
+      value: 13.905999999999999
+    - type: precision_at_5
+      value: 9.386999999999999
+    - type: recall_at_1
+      value: 25.653
+    - type: recall_at_10
+      value: 44.962
+    - type: recall_at_100
+      value: 66.405
+    - type: recall_at_1000
+      value: 83.88799999999999
+    - type: recall_at_20
+      value: 51.79899999999999
+    - type: recall_at_3
+      value: 35.144999999999996
+    - type: recall_at_5
+      value: 38.814
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+    metrics:
+    - type: map_at_1
+      value: 17.825
+    - type: map_at_10
+      value: 25.592
+    - type: map_at_100
+      value: 26.613999999999997
+    - type: map_at_1000
+      value: 26.734
+    - type: map_at_20
+      value: 26.115
+    - type: map_at_3
+      value: 23.119
+    - type: map_at_5
+      value: 24.54
+    - type: mrr_at_1
+      value: 21.335
+    - type: mrr_at_10
+      value: 29.165000000000003
+    - type: mrr_at_100
+      value: 30.049
+    - type: mrr_at_1000
+      value: 30.121
+    - type: mrr_at_20
+      value: 29.639
+    - type: mrr_at_3
+      value: 26.863999999999997
+    - type: mrr_at_5
+      value: 28.185
+    - type: ndcg_at_1
+      value: 21.335
+    - type: ndcg_at_10
+      value: 30.357
+    - type: ndcg_at_100
+      value: 35.410000000000004
+    - type: ndcg_at_1000
+      value: 38.24
+    - type: ndcg_at_20
+      value: 32.08
+    - type: ndcg_at_3
+      value: 25.95
+    - type: ndcg_at_5
+      value: 28.081
+    - type: precision_at_1
+      value: 21.335
+    - type: precision_at_10
+      value: 5.506
+    - type: precision_at_100
+      value: 0.928
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_20
+      value: 3.2550000000000003
+    - type: precision_at_3
+      value: 12.239
+    - type: precision_at_5
+      value: 8.885
+    - type: recall_at_1
+      value: 17.825
+    - type: recall_at_10
+      value: 41.105999999999995
+    - type: recall_at_100
+      value: 64.17
+    - type: recall_at_1000
+      value: 84.19200000000001
+    - type: recall_at_20
+      value: 47.497
+    - type: recall_at_3
+      value: 28.862
+    - type: recall_at_5
+      value: 34.348
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+    metrics:
+    - type: map_at_1
+      value: 29.435
+    - type: map_at_10
+      value: 38.261
+    - type: map_at_100
+      value: 39.242
+    - type: map_at_1000
+      value: 39.347
+    - type: map_at_20
+      value: 38.742
+    - type: map_at_3
+      value: 35.457
+    - type: map_at_5
+      value: 37.043
+    - type: mrr_at_1
+      value: 34.235
+    - type: mrr_at_10
+      value: 42.24
+    - type: mrr_at_100
+      value: 42.988
+    - type: mrr_at_1000
+      value: 43.043
+    - type: mrr_at_20
+      value: 42.613
+    - type: mrr_at_3
+      value: 39.832
+    - type: mrr_at_5
+      value: 41.227000000000004
+    - type: ndcg_at_1
+      value: 34.235
+    - type: ndcg_at_10
+      value: 43.384
+    - type: ndcg_at_100
+      value: 48.14
+    - type: ndcg_at_1000
+      value: 50.414
+    - type: ndcg_at_20
+      value: 44.913
+    - type: ndcg_at_3
+      value: 38.454
+    - type: ndcg_at_5
+      value: 40.776
+    - type: precision_at_1
+      value: 34.235
+    - type: precision_at_10
+      value: 7.164
+    - type: precision_at_100
+      value: 1.065
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_20
+      value: 4.021
+    - type: precision_at_3
+      value: 17.226
+    - type: precision_at_5
+      value: 12.071
+    - type: recall_at_1
+      value: 29.435
+    - type: recall_at_10
+      value: 54.93900000000001
+    - type: recall_at_100
+      value: 76.176
+    - type: recall_at_1000
+      value: 91.989
+    - type: recall_at_20
+      value: 60.451
+    - type: recall_at_3
+      value: 41.332
+    - type: recall_at_5
+      value: 47.316
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: map_at_1
+      value: 25.605
+    - type: map_at_10
+      value: 34.162
+    - type: map_at_100
+      value: 35.827999999999996
+    - type: map_at_1000
+      value: 36.04
+    - type: map_at_20
+      value: 35.016000000000005
+    - type: map_at_3
+      value: 30.984
+    - type: map_at_5
+      value: 32.717
+    - type: mrr_at_1
+      value: 30.435000000000002
+    - type: mrr_at_10
+      value: 38.681
+    - type: mrr_at_100
+      value: 39.656000000000006
+    - type: mrr_at_1000
+      value: 39.71
+    - type: mrr_at_20
+      value: 39.208999999999996
+    - type: mrr_at_3
+      value: 35.903
+    - type: mrr_at_5
+      value: 37.454
+    - type: ndcg_at_1
+      value: 30.435000000000002
+    - type: ndcg_at_10
+      value: 39.916000000000004
+    - type: ndcg_at_100
+      value: 45.958
+    - type: ndcg_at_1000
+      value: 48.449999999999996
+    - type: ndcg_at_20
+      value: 42.085
+    - type: ndcg_at_3
+      value: 34.696
+    - type: ndcg_at_5
+      value: 37.147000000000006
+    - type: precision_at_1
+      value: 30.435000000000002
+    - type: precision_at_10
+      value: 7.767
+    - type: precision_at_100
+      value: 1.547
+    - type: precision_at_1000
+      value: 0.23800000000000002
+    - type: precision_at_20
+      value: 4.941
+    - type: precision_at_3
+      value: 16.073999999999998
+    - type: precision_at_5
+      value: 11.937000000000001
+    - type: recall_at_1
+      value: 25.605
+    - type: recall_at_10
+      value: 50.654999999999994
+    - type: recall_at_100
+      value: 77.609
+    - type: recall_at_1000
+      value: 93.518
+    - type: recall_at_20
+      value: 58.845000000000006
+    - type: recall_at_3
+      value: 36.272
+    - type: recall_at_5
+      value: 42.596000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 23.666
+    - type: map_at_10
+      value: 30.980999999999998
+    - type: map_at_100
+      value: 32.0
+    - type: map_at_1000
+      value: 32.098
+    - type: map_at_20
+      value: 31.621
+    - type: map_at_3
+      value: 28.449999999999996
+    - type: map_at_5
+      value: 29.731999999999996
+    - type: mrr_at_1
+      value: 25.692999999999998
+    - type: mrr_at_10
+      value: 32.788000000000004
+    - type: mrr_at_100
+      value: 33.783
+    - type: mrr_at_1000
+      value: 33.849000000000004
+    - type: mrr_at_20
+      value: 33.408
+    - type: mrr_at_3
+      value: 30.561
+    - type: mrr_at_5
+      value: 31.716
+    - type: ndcg_at_1
+      value: 25.692999999999998
+    - type: ndcg_at_10
+      value: 35.428
+    - type: ndcg_at_100
+      value: 40.375
+    - type: ndcg_at_1000
+      value: 42.802
+    - type: ndcg_at_20
+      value: 37.621
+    - type: ndcg_at_3
+      value: 30.476999999999997
+    - type: ndcg_at_5
+      value: 32.621
+    - type: precision_at_1
+      value: 25.692999999999998
+    - type: precision_at_10
+      value: 5.508
+    - type: precision_at_100
+      value: 0.848
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_20
+      value: 3.272
+    - type: precision_at_3
+      value: 12.631
+    - type: precision_at_5
+      value: 8.872
+    - type: recall_at_1
+      value: 23.666
+    - type: recall_at_10
+      value: 47.532000000000004
+    - type: recall_at_100
+      value: 69.73700000000001
+    - type: recall_at_1000
+      value: 87.83800000000001
+    - type: recall_at_20
+      value: 55.61000000000001
+    - type: recall_at_3
+      value: 34.06
+    - type: recall_at_5
+      value: 39.254
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: map_at_1
+      value: 16.337
+    - type: map_at_10
+      value: 26.488
+    - type: map_at_100
+      value: 28.415000000000003
+    - type: map_at_1000
+      value: 28.584
+    - type: map_at_20
+      value: 27.557
+    - type: map_at_3
+      value: 22.665
+    - type: map_at_5
+      value: 24.542
+    - type: mrr_at_1
+      value: 36.417
+    - type: mrr_at_10
+      value: 48.001
+    - type: mrr_at_100
+      value: 48.784
+    - type: mrr_at_1000
+      value: 48.809000000000005
+    - type: mrr_at_20
+      value: 48.507
+    - type: mrr_at_3
+      value: 45.103
+    - type: mrr_at_5
+      value: 46.843
+    - type: ndcg_at_1
+      value: 36.417
+    - type: ndcg_at_10
+      value: 35.67
+    - type: ndcg_at_100
+      value: 42.716
+    - type: ndcg_at_1000
+      value: 45.639
+    - type: ndcg_at_20
+      value: 38.471
+    - type: ndcg_at_3
+      value: 30.444
+    - type: ndcg_at_5
+      value: 32.004
+    - type: precision_at_1
+      value: 36.417
+    - type: precision_at_10
+      value: 10.73
+    - type: precision_at_100
+      value: 1.833
+    - type: precision_at_1000
+      value: 0.23800000000000002
+    - type: precision_at_20
+      value: 6.596
+    - type: precision_at_3
+      value: 22.302
+    - type: precision_at_5
+      value: 16.521
+    - type: recall_at_1
+      value: 16.337
+    - type: recall_at_10
+      value: 40.671
+    - type: recall_at_100
+      value: 64.55300000000001
+    - type: recall_at_1000
+      value: 80.934
+    - type: recall_at_20
+      value: 48.381
+    - type: recall_at_3
+      value: 27.279999999999998
+    - type: recall_at_5
+      value: 32.621
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: map_at_1
+      value: 9.056000000000001
+    - type: map_at_10
+      value: 19.419
+    - type: map_at_100
+      value: 27.069
+    - type: map_at_1000
+      value: 28.666000000000004
+    - type: map_at_20
+      value: 22.434
+    - type: map_at_3
+      value: 13.895
+    - type: map_at_5
+      value: 16.121
+    - type: mrr_at_1
+      value: 69.0
+    - type: mrr_at_10
+      value: 75.804
+    - type: mrr_at_100
+      value: 76.117
+    - type: mrr_at_1000
+      value: 76.125
+    - type: mrr_at_20
+      value: 76.009
+    - type: mrr_at_3
+      value: 74.375
+    - type: mrr_at_5
+      value: 75.4
+    - type: ndcg_at_1
+      value: 57.49999999999999
+    - type: ndcg_at_10
+      value: 41.495
+    - type: ndcg_at_100
+      value: 45.208
+    - type: ndcg_at_1000
+      value: 52.221
+    - type: ndcg_at_20
+      value: 40.617999999999995
+    - type: ndcg_at_3
+      value: 46.592
+    - type: ndcg_at_5
+      value: 43.559
+    - type: precision_at_1
+      value: 69.0
+    - type: precision_at_10
+      value: 32.574999999999996
+    - type: precision_at_100
+      value: 10.205
+    - type: precision_at_1000
+      value: 2.036
+    - type: precision_at_20
+      value: 24.687
+    - type: precision_at_3
+      value: 49.75
+    - type: precision_at_5
+      value: 42.0
+    - type: recall_at_1
+      value: 9.056000000000001
+    - type: recall_at_10
+      value: 24.866
+    - type: recall_at_100
+      value: 50.097
+    - type: recall_at_1000
+      value: 72.038
+    - type: recall_at_20
+      value: 31.858999999999998
+    - type: recall_at_3
+      value: 15.096000000000002
+    - type: recall_at_5
+      value: 18.548000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 48.259999999999984
+    - type: f1
+      value: 43.1498589523159
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: map_at_1
+      value: 74.798
+    - type: map_at_10
+      value: 83.454
+    - type: map_at_100
+      value: 83.623
+    - type: map_at_1000
+      value: 83.635
+    - type: map_at_20
+      value: 83.55
+    - type: map_at_3
+      value: 82.392
+    - type: map_at_5
+      value: 83.167
+    - type: mrr_at_1
+      value: 80.708
+    - type: mrr_at_10
+      value: 88.377
+    - type: mrr_at_100
+      value: 88.411
+    - type: mrr_at_1000
+      value: 88.411
+    - type: mrr_at_20
+      value: 88.402
+    - type: mrr_at_3
+      value: 87.646
+    - type: mrr_at_5
+      value: 88.232
+    - type: ndcg_at_1
+      value: 80.708
+    - type: ndcg_at_10
+      value: 87.35199999999999
+    - type: ndcg_at_100
+      value: 87.91600000000001
+    - type: ndcg_at_1000
+      value: 88.12299999999999
+    - type: ndcg_at_20
+      value: 87.593
+    - type: ndcg_at_3
+      value: 85.738
+    - type: ndcg_at_5
+      value: 86.845
+    - type: precision_at_1
+      value: 80.708
+    - type: precision_at_10
+      value: 10.432
+    - type: precision_at_100
+      value: 1.091
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_20
+      value: 5.296
+    - type: precision_at_3
+      value: 32.778
+    - type: precision_at_5
+      value: 20.399
+    - type: recall_at_1
+      value: 74.798
+    - type: recall_at_10
+      value: 94.459
+    - type: recall_at_100
+      value: 96.614
+    - type: recall_at_1000
+      value: 97.868
+    - type: recall_at_20
+      value: 95.254
+    - type: recall_at_3
+      value: 90.144
+    - type: recall_at_5
+      value: 92.965
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: map_at_1
+      value: 20.008
+    - type: map_at_10
+      value: 32.731
+    - type: map_at_100
+      value: 34.467999999999996
+    - type: map_at_1000
+      value: 34.643
+    - type: map_at_20
+      value: 33.717000000000006
+    - type: map_at_3
+      value: 28.427999999999997
+    - type: map_at_5
+      value: 30.788
+    - type: mrr_at_1
+      value: 40.586
+    - type: mrr_at_10
+      value: 49.056
+    - type: mrr_at_100
+      value: 49.887
+    - type: mrr_at_1000
+      value: 49.929
+    - type: mrr_at_20
+      value: 49.552
+    - type: mrr_at_3
+      value: 46.785
+    - type: mrr_at_5
+      value: 48.004000000000005
+    - type: ndcg_at_1
+      value: 40.586
+    - type: ndcg_at_10
+      value: 40.589999999999996
+    - type: ndcg_at_100
+      value: 47.03
+    - type: ndcg_at_1000
+      value: 49.994
+    - type: ndcg_at_20
+      value: 43.229
+    - type: ndcg_at_3
+      value: 37.061
+    - type: ndcg_at_5
+      value: 37.992
+    - type: precision_at_1
+      value: 40.586
+    - type: precision_at_10
+      value: 11.219
+    - type: precision_at_100
+      value: 1.781
+    - type: precision_at_1000
+      value: 0.232
+    - type: precision_at_20
+      value: 6.705
+    - type: precision_at_3
+      value: 24.743000000000002
+    - type: precision_at_5
+      value: 18.086
+    - type: recall_at_1
+      value: 20.008
+    - type: recall_at_10
+      value: 47.412
+    - type: recall_at_100
+      value: 71.274
+    - type: recall_at_1000
+      value: 88.898
+    - type: recall_at_20
+      value: 55.706999999999994
+    - type: recall_at_3
+      value: 33.346
+    - type: recall_at_5
+      value: 39.112
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: map_at_1
+      value: 41.789
+    - type: map_at_10
+      value: 57.898
+    - type: map_at_100
+      value: 58.632
+    - type: map_at_1000
+      value: 58.693
+    - type: map_at_20
+      value: 58.314
+    - type: map_at_3
+      value: 55.236
+    - type: map_at_5
+      value: 56.852999999999994
+    - type: mrr_at_1
+      value: 83.57900000000001
+    - type: mrr_at_10
+      value: 87.631
+    - type: mrr_at_100
+      value: 87.764
+    - type: mrr_at_1000
+      value: 87.77000000000001
+    - type: mrr_at_20
+      value: 87.70700000000001
+    - type: mrr_at_3
+      value: 87.02499999999999
+    - type: mrr_at_5
+      value: 87.34100000000001
+    - type: ndcg_at_1
+      value: 83.57900000000001
+    - type: ndcg_at_10
+      value: 67.11399999999999
+    - type: ndcg_at_100
+      value: 69.686
+    - type: ndcg_at_1000
+      value: 70.926
+    - type: ndcg_at_20
+      value: 68.119
+    - type: ndcg_at_3
+      value: 63.402
+    - type: ndcg_at_5
+      value: 65.354
+    - type: precision_at_1
+      value: 83.57900000000001
+    - type: precision_at_10
+      value: 13.333
+    - type: precision_at_100
+      value: 1.537
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_20
+      value: 6.988999999999999
+    - type: precision_at_3
+      value: 38.929
+    - type: precision_at_5
+      value: 24.897
+    - type: recall_at_1
+      value: 41.789
+    - type: recall_at_10
+      value: 66.664
+    - type: recall_at_100
+      value: 76.833
+    - type: recall_at_1000
+      value: 85.14500000000001
+    - type: recall_at_20
+      value: 69.892
+    - type: recall_at_3
+      value: 58.392999999999994
+    - type: recall_at_5
+      value: 62.242
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 86.6108
+    - type: ap
+      value: 81.63890253106925
+    - type: f1
+      value: 86.54585789538082
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: map_at_1
+      value: 22.407
+    - type: map_at_10
+      value: 34.603
+    - type: map_at_100
+      value: 35.808
+    - type: map_at_1000
+      value: 35.855
+    - type: map_at_20
+      value: 35.368
+    - type: map_at_3
+      value: 30.764000000000003
+    - type: map_at_5
+      value: 32.964
+    - type: mrr_at_1
+      value: 23.009
+    - type: mrr_at_10
+      value: 35.136
+    - type: mrr_at_100
+      value: 36.284
+    - type: mrr_at_1000
+      value: 36.325
+    - type: mrr_at_20
+      value: 35.869
+    - type: mrr_at_3
+      value: 31.351000000000003
+    - type: mrr_at_5
+      value: 33.54
+    - type: ndcg_at_1
+      value: 23.009
+    - type: ndcg_at_10
+      value: 41.471999999999994
+    - type: ndcg_at_100
+      value: 47.211999999999996
+    - type: ndcg_at_1000
+      value: 48.361
+    - type: ndcg_at_20
+      value: 44.169000000000004
+    - type: ndcg_at_3
+      value: 33.646
+    - type: ndcg_at_5
+      value: 37.580000000000005
+    - type: precision_at_1
+      value: 23.009
+    - type: precision_at_10
+      value: 6.54
+    - type: precision_at_100
+      value: 0.941
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_20
+      value: 3.832
+    - type: precision_at_3
+      value: 14.283999999999999
+    - type: precision_at_5
+      value: 10.564
+    - type: recall_at_1
+      value: 22.407
+    - type: recall_at_10
+      value: 62.678999999999995
+    - type: recall_at_100
+      value: 89.09700000000001
+    - type: recall_at_1000
+      value: 97.822
+    - type: recall_at_20
+      value: 73.116
+    - type: recall_at_3
+      value: 41.4
+    - type: recall_at_5
+      value: 50.855
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 92.94573643410853
+    - type: f1
+      value: 92.73148878666994
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 77.86137710898313
+    - type: f1
+      value: 60.360562463738724
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 73.83322125084062
+    - type: f1
+      value: 71.61864304680206
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 77.50504371217215
+    - type: f1
+      value: 77.52039268347185
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.346952648910225
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 32.19992734583148
+    - type: v_measures
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+  - task:
+      type: Reranking
+    dataset:
+      type: None
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.62309561205373
+    - type: mrr
+      value: 31.707879717902554
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: map_at_1
+      value: 5.668
+    - type: map_at_10
+      value: 12.225999999999999
+    - type: map_at_100
+      value: 15.122
+    - type: map_at_1000
+      value: 16.422
+    - type: map_at_20
+      value: 13.361999999999998
+    - type: map_at_3
+      value: 9.083
+    - type: map_at_5
+      value: 10.5
+    - type: mrr_at_1
+      value: 46.44
+    - type: mrr_at_10
+      value: 53.553
+    - type: mrr_at_100
+      value: 54.15
+    - type: mrr_at_1000
+      value: 54.193000000000005
+    - type: mrr_at_20
+      value: 53.837
+    - type: mrr_at_3
+      value: 51.702999999999996
+    - type: mrr_at_5
+      value: 52.647
+    - type: ndcg_at_1
+      value: 44.272
+    - type: ndcg_at_10
+      value: 33.395
+    - type: ndcg_at_100
+      value: 29.976999999999997
+    - type: ndcg_at_1000
+      value: 38.388
+    - type: ndcg_at_20
+      value: 30.606
+    - type: ndcg_at_3
+      value: 39.212
+    - type: ndcg_at_5
+      value: 36.611
+    - type: precision_at_1
+      value: 46.129999999999995
+    - type: precision_at_10
+      value: 24.334
+    - type: precision_at_100
+      value: 7.553999999999999
+    - type: precision_at_1000
+      value: 1.994
+    - type: precision_at_20
+      value: 17.678
+    - type: precision_at_3
+      value: 36.326
+    - type: precision_at_5
+      value: 31.330999999999996
+    - type: recall_at_1
+      value: 5.668
+    - type: recall_at_10
+      value: 15.837000000000002
+    - type: recall_at_100
+      value: 29.845
+    - type: recall_at_1000
+      value: 60.563
+    - type: recall_at_20
+      value: 18.587999999999997
+    - type: recall_at_3
+      value: 10.096
+    - type: recall_at_5
+      value: 12.261
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: map_at_1
+      value: 39.335
+    - type: map_at_10
+      value: 54.932
+    - type: map_at_100
+      value: 55.742000000000004
+    - type: map_at_1000
+      value: 55.766000000000005
+    - type: map_at_20
+      value: 55.504
+    - type: map_at_3
+      value: 50.904
+    - type: map_at_5
+      value: 53.388999999999996
+    - type: mrr_at_1
+      value: 44.003
+    - type: mrr_at_10
+      value: 57.419
+    - type: mrr_at_100
+      value: 57.963
+    - type: mrr_at_1000
+      value: 57.981
+    - type: mrr_at_20
+      value: 57.80499999999999
+    - type: mrr_at_3
+      value: 54.30199999999999
+    - type: mrr_at_5
+      value: 56.257000000000005
+    - type: ndcg_at_1
+      value: 43.974999999999994
+    - type: ndcg_at_10
+      value: 62.153999999999996
+    - type: ndcg_at_100
+      value: 65.326
+    - type: ndcg_at_1000
+      value: 65.862
+    - type: ndcg_at_20
+      value: 63.922999999999995
+    - type: ndcg_at_3
+      value: 54.834
+    - type: ndcg_at_5
+      value: 58.857000000000006
+    - type: precision_at_1
+      value: 43.974999999999994
+    - type: precision_at_10
+      value: 9.722
+    - type: precision_at_100
+      value: 1.153
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_20
+      value: 5.3
+    - type: precision_at_3
+      value: 24.392
+    - type: precision_at_5
+      value: 16.993
+    - type: recall_at_1
+      value: 39.335
+    - type: recall_at_10
+      value: 81.501
+    - type: recall_at_100
+      value: 94.851
+    - type: recall_at_1000
+      value: 98.817
+    - type: recall_at_20
+      value: 87.968
+    - type: recall_at_3
+      value: 62.795
+    - type: recall_at_5
+      value: 71.985
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+    metrics:
+    - type: map_at_1
+      value: 71.222
+    - type: map_at_10
+      value: 85.193
+    - type: map_at_100
+      value: 85.802
+    - type: map_at_1000
+      value: 85.81800000000001
+    - type: map_at_20
+      value: 85.587
+    - type: map_at_3
+      value: 82.253
+    - type: map_at_5
+      value: 84.142
+    - type: mrr_at_1
+      value: 82.04
+    - type: mrr_at_10
+      value: 88.101
+    - type: mrr_at_100
+      value: 88.196
+    - type: mrr_at_1000
+      value: 88.196
+    - type: mrr_at_20
+      value: 88.175
+    - type: mrr_at_3
+      value: 87.145
+    - type: mrr_at_5
+      value: 87.825
+    - type: ndcg_at_1
+      value: 82.04
+    - type: ndcg_at_10
+      value: 88.849
+    - type: ndcg_at_100
+      value: 89.992
+    - type: ndcg_at_1000
+      value: 90.089
+    - type: ndcg_at_20
+      value: 89.468
+    - type: ndcg_at_3
+      value: 86.06899999999999
+    - type: ndcg_at_5
+      value: 87.669
+    - type: precision_at_1
+      value: 82.04
+    - type: precision_at_10
+      value: 13.447000000000001
+    - type: precision_at_100
+      value: 1.528
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_20
+      value: 7.116
+    - type: precision_at_3
+      value: 37.617
+    - type: precision_at_5
+      value: 24.776
+    - type: recall_at_1
+      value: 71.222
+    - type: recall_at_10
+      value: 95.73899999999999
+    - type: recall_at_100
+      value: 99.572
+    - type: recall_at_1000
+      value: 99.988
+    - type: recall_at_20
+      value: 97.725
+    - type: recall_at_3
+      value: 87.742
+    - type: recall_at_5
+      value: 92.23400000000001
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 56.502005725283524
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+    metrics:
+    - type: v_measure
+      value: 63.14989421688691
+    - type: v_measures
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0.7513770750973534]
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+    metrics:
+    - type: map_at_1
+      value: 4.4830000000000005
+    - type: map_at_10
+      value: 11.04
+    - type: map_at_100
+      value: 12.764000000000001
+    - type: map_at_1000
+      value: 13.04
+    - type: map_at_20
+      value: 11.953
+    - type: map_at_3
+      value: 8.125
+    - type: map_at_5
+      value: 9.565999999999999
+    - type: mrr_at_1
+      value: 22.1
+    - type: mrr_at_10
+      value: 32.494
+    - type: mrr_at_100
+      value: 33.525
+    - type: mrr_at_1000
+      value: 33.596
+    - type: mrr_at_20
+      value: 33.089
+    - type: mrr_at_3
+      value: 29.416999999999998
+    - type: mrr_at_5
+      value: 31.267
+    - type: ndcg_at_1
+      value: 22.1
+    - type: ndcg_at_10
+      value: 18.587
+    - type: ndcg_at_100
+      value: 25.482
+    - type: ndcg_at_1000
+      value: 30.581999999999997
+    - type: ndcg_at_20
+      value: 21.077
+    - type: ndcg_at_3
+      value: 18.165
+    - type: ndcg_at_5
+      value: 15.676000000000002
+    - type: precision_at_1
+      value: 22.1
+    - type: precision_at_10
+      value: 9.48
+    - type: precision_at_100
+      value: 1.942
+    - type: precision_at_1000
+      value: 0.316
+    - type: precision_at_20
+      value: 6.175
+    - type: precision_at_3
+      value: 17.033
+    - type: precision_at_5
+      value: 13.719999999999999
+    - type: recall_at_1
+      value: 4.4830000000000005
+    - type: recall_at_10
+      value: 19.208
+    - type: recall_at_100
+      value: 39.417
+    - type: recall_at_1000
+      value: 64.235
+    - type: recall_at_20
+      value: 25.057000000000002
+    - type: recall_at_3
+      value: 10.348
+    - type: recall_at_5
+      value: 13.893
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.50181312649208
+    - type: cos_sim_spearman
+      value: 79.92900705478993
+    - type: euclidean_pearson
+      value: 81.13482128094503
+    - type: euclidean_spearman
+      value: 79.92732266864367
+    - type: manhattan_pearson
+      value: 81.06702121654993
+    - type: manhattan_spearman
+      value: 79.86983106619135
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.85431681906961
+    - type: cos_sim_spearman
+      value: 77.61671419416626
+    - type: euclidean_pearson
+      value: 81.30538320520961
+    - type: euclidean_spearman
+      value: 77.62096481461272
+    - type: manhattan_pearson
+      value: 81.2306021173407
+    - type: manhattan_spearman
+      value: 77.58386300715222
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.98057702322754
+    - type: cos_sim_spearman
+      value: 86.13305071688859
+    - type: euclidean_pearson
+      value: 85.70903555966376
+    - type: euclidean_spearman
+      value: 86.13150222328171
+    - type: manhattan_pearson
+      value: 85.69380834788831
+    - type: manhattan_spearman
+      value: 86.10784739081191
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.43368314724589
+    - type: cos_sim_spearman
+      value: 81.26767916144169
+    - type: euclidean_pearson
+      value: 83.23234690932492
+    - type: euclidean_spearman
+      value: 81.2671726214706
+    - type: manhattan_pearson
+      value: 83.2381239261109
+    - type: manhattan_spearman
+      value: 81.27674961470714
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.8637546411748
+    - type: cos_sim_spearman
+      value: 88.25330888676139
+    - type: euclidean_pearson
+      value: 87.81194589390417
+    - type: euclidean_spearman
+      value: 88.25258669625579
+    - type: manhattan_pearson
+      value: 87.8131866998459
+    - type: manhattan_spearman
+      value: 88.26523268929576
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.83129743147286
+    - type: cos_sim_spearman
+      value: 85.73732687732624
+    - type: euclidean_pearson
+      value: 85.18051277328075
+    - type: euclidean_spearman
+      value: 85.73565846174445
+    - type: manhattan_pearson
+      value: 85.179029651079
+    - type: manhattan_spearman
+      value: 85.75709685404729
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.04715794253148
+    - type: cos_sim_spearman
+      value: 87.61577496386343
+    - type: euclidean_pearson
+      value: 88.34713614361046
+    - type: euclidean_spearman
+      value: 87.56541901567275
+    - type: manhattan_pearson
+      value: 88.26010824585985
+    - type: manhattan_spearman
+      value: 87.35211736948182
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 62.36160793264433
+    - type: cos_sim_spearman
+      value: 66.07767480051893
+    - type: euclidean_pearson
+      value: 66.4716471304865
+    - type: euclidean_spearman
+      value: 66.03999286501872
+    - type: manhattan_pearson
+      value: 66.46197824372902
+    - type: manhattan_spearman
+      value: 65.82936468127227
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.27768996785856
+    - type: cos_sim_spearman
+      value: 86.96704639052885
+    - type: euclidean_pearson
+      value: 86.48753189555983
+    - type: euclidean_spearman
+      value: 86.96981285751171
+    - type: manhattan_pearson
+      value: 86.49262465015401
+    - type: manhattan_spearman
+      value: 86.95378609580054
+  - task:
+      type: Reranking
+    dataset:
+      type: None
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 81.52012853393428
+    - type: mrr
+      value: 94.70817671798063
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+    metrics:
+    - type: map_at_1
+      value: 55.344
+    - type: map_at_10
+      value: 64.82900000000001
+    - type: map_at_100
+      value: 65.42
+    - type: map_at_1000
+      value: 65.443
+    - type: map_at_20
+      value: 65.2
+    - type: map_at_3
+      value: 61.8
+    - type: map_at_5
+      value: 63.510999999999996
+    - type: mrr_at_1
+      value: 58.333
+    - type: mrr_at_10
+      value: 66.24600000000001
+    - type: mrr_at_100
+      value: 66.742
+    - type: mrr_at_1000
+      value: 66.762
+    - type: mrr_at_20
+      value: 66.549
+    - type: mrr_at_3
+      value: 64.056
+    - type: mrr_at_5
+      value: 65.372
+    - type: ndcg_at_1
+      value: 58.333
+    - type: ndcg_at_10
+      value: 69.626
+    - type: ndcg_at_100
+      value: 72.236
+    - type: ndcg_at_1000
+      value: 72.872
+    - type: ndcg_at_20
+      value: 70.864
+    - type: ndcg_at_3
+      value: 64.50399999999999
+    - type: ndcg_at_5
+      value: 67.07600000000001
+    - type: precision_at_1
+      value: 58.333
+    - type: precision_at_10
+      value: 9.4
+    - type: precision_at_100
+      value: 1.073
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_20
+      value: 4.983
+    - type: precision_at_3
+      value: 25.222
+    - type: precision_at_5
+      value: 16.8
+    - type: recall_at_1
+      value: 55.344
+    - type: recall_at_10
+      value: 82.789
+    - type: recall_at_100
+      value: 94.6
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_20
+      value: 87.533
+    - type: recall_at_3
+      value: 69.18299999999999
+    - type: recall_at_5
+      value: 75.622
+  - task:
+      type: PairClassification
+    dataset:
+      type: None
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.69405940594059
+    - type: cos_sim_ap
+      value: 92.03642221694545
+    - type: cos_sim_f1
+      value: 84.06395048994327
+    - type: cos_sim_precision
+      value: 86.79446219382322
+    - type: cos_sim_recall
+      value: 81.5
+    - type: dot_accuracy
+      value: 99.6930693069307
+    - type: dot_ap
+      value: 91.9971441434875
+    - type: dot_f1
+      value: 83.8006230529595
+    - type: dot_precision
+      value: 87.14902807775377
+    - type: dot_recall
+      value: 80.7
+    - type: euclidean_accuracy
+      value: 99.69504950495049
+    - type: euclidean_ap
+      value: 92.03626548389335
+    - type: euclidean_f1
+      value: 84.10732714138285
+    - type: euclidean_precision
+      value: 86.88699360341151
+    - type: euclidean_recall
+      value: 81.5
+    - type: manhattan_accuracy
+      value: 99.69504950495049
+    - type: manhattan_ap
+      value: 92.02049659660081
+    - type: manhattan_f1
+      value: 84.34959349593495
+    - type: manhattan_precision
+      value: 85.74380165289256
+    - type: manhattan_recall
+      value: 83.0
+    - type: max_accuracy
+      value: 99.69504950495049
+    - type: max_ap
+      value: 92.03642221694545
+    - type: max_f1
+      value: 84.34959349593495
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 67.04916654680977
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 33.36641413495258
+    - type: v_measures
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+  - task:
+      type: Reranking
+    dataset:
+      type: None
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 51.19282080158746
+    - type: mrr
+      value: 51.871100713012474
+  - task:
+      type: Summarization
+    dataset:
+      type: None
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.437664703708485
+    - type: cos_sim_spearman
+      value: 31.391119208581575
+    - type: dot_pearson
+      value: 31.19925970504054
+    - type: dot_spearman
+      value: 31.38087224016694
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+    metrics:
+    - type: map_at_1
+      value: 0.249
+    - type: map_at_10
+      value: 2.163
+    - type: map_at_100
+      value: 13.242999999999999
+    - type: map_at_1000
+      value: 30.866
+    - type: map_at_20
+      value: 3.9539999999999997
+    - type: map_at_3
+      value: 0.718
+    - type: map_at_5
+      value: 1.169
+    - type: mrr_at_1
+      value: 96.0
+    - type: mrr_at_10
+      value: 98.0
+    - type: mrr_at_100
+      value: 98.0
+    - type: mrr_at_1000
+      value: 98.0
+    - type: mrr_at_20
+      value: 98.0
+    - type: mrr_at_3
+      value: 98.0
+    - type: mrr_at_5
+      value: 98.0
+    - type: ndcg_at_1
+      value: 92.0
+    - type: ndcg_at_10
+      value: 84.147
+    - type: ndcg_at_100
+      value: 65.143
+    - type: ndcg_at_1000
+      value: 56.038
+    - type: ndcg_at_20
+      value: 80.869
+    - type: ndcg_at_3
+      value: 89.11200000000001
+    - type: ndcg_at_5
+      value: 87.199
+    - type: precision_at_1
+      value: 96.0
+    - type: precision_at_10
+      value: 87.8
+    - type: precision_at_100
+      value: 66.72
+    - type: precision_at_1000
+      value: 24.684
+    - type: precision_at_20
+      value: 84.3
+    - type: precision_at_3
+      value: 94.0
+    - type: precision_at_5
+      value: 91.2
+    - type: recall_at_1
+      value: 0.249
+    - type: recall_at_10
+      value: 2.284
+    - type: recall_at_100
+      value: 16.025
+    - type: recall_at_1000
+      value: 52.068999999999996
+    - type: recall_at_20
+      value: 4.3180000000000005
+    - type: recall_at_3
+      value: 0.738
+    - type: recall_at_5
+      value: 1.212
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: map_at_1
+      value: 3.4520000000000004
+    - type: map_at_10
+      value: 13.045000000000002
+    - type: map_at_100
+      value: 19.442
+    - type: map_at_1000
+      value: 21.09
+    - type: map_at_20
+      value: 15.667
+    - type: map_at_3
+      value: 7.409000000000001
+    - type: map_at_5
+      value: 9.73
+    - type: mrr_at_1
+      value: 46.939
+    - type: mrr_at_10
+      value: 60.295
+    - type: mrr_at_100
+      value: 60.904
+    - type: mrr_at_1000
+      value: 60.919000000000004
+    - type: mrr_at_20
+      value: 60.77
+    - type: mrr_at_3
+      value: 58.50300000000001
+    - type: mrr_at_5
+      value: 59.014
+    - type: ndcg_at_1
+      value: 44.897999999999996
+    - type: ndcg_at_10
+      value: 31.911
+    - type: ndcg_at_100
+      value: 41.945
+    - type: ndcg_at_1000
+      value: 53.181999999999995
+    - type: ndcg_at_20
+      value: 31.505
+    - type: ndcg_at_3
+      value: 39.745000000000005
+    - type: ndcg_at_5
+      value: 35.528999999999996
+    - type: precision_at_1
+      value: 46.939
+    - type: precision_at_10
+      value: 26.531
+    - type: precision_at_100
+      value: 8.163
+    - type: precision_at_1000
+      value: 1.559
+    - type: precision_at_20
+      value: 19.387999999999998
+    - type: precision_at_3
+      value: 40.136
+    - type: precision_at_5
+      value: 33.878
+    - type: recall_at_1
+      value: 3.4520000000000004
+    - type: recall_at_10
+      value: 18.899
+    - type: recall_at_100
+      value: 50.207
+    - type: recall_at_1000
+      value: 83.871
+    - type: recall_at_20
+      value: 26.756999999999998
+    - type: recall_at_3
+      value: 8.729000000000001
+    - type: recall_at_5
+      value: 12.084999999999999
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+    metrics:
+    - type: accuracy
+      value: 67.4560546875
+    - type: ap
+      value: 12.720403845355294
+    - type: f1
+      value: 51.76062666567839
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 62.36276174306734
+    - type: f1
+      value: 62.69956906934332
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.473492910233965
+    - type: v_measures
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+  - task:
+      type: PairClassification
+    dataset:
+      type: None
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.75430649102938
+    - type: cos_sim_ap
+      value: 73.62842656477649
+    - type: cos_sim_f1
+      value: 67.76023680315738
+    - type: cos_sim_precision
+      value: 63.61741547012506
+    - type: cos_sim_recall
+      value: 72.4802110817942
+    - type: dot_accuracy
+      value: 85.7423854085951
+    - type: dot_ap
+      value: 73.59147637253723
+    - type: dot_f1
+      value: 67.69498693867396
+    - type: dot_precision
+      value: 64.03859731701577
+    - type: dot_recall
+      value: 71.79419525065963
+    - type: euclidean_accuracy
+      value: 85.7423854085951
+    - type: euclidean_ap
+      value: 73.6288990409654
+    - type: euclidean_f1
+      value: 67.80415430267064
+    - type: euclidean_precision
+      value: 63.79711493718009
+    - type: euclidean_recall
+      value: 72.34828496042216
+    - type: manhattan_accuracy
+      value: 85.69470107885796
+    - type: manhattan_ap
+      value: 73.49219614602531
+    - type: manhattan_f1
+      value: 67.60809797550613
+    - type: manhattan_precision
+      value: 64.22127255460589
+    - type: manhattan_recall
+      value: 71.37203166226914
+    - type: max_accuracy
+      value: 85.75430649102938
+    - type: max_ap
+      value: 73.6288990409654
+    - type: max_f1
+      value: 67.80415430267064
+  - task:
+      type: PairClassification
+    dataset:
+      type: None
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.08293553770326
+    - type: cos_sim_ap
+      value: 86.21246419992926
+    - type: cos_sim_f1
+      value: 78.49922526377924
+    - type: cos_sim_precision
+      value: 75.35769939084857
+    - type: cos_sim_recall
+      value: 81.9140745303357
+    - type: dot_accuracy
+      value: 89.08681647067955
+    - type: dot_ap
+      value: 86.19733517196862
+    - type: dot_f1
+      value: 78.51132446157838
+    - type: dot_precision
+      value: 75.70233755093287
+    - type: dot_recall
+      value: 81.53680320295658
+    - type: euclidean_accuracy
+      value: 89.07517367175069
+    - type: euclidean_ap
+      value: 86.21198725320203
+    - type: euclidean_f1
+      value: 78.49867139061116
+    - type: euclidean_precision
+      value: 75.38276155372839
+    - type: euclidean_recall
+      value: 81.88327687095781
+    - type: manhattan_accuracy
+      value: 89.0538285403811
+    - type: manhattan_ap
+      value: 86.17785515765131
+    - type: manhattan_f1
+      value: 78.48184098593084
+    - type: manhattan_precision
+      value: 74.34396308285694
+    - type: manhattan_recall
+      value: 83.10748383122882
+    - type: max_accuracy
+      value: 89.08681647067955
+    - type: max_ap
+      value: 86.21246419992926
+    - type: max_f1
+      value: 78.51132446157838
+license: apache-2.0
+language:
+- en
+base_model:
+- answerdotai/ModernBERT-base
+- nomic-ai/modernbert-embed-unsupervised
+---
+
+# ModernBERT Embed
+
+[![image/png](modernbertembed.png)](https://huggingface.co/nomic-ai/modernbert-embed-base)
+
+ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), bringing the new advances of ModernBERT to embeddings!
+
+Trained on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets, `modernbert-embed` also supports Matryoshka Representation Learning dimensions of 256, reducing memory by 3x with minimal performance loss.
+
+## Performance
+
+| Model                 | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10)  | Summarization (1) |
+|-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------|
+| nomic-embed-text-v1   | 768        | 62.4         | 74.1                | 43.9            | **85.2**                | 55.7          | 52.8           | 82.1      | 30.1             |
+| nomic-embed-text-v1.5 | 768        | 62.28        | 73.55               | 43.93           | 84.61                   | 55.78         | **53.01**      | **81.94** | 30.4             |
+| modernbert-embed-base      | 768        | **62.62**    | **74.31**           | **44.98**       | 83.96                   | **56.42**     | 52.89          | 81.78     | **31.39**        |
+| nomic-embed-text-v1.5 | 256        | 61.04        | 72.1                | 43.16           | 84.09                   | 55.18         | 50.81          | 81.34     |   30.05               |
+| modernbert-embed-base      | 256        | 61.17        | 72.40               | 43.82           | 83.45                   | 55.69         | 50.62          | 81.12     | 31.27            |
+
+
+
+## Usage
+
+You can use these models directly with the transformers library. Until the next transformers release, doing so requires installing `transformers` from `main`:
+
+```bash
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
+
+Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/modernbert-embed-base")
+
+query_embeddings = model.encode([
+    "search_query: What is TSNE?",
+    "search_query: Who is Laurens van der Maaten?",
+])
+doc_embeddings = model.encode([
+    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
+])
+print(query_embeddings.shape, doc_embeddings.shape)
+# (2, 768) (1, 768)
+
+similarities = model.similarity(query_embeddings, doc_embeddings)
+print(similarities)
+# tensor([[0.7214],
+#         [0.3260]])
+```
+
+<details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
+
+In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/modernbert-embed-base", truncate_dim=256)
+
+query_embeddings = model.encode([
+    "search_query: What is TSNE?",
+    "search_query: Who is Laurens van der Maaten?",
+])
+doc_embeddings = model.encode([
+    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
+])
+print(query_embeddings.shape, doc_embeddings.shape)
+# (2, 256) (1, 256)
+
+similarities = model.similarity(query_embeddings, doc_embeddings)
+print(similarities)
+# tensor([[0.7759],
+#         [0.3419]])
+```
+
+Note the small differences compared to the full 768-dimensional similarities.
+
+</details>
+
+### Transformers
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    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
+    )
+
+
+queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
+documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
+
+tokenizer = AutoTokenizer.from_pretrained("nomic-ai/modernbert-embed-base")
+model = AutoModel.from_pretrained("nomic-ai/modernbert-embed-base")
+
+encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
+encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+    queries_outputs = model(**encoded_queries)
+    documents_outputs = model(**encoded_documents)
+
+query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
+query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
+doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
+doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
+print(query_embeddings.shape, doc_embeddings.shape)
+# torch.Size([2, 768]) torch.Size([1, 768])
+
+similarities = query_embeddings @ doc_embeddings.T
+print(similarities)
+# tensor([[0.7214],
+#         [0.3260]])
+```
+
+<details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
+
+In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    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
+    )
+
+
+queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
+documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
+
+tokenizer = AutoTokenizer.from_pretrained(".")
+model = AutoModel.from_pretrained(".")
+truncate_dim = 256
+
+encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
+encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+    queries_outputs = model(**encoded_queries)
+    documents_outputs = model(**encoded_documents)
+
+query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
+query_embeddings = query_embeddings[:, :truncate_dim]
+query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
+doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
+doc_embeddings = doc_embeddings[:, :truncate_dim]
+doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
+print(query_embeddings.shape, doc_embeddings.shape)
+# torch.Size([2, 256]) torch.Size([1, 256])
+
+similarities = query_embeddings @ doc_embeddings.T
+print(similarities)
+# tensor([[0.7759],
+#         [0.3419]])
+```
+
+Note the small differences compared to the full 768-dimensional similarities.
+
+</details>
+
+### Transformers.js
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+Then, you can compute embeddings as follows:
+
+```javascript
+import { pipeline, matmul } from '@huggingface/transformers';
+
+// Create a feature extraction pipeline
+const extractor = await pipeline(
+  "feature-extraction",
+  "nomic-ai/modernbert-embed-base",
+  { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
+);
+
+// Embed queries and documents
+const query_embeddings = await extractor([
+    "search_query: What is TSNE?",
+    "search_query: Who is Laurens van der Maaten?",
+  ], { pooling: "mean", normalize: true },
+);
+const doc_embeddings = await extractor([
+    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
+  ], { pooling: "mean", normalize: true },
+);
+
+// Compute similarity scores
+const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
+console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]
+```
+
+
+## Training
+
+Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
+
+[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
+
+We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
+the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
+
+In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
+
+For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
+
+Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
+
+
+## Join the Nomic Community
+
+- Nomic: [https://nomic.ai](https://nomic.ai)
+- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
+- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
+
+## Citation
+
+If you find the model, dataset, or training code useful, please cite our work
+
+```bibtex
+@misc{nussbaum2024nomic,
+      title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, 
+      author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
+      year={2024},
+      eprint={2402.01613},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```
\ No newline at end of file