modernbert-embed-base_a1419.../README.md

434 KiB

pipeline_tag tags model-index license language base_model
sentence-similarity
sentence-transformers
feature-extraction
sentence-similarity
mteb
transformers.js
name results
binarize_False
task dataset metrics
type
Classification
type name config split revision
None MTEB AmazonCounterfactualClassification (en) en test e8379541af4e31359cca9fbcf4b00f2671dba205
type value
accuracy 78.13432835820896
type value
ap 42.190424731303246
type value
f1 72.34446401534811
task dataset metrics
type
Classification
type name config split revision
None MTEB AmazonPolarityClassification default test e2d317d38cd51312af73b3d32a06d1a08b442046
type value
accuracy 93.093825
type value
ap 90.03727505544286
type value
f1 93.0874055138833
task dataset metrics
type
Classification
type name config split revision
None MTEB AmazonReviewsClassification (en) en test 1399c76144fd37290681b995c656ef9b2e06e26d
type value
accuracy 48.428000000000004
type value
f1 47.74311520203536
task dataset metrics
type
Retrieval
type name config split revision
None MTEB ArguAna default test c22ab2a51041ffd869aaddef7af8d8215647e41a
type value
map_at_1 23.898
type value
map_at_10 39.775
type value
map_at_100 40.827000000000005
type value
map_at_1000 40.837
type value
map_at_20 40.604
type value
map_at_3 34.519
type value
map_at_5 37.307
type value
mrr_at_1 24.395
type value
mrr_at_10 39.963
type value
mrr_at_100 41.014
type value
mrr_at_1000 41.024
type value
mrr_at_20 40.791
type value
mrr_at_3 34.732
type value
mrr_at_5 37.480999999999995
type value
ndcg_at_1 23.898
type value
ndcg_at_10 48.962
type value
ndcg_at_100 53.386
type value
ndcg_at_1000 53.634
type value
ndcg_at_20 51.898999999999994
type value
ndcg_at_3 38.034
type value
ndcg_at_5 43.036
type value
precision_at_1 23.898
type value
precision_at_10 7.852
type value
precision_at_100 0.9769999999999999
type value
precision_at_1000 0.1
type value
precision_at_20 4.4990000000000006
type value
precision_at_3 16.073999999999998
type value
precision_at_5 12.063
type value
recall_at_1 23.898
type value
recall_at_10 78.521
type value
recall_at_100 97.724
type value
recall_at_1000 99.644
type value
recall_at_20 89.972
type value
recall_at_3 48.222
type value
recall_at_5 60.313
task dataset metrics
type
Clustering
type name config split revision
None MTEB ArxivClusteringP2P default test a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
type value
v_measure 47.69067314293749
type value
v_measures
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0.3479983114229163
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0.3073271865740206
1.0
0.2749401557058413
0.4953006738713271
0.500982950617211
0.490168788349858
0.4924060458428337
0.475176328561399
0.47446297663785564
0.46948807073019405
0.4772028638329531
0.48735189935310713
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0.561846457174852
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0.5514762015499715
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0.5148077891863135
0.29015461701593837
0.4430422977323321
0.40857527197890686
0.3479983114229163
0.27582001934225003
0.29595564003512503
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1.0
0.2749401557058413
0.4953006738713271
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0.475176328561399
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0.46948807073019405
0.4772028638329531
0.48735189935310713
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0.5575029526712674
0.5574020390232136
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0.5514762015499715
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0.5148077891863135
0.29015461701593837
0.4430422977323321
0.40857527197890686
0.3479983114229163
0.27582001934225003
0.29595564003512503
0.22528676611734755
0.3073271865740206
1.0
0.2749401557058413
task dataset metrics
type
Clustering
type name config split revision
None MTEB ArxivClusteringS2S default test f910caf1a6075f7329cdf8c1a6135696f37dbd53
type value
v_measure 38.0916537995626
type value
v_measures
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0.39235658929084877
0.3871170834588581
0.4042678213739614
0.3918486409557737
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task dataset metrics
type
Reranking
type name config split revision
None MTEB AskUbuntuDupQuestions default test 2000358ca161889fa9c082cb41daa8dcfb161a54
type value
map 62.33195643912506
type value
mrr 76.43978366970057
task dataset metrics
type
STS
type name config split revision
None MTEB BIOSSES default test d3fb88f8f02e40887cd149695127462bbcf29b4a
type value
cos_sim_pearson 81.20285894915236
type value
cos_sim_spearman 78.16322678527897
type value
euclidean_pearson 80.6118408638417
type value
euclidean_spearman 78.19033583671204
type value
manhattan_pearson 80.41282660275819
type value
manhattan_spearman 77.98611431591628
task dataset metrics
type
Classification
type name config split revision
None MTEB Banking77Classification default test 0fd18e25b25c072e09e0d92ab615fda904d66300
type value
accuracy 85.25324675324676
type value
f1 85.19854235582687
task dataset metrics
type
Clustering
type name config split revision
None MTEB BiorxivClusteringP2P default test 65b79d1d13f80053f67aca9498d9402c2d9f1f40
type value
v_measure 39.65216461057432
type value
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task dataset metrics
type
Clustering
type name config split revision
None MTEB BiorxivClusteringS2S default test 258694dd0231531bc1fd9de6ceb52a0853c6d908
type value
v_measure 33.28787287895752
type value
v_measures
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task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackAndroidRetrieval default test f46a197baaae43b4f621051089b82a364682dfeb
type value
map_at_1 32.677
type value
map_at_10 43.739
type value
map_at_100 45.152
type value
map_at_1000 45.279
type value
map_at_20 44.553
type value
map_at_3 40.321
type value
map_at_5 42.201
type value
mrr_at_1 40.2
type value
mrr_at_10 49.755
type value
mrr_at_100 50.468
type value
mrr_at_1000 50.513
type value
mrr_at_20 50.192
type value
mrr_at_3 47.163
type value
mrr_at_5 48.686
type value
ndcg_at_1 40.2
type value
ndcg_at_10 49.963
type value
ndcg_at_100 54.978
type value
ndcg_at_1000 56.979
type value
ndcg_at_20 51.983000000000004
type value
ndcg_at_3 45.086999999999996
type value
ndcg_at_5 47.309
type value
precision_at_1 40.2
type value
precision_at_10 9.328
type value
precision_at_100 1.443
type value
precision_at_1000 0.19
type value
precision_at_20 5.558
type value
precision_at_3 21.364
type value
precision_at_5 15.222
type value
recall_at_1 32.677
type value
recall_at_10 61.71
type value
recall_at_100 82.431
type value
recall_at_1000 94.896
type value
recall_at_20 68.73700000000001
type value
recall_at_3 47.431
type value
recall_at_5 53.739000000000004
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackEnglishRetrieval default test ad9991cb51e31e31e430383c75ffb2885547b5f0
type value
map_at_1 32.71
type value
map_at_10 43.297000000000004
type value
map_at_100 44.607
type value
map_at_1000 44.729
type value
map_at_20 44.013999999999996
type value
map_at_3 40.213
type value
map_at_5 42.004000000000005
type value
mrr_at_1 40.892
type value
mrr_at_10 49.394
type value
mrr_at_100 50.005
type value
mrr_at_1000 50.043000000000006
type value
mrr_at_20 49.764
type value
mrr_at_3 47.134
type value
mrr_at_5 48.522
type value
ndcg_at_1 40.892
type value
ndcg_at_10 49.047000000000004
type value
ndcg_at_100 53.266999999999996
type value
ndcg_at_1000 55.096999999999994
type value
ndcg_at_20 50.707
type value
ndcg_at_3 44.896
type value
ndcg_at_5 46.983000000000004
type value
precision_at_1 40.892
type value
precision_at_10 9.293
type value
precision_at_100 1.473
type value
precision_at_1000 0.192
type value
precision_at_20 5.446
type value
precision_at_3 21.592
type value
precision_at_5 15.540999999999999
type value
recall_at_1 32.71
type value
recall_at_10 58.592999999999996
type value
recall_at_100 76.242
type value
recall_at_1000 87.717
type value
recall_at_20 64.646
type value
recall_at_3 46.253
type value
recall_at_5 51.946999999999996
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackGamingRetrieval default test 4885aa143210c98657558c04aaf3dc47cfb54340
type value
map_at_1 41.644999999999996
type value
map_at_10 53.825
type value
map_at_100 54.82
type value
map_at_1000 54.87499999999999
type value
map_at_20 54.43
type value
map_at_3 50.705
type value
map_at_5 52.501
type value
mrr_at_1 47.524
type value
mrr_at_10 57.260999999999996
type value
mrr_at_100 57.902
type value
mrr_at_1000 57.931999999999995
type value
mrr_at_20 57.689
type value
mrr_at_3 55.089
type value
mrr_at_5 56.38999999999999
type value
ndcg_at_1 47.524
type value
ndcg_at_10 59.41499999999999
type value
ndcg_at_100 63.258
type value
ndcg_at_1000 64.376
type value
ndcg_at_20 61.149
type value
ndcg_at_3 54.381
type value
ndcg_at_5 56.89999999999999
type value
precision_at_1 47.524
type value
precision_at_10 9.386
type value
precision_at_100 1.221
type value
precision_at_1000 0.136
type value
precision_at_20 5.223
type value
precision_at_3 24.096
type value
precision_at_5 16.364
type value
recall_at_1 41.644999999999996
type value
recall_at_10 72.386
type value
recall_at_100 88.794
type value
recall_at_1000 96.75399999999999
type value
recall_at_20 78.74
type value
recall_at_3 59.028000000000006
type value
recall_at_5 65.197
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackGisRetrieval default test 5003b3064772da1887988e05400cf3806fe491f2
type value
map_at_1 28.648
type value
map_at_10 36.388999999999996
type value
map_at_100 37.372
type value
map_at_1000 37.457
type value
map_at_20 36.912
type value
map_at_3 34.076
type value
map_at_5 35.415
type value
mrr_at_1 30.508000000000003
type value
mrr_at_10 38.132
type value
mrr_at_100 39.04
type value
mrr_at_1000 39.106
type value
mrr_at_20 38.643
type value
mrr_at_3 35.876000000000005
type value
mrr_at_5 37.208999999999996
type value
ndcg_at_1 30.508000000000003
type value
ndcg_at_10 40.762
type value
ndcg_at_100 45.732
type value
ndcg_at_1000 47.799
type value
ndcg_at_20 42.591
type value
ndcg_at_3 36.266999999999996
type value
ndcg_at_5 38.58
type value
precision_at_1 30.508000000000003
type value
precision_at_10 6.010999999999999
type value
precision_at_100 0.897
type value
precision_at_1000 0.11100000000000002
type value
precision_at_20 3.412
type value
precision_at_3 14.991
type value
precision_at_5 10.328
type value
recall_at_1 28.648
type value
recall_at_10 52.342999999999996
type value
recall_at_100 75.268
type value
recall_at_1000 90.641
type value
recall_at_20 59.303
type value
recall_at_3 40.447
type value
recall_at_5 46.117000000000004
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackMathematicaRetrieval default test 90fceea13679c63fe563ded68f3b6f06e50061de
type value
map_at_1 18.476
type value
map_at_10 27.148
type value
map_at_100 28.317999999999998
type value
map_at_1000 28.427999999999997
type value
map_at_20 27.764
type value
map_at_3 24.801000000000002
type value
map_at_5 26.133
type value
mrr_at_1 22.886
type value
mrr_at_10 31.741000000000003
type value
mrr_at_100 32.708
type value
mrr_at_1000 32.769
type value
mrr_at_20 32.296
type value
mrr_at_3 29.498
type value
mrr_at_5 30.773
type value
ndcg_at_1 22.886
type value
ndcg_at_10 32.265
type value
ndcg_at_100 37.829
type value
ndcg_at_1000 40.558
type value
ndcg_at_20 34.372
type value
ndcg_at_3 28.105000000000004
type value
ndcg_at_5 30.04
type value
precision_at_1 22.886
type value
precision_at_10 5.808
type value
precision_at_100 0.985
type value
precision_at_1000 0.13699999999999998
type value
precision_at_20 3.495
type value
precision_at_3 13.639999999999999
type value
precision_at_5 9.577
type value
recall_at_1 18.476
type value
recall_at_10 43.442
type value
recall_at_100 67.376
type value
recall_at_1000 86.874
type value
recall_at_20 51.038
type value
recall_at_3 31.785999999999998
type value
recall_at_5 36.858999999999995
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackPhysicsRetrieval default test 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
type value
map_at_1 29.098000000000003
type value
map_at_10 38.97
type value
map_at_100 40.293
type value
map_at_1000 40.397
type value
map_at_20 39.778999999999996
type value
map_at_3 35.723
type value
map_at_5 37.519999999999996
type value
mrr_at_1 35.515
type value
mrr_at_10 44.55
type value
mrr_at_100 45.37
type value
mrr_at_1000 45.412
type value
mrr_at_20 45.054
type value
mrr_at_3 41.835
type value
mrr_at_5 43.356
type value
ndcg_at_1 35.515
type value
ndcg_at_10 44.91
type value
ndcg_at_100 50.27700000000001
type value
ndcg_at_1000 52.215
type value
ndcg_at_20 47.235
type value
ndcg_at_3 39.505
type value
ndcg_at_5 42.016
type value
precision_at_1 35.515
type value
precision_at_10 8.152
type value
precision_at_100 1.262
type value
precision_at_1000 0.16
type value
precision_at_20 4.851
type value
precision_at_3 18.447
type value
precision_at_5 13.321
type value
recall_at_1 29.098000000000003
type value
recall_at_10 57.115
type value
recall_at_100 79.467
type value
recall_at_1000 92.162
type value
recall_at_20 65.161
type value
recall_at_3 42.254000000000005
type value
recall_at_5 48.415
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackProgrammersRetrieval default test 6184bc1440d2dbc7612be22b50686b8826d22b32
type value
map_at_1 27.372000000000003
type value
map_at_10 37.781
type value
map_at_100 39.128
type value
map_at_1000 39.238
type value
map_at_20 38.592
type value
map_at_3 34.782999999999994
type value
map_at_5 36.466
type value
mrr_at_1 33.904
type value
mrr_at_10 43.15
type value
mrr_at_100 44.049
type value
mrr_at_1000 44.107
type value
mrr_at_20 43.721
type value
mrr_at_3 40.677
type value
mrr_at_5 42.19
type value
ndcg_at_1 33.904
type value
ndcg_at_10 43.527
type value
ndcg_at_100 49.004999999999995
type value
ndcg_at_1000 51.276999999999994
type value
ndcg_at_20 45.988
type value
ndcg_at_3 38.824999999999996
type value
ndcg_at_5 41.04
type value
precision_at_1 33.904
type value
precision_at_10 7.854
type value
precision_at_100 1.2309999999999999
type value
precision_at_1000 0.16
type value
precision_at_20 4.692
type value
precision_at_3 18.531
type value
precision_at_5 13.150999999999998
type value
recall_at_1 27.372000000000003
type value
recall_at_10 55.245999999999995
type value
recall_at_100 78.278
type value
recall_at_1000 93.718
type value
recall_at_20 64.095
type value
recall_at_3 41.665
type value
recall_at_5 47.632000000000005
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackRetrieval default test f46a197baaae43b4f621051089b82a364682dfeb
type value
map_at_1 27.734166666666667
type value
map_at_10 36.858
type value
map_at_100 38.043833333333325
type value
map_at_1000 38.15541666666667
type value
map_at_20 37.521249999999995
type value
map_at_3 34.07658333333333
type value
map_at_5 35.62683333333333
type value
mrr_at_1 32.676249999999996
type value
mrr_at_10 40.999
type value
mrr_at_100 41.835
type value
mrr_at_1000 41.8895
type value
mrr_at_20 41.4865
type value
mrr_at_3 38.645
type value
mrr_at_5 39.99725000000001
type value
ndcg_at_1 32.676249999999996
type value
ndcg_at_10 42.08016666666666
type value
ndcg_at_100 47.082750000000004
type value
ndcg_at_1000 49.276583333333335
type value
ndcg_at_20 44.04808333333334
type value
ndcg_at_3 37.43375
type value
ndcg_at_5 39.623000000000005
type value
precision_at_1 32.676249999999996
type value
precision_at_10 7.271
type value
precision_at_100 1.1458333333333333
type value
precision_at_1000 0.152
type value
precision_at_20 4.282916666666667
type value
precision_at_3 17.061416666666666
type value
precision_at_5 12.05466666666667
type value
recall_at_1 27.734166666666667
type value
recall_at_10 53.33574999999999
type value
recall_at_100 75.16275
type value
recall_at_1000 90.34891666666665
type value
recall_at_20 60.4935
type value
recall_at_3 40.377916666666664
type value
recall_at_5 46.0195
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackStatsRetrieval default test 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
type value
map_at_1 25.653
type value
map_at_10 32.151
type value
map_at_100 33.152
type value
map_at_1000 33.243
type value
map_at_20 32.717
type value
map_at_3 30.287
type value
map_at_5 31.25
type value
mrr_at_1 28.988000000000003
type value
mrr_at_10 35.131
type value
mrr_at_100 36.002
type value
mrr_at_1000 36.069
type value
mrr_at_20 35.61
type value
mrr_at_3 33.308
type value
mrr_at_5 34.259
type value
ndcg_at_1 28.988000000000003
type value
ndcg_at_10 35.988
type value
ndcg_at_100 40.764
type value
ndcg_at_1000 43.112
type value
ndcg_at_20 37.852999999999994
type value
ndcg_at_3 32.562000000000005
type value
ndcg_at_5 33.983000000000004
type value
precision_at_1 28.988000000000003
type value
precision_at_10 5.475
type value
precision_at_100 0.8500000000000001
type value
precision_at_1000 0.11199999999999999
type value
precision_at_20 3.229
type value
precision_at_3 13.905999999999999
type value
precision_at_5 9.386999999999999
type value
recall_at_1 25.653
type value
recall_at_10 44.962
type value
recall_at_100 66.405
type value
recall_at_1000 83.88799999999999
type value
recall_at_20 51.79899999999999
type value
recall_at_3 35.144999999999996
type value
recall_at_5 38.814
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackTexRetrieval default test 46989137a86843e03a6195de44b09deda022eec7
type value
map_at_1 17.825
type value
map_at_10 25.592
type value
map_at_100 26.613999999999997
type value
map_at_1000 26.734
type value
map_at_20 26.115
type value
map_at_3 23.119
type value
map_at_5 24.54
type value
mrr_at_1 21.335
type value
mrr_at_10 29.165000000000003
type value
mrr_at_100 30.049
type value
mrr_at_1000 30.121
type value
mrr_at_20 29.639
type value
mrr_at_3 26.863999999999997
type value
mrr_at_5 28.185
type value
ndcg_at_1 21.335
type value
ndcg_at_10 30.357
type value
ndcg_at_100 35.410000000000004
type value
ndcg_at_1000 38.24
type value
ndcg_at_20 32.08
type value
ndcg_at_3 25.95
type value
ndcg_at_5 28.081
type value
precision_at_1 21.335
type value
precision_at_10 5.506
type value
precision_at_100 0.928
type value
precision_at_1000 0.135
type value
precision_at_20 3.2550000000000003
type value
precision_at_3 12.239
type value
precision_at_5 8.885
type value
recall_at_1 17.825
type value
recall_at_10 41.105999999999995
type value
recall_at_100 64.17
type value
recall_at_1000 84.19200000000001
type value
recall_at_20 47.497
type value
recall_at_3 28.862
type value
recall_at_5 34.348
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackUnixRetrieval default test 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
type value
map_at_1 29.435
type value
map_at_10 38.261
type value
map_at_100 39.242
type value
map_at_1000 39.347
type value
map_at_20 38.742
type value
map_at_3 35.457
type value
map_at_5 37.043
type value
mrr_at_1 34.235
type value
mrr_at_10 42.24
type value
mrr_at_100 42.988
type value
mrr_at_1000 43.043
type value
mrr_at_20 42.613
type value
mrr_at_3 39.832
type value
mrr_at_5 41.227000000000004
type value
ndcg_at_1 34.235
type value
ndcg_at_10 43.384
type value
ndcg_at_100 48.14
type value
ndcg_at_1000 50.414
type value
ndcg_at_20 44.913
type value
ndcg_at_3 38.454
type value
ndcg_at_5 40.776
type value
precision_at_1 34.235
type value
precision_at_10 7.164
type value
precision_at_100 1.065
type value
precision_at_1000 0.13699999999999998
type value
precision_at_20 4.021
type value
precision_at_3 17.226
type value
precision_at_5 12.071
type value
recall_at_1 29.435
type value
recall_at_10 54.93900000000001
type value
recall_at_100 76.176
type value
recall_at_1000 91.989
type value
recall_at_20 60.451
type value
recall_at_3 41.332
type value
recall_at_5 47.316
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackWebmastersRetrieval default test 160c094312a0e1facb97e55eeddb698c0abe3571
type value
map_at_1 25.605
type value
map_at_10 34.162
type value
map_at_100 35.827999999999996
type value
map_at_1000 36.04
type value
map_at_20 35.016000000000005
type value
map_at_3 30.984
type value
map_at_5 32.717
type value
mrr_at_1 30.435000000000002
type value
mrr_at_10 38.681
type value
mrr_at_100 39.656000000000006
type value
mrr_at_1000 39.71
type value
mrr_at_20 39.208999999999996
type value
mrr_at_3 35.903
type value
mrr_at_5 37.454
type value
ndcg_at_1 30.435000000000002
type value
ndcg_at_10 39.916000000000004
type value
ndcg_at_100 45.958
type value
ndcg_at_1000 48.449999999999996
type value
ndcg_at_20 42.085
type value
ndcg_at_3 34.696
type value
ndcg_at_5 37.147000000000006
type value
precision_at_1 30.435000000000002
type value
precision_at_10 7.767
type value
precision_at_100 1.547
type value
precision_at_1000 0.23800000000000002
type value
precision_at_20 4.941
type value
precision_at_3 16.073999999999998
type value
precision_at_5 11.937000000000001
type value
recall_at_1 25.605
type value
recall_at_10 50.654999999999994
type value
recall_at_100 77.609
type value
recall_at_1000 93.518
type value
recall_at_20 58.845000000000006
type value
recall_at_3 36.272
type value
recall_at_5 42.596000000000004
task dataset metrics
type
Retrieval
type name config split revision
BeIR/cqadupstack MTEB CQADupstackWordpressRetrieval default test 4ffe81d471b1924886b33c7567bfb200e9eec5c4
type value
map_at_1 23.666
type value
map_at_10 30.980999999999998
type value
map_at_100 32.0
type value
map_at_1000 32.098
type value
map_at_20 31.621
type value
map_at_3 28.449999999999996
type value
map_at_5 29.731999999999996
type value
mrr_at_1 25.692999999999998
type value
mrr_at_10 32.788000000000004
type value
mrr_at_100 33.783
type value
mrr_at_1000 33.849000000000004
type value
mrr_at_20 33.408
type value
mrr_at_3 30.561
type value
mrr_at_5 31.716
type value
ndcg_at_1 25.692999999999998
type value
ndcg_at_10 35.428
type value
ndcg_at_100 40.375
type value
ndcg_at_1000 42.802
type value
ndcg_at_20 37.621
type value
ndcg_at_3 30.476999999999997
type value
ndcg_at_5 32.621
type value
precision_at_1 25.692999999999998
type value
precision_at_10 5.508
type value
precision_at_100 0.848
type value
precision_at_1000 0.116
type value
precision_at_20 3.272
type value
precision_at_3 12.631
type value
precision_at_5 8.872
type value
recall_at_1 23.666
type value
recall_at_10 47.532000000000004
type value
recall_at_100 69.73700000000001
type value
recall_at_1000 87.83800000000001
type value
recall_at_20 55.61000000000001
type value
recall_at_3 34.06
type value
recall_at_5 39.254
task dataset metrics
type
Retrieval
type name config split revision
None MTEB ClimateFEVER default test 47f2ac6acb640fc46020b02a5b59fdda04d39380
type value
map_at_1 16.337
type value
map_at_10 26.488
type value
map_at_100 28.415000000000003
type value
map_at_1000 28.584
type value
map_at_20 27.557
type value
map_at_3 22.665
type value
map_at_5 24.542
type value
mrr_at_1 36.417
type value
mrr_at_10 48.001
type value
mrr_at_100 48.784
type value
mrr_at_1000 48.809000000000005
type value
mrr_at_20 48.507
type value
mrr_at_3 45.103
type value
mrr_at_5 46.843
type value
ndcg_at_1 36.417
type value
ndcg_at_10 35.67
type value
ndcg_at_100 42.716
type value
ndcg_at_1000 45.639
type value
ndcg_at_20 38.471
type value
ndcg_at_3 30.444
type value
ndcg_at_5 32.004
type value
precision_at_1 36.417
type value
precision_at_10 10.73
type value
precision_at_100 1.833
type value
precision_at_1000 0.23800000000000002
type value
precision_at_20 6.596
type value
precision_at_3 22.302
type value
precision_at_5 16.521
type value
recall_at_1 16.337
type value
recall_at_10 40.671
type value
recall_at_100 64.55300000000001
type value
recall_at_1000 80.934
type value
recall_at_20 48.381
type value
recall_at_3 27.279999999999998
type value
recall_at_5 32.621
task dataset metrics
type
Retrieval
type name config split revision
None MTEB DBPedia default test c0f706b76e590d620bd6618b3ca8efdd34e2d659
type value
map_at_1 9.056000000000001
type value
map_at_10 19.419
type value
map_at_100 27.069
type value
map_at_1000 28.666000000000004
type value
map_at_20 22.434
type value
map_at_3 13.895
type value
map_at_5 16.121
type value
mrr_at_1 69.0
type value
mrr_at_10 75.804
type value
mrr_at_100 76.117
type value
mrr_at_1000 76.125
type value
mrr_at_20 76.009
type value
mrr_at_3 74.375
type value
mrr_at_5 75.4
type value
ndcg_at_1 57.49999999999999
type value
ndcg_at_10 41.495
type value
ndcg_at_100 45.208
type value
ndcg_at_1000 52.221
type value
ndcg_at_20 40.617999999999995
type value
ndcg_at_3 46.592
type value
ndcg_at_5 43.559
type value
precision_at_1 69.0
type value
precision_at_10 32.574999999999996
type value
precision_at_100 10.205
type value
precision_at_1000 2.036
type value
precision_at_20 24.687
type value
precision_at_3 49.75
type value
precision_at_5 42.0
type value
recall_at_1 9.056000000000001
type value
recall_at_10 24.866
type value
recall_at_100 50.097
type value
recall_at_1000 72.038
type value
recall_at_20 31.858999999999998
type value
recall_at_3 15.096000000000002
type value
recall_at_5 18.548000000000002
task dataset metrics
type
Classification
type name config split revision
None MTEB EmotionClassification default test 4f58c6b202a23cf9a4da393831edf4f9183cad37
type value
accuracy 48.259999999999984
type value
f1 43.1498589523159
task dataset metrics
type
Retrieval
type name config split revision
None MTEB FEVER default test bea83ef9e8fb933d90a2f1d5515737465d613e12
type value
map_at_1 74.798
type value
map_at_10 83.454
type value
map_at_100 83.623
type value
map_at_1000 83.635
type value
map_at_20 83.55
type value
map_at_3 82.392
type value
map_at_5 83.167
type value
mrr_at_1 80.708
type value
mrr_at_10 88.377
type value
mrr_at_100 88.411
type value
mrr_at_1000 88.411
type value
mrr_at_20 88.402
type value
mrr_at_3 87.646
type value
mrr_at_5 88.232
type value
ndcg_at_1 80.708
type value
ndcg_at_10 87.35199999999999
type value
ndcg_at_100 87.91600000000001
type value
ndcg_at_1000 88.12299999999999
type value
ndcg_at_20 87.593
type value
ndcg_at_3 85.738
type value
ndcg_at_5 86.845
type value
precision_at_1 80.708
type value
precision_at_10 10.432
type value
precision_at_100 1.091
type value
precision_at_1000 0.11299999999999999
type value
precision_at_20 5.296
type value
precision_at_3 32.778
type value
precision_at_5 20.399
type value
recall_at_1 74.798
type value
recall_at_10 94.459
type value
recall_at_100 96.614
type value
recall_at_1000 97.868
type value
recall_at_20 95.254
type value
recall_at_3 90.144
type value
recall_at_5 92.965
task dataset metrics
type
Retrieval
type name config split revision
None MTEB FiQA2018 default test 27a168819829fe9bcd655c2df245fb19452e8e06
type value
map_at_1 20.008
type value
map_at_10 32.731
type value
map_at_100 34.467999999999996
type value
map_at_1000 34.643
type value
map_at_20 33.717000000000006
type value
map_at_3 28.427999999999997
type value
map_at_5 30.788
type value
mrr_at_1 40.586
type value
mrr_at_10 49.056
type value
mrr_at_100 49.887
type value
mrr_at_1000 49.929
type value
mrr_at_20 49.552
type value
mrr_at_3 46.785
type value
mrr_at_5 48.004000000000005
type value
ndcg_at_1 40.586
type value
ndcg_at_10 40.589999999999996
type value
ndcg_at_100 47.03
type value
ndcg_at_1000 49.994
type value
ndcg_at_20 43.229
type value
ndcg_at_3 37.061
type value
ndcg_at_5 37.992
type value
precision_at_1 40.586
type value
precision_at_10 11.219
type value
precision_at_100 1.781
type value
precision_at_1000 0.232
type value
precision_at_20 6.705
type value
precision_at_3 24.743000000000002
type value
precision_at_5 18.086
type value
recall_at_1 20.008
type value
recall_at_10 47.412
type value
recall_at_100 71.274
type value
recall_at_1000 88.898
type value
recall_at_20 55.706999999999994
type value
recall_at_3 33.346
type value
recall_at_5 39.112
task dataset metrics
type
Retrieval
type name config split revision
None MTEB HotpotQA default test ab518f4d6fcca38d87c25209f94beba119d02014
type value
map_at_1 41.789
type value
map_at_10 57.898
type value
map_at_100 58.632
type value
map_at_1000 58.693
type value
map_at_20 58.314
type value
map_at_3 55.236
type value
map_at_5 56.852999999999994
type value
mrr_at_1 83.57900000000001
type value
mrr_at_10 87.631
type value
mrr_at_100 87.764
type value
mrr_at_1000 87.77000000000001
type value
mrr_at_20 87.70700000000001
type value
mrr_at_3 87.02499999999999
type value
mrr_at_5 87.34100000000001
type value
ndcg_at_1 83.57900000000001
type value
ndcg_at_10 67.11399999999999
type value
ndcg_at_100 69.686
type value
ndcg_at_1000 70.926
type value
ndcg_at_20 68.119
type value
ndcg_at_3 63.402
type value
ndcg_at_5 65.354
type value
precision_at_1 83.57900000000001
type value
precision_at_10 13.333
type value
precision_at_100 1.537
type value
precision_at_1000 0.16999999999999998
type value
precision_at_20 6.988999999999999
type value
precision_at_3 38.929
type value
precision_at_5 24.897
type value
recall_at_1 41.789
type value
recall_at_10 66.664
type value
recall_at_100 76.833
type value
recall_at_1000 85.14500000000001
type value
recall_at_20 69.892
type value
recall_at_3 58.392999999999994
type value
recall_at_5 62.242
task dataset metrics
type
Classification
type name config split revision
None MTEB ImdbClassification default test 3d86128a09e091d6018b6d26cad27f2739fc2db7
type value
accuracy 86.6108
type value
ap 81.63890253106925
type value
f1 86.54585789538082
task dataset metrics
type
Retrieval
type name config split revision
None MTEB MSMARCO default dev c5a29a104738b98a9e76336939199e264163d4a0
type value
map_at_1 22.407
type value
map_at_10 34.603
type value
map_at_100 35.808
type value
map_at_1000 35.855
type value
map_at_20 35.368
type value
map_at_3 30.764000000000003
type value
map_at_5 32.964
type value
mrr_at_1 23.009
type value
mrr_at_10 35.136
type value
mrr_at_100 36.284
type value
mrr_at_1000 36.325
type value
mrr_at_20 35.869
type value
mrr_at_3 31.351000000000003
type value
mrr_at_5 33.54
type value
ndcg_at_1 23.009
type value
ndcg_at_10 41.471999999999994
type value
ndcg_at_100 47.211999999999996
type value
ndcg_at_1000 48.361
type value
ndcg_at_20 44.169000000000004
type value
ndcg_at_3 33.646
type value
ndcg_at_5 37.580000000000005
type value
precision_at_1 23.009
type value
precision_at_10 6.54
type value
precision_at_100 0.941
type value
precision_at_1000 0.104
type value
precision_at_20 3.832
type value
precision_at_3 14.283999999999999
type value
precision_at_5 10.564
type value
recall_at_1 22.407
type value
recall_at_10 62.678999999999995
type value
recall_at_100 89.09700000000001
type value
recall_at_1000 97.822
type value
recall_at_20 73.116
type value
recall_at_3 41.4
type value
recall_at_5 50.855
task dataset metrics
type
Classification
type name config split revision
None MTEB MTOPDomainClassification (en) en test d80d48c1eb48d3562165c59d59d0034df9fff0bf
type value
accuracy 92.94573643410853
type value
f1 92.73148878666994
task dataset metrics
type
Classification
type name config split revision
None MTEB MTOPIntentClassification (en) en test ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
type value
accuracy 77.86137710898313
type value
f1 60.360562463738724
task dataset metrics
type
Classification
type name config split revision
None MTEB MassiveIntentClassification (en) en test 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
type value
accuracy 73.83322125084062
type value
f1 71.61864304680206
task dataset metrics
type
Classification
type name config split revision
None MTEB MassiveScenarioClassification (en) en test 7d571f92784cd94a019292a1f45445077d0ef634
type value
accuracy 77.50504371217215
type value
f1 77.52039268347185
task dataset metrics
type
Clustering
type name config split revision
None MTEB MedrxivClusteringP2P default test e7a26af6f3ae46b30dde8737f02c07b1505bcc73
type value
v_measure 34.346952648910225
type value
v_measures
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task dataset metrics
type
Clustering
type name config split revision
None MTEB MedrxivClusteringS2S default test 35191c8c0dca72d8ff3efcd72aa802307d469663
type value
v_measure 32.19992734583148
type value
v_measures
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0.32522623268021805
0.3410158265159116
0.33581770403870503
0.31539111636001027
0.31100967211136193
0.31302897733611235
0.3126922134381441
0.30243629014133017
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task dataset metrics
type
Reranking
type name config split revision
None MTEB MindSmallReranking default test 3bdac13927fdc888b903db93b2ffdbd90b295a69
type value
map 30.62309561205373
type value
mrr 31.707879717902554
task dataset metrics
type
Retrieval
type name config split revision
None MTEB NFCorpus default test ec0fa4fe99da2ff19ca1214b7966684033a58814
type value
map_at_1 5.668
type value
map_at_10 12.225999999999999
type value
map_at_100 15.122
type value
map_at_1000 16.422
type value
map_at_20 13.361999999999998
type value
map_at_3 9.083
type value
map_at_5 10.5
type value
mrr_at_1 46.44
type value
mrr_at_10 53.553
type value
mrr_at_100 54.15
type value
mrr_at_1000 54.193000000000005
type value
mrr_at_20 53.837
type value
mrr_at_3 51.702999999999996
type value
mrr_at_5 52.647
type value
ndcg_at_1 44.272
type value
ndcg_at_10 33.395
type value
ndcg_at_100 29.976999999999997
type value
ndcg_at_1000 38.388
type value
ndcg_at_20 30.606
type value
ndcg_at_3 39.212
type value
ndcg_at_5 36.611
type value
precision_at_1 46.129999999999995
type value
precision_at_10 24.334
type value
precision_at_100 7.553999999999999
type value
precision_at_1000 1.994
type value
precision_at_20 17.678
type value
precision_at_3 36.326
type value
precision_at_5 31.330999999999996
type value
recall_at_1 5.668
type value
recall_at_10 15.837000000000002
type value
recall_at_100 29.845
type value
recall_at_1000 60.563
type value
recall_at_20 18.587999999999997
type value
recall_at_3 10.096
type value
recall_at_5 12.261
task dataset metrics
type
Retrieval
type name config split revision
None MTEB NQ default test b774495ed302d8c44a3a7ea25c90dbce03968f31
type value
map_at_1 39.335
type value
map_at_10 54.932
type value
map_at_100 55.742000000000004
type value
map_at_1000 55.766000000000005
type value
map_at_20 55.504
type value
map_at_3 50.904
type value
map_at_5 53.388999999999996
type value
mrr_at_1 44.003
type value
mrr_at_10 57.419
type value
mrr_at_100 57.963
type value
mrr_at_1000 57.981
type value
mrr_at_20 57.80499999999999
type value
mrr_at_3 54.30199999999999
type value
mrr_at_5 56.257000000000005
type value
ndcg_at_1 43.974999999999994
type value
ndcg_at_10 62.153999999999996
type value
ndcg_at_100 65.326
type value
ndcg_at_1000 65.862
type value
ndcg_at_20 63.922999999999995
type value
ndcg_at_3 54.834
type value
ndcg_at_5 58.857000000000006
type value
precision_at_1 43.974999999999994
type value
precision_at_10 9.722
type value
precision_at_100 1.153
type value
precision_at_1000 0.12
type value
precision_at_20 5.3
type value
precision_at_3 24.392
type value
precision_at_5 16.993
type value
recall_at_1 39.335
type value
recall_at_10 81.501
type value
recall_at_100 94.851
type value
recall_at_1000 98.817
type value
recall_at_20 87.968
type value
recall_at_3 62.795
type value
recall_at_5 71.985
task dataset metrics
type
Retrieval
type name config split revision
None MTEB QuoraRetrieval default test e4e08e0b7dbe3c8700f0daef558ff32256715259
type value
map_at_1 71.222
type value
map_at_10 85.193
type value
map_at_100 85.802
type value
map_at_1000 85.81800000000001
type value
map_at_20 85.587
type value
map_at_3 82.253
type value
map_at_5 84.142
type value
mrr_at_1 82.04
type value
mrr_at_10 88.101
type value
mrr_at_100 88.196
type value
mrr_at_1000 88.196
type value
mrr_at_20 88.175
type value
mrr_at_3 87.145
type value
mrr_at_5 87.825
type value
ndcg_at_1 82.04
type value
ndcg_at_10 88.849
type value
ndcg_at_100 89.992
type value
ndcg_at_1000 90.089
type value
ndcg_at_20 89.468
type value
ndcg_at_3 86.06899999999999
type value
ndcg_at_5 87.669
type value
precision_at_1 82.04
type value
precision_at_10 13.447000000000001
type value
precision_at_100 1.528
type value
precision_at_1000 0.157
type value
precision_at_20 7.116
type value
precision_at_3 37.617
type value
precision_at_5 24.776
type value
recall_at_1 71.222
type value
recall_at_10 95.73899999999999
type value
recall_at_100 99.572
type value
recall_at_1000 99.988
type value
recall_at_20 97.725
type value
recall_at_3 87.742
type value
recall_at_5 92.23400000000001
task dataset metrics
type
Clustering
type name config split revision
None MTEB RedditClustering default test 24640382cdbf8abc73003fb0fa6d111a705499eb
type value
v_measure 56.502005725283524
type value
v_measures
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task dataset metrics
type
Clustering
type name config split revision
None MTEB RedditClusteringP2P default test 385e3cb46b4cfa89021f56c4380204149d0efe33
type value
v_measure 63.14989421688691
type value
v_measures
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task dataset metrics
type
Retrieval
type name config split revision
None MTEB SCIDOCS default test f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
type value
map_at_1 4.4830000000000005
type value
map_at_10 11.04
type value
map_at_100 12.764000000000001
type value
map_at_1000 13.04
type value
map_at_20 11.953
type value
map_at_3 8.125
type value
map_at_5 9.565999999999999
type value
mrr_at_1 22.1
type value
mrr_at_10 32.494
type value
mrr_at_100 33.525
type value
mrr_at_1000 33.596
type value
mrr_at_20 33.089
type value
mrr_at_3 29.416999999999998
type value
mrr_at_5 31.267
type value
ndcg_at_1 22.1
type value
ndcg_at_10 18.587
type value
ndcg_at_100 25.482
type value
ndcg_at_1000 30.581999999999997
type value
ndcg_at_20 21.077
type value
ndcg_at_3 18.165
type value
ndcg_at_5 15.676000000000002
type value
precision_at_1 22.1
type value
precision_at_10 9.48
type value
precision_at_100 1.942
type value
precision_at_1000 0.316
type value
precision_at_20 6.175
type value
precision_at_3 17.033
type value
precision_at_5 13.719999999999999
type value
recall_at_1 4.4830000000000005
type value
recall_at_10 19.208
type value
recall_at_100 39.417
type value
recall_at_1000 64.235
type value
recall_at_20 25.057000000000002
type value
recall_at_3 10.348
type value
recall_at_5 13.893
task dataset metrics
type
STS
type name config split revision
None MTEB SICK-R default test 20a6d6f312dd54037fe07a32d58e5e168867909d
type value
cos_sim_pearson 83.50181312649208
type value
cos_sim_spearman 79.92900705478993
type value
euclidean_pearson 81.13482128094503
type value
euclidean_spearman 79.92732266864367
type value
manhattan_pearson 81.06702121654993
type value
manhattan_spearman 79.86983106619135
task dataset metrics
type
STS
type name config split revision
None MTEB STS12 default test a0d554a64d88156834ff5ae9920b964011b16384
type value
cos_sim_pearson 83.85431681906961
type value
cos_sim_spearman 77.61671419416626
type value
euclidean_pearson 81.30538320520961
type value
euclidean_spearman 77.62096481461272
type value
manhattan_pearson 81.2306021173407
type value
manhattan_spearman 77.58386300715222
task dataset metrics
type
STS
type name config split revision
None MTEB STS13 default test 7e90230a92c190f1bf69ae9002b8cea547a64cca
type value
cos_sim_pearson 84.98057702322754
type value
cos_sim_spearman 86.13305071688859
type value
euclidean_pearson 85.70903555966376
type value
euclidean_spearman 86.13150222328171
type value
manhattan_pearson 85.69380834788831
type value
manhattan_spearman 86.10784739081191
task dataset metrics
type
STS
type name config split revision
None MTEB STS14 default test 6031580fec1f6af667f0bd2da0a551cf4f0b2375
type value
cos_sim_pearson 83.43368314724589
type value
cos_sim_spearman 81.26767916144169
type value
euclidean_pearson 83.23234690932492
type value
euclidean_spearman 81.2671726214706
type value
manhattan_pearson 83.2381239261109
type value
manhattan_spearman 81.27674961470714
task dataset metrics
type
STS
type name config split revision
None MTEB STS15 default test ae752c7c21bf194d8b67fd573edf7ae58183cbe3
type value
cos_sim_pearson 86.8637546411748
type value
cos_sim_spearman 88.25330888676139
type value
euclidean_pearson 87.81194589390417
type value
euclidean_spearman 88.25258669625579
type value
manhattan_pearson 87.8131866998459
type value
manhattan_spearman 88.26523268929576
task dataset metrics
type
STS
type name config split revision
None MTEB STS16 default test 4d8694f8f0e0100860b497b999b3dbed754a0513
type value
cos_sim_pearson 83.83129743147286
type value
cos_sim_spearman 85.73732687732624
type value
euclidean_pearson 85.18051277328075
type value
euclidean_spearman 85.73565846174445
type value
manhattan_pearson 85.179029651079
type value
manhattan_spearman 85.75709685404729
task dataset metrics
type
STS
type name config split revision
None MTEB STS17 (en-en) en-en test af5e6fb845001ecf41f4c1e033ce921939a2a68d
type value
cos_sim_pearson 87.04715794253148
type value
cos_sim_spearman 87.61577496386343
type value
euclidean_pearson 88.34713614361046
type value
euclidean_spearman 87.56541901567275
type value
manhattan_pearson 88.26010824585985
type value
manhattan_spearman 87.35211736948182
task dataset metrics
type
STS
type name config split revision
None MTEB STS22 (en) en test eea2b4fe26a775864c896887d910b76a8098ad3f
type value
cos_sim_pearson 62.36160793264433
type value
cos_sim_spearman 66.07767480051893
type value
euclidean_pearson 66.4716471304865
type value
euclidean_spearman 66.03999286501872
type value
manhattan_pearson 66.46197824372902
type value
manhattan_spearman 65.82936468127227
task dataset metrics
type
STS
type name config split revision
None MTEB STSBenchmark default test b0fddb56ed78048fa8b90373c8a3cfc37b684831
type value
cos_sim_pearson 85.27768996785856
type value
cos_sim_spearman 86.96704639052885
type value
euclidean_pearson 86.48753189555983
type value
euclidean_spearman 86.96981285751171
type value
manhattan_pearson 86.49262465015401
type value
manhattan_spearman 86.95378609580054
task dataset metrics
type
Reranking
type name config split revision
None MTEB SciDocsRR default test d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
type value
map 81.52012853393428
type value
mrr 94.70817671798063
task dataset metrics
type
Retrieval
type name config split revision
None MTEB SciFact default test 0228b52cf27578f30900b9e5271d331663a030d7
type value
map_at_1 55.344
type value
map_at_10 64.82900000000001
type value
map_at_100 65.42
type value
map_at_1000 65.443
type value
map_at_20 65.2
type value
map_at_3 61.8
type value
map_at_5 63.510999999999996
type value
mrr_at_1 58.333
type value
mrr_at_10 66.24600000000001
type value
mrr_at_100 66.742
type value
mrr_at_1000 66.762
type value
mrr_at_20 66.549
type value
mrr_at_3 64.056
type value
mrr_at_5 65.372
type value
ndcg_at_1 58.333
type value
ndcg_at_10 69.626
type value
ndcg_at_100 72.236
type value
ndcg_at_1000 72.872
type value
ndcg_at_20 70.864
type value
ndcg_at_3 64.50399999999999
type value
ndcg_at_5 67.07600000000001
type value
precision_at_1 58.333
type value
precision_at_10 9.4
type value
precision_at_100 1.073
type value
precision_at_1000 0.11299999999999999
type value
precision_at_20 4.983
type value
precision_at_3 25.222
type value
precision_at_5 16.8
type value
recall_at_1 55.344
type value
recall_at_10 82.789
type value
recall_at_100 94.6
type value
recall_at_1000 99.667
type value
recall_at_20 87.533
type value
recall_at_3 69.18299999999999
type value
recall_at_5 75.622
task dataset metrics
type
PairClassification
type name config split revision
None MTEB SprintDuplicateQuestions default test d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
type value
cos_sim_accuracy 99.69405940594059
type value
cos_sim_ap 92.03642221694545
type value
cos_sim_f1 84.06395048994327
type value
cos_sim_precision 86.79446219382322
type value
cos_sim_recall 81.5
type value
dot_accuracy 99.6930693069307
type value
dot_ap 91.9971441434875
type value
dot_f1 83.8006230529595
type value
dot_precision 87.14902807775377
type value
dot_recall 80.7
type value
euclidean_accuracy 99.69504950495049
type value
euclidean_ap 92.03626548389335
type value
euclidean_f1 84.10732714138285
type value
euclidean_precision 86.88699360341151
type value
euclidean_recall 81.5
type value
manhattan_accuracy 99.69504950495049
type value
manhattan_ap 92.02049659660081
type value
manhattan_f1 84.34959349593495
type value
manhattan_precision 85.74380165289256
type value
manhattan_recall 83.0
type value
max_accuracy 99.69504950495049
type value
max_ap 92.03642221694545
type value
max_f1 84.34959349593495
task dataset metrics
type
Clustering
type name config split revision
None MTEB StackExchangeClustering default test 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
type value
v_measure 67.04916654680977
type value
v_measures
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task dataset metrics
type
Clustering
type name config split revision
None MTEB StackExchangeClusteringP2P default test 815ca46b2622cec33ccafc3735d572c266efdb44
type value
v_measure 33.36641413495258
type value
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0.31882294716748927
0.31975204745764507
0.30752650651575314
0.3191185767616115
0.35880812225202774
0.3427515820677152
0.344097881083346
0.35390675395072985
0.3472606513458235
0.3245963448931168
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0.30752650651575314
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0.35880812225202774
0.3427515820677152
0.344097881083346
0.35390675395072985
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task dataset metrics
type
Reranking
type name config split revision
None MTEB StackOverflowDupQuestions default test e185fbe320c72810689fc5848eb6114e1ef5ec69
type value
map 51.19282080158746
type value
mrr 51.871100713012474
task dataset metrics
type
Summarization
type name config split revision
None MTEB SummEval default test cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
type value
cos_sim_pearson 31.437664703708485
type value
cos_sim_spearman 31.391119208581575
type value
dot_pearson 31.19925970504054
type value
dot_spearman 31.38087224016694
task dataset metrics
type
Retrieval
type name config split revision
None MTEB TRECCOVID default test bb9466bac8153a0349341eb1b22e06409e78ef4e
type value
map_at_1 0.249
type value
map_at_10 2.163
type value
map_at_100 13.242999999999999
type value
map_at_1000 30.866
type value
map_at_20 3.9539999999999997
type value
map_at_3 0.718
type value
map_at_5 1.169
type value
mrr_at_1 96.0
type value
mrr_at_10 98.0
type value
mrr_at_100 98.0
type value
mrr_at_1000 98.0
type value
mrr_at_20 98.0
type value
mrr_at_3 98.0
type value
mrr_at_5 98.0
type value
ndcg_at_1 92.0
type value
ndcg_at_10 84.147
type value
ndcg_at_100 65.143
type value
ndcg_at_1000 56.038
type value
ndcg_at_20 80.869
type value
ndcg_at_3 89.11200000000001
type value
ndcg_at_5 87.199
type value
precision_at_1 96.0
type value
precision_at_10 87.8
type value
precision_at_100 66.72
type value
precision_at_1000 24.684
type value
precision_at_20 84.3
type value
precision_at_3 94.0
type value
precision_at_5 91.2
type value
recall_at_1 0.249
type value
recall_at_10 2.284
type value
recall_at_100 16.025
type value
recall_at_1000 52.068999999999996
type value
recall_at_20 4.3180000000000005
type value
recall_at_3 0.738
type value
recall_at_5 1.212
task dataset metrics
type
Retrieval
type name config split revision
None MTEB Touche2020 default test a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
type value
map_at_1 3.4520000000000004
type value
map_at_10 13.045000000000002
type value
map_at_100 19.442
type value
map_at_1000 21.09
type value
map_at_20 15.667
type value
map_at_3 7.409000000000001
type value
map_at_5 9.73
type value
mrr_at_1 46.939
type value
mrr_at_10 60.295
type value
mrr_at_100 60.904
type value
mrr_at_1000 60.919000000000004
type value
mrr_at_20 60.77
type value
mrr_at_3 58.50300000000001
type value
mrr_at_5 59.014
type value
ndcg_at_1 44.897999999999996
type value
ndcg_at_10 31.911
type value
ndcg_at_100 41.945
type value
ndcg_at_1000 53.181999999999995
type value
ndcg_at_20 31.505
type value
ndcg_at_3 39.745000000000005
type value
ndcg_at_5 35.528999999999996
type value
precision_at_1 46.939
type value
precision_at_10 26.531
type value
precision_at_100 8.163
type value
precision_at_1000 1.559
type value
precision_at_20 19.387999999999998
type value
precision_at_3 40.136
type value
precision_at_5 33.878
type value
recall_at_1 3.4520000000000004
type value
recall_at_10 18.899
type value
recall_at_100 50.207
type value
recall_at_1000 83.871
type value
recall_at_20 26.756999999999998
type value
recall_at_3 8.729000000000001
type value
recall_at_5 12.084999999999999
task dataset metrics
type
Classification
type name config split revision
None MTEB ToxicConversationsClassification default test edfaf9da55d3dd50d43143d90c1ac476895ae6de
type value
accuracy 67.4560546875
type value
ap 12.720403845355294
type value
f1 51.76062666567839
task dataset metrics
type
Classification
type name config split revision
None MTEB TweetSentimentExtractionClassification default test d604517c81ca91fe16a244d1248fc021f9ecee7a
type value
accuracy 62.36276174306734
type value
f1 62.69956906934332
task dataset metrics
type
Clustering
type name config split revision
None MTEB TwentyNewsgroupsClustering default test 6125ec4e24fa026cec8a478383ee943acfbd5449
type value
v_measure 49.473492910233965
type value
v_measures
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0.5129628835129124
0.514824404624281
0.48829262296803855
0.49853262011854643
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0.5020774116970983
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0.4702417082210781
0.4763216048226018
0.49932879417585735
0.5129628835129124
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task dataset metrics
type
PairClassification
type name config split revision
None MTEB TwitterSemEval2015 default test 70970daeab8776df92f5ea462b6173c0b46fd2d1
type value
cos_sim_accuracy 85.75430649102938
type value
cos_sim_ap 73.62842656477649
type value
cos_sim_f1 67.76023680315738
type value
cos_sim_precision 63.61741547012506
type value
cos_sim_recall 72.4802110817942
type value
dot_accuracy 85.7423854085951
type value
dot_ap 73.59147637253723
type value
dot_f1 67.69498693867396
type value
dot_precision 64.03859731701577
type value
dot_recall 71.79419525065963
type value
euclidean_accuracy 85.7423854085951
type value
euclidean_ap 73.6288990409654
type value
euclidean_f1 67.80415430267064
type value
euclidean_precision 63.79711493718009
type value
euclidean_recall 72.34828496042216
type value
manhattan_accuracy 85.69470107885796
type value
manhattan_ap 73.49219614602531
type value
manhattan_f1 67.60809797550613
type value
manhattan_precision 64.22127255460589
type value
manhattan_recall 71.37203166226914
type value
max_accuracy 85.75430649102938
type value
max_ap 73.6288990409654
type value
max_f1 67.80415430267064
task dataset metrics
type
PairClassification
type name config split revision
None MTEB TwitterURLCorpus default test 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
type value
cos_sim_accuracy 89.08293553770326
type value
cos_sim_ap 86.21246419992926
type value
cos_sim_f1 78.49922526377924
type value
cos_sim_precision 75.35769939084857
type value
cos_sim_recall 81.9140745303357
type value
dot_accuracy 89.08681647067955
type value
dot_ap 86.19733517196862
type value
dot_f1 78.51132446157838
type value
dot_precision 75.70233755093287
type value
dot_recall 81.53680320295658
type value
euclidean_accuracy 89.07517367175069
type value
euclidean_ap 86.21198725320203
type value
euclidean_f1 78.49867139061116
type value
euclidean_precision 75.38276155372839
type value
euclidean_recall 81.88327687095781
type value
manhattan_accuracy 89.0538285403811
type value
manhattan_ap 86.17785515765131
type value
manhattan_f1 78.48184098593084
type value
manhattan_precision 74.34396308285694
type value
manhattan_recall 83.10748383122882
type value
max_accuracy 89.08681647067955
type value
max_ap 86.21246419992926
type value
max_f1 78.51132446157838
apache-2.0
en
answerdotai/ModernBERT-base
nomic-ai/modernbert-embed-unsupervised

ModernBERT Embed

image/png

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 transformers library. Until the next transformers release, doing so requires installing transformers from main:

pip install git+https://github.com/huggingface/transformers.git

Reminder, this model is trained similarly to Nomic Embed and REQUIRES prefixes to be added to the input. For more information, see the instructions in Nomic Embed.

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!

image/webp

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

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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}
}