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---
tags:
- mteb
- sentence-transformers
model-index:
- name: NV-Embed-v2
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 94.28358208955224
- type: accuracy_stderr
value: 0.40076780842082305
- type: ap
value: 76.49097318319616
- type: ap_stderr
value: 1.2418692675183929
- type: f1
value: 91.41982003001168
- type: f1_stderr
value: 0.5043921413093579
- type: main_score
value: 94.28358208955224
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 97.74185000000001
- type: accuracy_stderr
value: 0.07420471683120942
- type: ap
value: 96.4737144875525
- type: ap_stderr
value: 0.2977518241541558
- type: f1
value: 97.7417581594921
- type: f1_stderr
value: 0.07428763617010377
- type: main_score
value: 97.74185000000001
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 63.96000000000001
- type: accuracy_stderr
value: 1.815555011559825
- type: f1
value: 62.49361841640459
- type: f1_stderr
value: 2.829339314126457
- type: main_score
value: 63.96000000000001
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: map_at_1
value: 46.515
- type: map_at_10
value: 62.392
- type: map_at_100
value: 62.732
- type: map_at_1000
value: 62.733000000000004
- type: map_at_3
value: 58.701
- type: map_at_5
value: 61.027
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 46.515
- type: ndcg_at_10
value: 70.074
- type: ndcg_at_100
value: 71.395
- type: ndcg_at_1000
value: 71.405
- type: ndcg_at_3
value: 62.643
- type: ndcg_at_5
value: 66.803
- type: precision_at_1
value: 46.515
- type: precision_at_10
value: 9.41
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.68
- type: precision_at_5
value: 16.814
- type: recall_at_1
value: 46.515
- type: recall_at_10
value: 94.097
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 74.03999999999999
- type: recall_at_5
value: 84.068
- type: main_score
value: 70.074
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 55.79933795955242
- type: v_measure
value: 55.79933795955242
- type: v_measure_std
value: 14.575108141916148
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 51.262845995850334
- type: v_measure
value: 51.262845995850334
- type: v_measure_std
value: 14.727824473104173
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 67.46477327480808
- type: mrr
value: 79.50160488941653
- type: main_score
value: 67.46477327480808
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 89.74311007980987
- type: cosine_spearman
value: 87.41644967443246
- type: manhattan_pearson
value: 88.57457108347744
- type: manhattan_spearman
value: 87.59295972042997
- type: euclidean_pearson
value: 88.27108977118459
- type: euclidean_spearman
value: 87.41644967443246
- type: main_score
value: 87.41644967443246
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 92.41558441558443
- type: accuracy_stderr
value: 0.37701502251934443
- type: f1
value: 92.38130170447671
- type: f1_stderr
value: 0.39115151225617767
- type: main_score
value: 92.41558441558443
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: main_score
value: 54.08649516394218
- type: v_measure
value: 54.08649516394218
- type: v_measure_std
value: 0.5303233693045373
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: main_score
value: 49.60352214167779
- type: v_measure
value: 49.60352214167779
- type: v_measure_std
value: 0.7176198612516721
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: CQADupstackRetrieval_is_a_combined_dataset
metrics:
- type: map_at_1
value: 31.913249999999998
- type: map_at_10
value: 43.87733333333334
- type: map_at_100
value: 45.249916666666664
- type: map_at_1000
value: 45.350583333333326
- type: map_at_3
value: 40.316833333333335
- type: map_at_5
value: 42.317083333333336
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 38.30616666666667
- type: ndcg_at_10
value: 50.24175000000001
- type: ndcg_at_100
value: 55.345333333333336
- type: ndcg_at_1000
value: 56.91225000000001
- type: ndcg_at_3
value: 44.67558333333333
- type: ndcg_at_5
value: 47.32333333333334
- type: precision_at_1
value: 38.30616666666667
- type: precision_at_10
value: 9.007416666666666
- type: precision_at_100
value: 1.3633333333333333
- type: precision_at_1000
value: 0.16691666666666666
- type: precision_at_3
value: 20.895666666666667
- type: precision_at_5
value: 14.871666666666666
- type: recall_at_1
value: 31.913249999999998
- type: recall_at_10
value: 64.11891666666666
- type: recall_at_100
value: 85.91133333333333
- type: recall_at_1000
value: 96.28225
- type: recall_at_3
value: 48.54749999999999
- type: recall_at_5
value: 55.44283333333334
- type: main_score
value: 50.24175000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: map_at_1
value: 19.556
- type: map_at_10
value: 34.623
- type: map_at_100
value: 36.97
- type: map_at_1000
value: 37.123
- type: map_at_3
value: 28.904999999999998
- type: map_at_5
value: 31.955
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 44.104
- type: ndcg_at_10
value: 45.388
- type: ndcg_at_100
value: 52.793
- type: ndcg_at_1000
value: 55.108999999999995
- type: ndcg_at_3
value: 38.604
- type: ndcg_at_5
value: 40.806
- type: precision_at_1
value: 44.104
- type: precision_at_10
value: 14.143
- type: precision_at_100
value: 2.2190000000000003
- type: precision_at_1000
value: 0.266
- type: precision_at_3
value: 29.316
- type: precision_at_5
value: 21.98
- type: recall_at_1
value: 19.556
- type: recall_at_10
value: 52.120999999999995
- type: recall_at_100
value: 76.509
- type: recall_at_1000
value: 89.029
- type: recall_at_3
value: 34.919
- type: recall_at_5
value: 42.18
- type: main_score
value: 45.388
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
split: test
type: mteb/dbpedia
metrics:
- type: map_at_1
value: 10.714
- type: map_at_10
value: 25.814999999999998
- type: map_at_100
value: 37.845
- type: map_at_1000
value: 39.974
- type: map_at_3
value: 17.201
- type: map_at_5
value: 21.062
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 53.496
- type: ndcg_at_100
value: 58.053
- type: ndcg_at_1000
value: 64.886
- type: ndcg_at_3
value: 57.656
- type: ndcg_at_5
value: 55.900000000000006
- type: precision_at_1
value: 77.25
- type: precision_at_10
value: 43.65
- type: precision_at_100
value: 13.76
- type: precision_at_1000
value: 2.5940000000000003
- type: precision_at_3
value: 61.0
- type: precision_at_5
value: 54.65
- type: recall_at_1
value: 10.714
- type: recall_at_10
value: 31.173000000000002
- type: recall_at_100
value: 63.404
- type: recall_at_1000
value: 85.874
- type: recall_at_3
value: 18.249000000000002
- type: recall_at_5
value: 23.69
- type: main_score
value: 53.496
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 93.38499999999999
- type: accuracy_stderr
value: 0.13793114224133846
- type: f1
value: 90.12141028353496
- type: f1_stderr
value: 0.174640257706043
- type: main_score
value: 93.38499999999999
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
metrics:
- type: map_at_1
value: 84.66900000000001
- type: map_at_10
value: 91.52799999999999
- type: map_at_100
value: 91.721
- type: map_at_1000
value: 91.73
- type: map_at_3
value: 90.752
- type: map_at_5
value: 91.262
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 91.20899999999999
- type: ndcg_at_10
value: 93.74900000000001
- type: ndcg_at_100
value: 94.279
- type: ndcg_at_1000
value: 94.408
- type: ndcg_at_3
value: 92.923
- type: ndcg_at_5
value: 93.376
- type: precision_at_1
value: 91.20899999999999
- type: precision_at_10
value: 11.059
- type: precision_at_100
value: 1.1560000000000001
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 35.129
- type: precision_at_5
value: 21.617
- type: recall_at_1
value: 84.66900000000001
- type: recall_at_10
value: 97.03399999999999
- type: recall_at_100
value: 98.931
- type: recall_at_1000
value: 99.65899999999999
- type: recall_at_3
value: 94.76299999999999
- type: recall_at_5
value: 95.968
- type: main_score
value: 93.74900000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
split: test
type: mteb/fiqa
metrics:
- type: map_at_1
value: 34.866
- type: map_at_10
value: 58.06099999999999
- type: map_at_100
value: 60.028999999999996
- type: map_at_1000
value: 60.119
- type: map_at_3
value: 51.304
- type: map_at_5
value: 55.054
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 64.815
- type: ndcg_at_10
value: 65.729
- type: ndcg_at_100
value: 71.14
- type: ndcg_at_1000
value: 72.336
- type: ndcg_at_3
value: 61.973
- type: ndcg_at_5
value: 62.858000000000004
- type: precision_at_1
value: 64.815
- type: precision_at_10
value: 17.87
- type: precision_at_100
value: 2.373
- type: precision_at_1000
value: 0.258
- type: precision_at_3
value: 41.152
- type: precision_at_5
value: 29.568
- type: recall_at_1
value: 34.866
- type: recall_at_10
value: 72.239
- type: recall_at_100
value: 91.19
- type: recall_at_1000
value: 98.154
- type: recall_at_3
value: 56.472
- type: recall_at_5
value: 63.157
- type: main_score
value: 65.729
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
metrics:
- type: map_at_1
value: 44.651999999999994
- type: map_at_10
value: 79.95100000000001
- type: map_at_100
value: 80.51700000000001
- type: map_at_1000
value: 80.542
- type: map_at_3
value: 77.008
- type: map_at_5
value: 78.935
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 89.305
- type: ndcg_at_10
value: 85.479
- type: ndcg_at_100
value: 87.235
- type: ndcg_at_1000
value: 87.669
- type: ndcg_at_3
value: 81.648
- type: ndcg_at_5
value: 83.88600000000001
- type: precision_at_1
value: 89.305
- type: precision_at_10
value: 17.807000000000002
- type: precision_at_100
value: 1.9140000000000001
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 53.756
- type: precision_at_5
value: 34.018
- type: recall_at_1
value: 44.651999999999994
- type: recall_at_10
value: 89.034
- type: recall_at_100
value: 95.719
- type: recall_at_1000
value: 98.535
- type: recall_at_3
value: 80.635
- type: recall_at_5
value: 85.044
- type: main_score
value: 85.479
task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 97.1376
- type: accuracy_stderr
value: 0.04571914259913447
- type: ap
value: 95.92783808558808
- type: ap_stderr
value: 0.05063782483358255
- type: f1
value: 97.13755519177172
- type: f1_stderr
value: 0.04575943074086138
- type: main_score
value: 97.1376
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
- type: map_at_1
value: 0.0
- type: map_at_10
value: 38.342
- type: map_at_100
value: 0.0
- type: map_at_1000
value: 0.0
- type: map_at_3
value: 0.0
- type: map_at_5
value: 0.0
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 0.0
- type: ndcg_at_10
value: 45.629999999999995
- type: ndcg_at_100
value: 0.0
- type: ndcg_at_1000
value: 0.0
- type: ndcg_at_3
value: 0.0
- type: ndcg_at_5
value: 0.0
- type: precision_at_1
value: 0.0
- type: precision_at_10
value: 7.119000000000001
- type: precision_at_100
value: 0.0
- type: precision_at_1000
value: 0.0
- type: precision_at_3
value: 0.0
- type: precision_at_5
value: 0.0
- type: recall_at_1
value: 0.0
- type: recall_at_10
value: 67.972
- type: recall_at_100
value: 0.0
- type: recall_at_1000
value: 0.0
- type: recall_at_3
value: 0.0
- type: recall_at_5
value: 0.0
- type: main_score
value: 45.629999999999995
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 99.24988600091199
- type: accuracy_stderr
value: 0.04496826931900734
- type: f1
value: 99.15933275095276
- type: f1_stderr
value: 0.05565039139747446
- type: main_score
value: 99.24988600091199
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 94.3684450524396
- type: accuracy_stderr
value: 0.8436548701322188
- type: f1
value: 77.33022623133307
- type: f1_stderr
value: 0.9228425861187275
- type: main_score
value: 94.3684450524396
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 86.09616677874916
- type: accuracy_stderr
value: 0.9943208055590853
- type: f1
value: 83.4902056490062
- type: f1_stderr
value: 0.7626189310074184
- type: main_score
value: 86.09616677874916
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 92.17215870880968
- type: accuracy_stderr
value: 0.25949941333658166
- type: f1
value: 91.36757392422702
- type: f1_stderr
value: 0.29139507298154815
- type: main_score
value: 92.17215870880968
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: main_score
value: 46.09497344077905
- type: v_measure
value: 46.09497344077905
- type: v_measure_std
value: 1.44871520869784
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: main_score
value: 44.861049989560684
- type: v_measure
value: 44.861049989560684
- type: v_measure_std
value: 1.432199293162203
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 31.75936162919999
- type: mrr
value: 32.966812736541236
- type: main_score
value: 31.75936162919999
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
metrics:
- type: map_at_1
value: 7.893999999999999
- type: map_at_10
value: 17.95
- type: map_at_100
value: 23.474
- type: map_at_1000
value: 25.412000000000003
- type: map_at_3
value: 12.884
- type: map_at_5
value: 15.171000000000001
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 55.728
- type: ndcg_at_10
value: 45.174
- type: ndcg_at_100
value: 42.18
- type: ndcg_at_1000
value: 50.793
- type: ndcg_at_3
value: 50.322
- type: ndcg_at_5
value: 48.244
- type: precision_at_1
value: 57.276
- type: precision_at_10
value: 33.437
- type: precision_at_100
value: 10.671999999999999
- type: precision_at_1000
value: 2.407
- type: precision_at_3
value: 46.646
- type: precision_at_5
value: 41.672
- type: recall_at_1
value: 7.893999999999999
- type: recall_at_10
value: 22.831000000000003
- type: recall_at_100
value: 43.818
- type: recall_at_1000
value: 75.009
- type: recall_at_3
value: 14.371
- type: recall_at_5
value: 17.752000000000002
- type: main_score
value: 45.174
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
- type: map_at_1
value: 49.351
- type: map_at_10
value: 66.682
- type: map_at_100
value: 67.179
- type: map_at_1000
value: 67.18499999999999
- type: map_at_3
value: 62.958999999999996
- type: map_at_5
value: 65.364
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 55.417
- type: ndcg_at_10
value: 73.568
- type: ndcg_at_100
value: 75.35
- type: ndcg_at_1000
value: 75.478
- type: ndcg_at_3
value: 67.201
- type: ndcg_at_5
value: 70.896
- type: precision_at_1
value: 55.417
- type: precision_at_10
value: 11.036999999999999
- type: precision_at_100
value: 1.204
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 29.654000000000003
- type: precision_at_5
value: 20.006
- type: recall_at_1
value: 49.351
- type: recall_at_10
value: 91.667
- type: recall_at_100
value: 98.89
- type: recall_at_1000
value: 99.812
- type: recall_at_3
value: 75.715
- type: recall_at_5
value: 84.072
- type: main_score
value: 73.568
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
split: test
type: mteb/quora
metrics:
- type: map_at_1
value: 71.358
- type: map_at_10
value: 85.474
- type: map_at_100
value: 86.101
- type: map_at_1000
value: 86.114
- type: map_at_3
value: 82.562
- type: map_at_5
value: 84.396
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 82.12
- type: ndcg_at_10
value: 89.035
- type: ndcg_at_100
value: 90.17399999999999
- type: ndcg_at_1000
value: 90.243
- type: ndcg_at_3
value: 86.32300000000001
- type: ndcg_at_5
value: 87.85
- type: precision_at_1
value: 82.12
- type: precision_at_10
value: 13.55
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.89
- type: precision_at_5
value: 24.9
- type: recall_at_1
value: 71.358
- type: recall_at_10
value: 95.855
- type: recall_at_100
value: 99.711
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.02
- type: recall_at_5
value: 92.378
- type: main_score
value: 89.035
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: main_score
value: 71.0984522742521
- type: v_measure
value: 71.0984522742521
- type: v_measure_std
value: 3.5668139917058044
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: main_score
value: 74.94499641904133
- type: v_measure
value: 74.94499641904133
- type: v_measure_std
value: 11.419672879389248
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
metrics:
- type: map_at_1
value: 5.343
- type: map_at_10
value: 13.044
- type: map_at_100
value: 15.290999999999999
- type: map_at_1000
value: 15.609
- type: map_at_3
value: 9.227
- type: map_at_5
value: 11.158
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 26.3
- type: ndcg_at_10
value: 21.901
- type: ndcg_at_100
value: 30.316
- type: ndcg_at_1000
value: 35.547000000000004
- type: ndcg_at_3
value: 20.560000000000002
- type: ndcg_at_5
value: 18.187
- type: precision_at_1
value: 26.3
- type: precision_at_10
value: 11.34
- type: precision_at_100
value: 2.344
- type: precision_at_1000
value: 0.359
- type: precision_at_3
value: 18.967
- type: precision_at_5
value: 15.920000000000002
- type: recall_at_1
value: 5.343
- type: recall_at_10
value: 22.997
- type: recall_at_100
value: 47.562
- type: recall_at_1000
value: 72.94500000000001
- type: recall_at_3
value: 11.533
- type: recall_at_5
value: 16.148
- type: main_score
value: 21.901
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 87.3054603493591
- type: cosine_spearman
value: 82.14763206055602
- type: manhattan_pearson
value: 84.78737790237557
- type: manhattan_spearman
value: 81.88455356002758
- type: euclidean_pearson
value: 85.00668629311117
- type: euclidean_spearman
value: 82.14763037860851
- type: main_score
value: 82.14763206055602
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 86.6911864687294
- type: cosine_spearman
value: 77.89286260403269
- type: manhattan_pearson
value: 82.87240347680857
- type: manhattan_spearman
value: 78.10055393740326
- type: euclidean_pearson
value: 82.72282535777123
- type: euclidean_spearman
value: 77.89256648406325
- type: main_score
value: 77.89286260403269
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 87.7220832598633
- type: cosine_spearman
value: 88.30238972017452
- type: manhattan_pearson
value: 87.88214789140248
- type: manhattan_spearman
value: 88.24770220032391
- type: euclidean_pearson
value: 87.98610386257103
- type: euclidean_spearman
value: 88.30238972017452
- type: main_score
value: 88.30238972017452
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 85.70614623247714
- type: cosine_spearman
value: 84.29920990970672
- type: manhattan_pearson
value: 84.9836190531721
- type: manhattan_spearman
value: 84.40933470597638
- type: euclidean_pearson
value: 84.96652336693347
- type: euclidean_spearman
value: 84.29920989531965
- type: main_score
value: 84.29920990970672
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 88.4169972425264
- type: cosine_spearman
value: 89.03555007807218
- type: manhattan_pearson
value: 88.83068699455478
- type: manhattan_spearman
value: 89.21877175674125
- type: euclidean_pearson
value: 88.7251052947544
- type: euclidean_spearman
value: 89.03557389893083
- type: main_score
value: 89.03555007807218
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 85.63830579034632
- type: cosine_spearman
value: 86.77353371581373
- type: manhattan_pearson
value: 86.24830492396637
- type: manhattan_spearman
value: 86.96754348626189
- type: euclidean_pearson
value: 86.09837038778359
- type: euclidean_spearman
value: 86.77353371581373
- type: main_score
value: 86.77353371581373
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 91.2204675588959
- type: cosine_spearman
value: 90.66976712249057
- type: manhattan_pearson
value: 91.11007808242346
- type: manhattan_spearman
value: 90.51739232964488
- type: euclidean_pearson
value: 91.19588941007903
- type: euclidean_spearman
value: 90.66976712249057
- type: main_score
value: 90.66976712249057
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 69.34416749707114
- type: cosine_spearman
value: 68.11632448161046
- type: manhattan_pearson
value: 68.99243488935281
- type: manhattan_spearman
value: 67.8398546438258
- type: euclidean_pearson
value: 69.06376010216088
- type: euclidean_spearman
value: 68.11632448161046
- type: main_score
value: 68.11632448161046
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 88.10309739429758
- type: cosine_spearman
value: 88.40520383147418
- type: manhattan_pearson
value: 88.50753383813232
- type: manhattan_spearman
value: 88.66382629460927
- type: euclidean_pearson
value: 88.35050664609376
- type: euclidean_spearman
value: 88.40520383147418
- type: main_score
value: 88.40520383147418
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 87.58627126942797
- type: mrr
value: 97.01098103058887
- type: main_score
value: 87.58627126942797
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: map_at_1
value: 62.883
- type: map_at_10
value: 75.371
- type: map_at_100
value: 75.66000000000001
- type: map_at_1000
value: 75.667
- type: map_at_3
value: 72.741
- type: map_at_5
value: 74.74
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 80.12700000000001
- type: ndcg_at_100
value: 81.291
- type: ndcg_at_1000
value: 81.464
- type: ndcg_at_3
value: 76.19
- type: ndcg_at_5
value: 78.827
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 10.567
- type: precision_at_100
value: 1.117
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 30.333
- type: precision_at_5
value: 20.133000000000003
- type: recall_at_1
value: 62.883
- type: recall_at_10
value: 93.556
- type: recall_at_100
value: 98.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 83.322
- type: recall_at_5
value: 89.756
- type: main_score
value: 80.12700000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.87524752475248
- type: cos_sim_accuracy_threshold
value: 74.86587762832642
- type: cos_sim_ap
value: 97.02222446606328
- type: cos_sim_f1
value: 93.66197183098592
- type: cos_sim_f1_threshold
value: 74.74223375320435
- type: cos_sim_precision
value: 94.23076923076923
- type: cos_sim_recall
value: 93.10000000000001
- type: dot_accuracy
value: 99.87524752475248
- type: dot_accuracy_threshold
value: 74.86587762832642
- type: dot_ap
value: 97.02222688043362
- type: dot_f1
value: 93.66197183098592
- type: dot_f1_threshold
value: 74.74223375320435
- type: dot_precision
value: 94.23076923076923
- type: dot_recall
value: 93.10000000000001
- type: euclidean_accuracy
value: 99.87524752475248
- type: euclidean_accuracy_threshold
value: 70.9000825881958
- type: euclidean_ap
value: 97.02222446606329
- type: euclidean_f1
value: 93.66197183098592
- type: euclidean_f1_threshold
value: 71.07426524162292
- type: euclidean_precision
value: 94.23076923076923
- type: euclidean_recall
value: 93.10000000000001
- type: manhattan_accuracy
value: 99.87623762376238
- type: manhattan_accuracy_threshold
value: 3588.5040283203125
- type: manhattan_ap
value: 97.09194643777883
- type: manhattan_f1
value: 93.7375745526839
- type: manhattan_f1_threshold
value: 3664.3760681152344
- type: manhattan_precision
value: 93.18181818181817
- type: manhattan_recall
value: 94.3
- type: max_accuracy
value: 99.87623762376238
- type: max_ap
value: 97.09194643777883
- type: max_f1
value: 93.7375745526839
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: main_score
value: 82.10134099988541
- type: v_measure
value: 82.10134099988541
- type: v_measure_std
value: 2.7926349897769533
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: main_score
value: 48.357450742397404
- type: v_measure
value: 48.357450742397404
- type: v_measure_std
value: 1.520118876440547
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 55.79277200802986
- type: mrr
value: 56.742517082590616
- type: main_score
value: 55.79277200802986
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_spearman
value: 30.701215774712693
- type: cosine_pearson
value: 31.26740037278488
- type: dot_spearman
value: 30.701215774712693
- type: dot_pearson
value: 31.267404144879997
- type: main_score
value: 30.701215774712693
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
metrics:
- type: map_at_1
value: 0.23800000000000002
- type: map_at_10
value: 2.31
- type: map_at_100
value: 15.495000000000001
- type: map_at_1000
value: 38.829
- type: map_at_3
value: 0.72
- type: map_at_5
value: 1.185
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 71.39
- type: ndcg_at_1000
value: 64.153
- type: ndcg_at_3
value: 89.877
- type: ndcg_at_5
value: 89.562
- type: precision_at_1
value: 92.0
- type: precision_at_10
value: 92.60000000000001
- type: precision_at_100
value: 73.74000000000001
- type: precision_at_1000
value: 28.222
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 93.60000000000001
- type: recall_at_1
value: 0.23800000000000002
- type: recall_at_10
value: 2.428
- type: recall_at_100
value: 18.099999999999998
- type: recall_at_1000
value: 60.79599999999999
- type: recall_at_3
value: 0.749
- type: recall_at_5
value: 1.238
- type: main_score
value: 88.442
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: map_at_1
value: 3.4939999999999998
- type: map_at_10
value: 12.531999999999998
- type: map_at_100
value: 19.147
- type: map_at_1000
value: 20.861
- type: map_at_3
value: 7.558
- type: map_at_5
value: 9.49
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 47.959
- type: ndcg_at_10
value: 31.781
- type: ndcg_at_100
value: 42.131
- type: ndcg_at_1000
value: 53.493
- type: ndcg_at_3
value: 39.204
- type: ndcg_at_5
value: 34.635
- type: precision_at_1
value: 48.980000000000004
- type: precision_at_10
value: 27.143
- type: precision_at_100
value: 8.224
- type: precision_at_1000
value: 1.584
- type: precision_at_3
value: 38.775999999999996
- type: precision_at_5
value: 33.061
- type: recall_at_1
value: 3.4939999999999998
- type: recall_at_10
value: 18.895
- type: recall_at_100
value: 50.192
- type: recall_at_1000
value: 85.167
- type: recall_at_3
value: 8.703
- type: recall_at_5
value: 11.824
- type: main_score
value: 31.781
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 92.7402
- type: accuracy_stderr
value: 1.020764595781027
- type: ap
value: 44.38594756333084
- type: ap_stderr
value: 1.817150701258273
- type: f1
value: 79.95699280019547
- type: f1_stderr
value: 1.334582498702029
- type: main_score
value: 92.7402
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 80.86870401810978
- type: accuracy_stderr
value: 0.22688467782004712
- type: f1
value: 81.1829040745744
- type: f1_stderr
value: 0.19774920574849694
- type: main_score
value: 80.86870401810978
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: main_score
value: 64.82048869927482
- type: v_measure
value: 64.82048869927482
- type: v_measure_std
value: 0.9170394252450564
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 88.44251057996067
- type: cos_sim_accuracy_threshold
value: 70.2150285243988
- type: cos_sim_ap
value: 81.11422351199913
- type: cos_sim_f1
value: 73.71062868615887
- type: cos_sim_f1_threshold
value: 66.507488489151
- type: cos_sim_precision
value: 70.2799712849964
- type: cos_sim_recall
value: 77.4934036939314
- type: dot_accuracy
value: 88.44251057996067
- type: dot_accuracy_threshold
value: 70.2150285243988
- type: dot_ap
value: 81.11420529068658
- type: dot_f1
value: 73.71062868615887
- type: dot_f1_threshold
value: 66.50749444961548
- type: dot_precision
value: 70.2799712849964
- type: dot_recall
value: 77.4934036939314
- type: euclidean_accuracy
value: 88.44251057996067
- type: euclidean_accuracy_threshold
value: 77.18156576156616
- type: euclidean_ap
value: 81.11422421732487
- type: euclidean_f1
value: 73.71062868615887
- type: euclidean_f1_threshold
value: 81.84436559677124
- type: euclidean_precision
value: 70.2799712849964
- type: euclidean_recall
value: 77.4934036939314
- type: manhattan_accuracy
value: 88.26369434344639
- type: manhattan_accuracy_threshold
value: 3837.067413330078
- type: manhattan_ap
value: 80.81442360477725
- type: manhattan_f1
value: 73.39883099117024
- type: manhattan_f1_threshold
value: 4098.833847045898
- type: manhattan_precision
value: 69.41896024464832
- type: manhattan_recall
value: 77.86279683377309
- type: max_accuracy
value: 88.44251057996067
- type: max_ap
value: 81.11422421732487
- type: max_f1
value: 73.71062868615887
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy
value: 90.03182365040556
- type: cos_sim_accuracy_threshold
value: 64.46443796157837
- type: cos_sim_ap
value: 87.86649113691112
- type: cos_sim_f1
value: 80.45644844577821
- type: cos_sim_f1_threshold
value: 61.40774488449097
- type: cos_sim_precision
value: 77.54052702992216
- type: cos_sim_recall
value: 83.60024638127503
- type: dot_accuracy
value: 90.03182365040556
- type: dot_accuracy_threshold
value: 64.46444988250732
- type: dot_ap
value: 87.86649011954319
- type: dot_f1
value: 80.45644844577821
- type: dot_f1_threshold
value: 61.407750844955444
- type: dot_precision
value: 77.54052702992216
- type: dot_recall
value: 83.60024638127503
- type: euclidean_accuracy
value: 90.03182365040556
- type: euclidean_accuracy_threshold
value: 84.30368900299072
- type: euclidean_ap
value: 87.86649114275045
- type: euclidean_f1
value: 80.45644844577821
- type: euclidean_f1_threshold
value: 87.8547191619873
- type: euclidean_precision
value: 77.54052702992216
- type: euclidean_recall
value: 83.60024638127503
- type: manhattan_accuracy
value: 89.99883572010712
- type: manhattan_accuracy_threshold
value: 4206.838607788086
- type: manhattan_ap
value: 87.8600826607838
- type: manhattan_f1
value: 80.44054508120217
- type: manhattan_f1_threshold
value: 4372.755432128906
- type: manhattan_precision
value: 78.08219178082192
- type: manhattan_recall
value: 82.94579611949491
- type: max_accuracy
value: 90.03182365040556
- type: max_ap
value: 87.86649114275045
- type: max_f1
value: 80.45644844577821
task:
type: PairClassification
language:
- en
license: cc-by-nc-4.0
library_name: transformers
---
## Introduction
We present NV-Embed-v2, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark ([MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard))(as of Aug 30, 2024) with a score of 72.31 across 56 text embedding tasks. It also holds the No. 1 in the retrieval sub-category (a score of 62.65 across 15 tasks) in the leaderboard, which is essential to the development of RAG technology.
NV-Embed-v2 presents several new designs, including having the LLM attend to latent vectors for better pooled embedding output, and demonstrating a two-staged instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks. Additionally, NV-Embed-v2 incorporates a novel hard-negative mining methods that take into account the positive relevance score for better false negatives removal.
For more technical details, refer to our paper: [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://arxiv.org/pdf/2405.17428).
## Model Details
- Base Decoder-only LLM: [Mistral-7B-v0.1](https://huggingface.co/mistralai/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](https://huggingface.co/nvidia/NV-Embed-v2#2-required-packages).
### Usage (HuggingFace Transformers)
```python
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-v2', trust_remote_code=True)
# get the embeddings
max_length = 32768
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())
# [[87.42693328857422, 0.46283677220344543], [0.965264618396759, 86.03721618652344]]
```
### Usage (Sentence-Transformers)
```python
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-v2', trust_remote_code=True)
model.max_seq_length = 32768
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())
```
## License
This model should not be used for any commercial purpose. Refer the [license](https://spdx.org/licenses/CC-BY-NC-4.0) for the detailed terms.
For commercial purpose, we recommend you to use the models of [NeMo Retriever Microservices (NIMs)](https://build.nvidia.com/explore/retrieval).
## 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:
```bibtex
@article{lee2024nv,
title={NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models},
author={Lee, Chankyu and Roy, Rajarshi and Xu, Mengyao and Raiman, Jonathan and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
journal={arXiv preprint arXiv:2405.17428},
year={2024}
}
```
```bibtex
@article{moreira2024nv,
title={NV-Retriever: Improving text embedding models with effective hard-negative mining},
author={Moreira, Gabriel de Souza P and Osmulski, Radek and Xu, Mengyao and Ak, Ronay and Schifferer, Benedikt and Oldridge, Even},
journal={arXiv preprint arXiv:2407.15831},
year={2024}
}
```
## Troubleshooting
#### 1. Instruction template for MTEB benchmarks
For MTEB sub-tasks for retrieval, STS, summarization, please use the instruction prefix template in [instructions.json](https://huggingface.co/nvidia/NV-Embed-v2/blob/main/instructions.json). For classification, clustering and reranking, please use the instructions provided in Table. 7 in [NV-Embed paper](https://arxiv.org/pdf/2405.17428).
#### 2. Required Packages
If you have trouble, try installing the python packages as below
```python
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. How to enable Multi-GPU (Note, this is the case for HuggingFace Transformers)
```python
from transformers import AutoModel
from torch.nn import DataParallel
embedding_model = AutoModel.from_pretrained("nvidia/NV-Embed-v2")
for module_key, module in embedding_model._modules.items():
embedding_model._modules[module_key] = DataParallel(module)
```
#### 4. Fixing "nvidia/NV-Embed-v2 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.
#### 5. Access to model nvidia/NV-Embed-v2 is restricted. You must be authenticated to access it
Use your huggingface access [token](https://huggingface.co/settings/tokens) to execute *"huggingface-cli login"*.
#### 6. 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](https://github.com/UKPLab/sentence-transformers/blob/v2.7-release/sentence_transformers/SentenceTransformer.py#L353) as below.
```python
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 .
```
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