metadata
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:
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value: 47.800803622189214
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type: Clustering
dataset:
type: None
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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type: Reranking
dataset:
type: None
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 63.213756562834234
- type: mrr
value: 76.76493866244557
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type: STS
dataset:
type: None
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 87.90977143141957
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value: 86.31461733951959
- type: manhattan_pearson
value: 85.94280510342506
- type: manhattan_spearman
value: 85.61158927235539
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type: Classification
dataset:
type: None
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
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value: 86.17857142857144
- type: f1
value: 86.14192410600847
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type: Clustering
dataset:
type: None
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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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
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- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 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
value:
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
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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
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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
- type: recall_at_1000
value: 100
- 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
value:
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- task:
type: Clustering
dataset:
type: None
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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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
- type: mrr_at_10
value: 98
- type: mrr_at_100
value: 98
- type: mrr_at_1000
value: 98
- type: mrr_at_20
value: 98
- type: mrr_at_3
value: 98
- type: mrr_at_5
value: 98
- type: ndcg_at_1
value: 92
- 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
- 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
- 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
value:
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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, 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 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:
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("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, 1024) (1, 1024)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.6518],
# [0.4237]])
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("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([[0.6835],
# [0.3982]])
Note the small differences compared to the full 1024-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("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, 1024]) torch.Size([1, 1024])
similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.6518],
# [0.4237]])
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([[0.6835],
# [0.3982]])
Note the small differences compared to the full 1024-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",
"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());
Training
We train ModernBERT-embed-large using a multi-stage training pipeline. Starting from the pretrained 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 and corresponding blog post.
Training data to train the models is released in its entirety. For more details, see the contrastors repository
Acknowledgment
We wanted to thank Zach Nussbaum from 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:
@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},
}
@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:
@misc{ModernBERT-embed-large,
title={ModernBERT-embed-large},
author={Chaffin, Antoine},
url={https://huggingface.co/lightonai/modernbert-embed-large},
year={2025}
}