diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -1,3172 +1,141 @@
----
-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
----
-
-# ModernBERT-embed-large
-
-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.
-
-## Performance
-
-
-
-| 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            |
-
-
-## Usage
-
-You can use these models directly with the latest transformers release and requires installing `transformers>=4.48.0`:
-
-```bash
-pip install transformers>=4.48.0
-```
-
-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("lightonai/modernbert-embed-large")
-
-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("lightonai/modernbert-embed-large", 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 1024-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("lightonai/modernbert-embed-large")
-model = AutoModel.from_pretrained("lightonai/modernbert-embed-large")
-
-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",
-  "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}
-}
-```
\ No newline at end of file
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+base_model: lightonai/modernbert-embed-large
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on lightonai/modernbert-embed-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [lightonai/modernbert-embed-large](https://huggingface.co/lightonai/modernbert-embed-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [lightonai/modernbert-embed-large](https://huggingface.co/lightonai/modernbert-embed-large) <!-- at revision 0725fd5d07e2171f5b8084315485760c469648e2 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("lightonai/modernbert-embed-large")
+# Run inference
+sentences = [
+    'The weather is lovely today.',
+    "It's so sunny outside!",
+    'He drove to the stadium.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 3.4.0.dev0
+- Transformers: 4.48.0.dev0
+- PyTorch: 2.5.0+cu121
+- Accelerate: 0.35.0.dev0
+- Datasets: 2.20.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->
\ No newline at end of file