nomic-ai_modernbert-embed-base

Name: nomic-ai/modernbert-embed-base
License: apache-2.0

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pipeline_tag: sentence-similarity tags:

sentence-transformers
feature-extraction
sentence-similarity
mteb
transformers.js model-index:
name: binarize_False results:
- task: type: Classification dataset: type: None name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
  - type: accuracy value: 78.13432835820896
  - type: ap value: 42.190424731303246
  - type: f1 value: 72.34446401534811
- task: type: Classification dataset: type: None name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
  - type: accuracy value: 93.093825
  - type: ap value: 90.03727505544286
  - type: f1 value: 93.0874055138833
- task: type: Classification dataset: type: None name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
  - type: accuracy value: 48.428000000000004
  - type: f1 value: 47.74311520203536
- task: type: Retrieval dataset: type: None name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics:
  - type: map_at_1 value: 23.898
  - type: map_at_10 value: 39.775
  - type: map_at_100 value: 40.827000000000005
  - type: map_at_1000 value: 40.837
  - type: map_at_20 value: 40.604
  - type: map_at_3 value: 34.519
  - type: map_at_5 value: 37.307
  - type: mrr_at_1 value: 24.395
  - type: mrr_at_10 value: 39.963
  - type: mrr_at_100 value: 41.014
  - type: mrr_at_1000 value: 41.024
  - type: mrr_at_20 value: 40.791
  - type: mrr_at_3 value: 34.732
  - type: mrr_at_5 value: 37.480999999999995
  - type: ndcg_at_1 value: 23.898
  - type: ndcg_at_10 value: 48.962
  - type: ndcg_at_100 value: 53.386
  - type: ndcg_at_1000 value: 53.634
  - type: ndcg_at_20 value: 51.898999999999994
  - type: ndcg_at_3 value: 38.034
  - type: ndcg_at_5 value: 43.036
  - type: precision_at_1 value: 23.898
  - type: precision_at_10 value: 7.852
  - type: precision_at_100 value: 0.9769999999999999
  - type: precision_at_1000 value: 0.1
  - type: precision_at_20 value: 4.4990000000000006
  - type: precision_at_3 value: 16.073999999999998
  - type: precision_at_5 value: 12.063
  - type: recall_at_1 value: 23.898
  - type: recall_at_10 value: 78.521
  - type: recall_at_100 value: 97.724
  - type: recall_at_1000 value: 99.644
  - type: recall_at_20 value: 89.972
  - type: recall_at_3 value: 48.222
  - type: recall_at_5 value: 60.313
- task: type: Clustering dataset: type: None name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
  - type: v_measure value: 47.69067314293749
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- task: type: Clustering dataset: type: None name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
  - type: v_measure value: 38.0916537995626
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- task: type: Reranking dataset: type: None name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
  - type: map value: 62.33195643912506
  - type: mrr value: 76.43978366970057
- task: type: STS dataset: type: None name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
  - type: cos_sim_pearson value: 81.20285894915236
  - type: cos_sim_spearman value: 78.16322678527897
  - type: euclidean_pearson value: 80.6118408638417
  - type: euclidean_spearman value: 78.19033583671204
  - type: manhattan_pearson value: 80.41282660275819
  - type: manhattan_spearman value: 77.98611431591628
- task: type: Classification dataset: type: None name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
  - type: accuracy value: 85.25324675324676
  - type: f1 value: 85.19854235582687
- task: type: Clustering dataset: type: None name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
  - type: v_measure value: 39.65216461057432
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- task: type: Clustering dataset: type: None name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
  - type: v_measure value: 33.28787287895752
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- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
  - type: map_at_1 value: 32.677
  - type: map_at_10 value: 43.739
  - type: map_at_100 value: 45.152
  - type: map_at_1000 value: 45.279
  - type: map_at_20 value: 44.553
  - type: map_at_3 value: 40.321
  - type: map_at_5 value: 42.201
  - type: mrr_at_1 value: 40.2
  - type: mrr_at_10 value: 49.755
  - type: mrr_at_100 value: 50.468
  - type: mrr_at_1000 value: 50.513
  - type: mrr_at_20 value: 50.192
  - type: mrr_at_3 value: 47.163
  - type: mrr_at_5 value: 48.686
  - type: ndcg_at_1 value: 40.2
  - type: ndcg_at_10 value: 49.963
  - type: ndcg_at_100 value: 54.978
  - type: ndcg_at_1000 value: 56.979
  - type: ndcg_at_20 value: 51.983000000000004
  - type: ndcg_at_3 value: 45.086999999999996
  - type: ndcg_at_5 value: 47.309
  - type: precision_at_1 value: 40.2
  - type: precision_at_10 value: 9.328
  - type: precision_at_100 value: 1.443
  - type: precision_at_1000 value: 0.19
  - type: precision_at_20 value: 5.558
  - type: precision_at_3 value: 21.364
  - type: precision_at_5 value: 15.222
  - type: recall_at_1 value: 32.677
  - type: recall_at_10 value: 61.71
  - type: recall_at_100 value: 82.431
  - type: recall_at_1000 value: 94.896
  - type: recall_at_20 value: 68.73700000000001
  - type: recall_at_3 value: 47.431
  - type: recall_at_5 value: 53.739000000000004
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics:
  - type: map_at_1 value: 32.71
  - type: map_at_10 value: 43.297000000000004
  - type: map_at_100 value: 44.607
  - type: map_at_1000 value: 44.729
  - type: map_at_20 value: 44.013999999999996
  - type: map_at_3 value: 40.213
  - type: map_at_5 value: 42.004000000000005
  - type: mrr_at_1 value: 40.892
  - type: mrr_at_10 value: 49.394
  - type: mrr_at_100 value: 50.005
  - type: mrr_at_1000 value: 50.043000000000006
  - type: mrr_at_20 value: 49.764
  - type: mrr_at_3 value: 47.134
  - type: mrr_at_5 value: 48.522
  - type: ndcg_at_1 value: 40.892
  - type: ndcg_at_10 value: 49.047000000000004
  - type: ndcg_at_100 value: 53.266999999999996
  - type: ndcg_at_1000 value: 55.096999999999994
  - type: ndcg_at_20 value: 50.707
  - type: ndcg_at_3 value: 44.896
  - type: ndcg_at_5 value: 46.983000000000004
  - type: precision_at_1 value: 40.892
  - type: precision_at_10 value: 9.293
  - type: precision_at_100 value: 1.473
  - type: precision_at_1000 value: 0.192
  - type: precision_at_20 value: 5.446
  - type: precision_at_3 value: 21.592
  - type: precision_at_5 value: 15.540999999999999
  - type: recall_at_1 value: 32.71
  - type: recall_at_10 value: 58.592999999999996
  - type: recall_at_100 value: 76.242
  - type: recall_at_1000 value: 87.717
  - type: recall_at_20 value: 64.646
  - type: recall_at_3 value: 46.253
  - type: recall_at_5 value: 51.946999999999996
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics:
  - type: map_at_1 value: 41.644999999999996
  - type: map_at_10 value: 53.825
  - type: map_at_100 value: 54.82
  - type: map_at_1000 value: 54.87499999999999
  - type: map_at_20 value: 54.43
  - type: map_at_3 value: 50.705
  - type: map_at_5 value: 52.501
  - type: mrr_at_1 value: 47.524
  - type: mrr_at_10 value: 57.260999999999996
  - type: mrr_at_100 value: 57.902
  - type: mrr_at_1000 value: 57.931999999999995
  - type: mrr_at_20 value: 57.689
  - type: mrr_at_3 value: 55.089
  - type: mrr_at_5 value: 56.38999999999999
  - type: ndcg_at_1 value: 47.524
  - type: ndcg_at_10 value: 59.41499999999999
  - type: ndcg_at_100 value: 63.258
  - type: ndcg_at_1000 value: 64.376
  - type: ndcg_at_20 value: 61.149
  - type: ndcg_at_3 value: 54.381
  - type: ndcg_at_5 value: 56.89999999999999
  - type: precision_at_1 value: 47.524
  - type: precision_at_10 value: 9.386
  - type: precision_at_100 value: 1.221
  - type: precision_at_1000 value: 0.136
  - type: precision_at_20 value: 5.223
  - type: precision_at_3 value: 24.096
  - type: precision_at_5 value: 16.364
  - type: recall_at_1 value: 41.644999999999996
  - type: recall_at_10 value: 72.386
  - type: recall_at_100 value: 88.794
  - type: recall_at_1000 value: 96.75399999999999
  - type: recall_at_20 value: 78.74
  - type: recall_at_3 value: 59.028000000000006
  - type: recall_at_5 value: 65.197
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics:
  - type: map_at_1 value: 28.648
  - type: map_at_10 value: 36.388999999999996
  - type: map_at_100 value: 37.372
  - type: map_at_1000 value: 37.457
  - type: map_at_20 value: 36.912
  - type: map_at_3 value: 34.076
  - type: map_at_5 value: 35.415
  - type: mrr_at_1 value: 30.508000000000003
  - type: mrr_at_10 value: 38.132
  - type: mrr_at_100 value: 39.04
  - type: mrr_at_1000 value: 39.106
  - type: mrr_at_20 value: 38.643
  - type: mrr_at_3 value: 35.876000000000005
  - type: mrr_at_5 value: 37.208999999999996
  - type: ndcg_at_1 value: 30.508000000000003
  - type: ndcg_at_10 value: 40.762
  - type: ndcg_at_100 value: 45.732
  - type: ndcg_at_1000 value: 47.799
  - type: ndcg_at_20 value: 42.591
  - type: ndcg_at_3 value: 36.266999999999996
  - type: ndcg_at_5 value: 38.58
  - type: precision_at_1 value: 30.508000000000003
  - type: precision_at_10 value: 6.010999999999999
  - type: precision_at_100 value: 0.897
  - type: precision_at_1000 value: 0.11100000000000002
  - type: precision_at_20 value: 3.412
  - type: precision_at_3 value: 14.991
  - type: precision_at_5 value: 10.328
  - type: recall_at_1 value: 28.648
  - type: recall_at_10 value: 52.342999999999996
  - type: recall_at_100 value: 75.268
  - type: recall_at_1000 value: 90.641
  - type: recall_at_20 value: 59.303
  - type: recall_at_3 value: 40.447
  - type: recall_at_5 value: 46.117000000000004
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics:
  - type: map_at_1 value: 18.476
  - type: map_at_10 value: 27.148
  - type: map_at_100 value: 28.317999999999998
  - type: map_at_1000 value: 28.427999999999997
  - type: map_at_20 value: 27.764
  - type: map_at_3 value: 24.801000000000002
  - type: map_at_5 value: 26.133
  - type: mrr_at_1 value: 22.886
  - type: mrr_at_10 value: 31.741000000000003
  - type: mrr_at_100 value: 32.708
  - type: mrr_at_1000 value: 32.769
  - type: mrr_at_20 value: 32.296
  - type: mrr_at_3 value: 29.498
  - type: mrr_at_5 value: 30.773
  - type: ndcg_at_1 value: 22.886
  - type: ndcg_at_10 value: 32.265
  - type: ndcg_at_100 value: 37.829
  - type: ndcg_at_1000 value: 40.558
  - type: ndcg_at_20 value: 34.372
  - type: ndcg_at_3 value: 28.105000000000004
  - type: ndcg_at_5 value: 30.04
  - type: precision_at_1 value: 22.886
  - type: precision_at_10 value: 5.808
  - type: precision_at_100 value: 0.985
  - type: precision_at_1000 value: 0.13699999999999998
  - type: precision_at_20 value: 3.495
  - type: precision_at_3 value: 13.639999999999999
  - type: precision_at_5 value: 9.577
  - type: recall_at_1 value: 18.476
  - type: recall_at_10 value: 43.442
  - type: recall_at_100 value: 67.376
  - type: recall_at_1000 value: 86.874
  - type: recall_at_20 value: 51.038
  - type: recall_at_3 value: 31.785999999999998
  - type: recall_at_5 value: 36.858999999999995
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics:
  - type: map_at_1 value: 29.098000000000003
  - type: map_at_10 value: 38.97
  - type: map_at_100 value: 40.293
  - type: map_at_1000 value: 40.397
  - type: map_at_20 value: 39.778999999999996
  - type: map_at_3 value: 35.723
  - type: map_at_5 value: 37.519999999999996
  - type: mrr_at_1 value: 35.515
  - type: mrr_at_10 value: 44.55
  - type: mrr_at_100 value: 45.37
  - type: mrr_at_1000 value: 45.412
  - type: mrr_at_20 value: 45.054
  - type: mrr_at_3 value: 41.835
  - type: mrr_at_5 value: 43.356
  - type: ndcg_at_1 value: 35.515
  - type: ndcg_at_10 value: 44.91
  - type: ndcg_at_100 value: 50.27700000000001
  - type: ndcg_at_1000 value: 52.215
  - type: ndcg_at_20 value: 47.235
  - type: ndcg_at_3 value: 39.505
  - type: ndcg_at_5 value: 42.016
  - type: precision_at_1 value: 35.515
  - type: precision_at_10 value: 8.152
  - type: precision_at_100 value: 1.262
  - type: precision_at_1000 value: 0.16
  - type: precision_at_20 value: 4.851
  - type: precision_at_3 value: 18.447
  - type: precision_at_5 value: 13.321
  - type: recall_at_1 value: 29.098000000000003
  - type: recall_at_10 value: 57.115
  - type: recall_at_100 value: 79.467
  - type: recall_at_1000 value: 92.162
  - type: recall_at_20 value: 65.161
  - type: recall_at_3 value: 42.254000000000005
  - type: recall_at_5 value: 48.415
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics:
  - type: map_at_1 value: 27.372000000000003
  - type: map_at_10 value: 37.781
  - type: map_at_100 value: 39.128
  - type: map_at_1000 value: 39.238
  - type: map_at_20 value: 38.592
  - type: map_at_3 value: 34.782999999999994
  - type: map_at_5 value: 36.466
  - type: mrr_at_1 value: 33.904
  - type: mrr_at_10 value: 43.15
  - type: mrr_at_100 value: 44.049
  - type: mrr_at_1000 value: 44.107
  - type: mrr_at_20 value: 43.721
  - type: mrr_at_3 value: 40.677
  - type: mrr_at_5 value: 42.19
  - type: ndcg_at_1 value: 33.904
  - type: ndcg_at_10 value: 43.527
  - type: ndcg_at_100 value: 49.004999999999995
  - type: ndcg_at_1000 value: 51.276999999999994
  - type: ndcg_at_20 value: 45.988
  - type: ndcg_at_3 value: 38.824999999999996
  - type: ndcg_at_5 value: 41.04
  - type: precision_at_1 value: 33.904
  - type: precision_at_10 value: 7.854
  - type: precision_at_100 value: 1.2309999999999999
  - type: precision_at_1000 value: 0.16
  - type: precision_at_20 value: 4.692
  - type: precision_at_3 value: 18.531
  - type: precision_at_5 value: 13.150999999999998
  - type: recall_at_1 value: 27.372000000000003
  - type: recall_at_10 value: 55.245999999999995
  - type: recall_at_100 value: 78.278
  - type: recall_at_1000 value: 93.718
  - type: recall_at_20 value: 64.095
  - type: recall_at_3 value: 41.665
  - type: recall_at_5 value: 47.632000000000005
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
  - type: map_at_1 value: 27.734166666666667
  - type: map_at_10 value: 36.858
  - type: map_at_100 value: 38.043833333333325
  - type: map_at_1000 value: 38.15541666666667
  - type: map_at_20 value: 37.521249999999995
  - type: map_at_3 value: 34.07658333333333
  - type: map_at_5 value: 35.62683333333333
  - type: mrr_at_1 value: 32.676249999999996
  - type: mrr_at_10 value: 40.999
  - type: mrr_at_100 value: 41.835
  - type: mrr_at_1000 value: 41.8895
  - type: mrr_at_20 value: 41.4865
  - type: mrr_at_3 value: 38.645
  - type: mrr_at_5 value: 39.99725000000001
  - type: ndcg_at_1 value: 32.676249999999996
  - type: ndcg_at_10 value: 42.08016666666666
  - type: ndcg_at_100 value: 47.082750000000004
  - type: ndcg_at_1000 value: 49.276583333333335
  - type: ndcg_at_20 value: 44.04808333333334
  - type: ndcg_at_3 value: 37.43375
  - type: ndcg_at_5 value: 39.623000000000005
  - type: precision_at_1 value: 32.676249999999996
  - type: precision_at_10 value: 7.271
  - type: precision_at_100 value: 1.1458333333333333
  - type: precision_at_1000 value: 0.152
  - type: precision_at_20 value: 4.282916666666667
  - type: precision_at_3 value: 17.061416666666666
  - type: precision_at_5 value: 12.05466666666667
  - type: recall_at_1 value: 27.734166666666667
  - type: recall_at_10 value: 53.33574999999999
  - type: recall_at_100 value: 75.16275
  - type: recall_at_1000 value: 90.34891666666665
  - type: recall_at_20 value: 60.4935
  - type: recall_at_3 value: 40.377916666666664
  - type: recall_at_5 value: 46.0195
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics:
  - type: map_at_1 value: 25.653
  - type: map_at_10 value: 32.151
  - type: map_at_100 value: 33.152
  - type: map_at_1000 value: 33.243
  - type: map_at_20 value: 32.717
  - type: map_at_3 value: 30.287
  - type: map_at_5 value: 31.25
  - type: mrr_at_1 value: 28.988000000000003
  - type: mrr_at_10 value: 35.131
  - type: mrr_at_100 value: 36.002
  - type: mrr_at_1000 value: 36.069
  - type: mrr_at_20 value: 35.61
  - type: mrr_at_3 value: 33.308
  - type: mrr_at_5 value: 34.259
  - type: ndcg_at_1 value: 28.988000000000003
  - type: ndcg_at_10 value: 35.988
  - type: ndcg_at_100 value: 40.764
  - type: ndcg_at_1000 value: 43.112
  - type: ndcg_at_20 value: 37.852999999999994
  - type: ndcg_at_3 value: 32.562000000000005
  - type: ndcg_at_5 value: 33.983000000000004
  - type: precision_at_1 value: 28.988000000000003
  - type: precision_at_10 value: 5.475
  - type: precision_at_100 value: 0.8500000000000001
  - type: precision_at_1000 value: 0.11199999999999999
  - type: precision_at_20 value: 3.229
  - type: precision_at_3 value: 13.905999999999999
  - type: precision_at_5 value: 9.386999999999999
  - type: recall_at_1 value: 25.653
  - type: recall_at_10 value: 44.962
  - type: recall_at_100 value: 66.405
  - type: recall_at_1000 value: 83.88799999999999
  - type: recall_at_20 value: 51.79899999999999
  - type: recall_at_3 value: 35.144999999999996
  - type: recall_at_5 value: 38.814
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics:
  - type: map_at_1 value: 17.825
  - type: map_at_10 value: 25.592
  - type: map_at_100 value: 26.613999999999997
  - type: map_at_1000 value: 26.734
  - type: map_at_20 value: 26.115
  - type: map_at_3 value: 23.119
  - type: map_at_5 value: 24.54
  - type: mrr_at_1 value: 21.335
  - type: mrr_at_10 value: 29.165000000000003
  - type: mrr_at_100 value: 30.049
  - type: mrr_at_1000 value: 30.121
  - type: mrr_at_20 value: 29.639
  - type: mrr_at_3 value: 26.863999999999997
  - type: mrr_at_5 value: 28.185
  - type: ndcg_at_1 value: 21.335
  - type: ndcg_at_10 value: 30.357
  - type: ndcg_at_100 value: 35.410000000000004
  - type: ndcg_at_1000 value: 38.24
  - type: ndcg_at_20 value: 32.08
  - type: ndcg_at_3 value: 25.95
  - type: ndcg_at_5 value: 28.081
  - type: precision_at_1 value: 21.335
  - type: precision_at_10 value: 5.506
  - type: precision_at_100 value: 0.928
  - type: precision_at_1000 value: 0.135
  - type: precision_at_20 value: 3.2550000000000003
  - type: precision_at_3 value: 12.239
  - type: precision_at_5 value: 8.885
  - type: recall_at_1 value: 17.825
  - type: recall_at_10 value: 41.105999999999995
  - type: recall_at_100 value: 64.17
  - type: recall_at_1000 value: 84.19200000000001
  - type: recall_at_20 value: 47.497
  - type: recall_at_3 value: 28.862
  - type: recall_at_5 value: 34.348
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics:
  - type: map_at_1 value: 29.435
  - type: map_at_10 value: 38.261
  - type: map_at_100 value: 39.242
  - type: map_at_1000 value: 39.347
  - type: map_at_20 value: 38.742
  - type: map_at_3 value: 35.457
  - type: map_at_5 value: 37.043
  - type: mrr_at_1 value: 34.235
  - type: mrr_at_10 value: 42.24
  - type: mrr_at_100 value: 42.988
  - type: mrr_at_1000 value: 43.043
  - type: mrr_at_20 value: 42.613
  - type: mrr_at_3 value: 39.832
  - type: mrr_at_5 value: 41.227000000000004
  - type: ndcg_at_1 value: 34.235
  - type: ndcg_at_10 value: 43.384
  - type: ndcg_at_100 value: 48.14
  - type: ndcg_at_1000 value: 50.414
  - type: ndcg_at_20 value: 44.913
  - type: ndcg_at_3 value: 38.454
  - type: ndcg_at_5 value: 40.776
  - type: precision_at_1 value: 34.235
  - type: precision_at_10 value: 7.164
  - type: precision_at_100 value: 1.065
  - type: precision_at_1000 value: 0.13699999999999998
  - type: precision_at_20 value: 4.021
  - type: precision_at_3 value: 17.226
  - type: precision_at_5 value: 12.071
  - type: recall_at_1 value: 29.435
  - type: recall_at_10 value: 54.93900000000001
  - type: recall_at_100 value: 76.176
  - type: recall_at_1000 value: 91.989
  - type: recall_at_20 value: 60.451
  - type: recall_at_3 value: 41.332
  - type: recall_at_5 value: 47.316
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics:
  - type: map_at_1 value: 25.605
  - type: map_at_10 value: 34.162
  - type: map_at_100 value: 35.827999999999996
  - type: map_at_1000 value: 36.04
  - type: map_at_20 value: 35.016000000000005
  - type: map_at_3 value: 30.984
  - type: map_at_5 value: 32.717
  - type: mrr_at_1 value: 30.435000000000002
  - type: mrr_at_10 value: 38.681
  - type: mrr_at_100 value: 39.656000000000006
  - type: mrr_at_1000 value: 39.71
  - type: mrr_at_20 value: 39.208999999999996
  - type: mrr_at_3 value: 35.903
  - type: mrr_at_5 value: 37.454
  - type: ndcg_at_1 value: 30.435000000000002
  - type: ndcg_at_10 value: 39.916000000000004
  - type: ndcg_at_100 value: 45.958
  - type: ndcg_at_1000 value: 48.449999999999996
  - type: ndcg_at_20 value: 42.085
  - type: ndcg_at_3 value: 34.696
  - type: ndcg_at_5 value: 37.147000000000006
  - type: precision_at_1 value: 30.435000000000002
  - type: precision_at_10 value: 7.767
  - type: precision_at_100 value: 1.547
  - type: precision_at_1000 value: 0.23800000000000002
  - type: precision_at_20 value: 4.941
  - type: precision_at_3 value: 16.073999999999998
  - type: precision_at_5 value: 11.937000000000001
  - type: recall_at_1 value: 25.605
  - type: recall_at_10 value: 50.654999999999994
  - type: recall_at_100 value: 77.609
  - type: recall_at_1000 value: 93.518
  - type: recall_at_20 value: 58.845000000000006
  - type: recall_at_3 value: 36.272
  - type: recall_at_5 value: 42.596000000000004
- task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
  - type: map_at_1 value: 23.666
  - type: map_at_10 value: 30.980999999999998
  - type: map_at_100 value: 32.0
  - type: map_at_1000 value: 32.098
  - type: map_at_20 value: 31.621
  - type: map_at_3 value: 28.449999999999996
  - type: map_at_5 value: 29.731999999999996
  - type: mrr_at_1 value: 25.692999999999998
  - type: mrr_at_10 value: 32.788000000000004
  - type: mrr_at_100 value: 33.783
  - type: mrr_at_1000 value: 33.849000000000004
  - type: mrr_at_20 value: 33.408
  - type: mrr_at_3 value: 30.561
  - type: mrr_at_5 value: 31.716
  - type: ndcg_at_1 value: 25.692999999999998
  - type: ndcg_at_10 value: 35.428
  - type: ndcg_at_100 value: 40.375
  - type: ndcg_at_1000 value: 42.802
  - type: ndcg_at_20 value: 37.621
  - type: ndcg_at_3 value: 30.476999999999997
  - type: ndcg_at_5 value: 32.621
  - type: precision_at_1 value: 25.692999999999998
  - type: precision_at_10 value: 5.508
  - type: precision_at_100 value: 0.848
  - type: precision_at_1000 value: 0.116
  - type: precision_at_20 value: 3.272
  - type: precision_at_3 value: 12.631
  - type: precision_at_5 value: 8.872
  - type: recall_at_1 value: 23.666
  - type: recall_at_10 value: 47.532000000000004
  - type: recall_at_100 value: 69.73700000000001
  - type: recall_at_1000 value: 87.83800000000001
  - type: recall_at_20 value: 55.61000000000001
  - type: recall_at_3 value: 34.06
  - type: recall_at_5 value: 39.254
- task: type: Retrieval dataset: type: None name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics:
  - type: map_at_1 value: 16.337
  - type: map_at_10 value: 26.488
  - type: map_at_100 value: 28.415000000000003
  - type: map_at_1000 value: 28.584
  - type: map_at_20 value: 27.557
  - type: map_at_3 value: 22.665
  - type: map_at_5 value: 24.542
  - type: mrr_at_1 value: 36.417
  - type: mrr_at_10 value: 48.001
  - type: mrr_at_100 value: 48.784
  - type: mrr_at_1000 value: 48.809000000000005
  - type: mrr_at_20 value: 48.507
  - type: mrr_at_3 value: 45.103
  - type: mrr_at_5 value: 46.843
  - type: ndcg_at_1 value: 36.417
  - type: ndcg_at_10 value: 35.67
  - type: ndcg_at_100 value: 42.716
  - type: ndcg_at_1000 value: 45.639
  - type: ndcg_at_20 value: 38.471
  - type: ndcg_at_3 value: 30.444
  - type: ndcg_at_5 value: 32.004
  - type: precision_at_1 value: 36.417
  - type: precision_at_10 value: 10.73
  - type: precision_at_100 value: 1.833
  - type: precision_at_1000 value: 0.23800000000000002
  - type: precision_at_20 value: 6.596
  - type: precision_at_3 value: 22.302
  - type: precision_at_5 value: 16.521
  - type: recall_at_1 value: 16.337
  - type: recall_at_10 value: 40.671
  - type: recall_at_100 value: 64.55300000000001
  - type: recall_at_1000 value: 80.934
  - type: recall_at_20 value: 48.381
  - type: recall_at_3 value: 27.279999999999998
  - type: recall_at_5 value: 32.621
- task: type: Retrieval dataset: type: None name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics:
  - type: map_at_1 value: 9.056000000000001
  - type: map_at_10 value: 19.419
  - type: map_at_100 value: 27.069
  - type: map_at_1000 value: 28.666000000000004
  - type: map_at_20 value: 22.434
  - type: map_at_3 value: 13.895
  - type: map_at_5 value: 16.121
  - type: mrr_at_1 value: 69.0
  - type: mrr_at_10 value: 75.804
  - type: mrr_at_100 value: 76.117
  - type: mrr_at_1000 value: 76.125
  - type: mrr_at_20 value: 76.009
  - type: mrr_at_3 value: 74.375
  - type: mrr_at_5 value: 75.4
  - type: ndcg_at_1 value: 57.49999999999999
  - type: ndcg_at_10 value: 41.495
  - type: ndcg_at_100 value: 45.208
  - type: ndcg_at_1000 value: 52.221
  - type: ndcg_at_20 value: 40.617999999999995
  - type: ndcg_at_3 value: 46.592
  - type: ndcg_at_5 value: 43.559
  - type: precision_at_1 value: 69.0
  - type: precision_at_10 value: 32.574999999999996
  - type: precision_at_100 value: 10.205
  - type: precision_at_1000 value: 2.036
  - type: precision_at_20 value: 24.687
  - type: precision_at_3 value: 49.75
  - type: precision_at_5 value: 42.0
  - type: recall_at_1 value: 9.056000000000001
  - type: recall_at_10 value: 24.866
  - type: recall_at_100 value: 50.097
  - type: recall_at_1000 value: 72.038
  - type: recall_at_20 value: 31.858999999999998
  - type: recall_at_3 value: 15.096000000000002
  - type: recall_at_5 value: 18.548000000000002
- task: type: Classification dataset: type: None name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
  - type: accuracy value: 48.259999999999984
  - type: f1 value: 43.1498589523159
- task: type: Retrieval dataset: type: None name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics:
  - type: map_at_1 value: 74.798
  - type: map_at_10 value: 83.454
  - type: map_at_100 value: 83.623
  - type: map_at_1000 value: 83.635
  - type: map_at_20 value: 83.55
  - type: map_at_3 value: 82.392
  - type: map_at_5 value: 83.167
  - type: mrr_at_1 value: 80.708
  - type: mrr_at_10 value: 88.377
  - type: mrr_at_100 value: 88.411
  - type: mrr_at_1000 value: 88.411
  - type: mrr_at_20 value: 88.402
  - type: mrr_at_3 value: 87.646
  - type: mrr_at_5 value: 88.232
  - type: ndcg_at_1 value: 80.708
  - type: ndcg_at_10 value: 87.35199999999999
  - type: ndcg_at_100 value: 87.91600000000001
  - type: ndcg_at_1000 value: 88.12299999999999
  - type: ndcg_at_20 value: 87.593
  - type: ndcg_at_3 value: 85.738
  - type: ndcg_at_5 value: 86.845
  - type: precision_at_1 value: 80.708
  - type: precision_at_10 value: 10.432
  - type: precision_at_100 value: 1.091
  - type: precision_at_1000 value: 0.11299999999999999
  - type: precision_at_20 value: 5.296
  - type: precision_at_3 value: 32.778
  - type: precision_at_5 value: 20.399
  - type: recall_at_1 value: 74.798
  - type: recall_at_10 value: 94.459
  - type: recall_at_100 value: 96.614
  - type: recall_at_1000 value: 97.868
  - type: recall_at_20 value: 95.254
  - type: recall_at_3 value: 90.144
  - type: recall_at_5 value: 92.965
- task: type: Retrieval dataset: type: None name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics:
  - type: map_at_1 value: 20.008
  - type: map_at_10 value: 32.731
  - type: map_at_100 value: 34.467999999999996
  - type: map_at_1000 value: 34.643
  - type: map_at_20 value: 33.717000000000006
  - type: map_at_3 value: 28.427999999999997
  - type: map_at_5 value: 30.788
  - type: mrr_at_1 value: 40.586
  - type: mrr_at_10 value: 49.056
  - type: mrr_at_100 value: 49.887
  - type: mrr_at_1000 value: 49.929
  - type: mrr_at_20 value: 49.552
  - type: mrr_at_3 value: 46.785
  - type: mrr_at_5 value: 48.004000000000005
  - type: ndcg_at_1 value: 40.586
  - type: ndcg_at_10 value: 40.589999999999996
  - type: ndcg_at_100 value: 47.03
  - type: ndcg_at_1000 value: 49.994
  - type: ndcg_at_20 value: 43.229
  - type: ndcg_at_3 value: 37.061
  - type: ndcg_at_5 value: 37.992
  - type: precision_at_1 value: 40.586
  - type: precision_at_10 value: 11.219
  - type: precision_at_100 value: 1.781
  - type: precision_at_1000 value: 0.232
  - type: precision_at_20 value: 6.705
  - type: precision_at_3 value: 24.743000000000002
  - type: precision_at_5 value: 18.086
  - type: recall_at_1 value: 20.008
  - type: recall_at_10 value: 47.412
  - type: recall_at_100 value: 71.274
  - type: recall_at_1000 value: 88.898
  - type: recall_at_20 value: 55.706999999999994
  - type: recall_at_3 value: 33.346
  - type: recall_at_5 value: 39.112
- task: type: Retrieval dataset: type: None name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics:
  - type: map_at_1 value: 41.789
  - type: map_at_10 value: 57.898
  - type: map_at_100 value: 58.632
  - type: map_at_1000 value: 58.693
  - type: map_at_20 value: 58.314
  - type: map_at_3 value: 55.236
  - type: map_at_5 value: 56.852999999999994
  - type: mrr_at_1 value: 83.57900000000001
  - type: mrr_at_10 value: 87.631
  - type: mrr_at_100 value: 87.764
  - type: mrr_at_1000 value: 87.77000000000001
  - type: mrr_at_20 value: 87.70700000000001
  - type: mrr_at_3 value: 87.02499999999999
  - type: mrr_at_5 value: 87.34100000000001
  - type: ndcg_at_1 value: 83.57900000000001
  - type: ndcg_at_10 value: 67.11399999999999
  - type: ndcg_at_100 value: 69.686
  - type: ndcg_at_1000 value: 70.926
  - type: ndcg_at_20 value: 68.119
  - type: ndcg_at_3 value: 63.402
  - type: ndcg_at_5 value: 65.354
  - type: precision_at_1 value: 83.57900000000001
  - type: precision_at_10 value: 13.333
  - type: precision_at_100 value: 1.537
  - type: precision_at_1000 value: 0.16999999999999998
  - type: precision_at_20 value: 6.988999999999999
  - type: precision_at_3 value: 38.929
  - type: precision_at_5 value: 24.897
  - type: recall_at_1 value: 41.789
  - type: recall_at_10 value: 66.664
  - type: recall_at_100 value: 76.833
  - type: recall_at_1000 value: 85.14500000000001
  - type: recall_at_20 value: 69.892
  - type: recall_at_3 value: 58.392999999999994
  - type: recall_at_5 value: 62.242
- task: type: Classification dataset: type: None name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
  - type: accuracy value: 86.6108
  - type: ap value: 81.63890253106925
  - type: f1 value: 86.54585789538082
- task: type: Retrieval dataset: type: None name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics:
  - type: map_at_1 value: 22.407
  - type: map_at_10 value: 34.603
  - type: map_at_100 value: 35.808
  - type: map_at_1000 value: 35.855
  - type: map_at_20 value: 35.368
  - type: map_at_3 value: 30.764000000000003
  - type: map_at_5 value: 32.964
  - type: mrr_at_1 value: 23.009
  - type: mrr_at_10 value: 35.136
  - type: mrr_at_100 value: 36.284
  - type: mrr_at_1000 value: 36.325
  - type: mrr_at_20 value: 35.869
  - type: mrr_at_3 value: 31.351000000000003
  - type: mrr_at_5 value: 33.54
  - type: ndcg_at_1 value: 23.009
  - type: ndcg_at_10 value: 41.471999999999994
  - type: ndcg_at_100 value: 47.211999999999996
  - type: ndcg_at_1000 value: 48.361
  - type: ndcg_at_20 value: 44.169000000000004
  - type: ndcg_at_3 value: 33.646
  - type: ndcg_at_5 value: 37.580000000000005
  - type: precision_at_1 value: 23.009
  - type: precision_at_10 value: 6.54
  - type: precision_at_100 value: 0.941
  - type: precision_at_1000 value: 0.104
  - type: precision_at_20 value: 3.832
  - type: precision_at_3 value: 14.283999999999999
  - type: precision_at_5 value: 10.564
  - type: recall_at_1 value: 22.407
  - type: recall_at_10 value: 62.678999999999995
  - type: recall_at_100 value: 89.09700000000001
  - type: recall_at_1000 value: 97.822
  - type: recall_at_20 value: 73.116
  - type: recall_at_3 value: 41.4
  - type: recall_at_5 value: 50.855
- task: type: Classification dataset: type: None name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
  - type: accuracy value: 92.94573643410853
  - type: f1 value: 92.73148878666994
- task: type: Classification dataset: type: None name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
  - type: accuracy value: 77.86137710898313
  - type: f1 value: 60.360562463738724
- task: type: Classification dataset: type: None name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
  - type: accuracy value: 73.83322125084062
  - type: f1 value: 71.61864304680206
- task: type: Classification dataset: type: None name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
  - type: accuracy value: 77.50504371217215
  - type: f1 value: 77.52039268347185
- task: type: Clustering dataset: type: None name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
  - type: v_measure value: 34.346952648910225
  - type: v_measures value: [0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 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- task: type: Clustering dataset: type: None name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
  - type: v_measure value: 32.19992734583148
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- task: type: Reranking dataset: type: None name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
  - type: map value: 30.62309561205373
  - type: mrr value: 31.707879717902554
- task: type: Retrieval dataset: type: None name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics:
  - type: map_at_1 value: 5.668
  - type: map_at_10 value: 12.225999999999999
  - type: map_at_100 value: 15.122
  - type: map_at_1000 value: 16.422
  - type: map_at_20 value: 13.361999999999998
  - type: map_at_3 value: 9.083
  - type: map_at_5 value: 10.5
  - type: mrr_at_1 value: 46.44
  - type: mrr_at_10 value: 53.553
  - type: mrr_at_100 value: 54.15
  - type: mrr_at_1000 value: 54.193000000000005
  - type: mrr_at_20 value: 53.837
  - type: mrr_at_3 value: 51.702999999999996
  - type: mrr_at_5 value: 52.647
  - type: ndcg_at_1 value: 44.272
  - type: ndcg_at_10 value: 33.395
  - type: ndcg_at_100 value: 29.976999999999997
  - type: ndcg_at_1000 value: 38.388
  - type: ndcg_at_20 value: 30.606
  - type: ndcg_at_3 value: 39.212
  - type: ndcg_at_5 value: 36.611
  - type: precision_at_1 value: 46.129999999999995
  - type: precision_at_10 value: 24.334
  - type: precision_at_100 value: 7.553999999999999
  - type: precision_at_1000 value: 1.994
  - type: precision_at_20 value: 17.678
  - type: precision_at_3 value: 36.326
  - type: precision_at_5 value: 31.330999999999996
  - type: recall_at_1 value: 5.668
  - type: recall_at_10 value: 15.837000000000002
  - type: recall_at_100 value: 29.845
  - type: recall_at_1000 value: 60.563
  - type: recall_at_20 value: 18.587999999999997
  - type: recall_at_3 value: 10.096
  - type: recall_at_5 value: 12.261
- task: type: Retrieval dataset: type: None name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics:
  - type: map_at_1 value: 39.335
  - type: map_at_10 value: 54.932
  - type: map_at_100 value: 55.742000000000004
  - type: map_at_1000 value: 55.766000000000005
  - type: map_at_20 value: 55.504
  - type: map_at_3 value: 50.904
  - type: map_at_5 value: 53.388999999999996
  - type: mrr_at_1 value: 44.003
  - type: mrr_at_10 value: 57.419
  - type: mrr_at_100 value: 57.963
  - type: mrr_at_1000 value: 57.981
  - type: mrr_at_20 value: 57.80499999999999
  - type: mrr_at_3 value: 54.30199999999999
  - type: mrr_at_5 value: 56.257000000000005
  - type: ndcg_at_1 value: 43.974999999999994
  - type: ndcg_at_10 value: 62.153999999999996
  - type: ndcg_at_100 value: 65.326
  - type: ndcg_at_1000 value: 65.862
  - type: ndcg_at_20 value: 63.922999999999995
  - type: ndcg_at_3 value: 54.834
  - type: ndcg_at_5 value: 58.857000000000006
  - type: precision_at_1 value: 43.974999999999994
  - type: precision_at_10 value: 9.722
  - type: precision_at_100 value: 1.153
  - type: precision_at_1000 value: 0.12
  - type: precision_at_20 value: 5.3
  - type: precision_at_3 value: 24.392
  - type: precision_at_5 value: 16.993
  - type: recall_at_1 value: 39.335
  - type: recall_at_10 value: 81.501
  - type: recall_at_100 value: 94.851
  - type: recall_at_1000 value: 98.817
  - type: recall_at_20 value: 87.968
  - type: recall_at_3 value: 62.795
  - type: recall_at_5 value: 71.985
- task: type: Retrieval dataset: type: None name: MTEB QuoraRetrieval config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics:
  - type: map_at_1 value: 71.222
  - type: map_at_10 value: 85.193
  - type: map_at_100 value: 85.802
  - type: map_at_1000 value: 85.81800000000001
  - type: map_at_20 value: 85.587
  - type: map_at_3 value: 82.253
  - type: map_at_5 value: 84.142
  - type: mrr_at_1 value: 82.04
  - type: mrr_at_10 value: 88.101
  - type: mrr_at_100 value: 88.196
  - type: mrr_at_1000 value: 88.196
  - type: mrr_at_20 value: 88.175
  - type: mrr_at_3 value: 87.145
  - type: mrr_at_5 value: 87.825
  - type: ndcg_at_1 value: 82.04
  - type: ndcg_at_10 value: 88.849
  - type: ndcg_at_100 value: 89.992
  - type: ndcg_at_1000 value: 90.089
  - type: ndcg_at_20 value: 89.468
  - type: ndcg_at_3 value: 86.06899999999999
  - type: ndcg_at_5 value: 87.669
  - type: precision_at_1 value: 82.04
  - type: precision_at_10 value: 13.447000000000001
  - type: precision_at_100 value: 1.528
  - type: precision_at_1000 value: 0.157
  - type: precision_at_20 value: 7.116
  - type: precision_at_3 value: 37.617
  - type: precision_at_5 value: 24.776
  - type: recall_at_1 value: 71.222
  - type: recall_at_10 value: 95.73899999999999
  - type: recall_at_100 value: 99.572
  - type: recall_at_1000 value: 99.988
  - type: recall_at_20 value: 97.725
  - type: recall_at_3 value: 87.742
  - type: recall_at_5 value: 92.23400000000001
- task: type: Clustering dataset: type: None name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
  - type: v_measure value: 56.502005725283524
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- task: type: Clustering dataset: type: None name: MTEB RedditClusteringP2P config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics:
  - type: v_measure value: 63.14989421688691
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- task: type: Retrieval dataset: type: None name: MTEB SCIDOCS config: default split: test revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 metrics:
  - type: map_at_1 value: 4.4830000000000005
  - type: map_at_10 value: 11.04
  - type: map_at_100 value: 12.764000000000001
  - type: map_at_1000 value: 13.04
  - type: map_at_20 value: 11.953
  - type: map_at_3 value: 8.125
  - type: map_at_5 value: 9.565999999999999
  - type: mrr_at_1 value: 22.1
  - type: mrr_at_10 value: 32.494
  - type: mrr_at_100 value: 33.525
  - type: mrr_at_1000 value: 33.596
  - type: mrr_at_20 value: 33.089
  - type: mrr_at_3 value: 29.416999999999998
  - type: mrr_at_5 value: 31.267
  - type: ndcg_at_1 value: 22.1
  - type: ndcg_at_10 value: 18.587
  - type: ndcg_at_100 value: 25.482
  - type: ndcg_at_1000 value: 30.581999999999997
  - type: ndcg_at_20 value: 21.077
  - type: ndcg_at_3 value: 18.165
  - type: ndcg_at_5 value: 15.676000000000002
  - type: precision_at_1 value: 22.1
  - type: precision_at_10 value: 9.48
  - type: precision_at_100 value: 1.942
  - type: precision_at_1000 value: 0.316
  - type: precision_at_20 value: 6.175
  - type: precision_at_3 value: 17.033
  - type: precision_at_5 value: 13.719999999999999
  - type: recall_at_1 value: 4.4830000000000005
  - type: recall_at_10 value: 19.208
  - type: recall_at_100 value: 39.417
  - type: recall_at_1000 value: 64.235
  - type: recall_at_20 value: 25.057000000000002
  - type: recall_at_3 value: 10.348
  - type: recall_at_5 value: 13.893
- task: type: STS dataset: type: None name: MTEB SICK-R config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics:
  - type: cos_sim_pearson value: 83.50181312649208
  - type: cos_sim_spearman value: 79.92900705478993
  - type: euclidean_pearson value: 81.13482128094503
  - type: euclidean_spearman value: 79.92732266864367
  - type: manhattan_pearson value: 81.06702121654993
  - type: manhattan_spearman value: 79.86983106619135
- task: type: STS dataset: type: None name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
  - type: cos_sim_pearson value: 83.85431681906961
  - type: cos_sim_spearman value: 77.61671419416626
  - type: euclidean_pearson value: 81.30538320520961
  - type: euclidean_spearman value: 77.62096481461272
  - type: manhattan_pearson value: 81.2306021173407
  - type: manhattan_spearman value: 77.58386300715222
- task: type: STS dataset: type: None name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
  - type: cos_sim_pearson value: 84.98057702322754
  - type: cos_sim_spearman value: 86.13305071688859
  - type: euclidean_pearson value: 85.70903555966376
  - type: euclidean_spearman value: 86.13150222328171
  - type: manhattan_pearson value: 85.69380834788831
  - type: manhattan_spearman value: 86.10784739081191
- task: type: STS dataset: type: None name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
  - type: cos_sim_pearson value: 83.43368314724589
  - type: cos_sim_spearman value: 81.26767916144169
  - type: euclidean_pearson value: 83.23234690932492
  - type: euclidean_spearman value: 81.2671726214706
  - type: manhattan_pearson value: 83.2381239261109
  - type: manhattan_spearman value: 81.27674961470714
- task: type: STS dataset: type: None name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
  - type: cos_sim_pearson value: 86.8637546411748
  - type: cos_sim_spearman value: 88.25330888676139
  - type: euclidean_pearson value: 87.81194589390417
  - type: euclidean_spearman value: 88.25258669625579
  - type: manhattan_pearson value: 87.8131866998459
  - type: manhattan_spearman value: 88.26523268929576
- task: type: STS dataset: type: None name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
  - type: cos_sim_pearson value: 83.83129743147286
  - type: cos_sim_spearman value: 85.73732687732624
  - type: euclidean_pearson value: 85.18051277328075
  - type: euclidean_spearman value: 85.73565846174445
  - type: manhattan_pearson value: 85.179029651079
  - type: manhattan_spearman value: 85.75709685404729
- task: type: STS dataset: type: None name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
  - type: cos_sim_pearson value: 87.04715794253148
  - type: cos_sim_spearman value: 87.61577496386343
  - type: euclidean_pearson value: 88.34713614361046
  - type: euclidean_spearman value: 87.56541901567275
  - type: manhattan_pearson value: 88.26010824585985
  - type: manhattan_spearman value: 87.35211736948182
- task: type: STS dataset: type: None name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
  - type: cos_sim_pearson value: 62.36160793264433
  - type: cos_sim_spearman value: 66.07767480051893
  - type: euclidean_pearson value: 66.4716471304865
  - type: euclidean_spearman value: 66.03999286501872
  - type: manhattan_pearson value: 66.46197824372902
  - type: manhattan_spearman value: 65.82936468127227
- task: type: STS dataset: type: None name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
  - type: cos_sim_pearson value: 85.27768996785856
  - type: cos_sim_spearman value: 86.96704639052885
  - type: euclidean_pearson value: 86.48753189555983
  - type: euclidean_spearman value: 86.96981285751171
  - type: manhattan_pearson value: 86.49262465015401
  - type: manhattan_spearman value: 86.95378609580054
- task: type: Reranking dataset: type: None name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
  - type: map value: 81.52012853393428
  - type: mrr value: 94.70817671798063
- task: type: Retrieval dataset: type: None name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics:
  - type: map_at_1 value: 55.344
  - type: map_at_10 value: 64.82900000000001
  - type: map_at_100 value: 65.42
  - type: map_at_1000 value: 65.443
  - type: map_at_20 value: 65.2
  - type: map_at_3 value: 61.8
  - type: map_at_5 value: 63.510999999999996
  - type: mrr_at_1 value: 58.333
  - type: mrr_at_10 value: 66.24600000000001
  - type: mrr_at_100 value: 66.742
  - type: mrr_at_1000 value: 66.762
  - type: mrr_at_20 value: 66.549
  - type: mrr_at_3 value: 64.056
  - type: mrr_at_5 value: 65.372
  - type: ndcg_at_1 value: 58.333
  - type: ndcg_at_10 value: 69.626
  - type: ndcg_at_100 value: 72.236
  - type: ndcg_at_1000 value: 72.872
  - type: ndcg_at_20 value: 70.864
  - type: ndcg_at_3 value: 64.50399999999999
  - type: ndcg_at_5 value: 67.07600000000001
  - type: precision_at_1 value: 58.333
  - type: precision_at_10 value: 9.4
  - type: precision_at_100 value: 1.073
  - type: precision_at_1000 value: 0.11299999999999999
  - type: precision_at_20 value: 4.983
  - type: precision_at_3 value: 25.222
  - type: precision_at_5 value: 16.8
  - type: recall_at_1 value: 55.344
  - type: recall_at_10 value: 82.789
  - type: recall_at_100 value: 94.6
  - type: recall_at_1000 value: 99.667
  - type: recall_at_20 value: 87.533
  - type: recall_at_3 value: 69.18299999999999
  - type: recall_at_5 value: 75.622
- task: type: PairClassification dataset: type: None name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
  - type: cos_sim_accuracy value: 99.69405940594059
  - type: cos_sim_ap value: 92.03642221694545
  - type: cos_sim_f1 value: 84.06395048994327
  - type: cos_sim_precision value: 86.79446219382322
  - type: cos_sim_recall value: 81.5
  - type: dot_accuracy value: 99.6930693069307
  - type: dot_ap value: 91.9971441434875
  - type: dot_f1 value: 83.8006230529595
  - type: dot_precision value: 87.14902807775377
  - type: dot_recall value: 80.7
  - type: euclidean_accuracy value: 99.69504950495049
  - type: euclidean_ap value: 92.03626548389335
  - type: euclidean_f1 value: 84.10732714138285
  - type: euclidean_precision value: 86.88699360341151
  - type: euclidean_recall value: 81.5
  - type: manhattan_accuracy value: 99.69504950495049
  - type: manhattan_ap value: 92.02049659660081
  - type: manhattan_f1 value: 84.34959349593495
  - type: manhattan_precision value: 85.74380165289256
  - type: manhattan_recall value: 83.0
  - type: max_accuracy value: 99.69504950495049
  - type: max_ap value: 92.03642221694545
  - type: max_f1 value: 84.34959349593495
- task: type: Clustering dataset: type: None name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
  - type: v_measure value: 67.04916654680977
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- task: type: Clustering dataset: type: None name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
  - type: v_measure value: 33.36641413495258
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- task: type: Reranking dataset: type: None name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
  - type: map value: 51.19282080158746
  - type: mrr value: 51.871100713012474
- task: type: Summarization dataset: type: None name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
  - type: cos_sim_pearson value: 31.437664703708485
  - type: cos_sim_spearman value: 31.391119208581575
  - type: dot_pearson value: 31.19925970504054
  - type: dot_spearman value: 31.38087224016694
- task: type: Retrieval dataset: type: None name: MTEB TRECCOVID config: default split: test revision: bb9466bac8153a0349341eb1b22e06409e78ef4e metrics:
  - type: map_at_1 value: 0.249
  - type: map_at_10 value: 2.163
  - type: map_at_100 value: 13.242999999999999
  - type: map_at_1000 value: 30.866
  - type: map_at_20 value: 3.9539999999999997
  - type: map_at_3 value: 0.718
  - type: map_at_5 value: 1.169
  - type: mrr_at_1 value: 96.0
  - type: mrr_at_10 value: 98.0
  - type: mrr_at_100 value: 98.0
  - type: mrr_at_1000 value: 98.0
  - type: mrr_at_20 value: 98.0
  - type: mrr_at_3 value: 98.0
  - type: mrr_at_5 value: 98.0
  - type: ndcg_at_1 value: 92.0
  - type: ndcg_at_10 value: 84.147
  - type: ndcg_at_100 value: 65.143
  - type: ndcg_at_1000 value: 56.038
  - type: ndcg_at_20 value: 80.869
  - type: ndcg_at_3 value: 89.11200000000001
  - type: ndcg_at_5 value: 87.199
  - type: precision_at_1 value: 96.0
  - type: precision_at_10 value: 87.8
  - type: precision_at_100 value: 66.72
  - type: precision_at_1000 value: 24.684
  - type: precision_at_20 value: 84.3
  - type: precision_at_3 value: 94.0
  - type: precision_at_5 value: 91.2
  - type: recall_at_1 value: 0.249
  - type: recall_at_10 value: 2.284
  - type: recall_at_100 value: 16.025
  - type: recall_at_1000 value: 52.068999999999996
  - type: recall_at_20 value: 4.3180000000000005
  - type: recall_at_3 value: 0.738
  - type: recall_at_5 value: 1.212
- task: type: Retrieval dataset: type: None name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics:
  - type: map_at_1 value: 3.4520000000000004
  - type: map_at_10 value: 13.045000000000002
  - type: map_at_100 value: 19.442
  - type: map_at_1000 value: 21.09
  - type: map_at_20 value: 15.667
  - type: map_at_3 value: 7.409000000000001
  - type: map_at_5 value: 9.73
  - type: mrr_at_1 value: 46.939
  - type: mrr_at_10 value: 60.295
  - type: mrr_at_100 value: 60.904
  - type: mrr_at_1000 value: 60.919000000000004
  - type: mrr_at_20 value: 60.77
  - type: mrr_at_3 value: 58.50300000000001
  - type: mrr_at_5 value: 59.014
  - type: ndcg_at_1 value: 44.897999999999996
  - type: ndcg_at_10 value: 31.911
  - type: ndcg_at_100 value: 41.945
  - type: ndcg_at_1000 value: 53.181999999999995
  - type: ndcg_at_20 value: 31.505
  - type: ndcg_at_3 value: 39.745000000000005
  - type: ndcg_at_5 value: 35.528999999999996
  - type: precision_at_1 value: 46.939
  - type: precision_at_10 value: 26.531
  - type: precision_at_100 value: 8.163
  - type: precision_at_1000 value: 1.559
  - type: precision_at_20 value: 19.387999999999998
  - type: precision_at_3 value: 40.136
  - type: precision_at_5 value: 33.878
  - type: recall_at_1 value: 3.4520000000000004
  - type: recall_at_10 value: 18.899
  - type: recall_at_100 value: 50.207
  - type: recall_at_1000 value: 83.871
  - type: recall_at_20 value: 26.756999999999998
  - type: recall_at_3 value: 8.729000000000001
  - type: recall_at_5 value: 12.084999999999999
- task: type: Classification dataset: type: None name: MTEB ToxicConversationsClassification config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics:
  - type: accuracy value: 67.4560546875
  - type: ap value: 12.720403845355294
  - type: f1 value: 51.76062666567839
- task: type: Classification dataset: type: None name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
  - type: accuracy value: 62.36276174306734
  - type: f1 value: 62.69956906934332
- task: type: Clustering dataset: type: None name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
  - type: v_measure value: 49.473492910233965
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- task: type: PairClassification dataset: type: None name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
  - type: cos_sim_accuracy value: 85.75430649102938
  - type: cos_sim_ap value: 73.62842656477649
  - type: cos_sim_f1 value: 67.76023680315738
  - type: cos_sim_precision value: 63.61741547012506
  - type: cos_sim_recall value: 72.4802110817942
  - type: dot_accuracy value: 85.7423854085951
  - type: dot_ap value: 73.59147637253723
  - type: dot_f1 value: 67.69498693867396
  - type: dot_precision value: 64.03859731701577
  - type: dot_recall value: 71.79419525065963
  - type: euclidean_accuracy value: 85.7423854085951
  - type: euclidean_ap value: 73.6288990409654
  - type: euclidean_f1 value: 67.80415430267064
  - type: euclidean_precision value: 63.79711493718009
  - type: euclidean_recall value: 72.34828496042216
  - type: manhattan_accuracy value: 85.69470107885796
  - type: manhattan_ap value: 73.49219614602531
  - type: manhattan_f1 value: 67.60809797550613
  - type: manhattan_precision value: 64.22127255460589
  - type: manhattan_recall value: 71.37203166226914
  - type: max_accuracy value: 85.75430649102938
  - type: max_ap value: 73.6288990409654
  - type: max_f1 value: 67.80415430267064
- task: type: PairClassification dataset: type: None name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
  - type: cos_sim_accuracy value: 89.08293553770326
  - type: cos_sim_ap value: 86.21246419992926
  - type: cos_sim_f1 value: 78.49922526377924
  - type: cos_sim_precision value: 75.35769939084857
  - type: cos_sim_recall value: 81.9140745303357
  - type: dot_accuracy value: 89.08681647067955
  - type: dot_ap value: 86.19733517196862
  - type: dot_f1 value: 78.51132446157838
  - type: dot_precision value: 75.70233755093287
  - type: dot_recall value: 81.53680320295658
  - type: euclidean_accuracy value: 89.07517367175069
  - type: euclidean_ap value: 86.21198725320203
  - type: euclidean_f1 value: 78.49867139061116
  - type: euclidean_precision value: 75.38276155372839
  - type: euclidean_recall value: 81.88327687095781
  - type: manhattan_accuracy value: 89.0538285403811
  - type: manhattan_ap value: 86.17785515765131
  - type: manhattan_f1 value: 78.48184098593084
  - type: manhattan_precision value: 74.34396308285694
  - type: manhattan_recall value: 83.10748383122882
  - type: max_accuracy value: 89.08681647067955
  - type: max_ap value: 86.21246419992926
  - type: max_f1 value: 78.51132446157838

license: apache-2.0 language:

en base_model:
answerdotai/ModernBERT-base
nomic-ai/modernbert-embed-unsupervised base_model_relation: finetune

ModernBERT Embed

ModernBERT Embed is an embedding model trained from ModernBERT-base, bringing the new advances of ModernBERT to embeddings!

Trained on the Nomic Embed weakly-supervised and supervised datasets, modernbert-embed also supports Matryoshka Representation Learning dimensions of 256, reducing memory by 3x with minimal performance loss.

Performance

Model	Dimensions	Average (56)	Classification (12)	Clustering (11)	Pair Classification (3)	Reranking (4)	Retrieval (15)	STS (10)	Summarization (1)
nomic-embed-text-v1	768	62.4	74.1	43.9	85.2	55.7	52.8	82.1	30.1
nomic-embed-text-v1.5	768	62.28	73.55	43.93	84.61	55.78	53.01	81.94	30.4
modernbert-embed-base	768	62.62	74.31	44.98	83.96	56.42	52.89	81.78	31.39
nomic-embed-text-v1.5	256	61.04	72.1	43.16	84.09	55.18	50.81	81.34	30.05
modernbert-embed-base	256	61.17	72.40	43.82	83.45	55.69	50.62	81.12	31.27

Usage

You can use these models directly with the latest transformers release and requires installing transformers>=4.48.0:

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.

Most use cases, adding search_query: to the query and search_document: to the documents will be sufficient.

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/modernbert-embed-base")

query_embeddings = model.encode([
    "search_query: What is TSNE?",
    "search_query: Who is Laurens van der Maaten?",
])
doc_embeddings = model.encode([
    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
])
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 768) (1, 768)

similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.7214],
#         [0.3260]])

Click to see Sentence Transformers usage with Matryoshka Truncation

In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the truncate_dim parameter when loading the SentenceTransformer model.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/modernbert-embed-base", truncate_dim=256)

query_embeddings = model.encode([
    "search_query: What is TSNE?",
    "search_query: Who is Laurens van der Maaten?",
])
doc_embeddings = model.encode([
    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
])
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 256) (1, 256)

similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.7759],
#         [0.3419]])

Note the small differences compared to the full 768-dimensional similarities.

Transformers

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = (
        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    )
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
        input_mask_expanded.sum(1), min=1e-9
    )


queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]

tokenizer = AutoTokenizer.from_pretrained("nomic-ai/modernbert-embed-base")
model = AutoModel.from_pretrained("nomic-ai/modernbert-embed-base")

encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    queries_outputs = model(**encoded_queries)
    documents_outputs = model(**encoded_documents)

query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
print(query_embeddings.shape, doc_embeddings.shape)
# torch.Size([2, 768]) torch.Size([1, 768])

similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.7214],
#         [0.3260]])

Click to see Transformers usage with Matryoshka Truncation

In transformers, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.

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.

Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

Then, you can compute embeddings as follows:

import { pipeline, matmul } from '@huggingface/transformers';

// Create a feature extraction pipeline
const extractor = await pipeline(
  "feature-extraction",
  "nomic-ai/modernbert-embed-base",
  { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
);

// Embed queries and documents
const query_embeddings = await extractor([
    "search_query: What is TSNE?",
    "search_query: Who is Laurens van der Maaten?",
  ], { pooling: "mean", normalize: true },
);
const doc_embeddings = await extractor([
    "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
  ], { pooling: "mean", normalize: true },
);

// Compute similarity scores
const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]

Training

Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!

We train our embedder using a multi-stage training pipeline. Starting from a long-context BERT 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 and corresponding blog post.

Training data to train the models is released in its entirety. For more details, see the contrastors repository

Join the Nomic Community

Nomic: https://nomic.ai
Discord: https://discord.gg/myY5YDR8z8
Twitter: https://twitter.com/nomic_ai

Citation

If you find the model, dataset, or training code useful, please cite our work

@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}
}

Magnet link

Opens the swarm directly in your torrent client — no file download needed. Copy-paste works too:

magnet:?xt=urn:btih:cdef8865873f3ef768c763c217b901adbdad1626&dn=nomic-ai_modernbert-embed-base

Open magnet in torrent client · infohash cdef8865873f3ef768c763c217b901adbdad1626

Files & hashes

Path	Size	sha1	sha256
1_Pooling/config.json	296 B (296 B)	9213783a33aa495a3c2d3791c7c5888d90607a4e	1c8bc7bd750c5c20d8707f1c5c578f5d69bb3d1d5ebcf4b2fde5128de154ec1c
README.md	434.2 KB (444,593 B)	6ba47dd940854a93aa1b078ab14dfa14e1931b49	c3f3cf74b304397141dfb4c2dcf649d3c5d2a5e4003232c3fe80d50b3ee6ca98
config.json	1.2 KB (1,261 B)	cacbb2f407c5d781e94c9c14ac9c38b8224f0ff7	dea4708ed1a6df7306b483037750464a96e77ccb0c7e88f4f61a0116944a71dd
config_sentence_transformers.json	210 B (210 B)	05c66ec49cf9d1ad2e7e11dfc91b56658cb51080	4d3adefab0a4c1eccd38fac58b37c240573a6008087ba33b9e7c674940067588
model.safetensors	568.5 MB (596,070,136 B)	7639a8cb449255cae9d195333d29492b379ed1a1	69f22ccd1ee0971b6173678c4fcadd29a49a3f5b6b8a6927002418abe4bd5b9c
modernbertembed.png	1.2 MB (1,284,778 B)	f910c67f6b0b7ca5b52a417c6a09178fc3a2468c	c97e3ae9104ae231d4f728dc6bdb70eb1f56c46a8bd4c7baf43bf882955607af
modules.json	349 B (349 B)	952a9b81c0bfd99800fabf352f69c7ccd46c5e43	84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf
sentence_bert_config.json	54 B (54 B)	0140ba1eac83a3c9b857d64baba91969d988624b	eb9b44b13c0f52a3b3685c3b1cbdea1ba8b04bea123b98f61610048940776eb1
special_tokens_map.json	694 B (694 B)	6bf65ea14185a88eef7e24b41c86a953a58dcbad	ea97ecdbcc73713039d8d64dbb05e3689495c96657fbd9a18f5bed381be81049
tokenizer.json	3.4 MB (3,583,228 B)	2f4d8583e507b7466d2490e2d6c045647a822698	6c8aaa9a542084f2457eab775d4eeb51f92a70c0fd9de28d5edb0ddec3c08d30
tokenizer_config.json	20.4 KB (20,840 B)	708dcd477ee11392ab611bcd98e298b5bccdcfd7	2966a59b9e9cf122279aec1249e22e5bc7ad8430c754e95031b13fd128d4e560

Cite this release

Canonical URL: https://aiseedbank.org/models/nomic-ai_modernbert-embed-base/
Slug: nomic-ai_modernbert-embed-base
Infohash: cdef8865873f3ef768c763c217b901adbdad1626
License: apache-2.0
Signing key fingerprint: 85a3b32c3712427b

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Upstream repository	nomic-ai/modernbert-embed-base
Revision (pinned)	d556a88e332558790b210f7bdbe87da2fa94a8d8
Fetched at	2026-09-04T03:20:47Z
License at fetch	apache-2.0
Snapshot tool	huggingface · seedbank 0.1.0

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✓ verified · rehash-vs-hf-metadata at 2026-09-04T03:20:55Z

apache-2.0573.5 MB (601,406,439 bytes)sentence-transformersonnxsafetensorsmodernbertfeature-extractionsentence-similaritymtebtransformers.jsmodel-indextext-embeddings-inferenceendpoints_compatible1 language (en)paper: 2402.01613