metadata
tags:
- mteb
- sentence-transformers
model-index:
- name: NV-Embed-v1
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 95.11940298507461
- type: ap
value: 79.21521293687752
- type: f1
value: 92.45575440759485
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.143125
- type: ap
value: 95.28635983806933
- type: f1
value: 97.1426073127198
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.465999999999994
- type: f1
value: 52.70196166254287
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 44.879000000000005
- type: map_at_10
value: 60.146
- type: map_at_100
value: 60.533
- type: map_at_1000
value: 60.533
- type: map_at_3
value: 55.725
- type: map_at_5
value: 58.477999999999994
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 44.879000000000005
- type: ndcg_at_10
value: 68.205
- type: ndcg_at_100
value: 69.646
- type: ndcg_at_1000
value: 69.65599999999999
- type: ndcg_at_3
value: 59.243
- type: ndcg_at_5
value: 64.214
- type: precision_at_1
value: 44.879000000000005
- type: precision_at_10
value: 9.374
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 23.139000000000003
- type: precision_at_5
value: 16.302
- type: recall_at_1
value: 44.879000000000005
- type: recall_at_10
value: 93.741
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 69.417
- type: recall_at_5
value: 81.50800000000001
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 53.76391569504432
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 49.589284930659005
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.49860736554155
- type: mrr
value: 80.77771182341819
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.87900681188576
- type: cos_sim_spearman
value: 85.5905044545741
- type: euclidean_pearson
value: 86.80150192033507
- type: euclidean_spearman
value: 85.5905044545741
- type: manhattan_pearson
value: 86.79080500635683
- type: manhattan_spearman
value: 85.69351885001977
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 90.33766233766235
- type: f1
value: 90.20736178753944
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 48.152262077598465
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 44.742970683037235
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 31.825333333333326
- type: map_at_10
value: 44.019999999999996
- type: map_at_100
value: 45.37291666666667
- type: map_at_1000
value: 45.46991666666666
- type: map_at_3
value: 40.28783333333333
- type: map_at_5
value: 42.39458333333334
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 37.79733333333333
- type: ndcg_at_10
value: 50.50541666666667
- type: ndcg_at_100
value: 55.59125
- type: ndcg_at_1000
value: 57.06325
- type: ndcg_at_3
value: 44.595666666666666
- type: ndcg_at_5
value: 47.44875
- type: precision_at_1
value: 37.79733333333333
- type: precision_at_10
value: 9.044083333333333
- type: precision_at_100
value: 1.3728333333333336
- type: precision_at_1000
value: 0.16733333333333333
- type: precision_at_3
value: 20.842166666666667
- type: precision_at_5
value: 14.921916666666668
- type: recall_at_1
value: 31.825333333333326
- type: recall_at_10
value: 65.11916666666666
- type: recall_at_100
value: 86.72233333333335
- type: recall_at_1000
value: 96.44200000000001
- type: recall_at_3
value: 48.75691666666667
- type: recall_at_5
value: 56.07841666666666
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 14.698
- type: map_at_10
value: 25.141999999999996
- type: map_at_100
value: 27.1
- type: map_at_1000
value: 27.277
- type: map_at_3
value: 21.162
- type: map_at_5
value: 23.154
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 32.704
- type: ndcg_at_10
value: 34.715
- type: ndcg_at_100
value: 41.839
- type: ndcg_at_1000
value: 44.82
- type: ndcg_at_3
value: 28.916999999999998
- type: ndcg_at_5
value: 30.738
- type: precision_at_1
value: 32.704
- type: precision_at_10
value: 10.795
- type: precision_at_100
value: 1.8530000000000002
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 21.564
- type: precision_at_5
value: 16.261
- type: recall_at_1
value: 14.698
- type: recall_at_10
value: 41.260999999999996
- type: recall_at_100
value: 65.351
- type: recall_at_1000
value: 81.759
- type: recall_at_3
value: 26.545999999999996
- type: recall_at_5
value: 32.416
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.959
- type: map_at_10
value: 23.104
- type: map_at_100
value: 33.202
- type: map_at_1000
value: 35.061
- type: map_at_3
value: 15.911
- type: map_at_5
value: 18.796
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 63.5
- type: ndcg_at_10
value: 48.29
- type: ndcg_at_100
value: 52.949999999999996
- type: ndcg_at_1000
value: 60.20100000000001
- type: ndcg_at_3
value: 52.92
- type: ndcg_at_5
value: 50.375
- type: precision_at_1
value: 73.75
- type: precision_at_10
value: 38.65
- type: precision_at_100
value: 12.008000000000001
- type: precision_at_1000
value: 2.409
- type: precision_at_3
value: 56.083000000000006
- type: precision_at_5
value: 48.449999999999996
- type: recall_at_1
value: 9.959
- type: recall_at_10
value: 28.666999999999998
- type: recall_at_100
value: 59.319
- type: recall_at_1000
value: 81.973
- type: recall_at_3
value: 17.219
- type: recall_at_5
value: 21.343999999999998
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 91.705
- type: f1
value: 87.98464515154814
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 74.297
- type: map_at_10
value: 83.931
- type: map_at_100
value: 84.152
- type: map_at_1000
value: 84.164
- type: map_at_3
value: 82.708
- type: map_at_5
value: 83.536
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 80.048
- type: ndcg_at_10
value: 87.77000000000001
- type: ndcg_at_100
value: 88.467
- type: ndcg_at_1000
value: 88.673
- type: ndcg_at_3
value: 86.003
- type: ndcg_at_5
value: 87.115
- type: precision_at_1
value: 80.048
- type: precision_at_10
value: 10.711
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 33.248
- type: precision_at_5
value: 20.744
- type: recall_at_1
value: 74.297
- type: recall_at_10
value: 95.402
- type: recall_at_100
value: 97.97
- type: recall_at_1000
value: 99.235
- type: recall_at_3
value: 90.783
- type: recall_at_5
value: 93.55499999999999
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.986
- type: map_at_10
value: 55.173
- type: map_at_100
value: 57.077
- type: map_at_1000
value: 57.176
- type: map_at_3
value: 48.182
- type: map_at_5
value: 52.303999999999995
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 62.037
- type: ndcg_at_10
value: 63.096
- type: ndcg_at_100
value: 68.42200000000001
- type: ndcg_at_1000
value: 69.811
- type: ndcg_at_3
value: 58.702
- type: ndcg_at_5
value: 60.20100000000001
- type: precision_at_1
value: 62.037
- type: precision_at_10
value: 17.269000000000002
- type: precision_at_100
value: 2.309
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 38.992
- type: precision_at_5
value: 28.610999999999997
- type: recall_at_1
value: 32.986
- type: recall_at_10
value: 70.61800000000001
- type: recall_at_100
value: 89.548
- type: recall_at_1000
value: 97.548
- type: recall_at_3
value: 53.400000000000006
- type: recall_at_5
value: 61.29599999999999
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 41.357
- type: map_at_10
value: 72.91499999999999
- type: map_at_100
value: 73.64699999999999
- type: map_at_1000
value: 73.67899999999999
- type: map_at_3
value: 69.113
- type: map_at_5
value: 71.68299999999999
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 82.714
- type: ndcg_at_10
value: 79.92
- type: ndcg_at_100
value: 82.232
- type: ndcg_at_1000
value: 82.816
- type: ndcg_at_3
value: 74.875
- type: ndcg_at_5
value: 77.969
- type: precision_at_1
value: 82.714
- type: precision_at_10
value: 17.037
- type: precision_at_100
value: 1.879
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 49.471
- type: precision_at_5
value: 32.124
- type: recall_at_1
value: 41.357
- type: recall_at_10
value: 85.18599999999999
- type: recall_at_100
value: 93.964
- type: recall_at_1000
value: 97.765
- type: recall_at_3
value: 74.207
- type: recall_at_5
value: 80.31099999999999
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 97.05799999999998
- type: ap
value: 95.51324940484382
- type: f1
value: 97.05788617110184
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: test
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 25.608999999999998
- type: map_at_10
value: 39.098
- type: map_at_100
value: 0
- type: map_at_1000
value: 0
- type: map_at_3
value: 0
- type: map_at_5
value: 37.383
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 26.404
- type: ndcg_at_10
value: 46.493
- type: ndcg_at_100
value: 0
- type: ndcg_at_1000
value: 0
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 42.459
- type: precision_at_1
value: 26.404
- type: precision_at_10
value: 7.249
- type: precision_at_100
value: 0
- type: precision_at_1000
value: 0
- type: precision_at_3
value: 0
- type: precision_at_5
value: 11.874
- type: recall_at_1
value: 25.608999999999998
- type: recall_at_10
value: 69.16799999999999
- type: recall_at_100
value: 0
- type: recall_at_1000
value: 0
- type: recall_at_3
value: 0
- type: recall_at_5
value: 56.962
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.50706794345645
- type: f1
value: 96.3983656000426
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 89.77428180574556
- type: f1
value: 70.47378359921777
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.07061197041023
- type: f1
value: 77.8633288994029
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.74176193678547
- type: f1
value: 79.8943810025071
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 39.239199736486334
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 36.98167653792483
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.815595271130718
- type: mrr
value: 31.892823243368795
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.214
- type: map_at_10
value: 14.393
- type: map_at_100
value: 18.163999999999998
- type: map_at_1000
value: 19.753999999999998
- type: map_at_3
value: 10.737
- type: map_at_5
value: 12.325
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 48.297000000000004
- type: ndcg_at_10
value: 38.035000000000004
- type: ndcg_at_100
value: 34.772
- type: ndcg_at_1000
value: 43.631
- type: ndcg_at_3
value: 44.252
- type: ndcg_at_5
value: 41.307
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 27.647
- type: precision_at_100
value: 8.824
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 40.97
- type: precision_at_5
value: 35.17
- type: recall_at_1
value: 6.214
- type: recall_at_10
value: 18.566
- type: recall_at_100
value: 34.411
- type: recall_at_1000
value: 67.331
- type: recall_at_3
value: 12.277000000000001
- type: recall_at_5
value: 14.734
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 47.11
- type: map_at_10
value: 64.404
- type: map_at_100
value: 65.005
- type: map_at_1000
value: 65.01400000000001
- type: map_at_3
value: 60.831
- type: map_at_5
value: 63.181
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 52.983999999999995
- type: ndcg_at_10
value: 71.219
- type: ndcg_at_100
value: 73.449
- type: ndcg_at_1000
value: 73.629
- type: ndcg_at_3
value: 65.07
- type: ndcg_at_5
value: 68.715
- type: precision_at_1
value: 52.983999999999995
- type: precision_at_10
value: 10.756
- type: precision_at_100
value: 1.198
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 28.977999999999998
- type: precision_at_5
value: 19.583000000000002
- type: recall_at_1
value: 47.11
- type: recall_at_10
value: 89.216
- type: recall_at_100
value: 98.44500000000001
- type: recall_at_1000
value: 99.744
- type: recall_at_3
value: 73.851
- type: recall_at_5
value: 82.126
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.641
- type: map_at_10
value: 85.687
- type: map_at_100
value: 86.304
- type: map_at_1000
value: 86.318
- type: map_at_3
value: 82.811
- type: map_at_5
value: 84.641
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 82.48
- type: ndcg_at_10
value: 89.212
- type: ndcg_at_100
value: 90.321
- type: ndcg_at_1000
value: 90.405
- type: ndcg_at_3
value: 86.573
- type: ndcg_at_5
value: 88.046
- type: precision_at_1
value: 82.48
- type: precision_at_10
value: 13.522
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.95
- type: precision_at_5
value: 24.932000000000002
- type: recall_at_1
value: 71.641
- type: recall_at_10
value: 95.91499999999999
- type: recall_at_100
value: 99.63300000000001
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.248
- type: recall_at_5
value: 92.428
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.19631707795757
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 68.01353074322002
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.67
- type: map_at_10
value: 11.991999999999999
- type: map_at_100
value: 14.263
- type: map_at_1000
value: 14.59
- type: map_at_3
value: 8.468
- type: map_at_5
value: 10.346
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 23.1
- type: ndcg_at_10
value: 20.19
- type: ndcg_at_100
value: 28.792
- type: ndcg_at_1000
value: 34.406
- type: ndcg_at_3
value: 19.139
- type: ndcg_at_5
value: 16.916
- type: precision_at_1
value: 23.1
- type: precision_at_10
value: 10.47
- type: precision_at_100
value: 2.2849999999999997
- type: precision_at_1000
value: 0.363
- type: precision_at_3
value: 17.9
- type: precision_at_5
value: 14.979999999999999
- type: recall_at_1
value: 4.67
- type: recall_at_10
value: 21.21
- type: recall_at_100
value: 46.36
- type: recall_at_1000
value: 73.72999999999999
- type: recall_at_3
value: 10.865
- type: recall_at_5
value: 15.185
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 84.31392081916142
- type: cos_sim_spearman
value: 82.80375234068289
- type: euclidean_pearson
value: 81.4159066418654
- type: euclidean_spearman
value: 82.80377112831907
- type: manhattan_pearson
value: 81.48376861134983
- type: manhattan_spearman
value: 82.86696725667119
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1940844467158
- type: cos_sim_spearman
value: 76.22474792649982
- type: euclidean_pearson
value: 79.87714243582901
- type: euclidean_spearman
value: 76.22462054296349
- type: manhattan_pearson
value: 80.19242023327877
- type: manhattan_spearman
value: 76.53202564089719
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.58028303401805
- type: cos_sim_spearman
value: 86.30355131725051
- type: euclidean_pearson
value: 85.9027489087145
- type: euclidean_spearman
value: 86.30352515906158
- type: manhattan_pearson
value: 85.74953930990678
- type: manhattan_spearman
value: 86.21878393891001
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.92370135244734
- type: cos_sim_spearman
value: 82.09196894621044
- type: euclidean_pearson
value: 81.83198023906334
- type: euclidean_spearman
value: 82.09196482328333
- type: manhattan_pearson
value: 81.8951479497964
- type: manhattan_spearman
value: 82.2392819738236
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.05662816919057
- type: cos_sim_spearman
value: 87.24083005603993
- type: euclidean_pearson
value: 86.54673655650183
- type: euclidean_spearman
value: 87.24083428218053
- type: manhattan_pearson
value: 86.51248710513431
- type: manhattan_spearman
value: 87.24796986335883
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.06330254316376
- type: cos_sim_spearman
value: 84.76788840323285
- type: euclidean_pearson
value: 84.15438606134029
- type: euclidean_spearman
value: 84.76788840323285
- type: manhattan_pearson
value: 83.97986968570088
- type: manhattan_spearman
value: 84.52468572953663
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.08627867173213
- type: cos_sim_spearman
value: 87.41531216247836
- type: euclidean_pearson
value: 87.92912483282956
- type: euclidean_spearman
value: 87.41531216247836
- type: manhattan_pearson
value: 87.85418528366228
- type: manhattan_spearman
value: 87.32655499883539
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 70.74143864859911
- type: cos_sim_spearman
value: 69.84863549051433
- type: euclidean_pearson
value: 71.07346533903932
- type: euclidean_spearman
value: 69.84863549051433
- type: manhattan_pearson
value: 71.32285810342451
- type: manhattan_spearman
value: 70.13063960824287
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.05702492574339
- type: cos_sim_spearman
value: 86.13895001731495
- type: euclidean_pearson
value: 85.86694514265486
- type: euclidean_spearman
value: 86.13895001731495
- type: manhattan_pearson
value: 85.96382530570494
- type: manhattan_spearman
value: 86.30950247235928
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.26225076335467
- type: mrr
value: 96.60696329813977
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 64.494
- type: map_at_10
value: 74.102
- type: map_at_100
value: 74.571
- type: map_at_1000
value: 74.58
- type: map_at_3
value: 71.111
- type: map_at_5
value: 73.184
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 67.667
- type: ndcg_at_10
value: 78.427
- type: ndcg_at_100
value: 80.167
- type: ndcg_at_1000
value: 80.41
- type: ndcg_at_3
value: 73.804
- type: ndcg_at_5
value: 76.486
- type: precision_at_1
value: 67.667
- type: precision_at_10
value: 10.167
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.222
- type: precision_at_5
value: 18.867
- type: recall_at_1
value: 64.494
- type: recall_at_10
value: 90.422
- type: recall_at_100
value: 97.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 78.278
- type: recall_at_5
value: 84.828
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82772277227723
- type: cos_sim_ap
value: 95.93881941923254
- type: cos_sim_f1
value: 91.12244897959184
- type: cos_sim_precision
value: 93.02083333333333
- type: cos_sim_recall
value: 89.3
- type: dot_accuracy
value: 99.82772277227723
- type: dot_ap
value: 95.93886287716076
- type: dot_f1
value: 91.12244897959184
- type: dot_precision
value: 93.02083333333333
- type: dot_recall
value: 89.3
- type: euclidean_accuracy
value: 99.82772277227723
- type: euclidean_ap
value: 95.93881941923253
- type: euclidean_f1
value: 91.12244897959184
- type: euclidean_precision
value: 93.02083333333333
- type: euclidean_recall
value: 89.3
- type: manhattan_accuracy
value: 99.83366336633664
- type: manhattan_ap
value: 96.07286531485964
- type: manhattan_f1
value: 91.34912461380021
- type: manhattan_precision
value: 94.16135881104034
- type: manhattan_recall
value: 88.7
- type: max_accuracy
value: 99.83366336633664
- type: max_ap
value: 96.07286531485964
- type: max_f1
value: 91.34912461380021
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 74.98877944689897
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 42.0365286267706
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 56.5797777961647
- type: mrr
value: 57.57701754944402
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.673216240991756
- type: cos_sim_spearman
value: 31.198648165051225
- type: dot_pearson
value: 30.67321511262982
- type: dot_spearman
value: 31.198648165051225
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.23500000000000001
- type: map_at_10
value: 2.274
- type: map_at_100
value: 14.002
- type: map_at_1000
value: 34.443
- type: map_at_3
value: 0.705
- type: map_at_5
value: 1.162
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 88
- type: ndcg_at_10
value: 85.883
- type: ndcg_at_100
value: 67.343
- type: ndcg_at_1000
value: 59.999
- type: ndcg_at_3
value: 87.70400000000001
- type: ndcg_at_5
value: 85.437
- type: precision_at_1
value: 92
- type: precision_at_10
value: 91.2
- type: precision_at_100
value: 69.19999999999999
- type: precision_at_1000
value: 26.6
- type: precision_at_3
value: 92.667
- type: precision_at_5
value: 90.8
- type: recall_at_1
value: 0.23500000000000001
- type: recall_at_10
value: 2.409
- type: recall_at_100
value: 16.706
- type: recall_at_1000
value: 56.396
- type: recall_at_3
value: 0.734
- type: recall_at_5
value: 1.213
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.4819999999999998
- type: map_at_10
value: 10.985
- type: map_at_100
value: 17.943
- type: map_at_1000
value: 19.591
- type: map_at_3
value: 5.86
- type: map_at_5
value: 8.397
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 37.755
- type: ndcg_at_10
value: 28.383000000000003
- type: ndcg_at_100
value: 40.603
- type: ndcg_at_1000
value: 51.469
- type: ndcg_at_3
value: 32.562000000000005
- type: ndcg_at_5
value: 31.532
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 24.898
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.582
- type: precision_at_3
value: 31.973000000000003
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 2.4819999999999998
- type: recall_at_10
value: 17.079
- type: recall_at_100
value: 51.406
- type: recall_at_1000
value: 84.456
- type: recall_at_3
value: 6.802
- type: recall_at_5
value: 10.856
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 92.5984
- type: ap
value: 41.969971606260906
- type: f1
value: 78.95995145145926
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 80.63950198075835
- type: f1
value: 80.93345710055597
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 60.13491858535076
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.42325803182929
- type: cos_sim_ap
value: 78.72789856051176
- type: cos_sim_f1
value: 71.83879093198993
- type: cos_sim_precision
value: 68.72289156626506
- type: cos_sim_recall
value: 75.25065963060686
- type: dot_accuracy
value: 87.42325803182929
- type: dot_ap
value: 78.72789755269454
- type: dot_f1
value: 71.83879093198993
- type: dot_precision
value: 68.72289156626506
- type: dot_recall
value: 75.25065963060686
- type: euclidean_accuracy
value: 87.42325803182929
- type: euclidean_ap
value: 78.7278973892869
- type: euclidean_f1
value: 71.83879093198993
- type: euclidean_precision
value: 68.72289156626506
- type: euclidean_recall
value: 75.25065963060686
- type: manhattan_accuracy
value: 87.59015318590929
- type: manhattan_ap
value: 78.99631410090865
- type: manhattan_f1
value: 72.11323565929972
- type: manhattan_precision
value: 68.10506566604127
- type: manhattan_recall
value: 76.62269129287598
- type: max_accuracy
value: 87.59015318590929
- type: max_ap
value: 78.99631410090865
- type: max_f1
value: 72.11323565929972
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.15473279776458
- type: cos_sim_ap
value: 86.05463278065247
- type: cos_sim_f1
value: 78.63797449855686
- type: cos_sim_precision
value: 74.82444552596816
- type: cos_sim_recall
value: 82.86110255620572
- type: dot_accuracy
value: 89.15473279776458
- type: dot_ap
value: 86.05463366261054
- type: dot_f1
value: 78.63797449855686
- type: dot_precision
value: 74.82444552596816
- type: dot_recall
value: 82.86110255620572
- type: euclidean_accuracy
value: 89.15473279776458
- type: euclidean_ap
value: 86.05463195314907
- type: euclidean_f1
value: 78.63797449855686
- type: euclidean_precision
value: 74.82444552596816
- type: euclidean_recall
value: 82.86110255620572
- type: manhattan_accuracy
value: 89.15861373074087
- type: manhattan_ap
value: 86.08743411620402
- type: manhattan_f1
value: 78.70125023325248
- type: manhattan_precision
value: 76.36706018686174
- type: manhattan_recall
value: 81.18263012011087
- type: max_accuracy
value: 89.15861373074087
- type: max_ap
value: 86.08743411620402
- type: max_f1
value: 78.70125023325248
language:
- en
license: cc-by-nc-4.0
Introduction
We introduce NV-Embed, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks. Notably, our model also achieves the highest score of 59.36 on 15 retrieval tasks within this benchmark.
NV-Embed presents several new designs, including having the LLM attend to latent vectors for better pooled embedding output, and demonstrating a two-stage instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks.
For more technical details, refer to our paper: NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.
For more benchmark results (other than MTEB), please find the AIR-Bench for QA (English only) and Long-Doc.
Model Details
- Base Decoder-only LLM: Mistral-7B-v0.1
- Pooling Type: Latent-Attention
- Embedding Dimension: 4096
How to use
Here is an example of how to encode queries and passages using Huggingface-transformer and Sentence-transformer. Please find the required package version here.
Usage (HuggingFace Transformers)
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}
query_prefix = "Instruct: "+task_name_to_instruct["example"]+"\nQuery: "
queries = [
'are judo throws allowed in wrestling?',
'how to become a radiology technician in michigan?'
]
# No instruction needed for retrieval passages
passage_prefix = ""
passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]
# load model with tokenizer
model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True)
# get the embeddings
max_length = 4096
query_embeddings = model.encode(queries, instruction=query_prefix, max_length=max_length)
passage_embeddings = model.encode(passages, instruction=passage_prefix, max_length=max_length)
# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)
# get the embeddings with DataLoader (spliting the datasets into multiple mini-batches)
# batch_size=2
# query_embeddings = model._do_encode(queries, batch_size=batch_size, instruction=query_prefix, max_length=max_length, num_workers=32, return_numpy=True)
# passage_embeddings = model._do_encode(passages, batch_size=batch_size, instruction=passage_prefix, max_length=max_length, num_workers=32, return_numpy=True)
scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
#[[77.9402084350586, 0.4248958230018616], [3.757718086242676, 79.60113525390625]]
Usage (Sentence-Transformers)
import torch
from sentence_transformers import SentenceTransformer
# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}
query_prefix = "Instruct: "+task_name_to_instruct["example"]+"\nQuery: "
queries = [
'are judo throws allowed in wrestling?',
'how to become a radiology technician in michigan?'
]
# No instruction needed for retrieval passages
passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]
# load model with tokenizer
model = SentenceTransformer('nvidia/NV-Embed-v1', trust_remote_code=True)
model.max_seq_length = 4096
model.tokenizer.padding_side="right"
def add_eos(input_examples):
input_examples = [input_example + model.tokenizer.eos_token for input_example in input_examples]
return input_examples
# get the embeddings
batch_size = 2
query_embeddings = model.encode(add_eos(queries), batch_size=batch_size, prompt=query_prefix, normalize_embeddings=True)
passage_embeddings = model.encode(add_eos(passages), batch_size=batch_size, normalize_embeddings=True)
scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
Correspondence to
Chankyu Lee (chankyul@nvidia.com), Wei Ping (wping@nvidia.com)
Citation
If you find this code useful in your research, please consider citing:
@misc{lee2024nvembed,
title={NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models},
author={Chankyu Lee and Rajarshi Roy and Mengyao Xu and Jonathan Raiman and Mohammad Shoeybi and Bryan Catanzaro and Wei Ping},
year={2024},
eprint={2405.17428},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
License
This model should not be used for any commercial purpose. Refer the license for the detailed terms.
For commercial purpose, we recommend you to use the models of NeMo Retriever Microservices (NIMs).
Troubleshooting
1. How to enable Multi-GPU (Note, this is the case for HuggingFace Transformers)
from transformers import AutoModel
from torch.nn import DataParallel
embedding_model = AutoModel.from_pretrained("nvidia/NV-Embed-v1")
for module_key, module in embedding_model._modules.items():
embedding_model._modules[module_key] = DataParallel(module)
2. Required Packages
If you have trouble, try installing the python packages as below
pip uninstall -y transformer-engine
pip install torch==2.2.0
pip install transformers==4.42.4
pip install flash-attn==2.2.0
pip install sentence-transformers==2.7.0
3. Fixing "nvidia/NV-Embed-v1 is not the path to a directory containing a file named config.json"
Switch to your local model path，and open config.json and change the value of "_name_or_path" and replace it with your local model path.
4. Access to model nvidia/NV-Embed-v1 is restricted. You must be authenticated to access it
Use your huggingface access token to execute "huggingface-cli login".
5. How to resolve slight mismatch in Sentence transformer results.
A slight mismatch in the Sentence Transformer implementation is caused by a discrepancy in the calculation of the instruction prefix length within the Sentence Transformer package.
To fix this issue, you need to build the Sentence Transformer package from source, making the necessary modification in this line as below.
git clone https://github.com/UKPLab/sentence-transformers.git
cd sentence-transformers
git checkout v2.7-release
# Modify L353 in SentenceTransformer.py to **'extra_features["prompt_length"] = tokenized_prompt["input_ids"].shape[-1]'**.
pip install -e .