--- 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 . ```