NV-Embed-v1#
README(From Huggingface)#
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 paddle
import paddle.nn.functional as F
from paddlenlp.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 paddle
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), Rajarshi Roy (rajarshir@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 paddlenlp.transformers import AutoModel
from paddle.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 .
Model Files#
README.md (51.3 KB)
config.json (707.0 B)
model_state-00001-of-00004.pdparams (3.7 GB)
model_state-00002-of-00004.pdparams (3.7 GB)
model_state-00003-of-00004.pdparams (3.7 GB)
model_state-00004-of-00004.pdparams (3.6 GB)
model_state.pdparams.index.json (23.8 KB)
sentencepiece.bpe.model (481.9 KB)
special_tokens_map.json (72.0 B)
tokenizer.json (1.7 MB)
tokenizer_config.json (1.4 KB)