diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -1,199 +1,3172 @@
 ---
-library_name: transformers
-tags: []
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+- transformers.js
+model-index:
+- name: modernbert-embed-large
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 76.7910447761194
+    - type: ap
+      value: 39.79562424828666
+    - type: f1
+      value: 70.69575548517653
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 94.19505000000001
+    - type: ap
+      value: 91.75071069741077
+    - type: f1
+      value: 94.19151001437368
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 47.664
+    - type: f1
+      value: 46.932904638602466
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: map_at_1
+      value: 25.178
+    - type: map_at_10
+      value: 41.088
+    - type: map_at_100
+      value: 42.143
+    - type: map_at_1000
+      value: 42.152
+    - type: map_at_20
+      value: 41.946
+    - type: map_at_3
+      value: 36.048
+    - type: map_at_5
+      value: 38.619
+    - type: mrr_at_1
+      value: 25.533
+    - type: mrr_at_10
+      value: 41.238
+    - type: mrr_at_100
+      value: 42.293
+    - type: mrr_at_1000
+      value: 42.302
+    - type: mrr_at_20
+      value: 42.096000000000004
+    - type: mrr_at_3
+      value: 36.260999999999996
+    - type: mrr_at_5
+      value: 38.797
+    - type: ndcg_at_1
+      value: 25.178
+    - type: ndcg_at_10
+      value: 50.352
+    - type: ndcg_at_100
+      value: 54.583000000000006
+    - type: ndcg_at_1000
+      value: 54.797
+    - type: ndcg_at_20
+      value: 53.36
+    - type: ndcg_at_3
+      value: 39.781
+    - type: ndcg_at_5
+      value: 44.412
+    - type: precision_at_1
+      value: 25.178
+    - type: precision_at_10
+      value: 8.016
+    - type: precision_at_100
+      value: 0.98
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_20
+      value: 4.591
+    - type: precision_at_3
+      value: 16.88
+    - type: precision_at_5
+      value: 12.376
+    - type: recall_at_1
+      value: 25.178
+    - type: recall_at_10
+      value: 80.156
+    - type: recall_at_100
+      value: 98.009
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_20
+      value: 91.821
+    - type: recall_at_3
+      value: 50.63999999999999
+    - type: recall_at_5
+      value: 61.878
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.800803622189214
+    - 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: 39.46617889287484
+    - 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: 63.213756562834234
+    - type: mrr
+      value: 76.76493866244557
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.90977143141957
+    - type: cos_sim_spearman
+      value: 87.47729443431557
+    - type: euclidean_pearson
+      value: 86.45663786393041
+    - type: euclidean_spearman
+      value: 86.31461733951959
+    - type: manhattan_pearson
+      value: 85.94280510342506
+    - type: manhattan_spearman
+      value: 85.61158927235539
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 86.17857142857144
+    - type: f1
+      value: 86.14192410600847
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.466770895318334
+    - 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: 34.668146108668715
+    - 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: 34.394999999999996
+    - type: map_at_10
+      value: 45.882
+    - type: map_at_100
+      value: 47.4
+    - type: map_at_1000
+      value: 47.509
+    - type: map_at_20
+      value: 46.822
+    - type: map_at_3
+      value: 42.408
+    - type: map_at_5
+      value: 44.586
+    - type: mrr_at_1
+      value: 41.202
+    - type: mrr_at_10
+      value: 51.134
+    - type: mrr_at_100
+      value: 51.943
+    - type: mrr_at_1000
+      value: 51.986
+    - type: mrr_at_20
+      value: 51.717
+    - type: mrr_at_3
+      value: 48.784
+    - type: mrr_at_5
+      value: 50.336000000000006
+    - type: ndcg_at_1
+      value: 41.202
+    - type: ndcg_at_10
+      value: 51.842999999999996
+    - type: ndcg_at_100
+      value: 57.177
+    - type: ndcg_at_1000
+      value: 58.89
+    - type: ndcg_at_20
+      value: 54.357
+    - type: ndcg_at_3
+      value: 47.286
+    - type: ndcg_at_5
+      value: 49.829
+    - type: precision_at_1
+      value: 41.202
+    - type: precision_at_10
+      value: 9.585
+    - type: precision_at_100
+      value: 1.5150000000000001
+    - type: precision_at_1000
+      value: 0.194
+    - type: precision_at_20
+      value: 5.808
+    - type: precision_at_3
+      value: 22.508
+    - type: precision_at_5
+      value: 16.366
+    - type: recall_at_1
+      value: 34.394999999999996
+    - type: recall_at_10
+      value: 63.17
+    - type: recall_at_100
+      value: 84.867
+    - type: recall_at_1000
+      value: 95.733
+    - type: recall_at_20
+      value: 72.011
+    - type: recall_at_3
+      value: 49.966
+    - type: recall_at_5
+      value: 56.802
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+    metrics:
+    - type: map_at_1
+      value: 34.324
+    - type: map_at_10
+      value: 46.123
+    - type: map_at_100
+      value: 47.455999999999996
+    - type: map_at_1000
+      value: 47.576
+    - type: map_at_20
+      value: 46.851
+    - type: map_at_3
+      value: 42.945
+    - type: map_at_5
+      value: 44.751000000000005
+    - type: mrr_at_1
+      value: 43.248
+    - type: mrr_at_10
+      value: 52.544000000000004
+    - type: mrr_at_100
+      value: 53.102000000000004
+    - type: mrr_at_1000
+      value: 53.138
+    - type: mrr_at_20
+      value: 52.861000000000004
+    - type: mrr_at_3
+      value: 50.37199999999999
+    - type: mrr_at_5
+      value: 51.712
+    - type: ndcg_at_1
+      value: 43.248
+    - type: ndcg_at_10
+      value: 52.235
+    - type: ndcg_at_100
+      value: 56.355
+    - type: ndcg_at_1000
+      value: 58.053
+    - type: ndcg_at_20
+      value: 53.849000000000004
+    - type: ndcg_at_3
+      value: 48.208
+    - type: ndcg_at_5
+      value: 50.134
+    - type: precision_at_1
+      value: 43.248
+    - type: precision_at_10
+      value: 9.917
+    - type: precision_at_100
+      value: 1.532
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_20
+      value: 5.779999999999999
+    - type: precision_at_3
+      value: 23.588
+    - type: precision_at_5
+      value: 16.586000000000002
+    - type: recall_at_1
+      value: 34.324
+    - type: recall_at_10
+      value: 62.56
+    - type: recall_at_100
+      value: 79.745
+    - type: recall_at_1000
+      value: 90.082
+    - type: recall_at_20
+      value: 68.367
+    - type: recall_at_3
+      value: 50.171
+    - type: recall_at_5
+      value: 55.889
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+    metrics:
+    - type: map_at_1
+      value: 44.143
+    - type: map_at_10
+      value: 56.53
+    - type: map_at_100
+      value: 57.48799999999999
+    - type: map_at_1000
+      value: 57.535000000000004
+    - type: map_at_20
+      value: 57.152
+    - type: map_at_3
+      value: 53.382
+    - type: map_at_5
+      value: 55.156000000000006
+    - type: mrr_at_1
+      value: 50.09400000000001
+    - type: mrr_at_10
+      value: 59.819
+    - type: mrr_at_100
+      value: 60.431000000000004
+    - type: mrr_at_1000
+      value: 60.455000000000005
+    - type: mrr_at_20
+      value: 60.251999999999995
+    - type: mrr_at_3
+      value: 57.544
+    - type: mrr_at_5
+      value: 58.904999999999994
+    - type: ndcg_at_1
+      value: 50.09400000000001
+    - type: ndcg_at_10
+      value: 62.141999999999996
+    - type: ndcg_at_100
+      value: 65.755
+    - type: ndcg_at_1000
+      value: 66.674
+    - type: ndcg_at_20
+      value: 63.92400000000001
+    - type: ndcg_at_3
+      value: 56.986000000000004
+    - type: ndcg_at_5
+      value: 59.519999999999996
+    - type: precision_at_1
+      value: 50.09400000000001
+    - type: precision_at_10
+      value: 9.743
+    - type: precision_at_100
+      value: 1.246
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_20
+      value: 5.439
+    - type: precision_at_3
+      value: 25.119999999999997
+    - type: precision_at_5
+      value: 17.052999999999997
+    - type: recall_at_1
+      value: 44.143
+    - type: recall_at_10
+      value: 75.372
+    - type: recall_at_100
+      value: 90.602
+    - type: recall_at_1000
+      value: 97.043
+    - type: recall_at_20
+      value: 81.83500000000001
+    - type: recall_at_3
+      value: 61.607
+    - type: recall_at_5
+      value: 67.755
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+    metrics:
+    - type: map_at_1
+      value: 26.621
+    - type: map_at_10
+      value: 35.865
+    - type: map_at_100
+      value: 36.93
+    - type: map_at_1000
+      value: 37.008
+    - type: map_at_20
+      value: 36.509
+    - type: map_at_3
+      value: 33.532000000000004
+    - type: map_at_5
+      value: 34.745
+    - type: mrr_at_1
+      value: 28.588
+    - type: mrr_at_10
+      value: 37.828
+    - type: mrr_at_100
+      value: 38.779
+    - type: mrr_at_1000
+      value: 38.834
+    - type: mrr_at_20
+      value: 38.419
+    - type: mrr_at_3
+      value: 35.725
+    - type: mrr_at_5
+      value: 36.803999999999995
+    - type: ndcg_at_1
+      value: 28.588
+    - type: ndcg_at_10
+      value: 40.983999999999995
+    - type: ndcg_at_100
+      value: 46.117000000000004
+    - type: ndcg_at_1000
+      value: 47.959
+    - type: ndcg_at_20
+      value: 43.22
+    - type: ndcg_at_3
+      value: 36.455
+    - type: ndcg_at_5
+      value: 38.393
+    - type: precision_at_1
+      value: 28.588
+    - type: precision_at_10
+      value: 6.282
+    - type: precision_at_100
+      value: 0.927
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_20
+      value: 3.689
+    - type: precision_at_3
+      value: 15.744
+    - type: precision_at_5
+      value: 10.644
+    - type: recall_at_1
+      value: 26.621
+    - type: recall_at_10
+      value: 54.80199999999999
+    - type: recall_at_100
+      value: 78.171
+    - type: recall_at_1000
+      value: 91.786
+    - type: recall_at_20
+      value: 63.195
+    - type: recall_at_3
+      value: 42.164
+    - type: recall_at_5
+      value: 46.936
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: map_at_1
+      value: 18.619
+    - type: map_at_10
+      value: 27.577
+    - type: map_at_100
+      value: 28.717
+    - type: map_at_1000
+      value: 28.835
+    - type: map_at_20
+      value: 28.18
+    - type: map_at_3
+      value: 24.462999999999997
+    - type: map_at_5
+      value: 26.230999999999998
+    - type: mrr_at_1
+      value: 22.886
+    - type: mrr_at_10
+      value: 32.089
+    - type: mrr_at_100
+      value: 32.998
+    - type: mrr_at_1000
+      value: 33.06
+    - type: mrr_at_20
+      value: 32.633
+    - type: mrr_at_3
+      value: 29.125
+    - type: mrr_at_5
+      value: 30.792
+    - type: ndcg_at_1
+      value: 22.886
+    - type: ndcg_at_10
+      value: 33.343
+    - type: ndcg_at_100
+      value: 38.735
+    - type: ndcg_at_1000
+      value: 41.393
+    - type: ndcg_at_20
+      value: 35.455
+    - type: ndcg_at_3
+      value: 27.575
+    - type: ndcg_at_5
+      value: 30.361
+    - type: precision_at_1
+      value: 22.886
+    - type: precision_at_10
+      value: 6.256
+    - type: precision_at_100
+      value: 1.03
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_20
+      value: 3.7130000000000005
+    - type: precision_at_3
+      value: 13.267000000000001
+    - type: precision_at_5
+      value: 9.851
+    - type: recall_at_1
+      value: 18.619
+    - type: recall_at_10
+      value: 46.478
+    - type: recall_at_100
+      value: 69.614
+    - type: recall_at_1000
+      value: 88.331
+    - type: recall_at_20
+      value: 54.254000000000005
+    - type: recall_at_3
+      value: 30.897999999999996
+    - type: recall_at_5
+      value: 37.785000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+    metrics:
+    - type: map_at_1
+      value: 31.087999999999997
+    - type: map_at_10
+      value: 41.668
+    - type: map_at_100
+      value: 42.983
+    - type: map_at_1000
+      value: 43.081
+    - type: map_at_20
+      value: 42.373
+    - type: map_at_3
+      value: 38.481
+    - type: map_at_5
+      value: 40.196
+    - type: mrr_at_1
+      value: 37.824999999999996
+    - type: mrr_at_10
+      value: 47.471999999999994
+    - type: mrr_at_100
+      value: 48.311
+    - type: mrr_at_1000
+      value: 48.351
+    - type: mrr_at_20
+      value: 47.981
+    - type: mrr_at_3
+      value: 45.074999999999996
+    - type: mrr_at_5
+      value: 46.37
+    - type: ndcg_at_1
+      value: 37.824999999999996
+    - type: ndcg_at_10
+      value: 47.63
+    - type: ndcg_at_100
+      value: 52.979
+    - type: ndcg_at_1000
+      value: 54.771
+    - type: ndcg_at_20
+      value: 49.733
+    - type: ndcg_at_3
+      value: 42.657000000000004
+    - type: ndcg_at_5
+      value: 44.878
+    - type: precision_at_1
+      value: 37.824999999999996
+    - type: precision_at_10
+      value: 8.527
+    - type: precision_at_100
+      value: 1.303
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_20
+      value: 4.966
+    - type: precision_at_3
+      value: 19.955000000000002
+    - type: precision_at_5
+      value: 14.033000000000001
+    - type: recall_at_1
+      value: 31.087999999999997
+    - type: recall_at_10
+      value: 59.585
+    - type: recall_at_100
+      value: 81.625
+    - type: recall_at_1000
+      value: 93.297
+    - type: recall_at_20
+      value: 66.813
+    - type: recall_at_3
+      value: 45.492
+    - type: recall_at_5
+      value: 51.283
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+    metrics:
+    - type: map_at_1
+      value: 28.756999999999998
+    - type: map_at_10
+      value: 40.275
+    - type: map_at_100
+      value: 41.655
+    - type: map_at_1000
+      value: 41.752
+    - type: map_at_20
+      value: 41.118
+    - type: map_at_3
+      value: 36.815
+    - type: map_at_5
+      value: 38.662
+    - type: mrr_at_1
+      value: 35.502
+    - type: mrr_at_10
+      value: 45.818
+    - type: mrr_at_100
+      value: 46.704
+    - type: mrr_at_1000
+      value: 46.745999999999995
+    - type: mrr_at_20
+      value: 46.387
+    - type: mrr_at_3
+      value: 43.322
+    - type: mrr_at_5
+      value: 44.675
+    - type: ndcg_at_1
+      value: 35.502
+    - type: ndcg_at_10
+      value: 46.658
+    - type: ndcg_at_100
+      value: 52.097
+    - type: ndcg_at_1000
+      value: 53.928
+    - type: ndcg_at_20
+      value: 49.134
+    - type: ndcg_at_3
+      value: 41.234
+    - type: ndcg_at_5
+      value: 43.579
+    - type: precision_at_1
+      value: 35.502
+    - type: precision_at_10
+      value: 8.652999999999999
+    - type: precision_at_100
+      value: 1.306
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_20
+      value: 5.086
+    - type: precision_at_3
+      value: 19.825
+    - type: precision_at_5
+      value: 13.995
+    - type: recall_at_1
+      value: 28.756999999999998
+    - type: recall_at_10
+      value: 59.79
+    - type: recall_at_100
+      value: 82.597
+    - type: recall_at_1000
+      value: 94.663
+    - type: recall_at_20
+      value: 68.74
+    - type: recall_at_3
+      value: 44.736
+    - type: recall_at_5
+      value: 51.047
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: map_at_1
+      value: 28.592666666666673
+    - type: map_at_10
+      value: 38.50391666666667
+    - type: map_at_100
+      value: 39.719166666666666
+    - type: map_at_1000
+      value: 39.82683333333334
+    - type: map_at_20
+      value: 39.18608333333333
+    - type: map_at_3
+      value: 35.561833333333325
+    - type: map_at_5
+      value: 37.181000000000004
+    - type: mrr_at_1
+      value: 33.67625
+    - type: mrr_at_10
+      value: 42.727
+    - type: mrr_at_100
+      value: 43.55041666666667
+    - type: mrr_at_1000
+      value: 43.60058333333334
+    - type: mrr_at_20
+      value: 43.21508333333333
+    - type: mrr_at_3
+      value: 40.32983333333334
+    - type: mrr_at_5
+      value: 41.699333333333335
+    - type: ndcg_at_1
+      value: 33.67625
+    - type: ndcg_at_10
+      value: 44.064416666666666
+    - type: ndcg_at_100
+      value: 49.085
+    - type: ndcg_at_1000
+      value: 51.09325
+    - type: ndcg_at_20
+      value: 46.07716666666666
+    - type: ndcg_at_3
+      value: 39.22225
+    - type: ndcg_at_5
+      value: 41.47508333333333
+    - type: precision_at_1
+      value: 33.67625
+    - type: precision_at_10
+      value: 7.689916666666667
+    - type: precision_at_100
+      value: 1.1995833333333334
+    - type: precision_at_1000
+      value: 0.15541666666666665
+    - type: precision_at_20
+      value: 4.515500000000001
+    - type: precision_at_3
+      value: 18.07241666666667
+    - type: precision_at_5
+      value: 12.732833333333332
+    - type: recall_at_1
+      value: 28.592666666666673
+    - type: recall_at_10
+      value: 56.15700000000001
+    - type: recall_at_100
+      value: 77.97075000000001
+    - type: recall_at_1000
+      value: 91.73058333333333
+    - type: recall_at_20
+      value: 63.49649999999999
+    - type: recall_at_3
+      value: 42.612833333333334
+    - type: recall_at_5
+      value: 48.44591666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+    metrics:
+    - type: map_at_1
+      value: 25.381999999999998
+    - type: map_at_10
+      value: 33.311
+    - type: map_at_100
+      value: 34.171
+    - type: map_at_1000
+      value: 34.254
+    - type: map_at_20
+      value: 33.732
+    - type: map_at_3
+      value: 31.025999999999996
+    - type: map_at_5
+      value: 32.253
+    - type: mrr_at_1
+      value: 28.221
+    - type: mrr_at_10
+      value: 36.132999999999996
+    - type: mrr_at_100
+      value: 36.848
+    - type: mrr_at_1000
+      value: 36.902
+    - type: mrr_at_20
+      value: 36.497
+    - type: mrr_at_3
+      value: 33.947
+    - type: mrr_at_5
+      value: 35.174
+    - type: ndcg_at_1
+      value: 28.221
+    - type: ndcg_at_10
+      value: 37.882
+    - type: ndcg_at_100
+      value: 42.283
+    - type: ndcg_at_1000
+      value: 44.458
+    - type: ndcg_at_20
+      value: 39.268
+    - type: ndcg_at_3
+      value: 33.611999999999995
+    - type: ndcg_at_5
+      value: 35.583
+    - type: precision_at_1
+      value: 28.221
+    - type: precision_at_10
+      value: 6.043
+    - type: precision_at_100
+      value: 0.8909999999999999
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_20
+      value: 3.405
+    - type: precision_at_3
+      value: 14.673
+    - type: precision_at_5
+      value: 10.152999999999999
+    - type: recall_at_1
+      value: 25.381999999999998
+    - type: recall_at_10
+      value: 48.980000000000004
+    - type: recall_at_100
+      value: 69.625
+    - type: recall_at_1000
+      value: 85.946
+    - type: recall_at_20
+      value: 54.041
+    - type: recall_at_3
+      value: 37.077
+    - type: recall_at_5
+      value: 42.097
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+    metrics:
+    - type: map_at_1
+      value: 18.186
+    - type: map_at_10
+      value: 26.450000000000003
+    - type: map_at_100
+      value: 27.62
+    - type: map_at_1000
+      value: 27.746
+    - type: map_at_20
+      value: 27.105
+    - type: map_at_3
+      value: 23.982999999999997
+    - type: map_at_5
+      value: 25.306
+    - type: mrr_at_1
+      value: 22.092
+    - type: mrr_at_10
+      value: 30.326999999999998
+    - type: mrr_at_100
+      value: 31.322
+    - type: mrr_at_1000
+      value: 31.394
+    - type: mrr_at_20
+      value: 30.923000000000002
+    - type: mrr_at_3
+      value: 28.063
+    - type: mrr_at_5
+      value: 29.284
+    - type: ndcg_at_1
+      value: 22.092
+    - type: ndcg_at_10
+      value: 31.418000000000003
+    - type: ndcg_at_100
+      value: 36.924
+    - type: ndcg_at_1000
+      value: 39.645
+    - type: ndcg_at_20
+      value: 33.597
+    - type: ndcg_at_3
+      value: 27.045
+    - type: ndcg_at_5
+      value: 28.971999999999998
+    - type: precision_at_1
+      value: 22.092
+    - type: precision_at_10
+      value: 5.785
+    - type: precision_at_100
+      value: 0.989
+    - type: precision_at_1000
+      value: 0.13999999999999999
+    - type: precision_at_20
+      value: 3.517
+    - type: precision_at_3
+      value: 12.985
+    - type: precision_at_5
+      value: 9.291
+    - type: recall_at_1
+      value: 18.186
+    - type: recall_at_10
+      value: 42.443
+    - type: recall_at_100
+      value: 66.964
+    - type: recall_at_1000
+      value: 86.005
+    - type: recall_at_20
+      value: 50.52799999999999
+    - type: recall_at_3
+      value: 30.095
+    - type: recall_at_5
+      value: 35.148
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+    metrics:
+    - type: map_at_1
+      value: 31.049
+    - type: map_at_10
+      value: 40.217000000000006
+    - type: map_at_100
+      value: 41.345
+    - type: map_at_1000
+      value: 41.447
+    - type: map_at_20
+      value: 40.818
+    - type: map_at_3
+      value: 37.413999999999994
+    - type: map_at_5
+      value: 39.001000000000005
+    - type: mrr_at_1
+      value: 36.474000000000004
+    - type: mrr_at_10
+      value: 44.655
+    - type: mrr_at_100
+      value: 45.399
+    - type: mrr_at_1000
+      value: 45.454
+    - type: mrr_at_20
+      value: 45.011
+    - type: mrr_at_3
+      value: 42.226
+    - type: mrr_at_5
+      value: 43.653999999999996
+    - type: ndcg_at_1
+      value: 36.474000000000004
+    - type: ndcg_at_10
+      value: 45.509
+    - type: ndcg_at_100
+      value: 50.571
+    - type: ndcg_at_1000
+      value: 52.605999999999995
+    - type: ndcg_at_20
+      value: 47.275
+    - type: ndcg_at_3
+      value: 40.766000000000005
+    - type: ndcg_at_5
+      value: 42.979
+    - type: precision_at_1
+      value: 36.474000000000004
+    - type: precision_at_10
+      value: 7.509
+    - type: precision_at_100
+      value: 1.1320000000000001
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_20
+      value: 4.3
+    - type: precision_at_3
+      value: 18.315
+    - type: precision_at_5
+      value: 12.705
+    - type: recall_at_1
+      value: 31.049
+    - type: recall_at_10
+      value: 57.135999999999996
+    - type: recall_at_100
+      value: 79.196
+    - type: recall_at_1000
+      value: 93.002
+    - type: recall_at_20
+      value: 63.416
+    - type: recall_at_3
+      value: 43.893
+    - type: recall_at_5
+      value: 49.675999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: map_at_1
+      value: 27.929
+    - type: map_at_10
+      value: 36.897000000000006
+    - type: map_at_100
+      value: 38.635000000000005
+    - type: map_at_1000
+      value: 38.842
+    - type: map_at_20
+      value: 37.814
+    - type: map_at_3
+      value: 33.522
+    - type: map_at_5
+      value: 35.128
+    - type: mrr_at_1
+      value: 33.399
+    - type: mrr_at_10
+      value: 41.817
+    - type: mrr_at_100
+      value: 42.797000000000004
+    - type: mrr_at_1000
+      value: 42.842999999999996
+    - type: mrr_at_20
+      value: 42.381
+    - type: mrr_at_3
+      value: 38.999
+    - type: mrr_at_5
+      value: 40.57
+    - type: ndcg_at_1
+      value: 33.399
+    - type: ndcg_at_10
+      value: 43.134
+    - type: ndcg_at_100
+      value: 49.009
+    - type: ndcg_at_1000
+      value: 51.199
+    - type: ndcg_at_20
+      value: 45.391999999999996
+    - type: ndcg_at_3
+      value: 37.645
+    - type: ndcg_at_5
+      value: 39.940999999999995
+    - type: precision_at_1
+      value: 33.399
+    - type: precision_at_10
+      value: 8.36
+    - type: precision_at_100
+      value: 1.646
+    - type: precision_at_1000
+      value: 0.244
+    - type: precision_at_20
+      value: 5.257
+    - type: precision_at_3
+      value: 17.457
+    - type: precision_at_5
+      value: 12.727
+    - type: recall_at_1
+      value: 27.929
+    - type: recall_at_10
+      value: 54.822
+    - type: recall_at_100
+      value: 80.63900000000001
+    - type: recall_at_1000
+      value: 94.382
+    - type: recall_at_20
+      value: 63.432
+    - type: recall_at_3
+      value: 39.291
+    - type: recall_at_5
+      value: 45.385999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 22.619
+    - type: map_at_10
+      value: 31.252000000000002
+    - type: map_at_100
+      value: 32.23
+    - type: map_at_1000
+      value: 32.336999999999996
+    - type: map_at_20
+      value: 31.758999999999997
+    - type: map_at_3
+      value: 28.771
+    - type: map_at_5
+      value: 30.157
+    - type: mrr_at_1
+      value: 24.584
+    - type: mrr_at_10
+      value: 33.088
+    - type: mrr_at_100
+      value: 33.971000000000004
+    - type: mrr_at_1000
+      value: 34.044000000000004
+    - type: mrr_at_20
+      value: 33.519
+    - type: mrr_at_3
+      value: 30.775999999999996
+    - type: mrr_at_5
+      value: 32.116
+    - type: ndcg_at_1
+      value: 24.584
+    - type: ndcg_at_10
+      value: 35.995
+    - type: ndcg_at_100
+      value: 41.018
+    - type: ndcg_at_1000
+      value: 43.543
+    - type: ndcg_at_20
+      value: 37.722
+    - type: ndcg_at_3
+      value: 31.197999999999997
+    - type: ndcg_at_5
+      value: 33.532000000000004
+    - type: precision_at_1
+      value: 24.584
+    - type: precision_at_10
+      value: 5.619
+    - type: precision_at_100
+      value: 0.878
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_20
+      value: 3.2259999999999995
+    - type: precision_at_3
+      value: 13.431999999999999
+    - type: precision_at_5
+      value: 9.39
+    - type: recall_at_1
+      value: 22.619
+    - type: recall_at_10
+      value: 48.746
+    - type: recall_at_100
+      value: 72.004
+    - type: recall_at_1000
+      value: 90.497
+    - type: recall_at_20
+      value: 55.326
+    - type: recall_at_3
+      value: 35.964
+    - type: recall_at_5
+      value: 41.547
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: map_at_1
+      value: 16.493
+    - type: map_at_10
+      value: 28.988999999999997
+    - type: map_at_100
+      value: 30.964999999999996
+    - type: map_at_1000
+      value: 31.142999999999997
+    - type: map_at_20
+      value: 30.103
+    - type: map_at_3
+      value: 24.006
+    - type: map_at_5
+      value: 26.535999999999998
+    - type: mrr_at_1
+      value: 37.915
+    - type: mrr_at_10
+      value: 50.736000000000004
+    - type: mrr_at_100
+      value: 51.361999999999995
+    - type: mrr_at_1000
+      value: 51.388999999999996
+    - type: mrr_at_20
+      value: 51.148
+    - type: mrr_at_3
+      value: 47.589999999999996
+    - type: mrr_at_5
+      value: 49.55
+    - type: ndcg_at_1
+      value: 37.915
+    - type: ndcg_at_10
+      value: 39.139
+    - type: ndcg_at_100
+      value: 45.993
+    - type: ndcg_at_1000
+      value: 48.861
+    - type: ndcg_at_20
+      value: 41.923
+    - type: ndcg_at_3
+      value: 32.491
+    - type: ndcg_at_5
+      value: 34.775
+    - type: precision_at_1
+      value: 37.915
+    - type: precision_at_10
+      value: 12.293
+    - type: precision_at_100
+      value: 1.9709999999999999
+    - type: precision_at_1000
+      value: 0.251
+    - type: precision_at_20
+      value: 7.3389999999999995
+    - type: precision_at_3
+      value: 24.407999999999998
+    - type: precision_at_5
+      value: 18.775
+    - type: recall_at_1
+      value: 16.493
+    - type: recall_at_10
+      value: 45.904
+    - type: recall_at_100
+      value: 69.037
+    - type: recall_at_1000
+      value: 84.815
+    - type: recall_at_20
+      value: 53.657
+    - type: recall_at_3
+      value: 29.629
+    - type: recall_at_5
+      value: 36.325
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: map_at_1
+      value: 9.180000000000001
+    - type: map_at_10
+      value: 20.714
+    - type: map_at_100
+      value: 28.801
+    - type: map_at_1000
+      value: 30.43
+    - type: map_at_20
+      value: 23.673
+    - type: map_at_3
+      value: 14.551
+    - type: map_at_5
+      value: 17.067
+    - type: mrr_at_1
+      value: 68.25
+    - type: mrr_at_10
+      value: 75.83
+    - type: mrr_at_100
+      value: 76.225
+    - type: mrr_at_1000
+      value: 76.232
+    - type: mrr_at_20
+      value: 76.14
+    - type: mrr_at_3
+      value: 74.375
+    - type: mrr_at_5
+      value: 75.225
+    - type: ndcg_at_1
+      value: 56.99999999999999
+    - type: ndcg_at_10
+      value: 43.071
+    - type: ndcg_at_100
+      value: 47.189
+    - type: ndcg_at_1000
+      value: 54.125
+    - type: ndcg_at_20
+      value: 42.111
+    - type: ndcg_at_3
+      value: 47.67
+    - type: ndcg_at_5
+      value: 44.983000000000004
+    - type: precision_at_1
+      value: 68.25
+    - type: precision_at_10
+      value: 34.599999999999994
+    - type: precision_at_100
+      value: 10.8
+    - type: precision_at_1000
+      value: 2.12
+    - type: precision_at_20
+      value: 25.7
+    - type: precision_at_3
+      value: 51.417
+    - type: precision_at_5
+      value: 43.85
+    - type: recall_at_1
+      value: 9.180000000000001
+    - type: recall_at_10
+      value: 26.212000000000003
+    - type: recall_at_100
+      value: 52.443
+    - type: recall_at_1000
+      value: 73.939
+    - type: recall_at_20
+      value: 33.101
+    - type: recall_at_3
+      value: 15.787999999999998
+    - type: recall_at_5
+      value: 19.691
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 49.625
+    - type: f1
+      value: 44.48944228050152
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: map_at_1
+      value: 76.773
+    - type: map_at_10
+      value: 85.175
+    - type: map_at_100
+      value: 85.353
+    - type: map_at_1000
+      value: 85.36500000000001
+    - type: map_at_20
+      value: 85.271
+    - type: map_at_3
+      value: 84.261
+    - type: map_at_5
+      value: 84.899
+    - type: mrr_at_1
+      value: 82.853
+    - type: mrr_at_10
+      value: 90.02
+    - type: mrr_at_100
+      value: 90.048
+    - type: mrr_at_1000
+      value: 90.048
+    - type: mrr_at_20
+      value: 90.039
+    - type: mrr_at_3
+      value: 89.51599999999999
+    - type: mrr_at_5
+      value: 89.92099999999999
+    - type: ndcg_at_1
+      value: 82.853
+    - type: ndcg_at_10
+      value: 88.75999999999999
+    - type: ndcg_at_100
+      value: 89.347
+    - type: ndcg_at_1000
+      value: 89.547
+    - type: ndcg_at_20
+      value: 88.994
+    - type: ndcg_at_3
+      value: 87.481
+    - type: ndcg_at_5
+      value: 88.31700000000001
+    - type: precision_at_1
+      value: 82.853
+    - type: precision_at_10
+      value: 10.519
+    - type: precision_at_100
+      value: 1.1039999999999999
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_20
+      value: 5.341
+    - type: precision_at_3
+      value: 33.323
+    - type: precision_at_5
+      value: 20.596999999999998
+    - type: recall_at_1
+      value: 76.773
+    - type: recall_at_10
+      value: 94.95700000000001
+    - type: recall_at_100
+      value: 97.167
+    - type: recall_at_1000
+      value: 98.354
+    - type: recall_at_20
+      value: 95.71
+    - type: recall_at_3
+      value: 91.47999999999999
+    - type: recall_at_5
+      value: 93.658
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: map_at_1
+      value: 21.629
+    - type: map_at_10
+      value: 36.394
+    - type: map_at_100
+      value: 38.308
+    - type: map_at_1000
+      value: 38.478
+    - type: map_at_20
+      value: 37.425999999999995
+    - type: map_at_3
+      value: 31.971
+    - type: map_at_5
+      value: 34.5
+    - type: mrr_at_1
+      value: 44.599
+    - type: mrr_at_10
+      value: 53.369
+    - type: mrr_at_100
+      value: 54.06999999999999
+    - type: mrr_at_1000
+      value: 54.114
+    - type: mrr_at_20
+      value: 53.754999999999995
+    - type: mrr_at_3
+      value: 51.415
+    - type: mrr_at_5
+      value: 52.479
+    - type: ndcg_at_1
+      value: 44.599
+    - type: ndcg_at_10
+      value: 44.425
+    - type: ndcg_at_100
+      value: 51.036
+    - type: ndcg_at_1000
+      value: 53.806
+    - type: ndcg_at_20
+      value: 46.934
+    - type: ndcg_at_3
+      value: 41.287
+    - type: ndcg_at_5
+      value: 42.143
+    - type: precision_at_1
+      value: 44.599
+    - type: precision_at_10
+      value: 12.222
+    - type: precision_at_100
+      value: 1.91
+    - type: precision_at_1000
+      value: 0.24
+    - type: precision_at_20
+      value: 7.176
+    - type: precision_at_3
+      value: 28.086
+    - type: precision_at_5
+      value: 20.369999999999997
+    - type: recall_at_1
+      value: 21.629
+    - type: recall_at_10
+      value: 51.168
+    - type: recall_at_100
+      value: 75.32600000000001
+    - type: recall_at_1000
+      value: 91.766
+    - type: recall_at_20
+      value: 58.923
+    - type: recall_at_3
+      value: 37.364999999999995
+    - type: recall_at_5
+      value: 43.322
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: map_at_1
+      value: 42.336
+    - type: map_at_10
+      value: 59.602999999999994
+    - type: map_at_100
+      value: 60.367000000000004
+    - type: map_at_1000
+      value: 60.428000000000004
+    - type: map_at_20
+      value: 60.068
+    - type: map_at_3
+      value: 56.842000000000006
+    - type: map_at_5
+      value: 58.669000000000004
+    - type: mrr_at_1
+      value: 84.673
+    - type: mrr_at_10
+      value: 88.713
+    - type: mrr_at_100
+      value: 88.852
+    - type: mrr_at_1000
+      value: 88.857
+    - type: mrr_at_20
+      value: 88.806
+    - type: mrr_at_3
+      value: 88.202
+    - type: mrr_at_5
+      value: 88.522
+    - type: ndcg_at_1
+      value: 84.673
+    - type: ndcg_at_10
+      value: 68.67
+    - type: ndcg_at_100
+      value: 71.277
+    - type: ndcg_at_1000
+      value: 72.47
+    - type: ndcg_at_20
+      value: 69.797
+    - type: ndcg_at_3
+      value: 64.971
+    - type: ndcg_at_5
+      value: 67.16
+    - type: precision_at_1
+      value: 84.673
+    - type: precision_at_10
+      value: 13.66
+    - type: precision_at_100
+      value: 1.5699999999999998
+    - type: precision_at_1000
+      value: 0.173
+    - type: precision_at_20
+      value: 7.19
+    - type: precision_at_3
+      value: 40.135
+    - type: precision_at_5
+      value: 25.81
+    - type: recall_at_1
+      value: 42.336
+    - type: recall_at_10
+      value: 68.298
+    - type: recall_at_100
+      value: 78.494
+    - type: recall_at_1000
+      value: 86.435
+    - type: recall_at_20
+      value: 71.904
+    - type: recall_at_3
+      value: 60.202999999999996
+    - type: recall_at_5
+      value: 64.524
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 89.0388
+    - type: ap
+      value: 84.768407855227
+    - type: f1
+      value: 89.00848365810504
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: map_at_1
+      value: 22.676
+    - type: map_at_10
+      value: 35.476
+    - type: map_at_100
+      value: 36.669000000000004
+    - type: map_at_1000
+      value: 36.714999999999996
+    - type: map_at_20
+      value: 36.253
+    - type: map_at_3
+      value: 31.430000000000003
+    - type: map_at_5
+      value: 33.891
+    - type: mrr_at_1
+      value: 23.281
+    - type: mrr_at_10
+      value: 35.994
+    - type: mrr_at_100
+      value: 37.128
+    - type: mrr_at_1000
+      value: 37.169000000000004
+    - type: mrr_at_20
+      value: 36.735
+    - type: mrr_at_3
+      value: 32.025
+    - type: mrr_at_5
+      value: 34.43
+    - type: ndcg_at_1
+      value: 23.281
+    - type: ndcg_at_10
+      value: 42.548
+    - type: ndcg_at_100
+      value: 48.138999999999996
+    - type: ndcg_at_1000
+      value: 49.26
+    - type: ndcg_at_20
+      value: 45.29
+    - type: ndcg_at_3
+      value: 34.414
+    - type: ndcg_at_5
+      value: 38.775999999999996
+    - type: precision_at_1
+      value: 23.281
+    - type: precision_at_10
+      value: 6.721000000000001
+    - type: precision_at_100
+      value: 0.9490000000000001
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_20
+      value: 3.93
+    - type: precision_at_3
+      value: 14.67
+    - type: precision_at_5
+      value: 11.003
+    - type: recall_at_1
+      value: 22.676
+    - type: recall_at_10
+      value: 64.33
+    - type: recall_at_100
+      value: 89.836
+    - type: recall_at_1000
+      value: 98.346
+    - type: recall_at_20
+      value: 74.958
+    - type: recall_at_3
+      value: 42.437000000000005
+    - type: recall_at_5
+      value: 52.89
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.26493388052896
+    - type: f1
+      value: 93.09322316606121
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 79.26356589147285
+    - type: f1
+      value: 62.91191113045691
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 75.4034969737727
+    - type: f1
+      value: 73.26712703676112
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.55749831876263
+    - type: f1
+      value: 78.59077417507389
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.39782367001404
+    - 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: 31.630415762081864
+    - 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: 30.989924085485676
+    - type: mrr
+      value: 31.985114880107695
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: map_at_1
+      value: 5.771
+    - type: map_at_10
+      value: 13.008000000000001
+    - type: map_at_100
+      value: 16.125999999999998
+    - type: map_at_1000
+      value: 17.482
+    - type: map_at_20
+      value: 14.324
+    - type: map_at_3
+      value: 9.69
+    - type: map_at_5
+      value: 11.174000000000001
+    - type: mrr_at_1
+      value: 45.201
+    - type: mrr_at_10
+      value: 53.989
+    - type: mrr_at_100
+      value: 54.50899999999999
+    - type: mrr_at_1000
+      value: 54.551
+    - type: mrr_at_20
+      value: 54.247
+    - type: mrr_at_3
+      value: 52.373999999999995
+    - type: mrr_at_5
+      value: 53.225
+    - type: ndcg_at_1
+      value: 43.808
+    - type: ndcg_at_10
+      value: 34.757
+    - type: ndcg_at_100
+      value: 31.174000000000003
+    - type: ndcg_at_1000
+      value: 39.607
+    - type: ndcg_at_20
+      value: 32.151999999999994
+    - type: ndcg_at_3
+      value: 40.458
+    - type: ndcg_at_5
+      value: 38.06
+    - type: precision_at_1
+      value: 45.201
+    - type: precision_at_10
+      value: 25.728
+    - type: precision_at_100
+      value: 7.82
+    - type: precision_at_1000
+      value: 2.032
+    - type: precision_at_20
+      value: 18.793000000000003
+    - type: precision_at_3
+      value: 38.080000000000005
+    - type: precision_at_5
+      value: 32.879000000000005
+    - type: recall_at_1
+      value: 5.771
+    - type: recall_at_10
+      value: 16.567
+    - type: recall_at_100
+      value: 30.447999999999997
+    - type: recall_at_1000
+      value: 60.941
+    - type: recall_at_20
+      value: 20.092
+    - type: recall_at_3
+      value: 10.928
+    - type: recall_at_5
+      value: 13.235
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: map_at_1
+      value: 40.716
+    - type: map_at_10
+      value: 56.599999999999994
+    - type: map_at_100
+      value: 57.389
+    - type: map_at_1000
+      value: 57.408
+    - type: map_at_20
+      value: 57.154
+    - type: map_at_3
+      value: 52.577
+    - type: map_at_5
+      value: 55.076
+    - type: mrr_at_1
+      value: 45.655
+    - type: mrr_at_10
+      value: 59.014
+    - type: mrr_at_100
+      value: 59.568
+    - type: mrr_at_1000
+      value: 59.580999999999996
+    - type: mrr_at_20
+      value: 59.41499999999999
+    - type: mrr_at_3
+      value: 55.88999999999999
+    - type: mrr_at_5
+      value: 57.879999999999995
+    - type: ndcg_at_1
+      value: 45.626
+    - type: ndcg_at_10
+      value: 63.778
+    - type: ndcg_at_100
+      value: 66.905
+    - type: ndcg_at_1000
+      value: 67.322
+    - type: ndcg_at_20
+      value: 65.521
+    - type: ndcg_at_3
+      value: 56.494
+    - type: ndcg_at_5
+      value: 60.553999999999995
+    - type: precision_at_1
+      value: 45.626
+    - type: precision_at_10
+      value: 9.942
+    - type: precision_at_100
+      value: 1.169
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_20
+      value: 5.390000000000001
+    - type: precision_at_3
+      value: 25.135
+    - type: precision_at_5
+      value: 17.451
+    - type: recall_at_1
+      value: 40.716
+    - type: recall_at_10
+      value: 82.998
+    - type: recall_at_100
+      value: 96.236
+    - type: recall_at_1000
+      value: 99.31400000000001
+    - type: recall_at_20
+      value: 89.402
+    - type: recall_at_3
+      value: 64.47699999999999
+    - type: recall_at_5
+      value: 73.774
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+    metrics:
+    - type: map_at_1
+      value: 71.679
+    - type: map_at_10
+      value: 85.63
+    - type: map_at_100
+      value: 86.24000000000001
+    - type: map_at_1000
+      value: 86.25500000000001
+    - type: map_at_20
+      value: 86.03
+    - type: map_at_3
+      value: 82.712
+    - type: map_at_5
+      value: 84.59400000000001
+    - type: mrr_at_1
+      value: 82.58
+    - type: mrr_at_10
+      value: 88.459
+    - type: mrr_at_100
+      value: 88.544
+    - type: mrr_at_1000
+      value: 88.545
+    - type: mrr_at_20
+      value: 88.521
+    - type: mrr_at_3
+      value: 87.548
+    - type: mrr_at_5
+      value: 88.19
+    - type: ndcg_at_1
+      value: 82.57
+    - type: ndcg_at_10
+      value: 89.205
+    - type: ndcg_at_100
+      value: 90.316
+    - type: ndcg_at_1000
+      value: 90.4
+    - type: ndcg_at_20
+      value: 89.802
+    - type: ndcg_at_3
+      value: 86.5
+    - type: ndcg_at_5
+      value: 88.06
+    - type: precision_at_1
+      value: 82.57
+    - type: precision_at_10
+      value: 13.511000000000001
+    - type: precision_at_100
+      value: 1.532
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_20
+      value: 7.1499999999999995
+    - type: precision_at_3
+      value: 37.82
+    - type: precision_at_5
+      value: 24.892
+    - type: recall_at_1
+      value: 71.679
+    - type: recall_at_10
+      value: 95.926
+    - type: recall_at_100
+      value: 99.653
+    - type: recall_at_1000
+      value: 99.99
+    - type: recall_at_20
+      value: 97.81
+    - type: recall_at_3
+      value: 88.124
+    - type: recall_at_5
+      value: 92.535
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 58.980204279295776
+    - 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: 64.68385650734866
+    - 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: 4.1930000000000005
+    - type: map_at_10
+      value: 10.993
+    - type: map_at_100
+      value: 12.821
+    - type: map_at_1000
+      value: 13.094
+    - type: map_at_20
+      value: 11.899999999999999
+    - type: map_at_3
+      value: 7.753
+    - type: map_at_5
+      value: 9.479
+    - type: mrr_at_1
+      value: 20.7
+    - type: mrr_at_10
+      value: 31.776
+    - type: mrr_at_100
+      value: 32.863
+    - type: mrr_at_1000
+      value: 32.921
+    - type: mrr_at_20
+      value: 32.374
+    - type: mrr_at_3
+      value: 28.499999999999996
+    - type: mrr_at_5
+      value: 30.464999999999996
+    - type: ndcg_at_1
+      value: 20.7
+    - type: ndcg_at_10
+      value: 18.602
+    - type: ndcg_at_100
+      value: 26.063
+    - type: ndcg_at_1000
+      value: 30.988
+    - type: ndcg_at_20
+      value: 21.124000000000002
+    - type: ndcg_at_3
+      value: 17.538999999999998
+    - type: ndcg_at_5
+      value: 15.604999999999999
+    - type: precision_at_1
+      value: 20.7
+    - type: precision_at_10
+      value: 9.69
+    - type: precision_at_100
+      value: 2.051
+    - type: precision_at_1000
+      value: 0.32299999999999995
+    - type: precision_at_20
+      value: 6.3
+    - type: precision_at_3
+      value: 16.567
+    - type: precision_at_5
+      value: 13.96
+    - type: recall_at_1
+      value: 4.1930000000000005
+    - type: recall_at_10
+      value: 19.618
+    - type: recall_at_100
+      value: 41.643
+    - type: recall_at_1000
+      value: 65.693
+    - type: recall_at_20
+      value: 25.562
+    - type: recall_at_3
+      value: 10.062999999999999
+    - type: recall_at_5
+      value: 14.127999999999998
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.46613174654865
+    - type: cos_sim_spearman
+      value: 80.3049357832415
+    - type: euclidean_pearson
+      value: 81.26631332583317
+    - type: euclidean_spearman
+      value: 80.3154745166346
+    - type: manhattan_pearson
+      value: 81.14703159845031
+    - type: manhattan_spearman
+      value: 80.20912001232311
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.54049067032975
+    - type: cos_sim_spearman
+      value: 80.96545866938635
+    - type: euclidean_pearson
+      value: 83.96265705630466
+    - type: euclidean_spearman
+      value: 79.93146623957664
+    - type: manhattan_pearson
+      value: 83.90680327172007
+    - type: manhattan_spearman
+      value: 79.9387741861374
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.88551701212096
+    - type: cos_sim_spearman
+      value: 87.86522961782607
+    - type: euclidean_pearson
+      value: 87.36290945594213
+    - type: euclidean_spearman
+      value: 87.83062393537139
+    - type: manhattan_pearson
+      value: 87.32544594269082
+    - type: manhattan_spearman
+      value: 87.81556963071229
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.30880458174929
+    - type: cos_sim_spearman
+      value: 83.80166079353091
+    - type: euclidean_pearson
+      value: 85.32128873266257
+    - type: euclidean_spearman
+      value: 83.86251092262333
+    - type: manhattan_pearson
+      value: 85.2712567451151
+    - type: manhattan_spearman
+      value: 83.80950203378747
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.26254668067915
+    - type: cos_sim_spearman
+      value: 88.58702965856746
+    - type: euclidean_pearson
+      value: 87.9969808017743
+    - type: euclidean_spearman
+      value: 88.48082129802832
+    - type: manhattan_pearson
+      value: 88.005385920726
+    - type: manhattan_spearman
+      value: 88.48824252319064
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.9048844772477
+    - type: cos_sim_spearman
+      value: 86.81864160521327
+    - type: euclidean_pearson
+      value: 86.28264402848413
+    - type: euclidean_spearman
+      value: 86.78000025418731
+    - type: manhattan_pearson
+      value: 86.2441248990138
+    - type: manhattan_spearman
+      value: 86.75021285222047
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.489340312079
+    - type: cos_sim_spearman
+      value: 87.98810146323362
+    - type: euclidean_pearson
+      value: 89.17657344753519
+    - type: euclidean_spearman
+      value: 88.96877394433339
+    - type: manhattan_pearson
+      value: 89.17489837230771
+    - type: manhattan_spearman
+      value: 88.87394331518345
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 63.020191114515576
+    - type: cos_sim_spearman
+      value: 66.81821028889179
+    - type: euclidean_pearson
+      value: 66.11102477309004
+    - type: euclidean_spearman
+      value: 66.59000262767655
+    - type: manhattan_pearson
+      value: 66.0319349852117
+    - type: manhattan_spearman
+      value: 66.51366211903893
+  - task:
+      type: STS
+    dataset:
+      type: None
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.05763458617234
+    - type: cos_sim_spearman
+      value: 87.40353901525121
+    - type: euclidean_pearson
+      value: 87.43632331678887
+    - type: euclidean_spearman
+      value: 87.58631222421829
+    - type: manhattan_pearson
+      value: 87.40408795218912
+    - type: manhattan_spearman
+      value: 87.55530395433567
+  - task:
+      type: Reranking
+    dataset:
+      type: None
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 83.40728647106346
+    - type: mrr
+      value: 95.39606725881237
+  - 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: 66.467
+    - type: map_at_100
+      value: 66.841
+    - type: map_at_1000
+      value: 66.86800000000001
+    - type: map_at_20
+      value: 66.728
+    - type: map_at_3
+      value: 62.888
+    - type: map_at_5
+      value: 65.10000000000001
+    - type: mrr_at_1
+      value: 58.333
+    - type: mrr_at_10
+      value: 67.471
+    - type: mrr_at_100
+      value: 67.75
+    - type: mrr_at_1000
+      value: 67.778
+    - type: mrr_at_20
+      value: 67.649
+    - type: mrr_at_3
+      value: 64.72200000000001
+    - type: mrr_at_5
+      value: 66.539
+    - type: ndcg_at_1
+      value: 58.333
+    - type: ndcg_at_10
+      value: 71.707
+    - type: ndcg_at_100
+      value: 73.301
+    - type: ndcg_at_1000
+      value: 74.053
+    - type: ndcg_at_20
+      value: 72.482
+    - type: ndcg_at_3
+      value: 65.561
+    - type: ndcg_at_5
+      value: 69.017
+    - type: precision_at_1
+      value: 58.333
+    - type: precision_at_10
+      value: 9.866999999999999
+    - type: precision_at_100
+      value: 1.0699999999999998
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_20
+      value: 5.1
+    - type: precision_at_3
+      value: 25.778000000000002
+    - type: precision_at_5
+      value: 17.533
+    - type: recall_at_1
+      value: 55.344
+    - type: recall_at_10
+      value: 86.76700000000001
+    - type: recall_at_100
+      value: 94.0
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_20
+      value: 89.60000000000001
+    - type: recall_at_3
+      value: 70.406
+    - type: recall_at_5
+      value: 79.106
+  - task:
+      type: PairClassification
+    dataset:
+      type: None
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.71089108910891
+    - type: cos_sim_ap
+      value: 91.82444380538519
+    - type: cos_sim_f1
+      value: 85.34525583705911
+    - type: cos_sim_precision
+      value: 84.79763079960513
+    - type: cos_sim_recall
+      value: 85.9
+    - type: dot_accuracy
+      value: 99.56039603960396
+    - type: dot_ap
+      value: 84.71022538609428
+    - type: dot_f1
+      value: 76.18100447538538
+    - type: dot_precision
+      value: 75.76656775469831
+    - type: dot_recall
+      value: 76.6
+    - type: euclidean_accuracy
+      value: 99.7
+    - type: euclidean_ap
+      value: 91.68317023504792
+    - type: euclidean_f1
+      value: 84.65712876171682
+    - type: euclidean_precision
+      value: 83.54430379746836
+    - type: euclidean_recall
+      value: 85.8
+    - type: manhattan_accuracy
+      value: 99.69900990099009
+    - type: manhattan_ap
+      value: 91.5749511659937
+    - type: manhattan_f1
+      value: 84.6989141164857
+    - type: manhattan_precision
+      value: 83.62573099415205
+    - type: manhattan_recall
+      value: 85.8
+    - type: max_accuracy
+      value: 99.71089108910891
+    - type: max_ap
+      value: 91.82444380538519
+    - type: max_f1
+      value: 85.34525583705911
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 69.36504474977566
+    - 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: 34.72911995025639
+    - 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: 52.975020393803675
+    - type: mrr
+      value: 53.87404772515067
+  - task:
+      type: Summarization
+    dataset:
+      type: None
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.205065693047615
+    - type: cos_sim_spearman
+      value: 28.307951294409406
+    - type: dot_pearson
+      value: 29.15581947828465
+    - type: dot_spearman
+      value: 28.222470759389505
+  - 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: 2.243
+    - type: map_at_100
+      value: 13.791
+    - type: map_at_1000
+      value: 32.539
+    - type: map_at_20
+      value: 4.112
+    - type: map_at_3
+      value: 0.7060000000000001
+    - type: map_at_5
+      value: 1.1860000000000002
+    - 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: 86.083
+    - type: ndcg_at_100
+      value: 66.471
+    - type: ndcg_at_1000
+      value: 57.31699999999999
+    - type: ndcg_at_20
+      value: 82.783
+    - type: ndcg_at_3
+      value: 88.805
+    - type: ndcg_at_5
+      value: 88.96
+    - type: precision_at_1
+      value: 96.0
+    - type: precision_at_10
+      value: 91.2
+    - type: precision_at_100
+      value: 68.16
+    - type: precision_at_1000
+      value: 25.290000000000003
+    - type: precision_at_20
+      value: 86.9
+    - type: precision_at_3
+      value: 94.0
+    - type: precision_at_5
+      value: 94.39999999999999
+    - type: recall_at_1
+      value: 0.249
+    - type: recall_at_10
+      value: 2.3800000000000003
+    - type: recall_at_100
+      value: 16.45
+    - type: recall_at_1000
+      value: 53.1
+    - type: recall_at_20
+      value: 4.4670000000000005
+    - type: recall_at_3
+      value: 0.734
+    - type: recall_at_5
+      value: 1.246
+  - task:
+      type: Retrieval
+    dataset:
+      type: None
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: map_at_1
+      value: 3.2520000000000002
+    - type: map_at_10
+      value: 11.805
+    - type: map_at_100
+      value: 18.749
+    - type: map_at_1000
+      value: 20.416999999999998
+    - type: map_at_20
+      value: 14.685
+    - type: map_at_3
+      value: 6.6739999999999995
+    - type: map_at_5
+      value: 8.863
+    - type: mrr_at_1
+      value: 42.857
+    - type: mrr_at_10
+      value: 57.635999999999996
+    - type: mrr_at_100
+      value: 58.034
+    - type: mrr_at_1000
+      value: 58.048
+    - type: mrr_at_20
+      value: 57.979
+    - type: mrr_at_3
+      value: 54.422000000000004
+    - type: mrr_at_5
+      value: 56.15599999999999
+    - type: ndcg_at_1
+      value: 39.796
+    - type: ndcg_at_10
+      value: 30.263
+    - type: ndcg_at_100
+      value: 40.825
+    - type: ndcg_at_1000
+      value: 52.447
+    - type: ndcg_at_20
+      value: 30.453000000000003
+    - type: ndcg_at_3
+      value: 35.086
+    - type: ndcg_at_5
+      value: 31.947
+    - type: precision_at_1
+      value: 42.857
+    - type: precision_at_10
+      value: 26.327
+    - type: precision_at_100
+      value: 8.041
+    - type: precision_at_1000
+      value: 1.582
+    - type: precision_at_20
+      value: 19.592000000000002
+    - type: precision_at_3
+      value: 36.054
+    - type: precision_at_5
+      value: 31.019999999999996
+    - type: recall_at_1
+      value: 3.2520000000000002
+    - type: recall_at_10
+      value: 18.471
+    - type: recall_at_100
+      value: 49.08
+    - type: recall_at_1000
+      value: 84.733
+    - type: recall_at_20
+      value: 26.389000000000003
+    - type: recall_at_3
+      value: 8.051
+    - type: recall_at_5
+      value: 11.672
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+    metrics:
+    - type: accuracy
+      value: 68.10546875
+    - type: ap
+      value: 12.899352291322325
+    - type: f1
+      value: 52.14484661172115
+  - task:
+      type: Classification
+    dataset:
+      type: None
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 62.323146576117715
+    - type: f1
+      value: 62.6518883448989
+  - task:
+      type: Clustering
+    dataset:
+      type: None
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 51.261957327618525
+    - 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: 87.09542826488645
+    - type: cos_sim_ap
+      value: 77.72170475021885
+    - type: cos_sim_f1
+      value: 70.67669172932331
+    - type: cos_sim_precision
+      value: 64.5238614077141
+    - type: cos_sim_recall
+      value: 78.12664907651715
+    - type: dot_accuracy
+      value: 83.96614412588663
+    - type: dot_ap
+      value: 68.08590796036842
+    - type: dot_f1
+      value: 63.934426229508205
+    - type: dot_precision
+      value: 58.854860186418115
+    - type: dot_recall
+      value: 69.9736147757256
+    - type: euclidean_accuracy
+      value: 87.20271800679502
+    - type: euclidean_ap
+      value: 77.87533191263717
+    - type: euclidean_f1
+      value: 70.92216475337455
+    - type: euclidean_precision
+      value: 67.94778825235677
+    - type: euclidean_recall
+      value: 74.1688654353562
+    - type: manhattan_accuracy
+      value: 87.20867854801216
+    - type: manhattan_ap
+      value: 77.84249032925085
+    - type: manhattan_f1
+      value: 71.11665626949471
+    - type: manhattan_precision
+      value: 67.45562130177515
+    - type: manhattan_recall
+      value: 75.19788918205805
+    - type: max_accuracy
+      value: 87.20867854801216
+    - type: max_ap
+      value: 77.87533191263717
+    - type: max_f1
+      value: 71.11665626949471
+  - task:
+      type: PairClassification
+    dataset:
+      type: None
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.22070865836147
+    - type: cos_sim_ap
+      value: 86.38617271379728
+    - type: cos_sim_f1
+      value: 78.946594085626
+    - type: cos_sim_precision
+      value: 75.5774647887324
+    - type: cos_sim_recall
+      value: 82.63012011087157
+    - type: dot_accuracy
+      value: 87.16963558039352
+    - type: dot_ap
+      value: 82.0965358395614
+    - type: dot_f1
+      value: 75.00997859138575
+    - type: dot_precision
+      value: 70.93541966920596
+    - type: dot_recall
+      value: 79.58115183246073
+    - type: euclidean_accuracy
+      value: 89.14891139830016
+    - type: euclidean_ap
+      value: 86.28000880804873
+    - type: euclidean_f1
+      value: 78.7341306347746
+    - type: euclidean_precision
+      value: 75.40706280397546
+    - type: euclidean_recall
+      value: 82.36834000615954
+    - type: manhattan_accuracy
+      value: 89.15279233127644
+    - type: manhattan_ap
+      value: 86.25024653784152
+    - type: manhattan_f1
+      value: 78.72760457406788
+    - type: manhattan_precision
+      value: 76.25369795800563
+    - type: manhattan_recall
+      value: 81.36741607637819
+    - type: max_accuracy
+      value: 89.22070865836147
+    - type: max_ap
+      value: 86.38617271379728
+    - type: max_f1
+      value: 78.946594085626
+license: apache-2.0
+language:
+- en
+base_model:
+- answerdotai/ModernBERT-large
+- lightonai/modernbert-embed-large-unsupervised
 ---
 
-# Model Card for Model ID
+# ModernBERT-embed-large
 
-<!-- Provide a quick summary of what the model is/does. -->
+ModernBERT-embed-large is an embedding model trained from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), bringing the new advances of ModernBERT to embeddings!
 
+Indeed, ModernBERT is a base model trained for Masked Language Modeling and can not directly be used to perform tasks such as retrieval without further fine-tuning.
 
+ModernBERT-embed-large is fine-tuned on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets and also supports Matryoshka Representation Learning dimensions of 256 to reduce memory with minimal performance loss.
 
-## Model Details
+## Performance
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+| Model                 | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10)  | Summarization (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**        |
+| modernbert-embed-large      | 1024        | **63,84**    | **75.03**           | **46.04**       | **85.31**                   | **57.64**     | **54.36**          | **83.80**     | 28.31        |
+| 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            |
+| modernbert-embed-large      | 256        | 62.43        | 73.60               | 44.59           | 84.89                   | 57.08         | 51.72          | 83.46     | 29.03            |
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+## Usage
 
-<!-- Provide the basic links for the model. -->
+You can use these models directly with the latest transformers release and requires installing `transformers>=4.48.0`:
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```bash
+pip install transformers>=4.48.0
+```
 
-## Uses
+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).
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
 
-### Direct Use
+### Sentence Transformers
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```python
+from sentence_transformers import SentenceTransformer
 
-[More Information Needed]
+model = SentenceTransformer("lightonai/modernbert-embed-large")
 
-### Downstream Use [optional]
+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)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+similarities = model.similarity(query_embeddings, doc_embeddings)
+print(similarities)
+# tensor([[0.7214],
+#         [0.3260]])
+```
 
-[More Information Needed]
+<details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
 
-### Out-of-Scope Use
+In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+```python
+from sentence_transformers import SentenceTransformer
 
-[More Information Needed]
+model = SentenceTransformer("lightonai/modernbert-embed-large", truncate_dim=256)
 
-## Bias, Risks, and Limitations
+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)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+similarities = model.similarity(query_embeddings, doc_embeddings)
+print(similarities)
+# tensor([[0.7759],
+#         [0.3419]])
+```
 
-[More Information Needed]
+Note the small differences compared to the full 1024-dimensional similarities.
 
-### Recommendations
+</details>
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Transformers
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
 
-## How to Get Started with the Model
 
-Use the code below to get started with the model.
+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
+    )
 
-[More Information Needed]
 
-## Training Details
+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"]
 
-### Training Data
+tokenizer = AutoTokenizer.from_pretrained("lightonai/modernbert-embed-large")
+model = AutoModel.from_pretrained("lightonai/modernbert-embed-large")
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
+encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
 
-[More Information Needed]
+with torch.no_grad():
+    queries_outputs = model(**encoded_queries)
+    documents_outputs = model(**encoded_documents)
 
-### Training Procedure
+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])
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+similarities = query_embeddings @ doc_embeddings.T
+print(similarities)
+# tensor([[0.7214],
+#         [0.3260]])
+```
 
-#### Preprocessing [optional]
+<details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
 
-[More Information Needed]
+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
 
-#### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+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
+    )
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+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"]
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(".")
+model = AutoModel.from_pretrained(".")
+truncate_dim = 256
 
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
\ No newline at end of file
+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",
+  "lightonai/modernbert-embed-large",
+  { 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
+We train ModernBERT-embed-large using a multi-stage training pipeline. Starting from the pretrained [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) model,
+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)
+
+## Acknowledgment 
+We wanted to thank [Zach Nussbaum](https://huggingface.co/zpn) from [Nomic AI](https://huggingface.co/nomic-ai) for building and sharing the Nomic Embed recipe and tools and its support during the training of this model!
+
+The training has been run on Orange Business Cloud Avenue infrastructure.
+
+## Citation
+
+If you find the model, dataset, or training code useful, please considering citing ModernBERT as well as Nomic Embed:
+```bibtex
+@misc{modernbert,
+      title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference}, 
+      author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli},
+      year={2024},
+      eprint={2412.13663},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.13663}, 
+}
+```
+
+```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}
+}
+```
+
+And if you want to cite this fine-tuning in particular, please use:
+```bibtex
+@misc{ModernBERT-embed-large,
+  title={ModernBERT-embed-large},
+  author={Chaffin, Antoine},
+  url={https://huggingface.co/lightonai/modernbert-embed-large},
+  year={2025}
+}
+```
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