# NV-Embed-v1 --- ## README([From Huggingface](https://huggingface.co/nvidia/NV-Embed-v1)) --- 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 - 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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 - 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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](https://arxiv.org/abs/2210.07316))(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](https://arxiv.org/pdf/2405.17428). For more benchmark results (other than MTEB), please find the [AIR-Bench](https://huggingface.co/spaces/AIR-Bench/leaderboard) for QA (English only) and Long-Doc. ## 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-v1#2-required-packages). ### Usage (HuggingFace Transformers) ```python 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) ```python 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: ```bibtex @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](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). ## Troubleshooting #### 1. How to enable Multi-GPU (Note, this is the case for HuggingFace Transformers) ```python 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 ```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. 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](https://huggingface.co/settings/tokens) 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](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 . ``` ## Model Files - [README.md](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/README.md) (51.3 KB) - [config.json](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/config.json) (707.0 B) - [model_state-00001-of-00004.pdparams](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/model_state-00001-of-00004.pdparams) (3.7 GB) - [model_state-00002-of-00004.pdparams](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/model_state-00002-of-00004.pdparams) (3.7 GB) - [model_state-00003-of-00004.pdparams](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/model_state-00003-of-00004.pdparams) (3.7 GB) - [model_state-00004-of-00004.pdparams](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/model_state-00004-of-00004.pdparams) (3.6 GB) - [model_state.pdparams.index.json](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/model_state.pdparams.index.json) (23.8 KB) - [sentencepiece.bpe.model](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/sentencepiece.bpe.model) (481.9 KB) - [special_tokens_map.json](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/special_tokens_map.json) (72.0 B) - [tokenizer.json](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/tokenizer.json) (1.7 MB) - [tokenizer_config.json](https://paddlenlp.bj.bcebos.com/models/community/nvidia/NV-Embed-v1/tokenizer_config.json) (1.4 KB) [Back to Main](../../)