--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit model-index: - name: intfloat/multilingual-e5-small results: - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 36.9996434842022 - type: f1 value: 67.95453679103099 task: type: Classification - dataset: config: de name: MTEB AmazonCounterfactualClassification (de) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 71.64882226980728 - type: ap value: 82.11942130026586 - type: f1 value: 69.87963421606715 task: type: Classification - dataset: config: en-ext name: MTEB AmazonCounterfactualClassification (en-ext) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 75.8095952023988 - type: ap value: 24.46869495579561 - type: f1 value: 63.00108480037597 task: type: Classification - dataset: config: ja name: MTEB AmazonCounterfactualClassification (ja) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 64.186295503212 - type: ap value: 15.496804690197042 - type: f1 value: 52.07153895475031 task: type: Classification - dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics: - type: accuracy value: 88.699325 - type: ap value: 85.27039559917269 - type: f1 value: 88.65556295032513 task: type: Classification - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 44.69799999999999 - type: f1 value: 43.73187348654165 task: type: Classification - dataset: config: de name: MTEB AmazonReviewsClassification (de) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 40.245999999999995 - type: f1 value: 39.3863530637684 task: type: Classification - dataset: config: es name: MTEB AmazonReviewsClassification (es) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 40.394 - type: f1 value: 39.301223469483446 task: type: Classification - dataset: config: fr name: MTEB AmazonReviewsClassification (fr) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 38.864 - type: f1 value: 37.97974261868003 task: type: Classification - dataset: config: ja name: MTEB AmazonReviewsClassification (ja) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 37.682 - type: f1 value: 37.07399369768313 task: type: Classification - dataset: config: zh name: MTEB AmazonReviewsClassification (zh) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 37.504 - type: f1 value: 36.62317273874278 task: type: Classification - dataset: config: default name: MTEB ArguAna revision: None split: test type: arguana metrics: - type: map_at_1 value: 19.061 - type: map_at_10 value: 31.703 - type: map_at_100 value: 32.967 - type: map_at_1000 value: 33.001000000000005 - type: map_at_3 value: 27.466 - type: map_at_5 value: 29.564 - type: mrr_at_1 value: 19.559 - type: mrr_at_10 value: 31.874999999999996 - type: mrr_at_100 value: 33.146 - type: mrr_at_1000 value: 33.18 - type: mrr_at_3 value: 27.667 - type: mrr_at_5 value: 29.74 - type: ndcg_at_1 value: 19.061 - type: ndcg_at_10 value: 39.062999999999995 - type: ndcg_at_100 value: 45.184000000000005 - type: ndcg_at_1000 value: 46.115 - type: ndcg_at_3 value: 30.203000000000003 - type: ndcg_at_5 value: 33.953 - type: precision_at_1 value: 19.061 - type: precision_at_10 value: 6.279999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 12.706999999999999 - type: precision_at_5 value: 9.431000000000001 - type: recall_at_1 value: 19.061 - type: recall_at_10 value: 62.802 - type: recall_at_100 value: 91.323 - type: recall_at_1000 value: 98.72 - type: recall_at_3 value: 38.122 - type: recall_at_5 value: 47.155 task: type: Retrieval - dataset: config: default name: MTEB ArxivClusteringP2P revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics: - type: v_measure value: 39.22266660528253 task: type: Clustering - dataset: config: default name: MTEB ArxivClusteringS2S revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics: - type: v_measure value: 30.79980849482483 task: type: Clustering - dataset: config: default name: MTEB AskUbuntuDupQuestions revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics: - type: map value: 57.8790068352054 - type: mrr value: 71.78791276436706 task: type: Reranking - dataset: config: default name: MTEB BIOSSES revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cos_sim_pearson value: 82.36328364043163 - type: cos_sim_spearman value: 82.26211536195868 - type: euclidean_pearson value: 80.3183865039173 - type: euclidean_spearman value: 79.88495276296132 - type: manhattan_pearson value: 80.14484480692127 - type: manhattan_spearman value: 80.39279565980743 task: type: STS - dataset: config: de-en name: MTEB BUCC (de-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining metrics: - type: accuracy value: 98.0375782881002 - type: f1 value: 97.86012526096033 - type: precision value: 97.77139874739039 - type: recall value: 98.0375782881002 task: type: BitextMining - dataset: config: fr-en name: MTEB BUCC (fr-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining metrics: - type: accuracy value: 93.35241030156286 - type: f1 value: 92.66050333846944 - type: precision value: 92.3306919069631 - type: recall value: 93.35241030156286 task: type: BitextMining - dataset: config: ru-en name: MTEB BUCC (ru-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining metrics: - type: accuracy value: 94.0699688257707 - type: f1 value: 93.50236693222492 - type: precision value: 93.22791825424315 - type: recall value: 94.0699688257707 task: type: BitextMining - dataset: config: zh-en name: MTEB BUCC (zh-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining metrics: - type: accuracy value: 89.25750394944708 - type: f1 value: 88.79234684921889 - type: precision value: 88.57293312269616 - type: recall value: 89.25750394944708 task: type: BitextMining - dataset: config: default name: MTEB Banking77Classification revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics: - type: accuracy value: 79.41558441558442 - type: f1 value: 79.25886487487219 task: type: Classification - dataset: config: default name: MTEB BiorxivClusteringP2P revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics: - type: v_measure value: 35.747820820329736 task: type: Clustering - dataset: config: default name: MTEB BiorxivClusteringS2S revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics: - type: v_measure value: 27.045143830596146 task: type: Clustering - dataset: config: default name: MTEB CQADupstackRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 24.252999999999997 - type: map_at_10 value: 31.655916666666666 - type: map_at_100 value: 32.680749999999996 - type: map_at_1000 value: 32.79483333333334 - type: map_at_3 value: 29.43691666666666 - type: map_at_5 value: 30.717416666666665 - type: mrr_at_1 value: 28.602750000000004 - type: mrr_at_10 value: 35.56875 - type: mrr_at_100 value: 36.3595 - type: mrr_at_1000 value: 36.427749999999996 - type: mrr_at_3 value: 33.586166666666664 - type: mrr_at_5 value: 34.73641666666666 - type: ndcg_at_1 value: 28.602750000000004 - type: ndcg_at_10 value: 36.06933333333334 - type: ndcg_at_100 value: 40.70141666666667 - type: ndcg_at_1000 value: 43.24341666666667 - type: ndcg_at_3 value: 32.307916666666664 - type: ndcg_at_5 value: 34.129999999999995 - type: precision_at_1 value: 28.602750000000004 - type: precision_at_10 value: 6.097666666666667 - type: precision_at_100 value: 0.9809166666666668 - type: precision_at_1000 value: 0.13766666666666663 - type: precision_at_3 value: 14.628166666666667 - type: precision_at_5 value: 10.266916666666667 - type: recall_at_1 value: 24.252999999999997 - type: recall_at_10 value: 45.31916666666667 - type: recall_at_100 value: 66.03575000000001 - type: recall_at_1000 value: 83.94708333333334 - type: recall_at_3 value: 34.71941666666666 - type: recall_at_5 value: 39.46358333333333 task: type: Retrieval - dataset: config: default name: MTEB ClimateFEVER revision: None split: test type: climate-fever metrics: - type: map_at_1 value: 9.024000000000001 - type: map_at_10 value: 15.644 - type: map_at_100 value: 17.154 - type: map_at_1000 value: 17.345 - type: map_at_3 value: 13.028 - type: map_at_5 value: 14.251 - type: mrr_at_1 value: 19.674 - type: mrr_at_10 value: 29.826999999999998 - type: mrr_at_100 value: 30.935000000000002 - type: mrr_at_1000 value: 30.987 - type: mrr_at_3 value: 26.645000000000003 - type: mrr_at_5 value: 28.29 - type: ndcg_at_1 value: 19.674 - type: ndcg_at_10 value: 22.545 - type: ndcg_at_100 value: 29.207 - type: ndcg_at_1000 value: 32.912 - type: ndcg_at_3 value: 17.952 - type: ndcg_at_5 value: 19.363 - type: precision_at_1 value: 19.674 - type: precision_at_10 value: 7.212000000000001 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 13.507 - type: precision_at_5 value: 10.397 - type: recall_at_1 value: 9.024000000000001 - type: recall_at_10 value: 28.077999999999996 - type: recall_at_100 value: 51.403 - type: recall_at_1000 value: 72.406 - type: recall_at_3 value: 16.768 - type: recall_at_5 value: 20.737 task: type: Retrieval - dataset: config: default name: MTEB DBPedia revision: None split: test type: dbpedia-entity metrics: - type: map_at_1 value: 8.012 - type: map_at_10 value: 17.138 - type: map_at_100 value: 24.146 - type: map_at_1000 value: 25.622 - type: map_at_3 value: 12.552 - type: map_at_5 value: 14.435 - type: mrr_at_1 value: 62.25000000000001 - type: mrr_at_10 value: 71.186 - type: mrr_at_100 value: 71.504 - type: mrr_at_1000 value: 71.514 - type: mrr_at_3 value: 69.333 - type: mrr_at_5 value: 70.408 - type: ndcg_at_1 value: 49.75 - type: ndcg_at_10 value: 37.76 - type: ndcg_at_100 value: 42.071 - type: ndcg_at_1000 value: 49.309 - type: ndcg_at_3 value: 41.644 - type: ndcg_at_5 value: 39.812999999999995 - type: precision_at_1 value: 62.25000000000001 - type: precision_at_10 value: 30.15 - type: precision_at_100 value: 9.753 - type: precision_at_1000 value: 1.9189999999999998 - type: precision_at_3 value: 45.667 - type: precision_at_5 value: 39.15 - type: recall_at_1 value: 8.012 - type: recall_at_10 value: 22.599 - type: recall_at_100 value: 48.068 - type: recall_at_1000 value: 71.328 - type: recall_at_3 value: 14.043 - type: recall_at_5 value: 17.124 task: type: Retrieval - dataset: config: default name: MTEB EmotionClassification revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 split: test type: mteb/emotion metrics: - type: accuracy value: 42.455 - type: f1 value: 37.59462649781862 task: type: Classification - dataset: config: default name: MTEB FEVER revision: None split: test type: fever metrics: - type: map_at_1 value: 58.092 - type: map_at_10 value: 69.586 - type: map_at_100 value: 69.968 - type: map_at_1000 value: 69.982 - type: map_at_3 value: 67.48100000000001 - type: map_at_5 value: 68.915 - type: mrr_at_1 value: 62.166 - type: mrr_at_10 value: 73.588 - type: mrr_at_100 value: 73.86399999999999 - type: mrr_at_1000 value: 73.868 - type: mrr_at_3 value: 71.6 - type: mrr_at_5 value: 72.99 - type: ndcg_at_1 value: 62.166 - type: ndcg_at_10 value: 75.27199999999999 - type: ndcg_at_100 value: 76.816 - type: ndcg_at_1000 value: 77.09700000000001 - type: ndcg_at_3 value: 71.36 - type: ndcg_at_5 value: 73.785 - type: precision_at_1 value: 62.166 - type: precision_at_10 value: 9.716 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 28.278 - type: precision_at_5 value: 18.343999999999998 - type: recall_at_1 value: 58.092 - type: recall_at_10 value: 88.73400000000001 - type: recall_at_100 value: 95.195 - type: recall_at_1000 value: 97.04599999999999 - type: recall_at_3 value: 78.45 - type: recall_at_5 value: 84.316 task: type: Retrieval - dataset: config: default name: MTEB FiQA2018 revision: None split: test type: fiqa metrics: - type: map_at_1 value: 16.649 - type: map_at_10 value: 26.457000000000004 - type: map_at_100 value: 28.169 - type: map_at_1000 value: 28.352 - type: map_at_3 value: 23.305 - type: map_at_5 value: 25.169000000000004 - type: mrr_at_1 value: 32.407000000000004 - type: mrr_at_10 value: 40.922 - type: mrr_at_100 value: 41.931000000000004 - type: mrr_at_1000 value: 41.983 - type: mrr_at_3 value: 38.786 - type: mrr_at_5 value: 40.205999999999996 - type: ndcg_at_1 value: 32.407000000000004 - type: ndcg_at_10 value: 33.314 - type: ndcg_at_100 value: 40.312 - type: ndcg_at_1000 value: 43.685 - type: ndcg_at_3 value: 30.391000000000002 - type: ndcg_at_5 value: 31.525 - type: precision_at_1 value: 32.407000000000004 - type: precision_at_10 value: 8.966000000000001 - type: precision_at_100 value: 1.6019999999999999 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 20.165 - type: precision_at_5 value: 14.722 - type: recall_at_1 value: 16.649 - type: recall_at_10 value: 39.117000000000004 - type: recall_at_100 value: 65.726 - type: recall_at_1000 value: 85.784 - type: recall_at_3 value: 27.914 - type: recall_at_5 value: 33.289 task: type: Retrieval - dataset: config: default name: MTEB HotpotQA revision: None split: test type: hotpotqa metrics: - type: map_at_1 value: 36.253 - type: map_at_10 value: 56.16799999999999 - type: map_at_100 value: 57.06099999999999 - type: map_at_1000 value: 57.126 - type: map_at_3 value: 52.644999999999996 - type: map_at_5 value: 54.909 - type: mrr_at_1 value: 72.505 - type: mrr_at_10 value: 79.66 - type: mrr_at_100 value: 79.869 - type: mrr_at_1000 value: 79.88 - type: mrr_at_3 value: 78.411 - type: mrr_at_5 value: 79.19800000000001 - type: ndcg_at_1 value: 72.505 - type: ndcg_at_10 value: 65.094 - type: ndcg_at_100 value: 68.219 - type: ndcg_at_1000 value: 69.515 - type: ndcg_at_3 value: 59.99 - type: ndcg_at_5 value: 62.909000000000006 - type: precision_at_1 value: 72.505 - type: precision_at_10 value: 13.749 - type: precision_at_100 value: 1.619 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.357 - type: precision_at_5 value: 25.313000000000002 - type: recall_at_1 value: 36.253 - type: recall_at_10 value: 68.744 - type: recall_at_100 value: 80.925 - type: recall_at_1000 value: 89.534 - type: recall_at_3 value: 57.535000000000004 - type: recall_at_5 value: 63.282000000000004 task: type: Retrieval - dataset: config: default name: MTEB ImdbClassification revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics: - type: accuracy value: 80.82239999999999 - type: ap value: 75.65895781725314 - type: f1 value: 80.75880969095746 task: type: Classification - dataset: config: default name: MTEB MSMARCO revision: None split: dev type: msmarco metrics: - type: map_at_1 value: 21.624 - type: map_at_10 value: 34.075 - type: map_at_100 value: 35.229 - type: map_at_1000 value: 35.276999999999994 - type: map_at_3 value: 30.245 - type: map_at_5 value: 32.42 - type: mrr_at_1 value: 22.264 - type: mrr_at_10 value: 34.638000000000005 - type: mrr_at_100 value: 35.744 - type: mrr_at_1000 value: 35.787 - type: mrr_at_3 value: 30.891000000000002 - type: mrr_at_5 value: 33.042 - type: ndcg_at_1 value: 22.264 - type: ndcg_at_10 value: 40.991 - type: ndcg_at_100 value: 46.563 - type: ndcg_at_1000 value: 47.743 - type: ndcg_at_3 value: 33.198 - type: ndcg_at_5 value: 37.069 - type: precision_at_1 value: 22.264 - type: precision_at_10 value: 6.5089999999999995 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 14.216999999999999 - type: precision_at_5 value: 10.487 - type: recall_at_1 value: 21.624 - type: recall_at_10 value: 62.303 - type: recall_at_100 value: 88.124 - type: recall_at_1000 value: 97.08 - type: recall_at_3 value: 41.099999999999994 - type: recall_at_5 value: 50.381 task: type: Retrieval - dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 91.06703146374831 - type: f1 value: 90.86867815863172 task: type: Classification - dataset: config: de name: MTEB MTOPDomainClassification (de) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 87.46970977740209 - type: f1 value: 86.36832872036588 task: type: Classification - dataset: config: es name: MTEB MTOPDomainClassification (es) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 89.26951300867245 - type: f1 value: 88.93561193959502 task: type: Classification - dataset: config: fr name: MTEB MTOPDomainClassification (fr) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 84.22799874725963 - type: f1 value: 84.30490069236556 task: type: Classification - dataset: config: hi name: MTEB MTOPDomainClassification (hi) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 86.02007888131948 - type: f1 value: 85.39376041027991 task: type: Classification - dataset: config: th name: MTEB MTOPDomainClassification (th) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 85.34900542495481 - type: f1 value: 85.39859673336713 task: type: Classification - dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 71.078431372549 - type: f1 value: 53.45071102002276 task: type: Classification - dataset: config: de name: MTEB MTOPIntentClassification (de) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 65.85798816568047 - type: f1 value: 46.53112748993529 task: type: Classification - dataset: config: es name: MTEB MTOPIntentClassification (es) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 67.96864576384256 - type: f1 value: 45.966703022829506 task: type: Classification - dataset: config: fr name: MTEB MTOPIntentClassification (fr) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 61.31537738803633 - type: f1 value: 45.52601712835461 task: type: Classification - dataset: config: hi name: MTEB MTOPIntentClassification (hi) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 66.29616349946218 - type: f1 value: 47.24166485726613 task: type: Classification - dataset: config: th name: MTEB MTOPIntentClassification (th) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 67.51537070524412 - type: f1 value: 49.463476319014276 task: type: Classification - dataset: config: af name: MTEB MassiveIntentClassification (af) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 57.06792199058508 - type: f1 value: 54.094921857502285 task: type: Classification - dataset: config: am name: MTEB MassiveIntentClassification (am) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 51.960322797579025 - type: f1 value: 48.547371223370945 task: type: Classification - dataset: config: ar name: MTEB MassiveIntentClassification (ar) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 54.425016812373904 - type: f1 value: 50.47069202054312 task: type: Classification - dataset: config: az name: MTEB MassiveIntentClassification (az) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - 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dataset: config: sq name: MTEB MassiveIntentClassification (sq) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 57.985877605917956 - type: f1 value: 54.46187524463802 task: type: Classification - dataset: config: sv name: MTEB MassiveIntentClassification (sv) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 65.03026227303296 - type: f1 value: 62.34377392877748 task: type: Classification - dataset: config: sw name: MTEB MassiveIntentClassification (sw) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 53.567585743106925 - type: f1 value: 50.73770655983206 task: type: Classification - dataset: config: ta name: MTEB MassiveIntentClassification (ta) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - 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type: accuracy value: 56.65770006724949 - type: f1 value: 55.84219135523227 task: type: Classification - dataset: config: ml name: MTEB MassiveScenarioClassification (ml) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 66.53665097511768 - type: f1 value: 65.09087787792639 task: type: Classification - dataset: config: mn name: MTEB MassiveScenarioClassification (mn) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 59.31405514458642 - type: f1 value: 58.06135303831491 task: type: Classification - dataset: config: ms name: MTEB MassiveScenarioClassification (ms) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 64.88231338264964 - type: f1 value: 62.751099407787926 task: type: Classification - dataset: config: my name: MTEB MassiveScenarioClassification (my) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 58.86012104909213 - type: f1 value: 56.29118323058282 task: type: Classification - dataset: config: nb name: MTEB MassiveScenarioClassification (nb) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 67.37390719569602 - type: f1 value: 66.27922244885102 task: type: Classification - dataset: config: nl name: MTEB MassiveScenarioClassification (nl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 70.8675184936113 - type: f1 value: 70.22146529932019 task: type: Classification - dataset: config: pl name: MTEB MassiveScenarioClassification (pl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 68.2212508406187 - type: f1 value: 67.77454802056282 task: type: Classification - dataset: config: pt name: MTEB MassiveScenarioClassification (pt) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 68.18090114324143 - type: f1 value: 68.03737625431621 task: type: Classification - dataset: config: ro name: MTEB MassiveScenarioClassification (ro) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 64.65030262273034 - type: f1 value: 63.792945486912856 task: type: Classification - dataset: config: ru name: MTEB MassiveScenarioClassification (ru) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 63.772749631087066 - type: f1 value: 63.4539101720024 - type: f1_weighted value: 62.778603897469566 - type: main_score value: 63.772749631087066 task: type: Classification - dataset: config: sl name: MTEB MassiveScenarioClassification (sl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 60.17821116341627 - type: f1 value: 59.3935969827171 task: type: Classification - dataset: config: sq name: MTEB MassiveScenarioClassification (sq) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 62.86146603900471 - type: f1 value: 60.133692735032376 task: type: Classification - dataset: config: sv name: MTEB MassiveScenarioClassification (sv) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 70.89441829186282 - type: f1 value: 70.03064076194089 task: type: Classification - dataset: config: sw name: MTEB MassiveScenarioClassification (sw) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 58.15063887020847 - type: f1 value: 56.23326278499678 task: type: Classification - dataset: config: ta name: MTEB MassiveScenarioClassification (ta) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 59.43846671149966 - type: f1 value: 57.70440450281974 task: type: Classification - dataset: config: te name: MTEB MassiveScenarioClassification (te) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 60.8507061197041 - type: f1 value: 59.22916396061171 task: type: Classification - dataset: config: th name: MTEB MassiveScenarioClassification (th) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 70.65568258238063 - type: f1 value: 69.90736239440633 task: type: Classification - dataset: config: tl name: MTEB MassiveScenarioClassification (tl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 60.8843308675185 - type: f1 value: 59.30332663713599 task: type: Classification - dataset: config: tr name: MTEB MassiveScenarioClassification (tr) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 68.05312710154674 - type: f1 value: 67.44024062594775 task: type: Classification - dataset: config: ur name: MTEB MassiveScenarioClassification (ur) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 62.111634162743776 - type: f1 value: 60.89083013084519 task: type: Classification - dataset: config: vi name: MTEB MassiveScenarioClassification (vi) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 67.44115669132482 - type: f1 value: 67.92227541674552 task: type: Classification - dataset: config: zh-CN name: MTEB MassiveScenarioClassification (zh-CN) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 74.4687289845326 - type: f1 value: 74.16376793486025 task: type: Classification - dataset: config: zh-TW name: MTEB MassiveScenarioClassification (zh-TW) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 68.31876260928043 - type: f1 value: 68.5246745215607 task: type: Classification - dataset: config: default name: MTEB MedrxivClusteringP2P revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics: - type: v_measure value: 30.90431696479766 task: type: Clustering - dataset: config: default name: MTEB MedrxivClusteringS2S revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 split: test type: mteb/medrxiv-clustering-s2s metrics: - type: v_measure value: 27.259158476693774 task: type: Clustering - dataset: config: default name: MTEB MindSmallReranking revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 split: test type: mteb/mind_small metrics: - type: map value: 30.28445330838555 - type: mrr value: 31.15758529581164 task: type: Reranking - dataset: config: default name: MTEB NFCorpus revision: None split: test type: nfcorpus metrics: - type: map_at_1 value: 5.353 - type: map_at_10 value: 11.565 - type: map_at_100 value: 14.097000000000001 - type: map_at_1000 value: 15.354999999999999 - type: map_at_3 value: 8.749 - type: map_at_5 value: 9.974 - type: mrr_at_1 value: 42.105 - type: mrr_at_10 value: 50.589 - type: mrr_at_100 value: 51.187000000000005 - type: mrr_at_1000 value: 51.233 - type: mrr_at_3 value: 48.246 - type: mrr_at_5 value: 49.546 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 31.009999999999998 - type: ndcg_at_100 value: 28.026 - type: ndcg_at_1000 value: 36.905 - type: ndcg_at_3 value: 35.983 - type: ndcg_at_5 value: 33.764 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 22.786 - type: precision_at_100 value: 6.916 - type: precision_at_1000 value: 1.981 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 28.731 - type: recall_at_1 value: 5.353 - type: recall_at_10 value: 15.039 - type: recall_at_100 value: 27.348 - type: recall_at_1000 value: 59.453 - type: recall_at_3 value: 9.792 - type: recall_at_5 value: 11.882 task: type: Retrieval - dataset: config: default name: MTEB NQ revision: None split: test type: nq metrics: - type: map_at_1 value: 33.852 - type: map_at_10 value: 48.924 - type: map_at_100 value: 49.854 - type: map_at_1000 value: 49.886 - type: map_at_3 value: 44.9 - type: map_at_5 value: 47.387 - type: mrr_at_1 value: 38.035999999999994 - type: mrr_at_10 value: 51.644 - type: mrr_at_100 value: 52.339 - type: mrr_at_1000 value: 52.35999999999999 - type: mrr_at_3 value: 48.421 - type: mrr_at_5 value: 50.468999999999994 - type: ndcg_at_1 value: 38.007000000000005 - type: ndcg_at_10 value: 56.293000000000006 - type: ndcg_at_100 value: 60.167 - type: ndcg_at_1000 value: 60.916000000000004 - type: ndcg_at_3 value: 48.903999999999996 - type: ndcg_at_5 value: 52.978 - type: precision_at_1 value: 38.007000000000005 - type: precision_at_10 value: 9.041 - type: precision_at_100 value: 1.1199999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 22.084 - type: precision_at_5 value: 15.608 - type: recall_at_1 value: 33.852 - type: recall_at_10 value: 75.893 - type: recall_at_100 value: 92.589 - type: recall_at_1000 value: 98.153 - type: recall_at_3 value: 56.969 - type: recall_at_5 value: 66.283 task: type: Retrieval - dataset: config: default name: MTEB QuoraRetrieval revision: None split: test type: quora metrics: - type: map_at_1 value: 69.174 - type: map_at_10 value: 82.891 - type: map_at_100 value: 83.545 - type: map_at_1000 value: 83.56700000000001 - type: map_at_3 value: 79.944 - type: map_at_5 value: 81.812 - type: mrr_at_1 value: 79.67999999999999 - type: mrr_at_10 value: 86.279 - type: mrr_at_100 value: 86.39 - type: mrr_at_1000 value: 86.392 - type: mrr_at_3 value: 85.21 - type: mrr_at_5 value: 85.92999999999999 - type: ndcg_at_1 value: 79.69000000000001 - type: ndcg_at_10 value: 86.929 - type: ndcg_at_100 value: 88.266 - type: ndcg_at_1000 value: 88.428 - type: ndcg_at_3 value: 83.899 - type: ndcg_at_5 value: 85.56700000000001 - type: precision_at_1 value: 79.69000000000001 - type: precision_at_10 value: 13.161000000000001 - type: precision_at_100 value: 1.513 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.603 - type: precision_at_5 value: 24.138 - type: recall_at_1 value: 69.174 - type: recall_at_10 value: 94.529 - type: recall_at_100 value: 99.15 - type: recall_at_1000 value: 99.925 - type: recall_at_3 value: 85.86200000000001 - type: recall_at_5 value: 90.501 task: type: Retrieval - dataset: config: default name: MTEB RedditClustering revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics: - type: v_measure value: 39.13064340585255 task: type: Clustering - dataset: config: default name: MTEB RedditClusteringP2P revision: 282350215ef01743dc01b456c7f5241fa8937f16 split: test type: mteb/reddit-clustering-p2p metrics: - type: v_measure value: 58.97884249325877 task: type: Clustering - dataset: config: default name: MTEB SCIDOCS revision: None split: test type: scidocs metrics: - type: map_at_1 value: 3.4680000000000004 - type: map_at_10 value: 7.865 - type: map_at_100 value: 9.332 - type: map_at_1000 value: 9.587 - type: map_at_3 value: 5.800000000000001 - type: map_at_5 value: 6.8790000000000004 - type: mrr_at_1 value: 17.0 - type: mrr_at_10 value: 25.629 - type: mrr_at_100 value: 26.806 - type: mrr_at_1000 value: 26.889000000000003 - type: mrr_at_3 value: 22.8 - type: mrr_at_5 value: 24.26 - type: ndcg_at_1 value: 17.0 - type: ndcg_at_10 value: 13.895 - type: ndcg_at_100 value: 20.491999999999997 - type: ndcg_at_1000 value: 25.759999999999998 - type: ndcg_at_3 value: 13.347999999999999 - type: ndcg_at_5 value: 11.61 - type: precision_at_1 value: 17.0 - type: precision_at_10 value: 7.090000000000001 - type: precision_at_100 value: 1.669 - type: precision_at_1000 value: 0.294 - type: precision_at_3 value: 12.3 - type: precision_at_5 value: 10.02 - type: recall_at_1 value: 3.4680000000000004 - type: recall_at_10 value: 14.363000000000001 - type: recall_at_100 value: 33.875 - type: recall_at_1000 value: 59.711999999999996 - type: recall_at_3 value: 7.483 - type: recall_at_5 value: 10.173 task: type: Retrieval - dataset: config: default name: MTEB SICK-R revision: a6ea5a8cab320b040a23452cc28066d9beae2cee split: test type: mteb/sickr-sts metrics: - type: cos_sim_pearson value: 83.04084311714061 - type: cos_sim_spearman value: 77.51342467443078 - type: euclidean_pearson value: 80.0321166028479 - type: euclidean_spearman value: 77.29249114733226 - type: manhattan_pearson value: 80.03105964262431 - type: manhattan_spearman value: 77.22373689514794 task: type: STS - dataset: config: default name: MTEB STS12 revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cos_sim_pearson value: 84.1680158034387 - type: cos_sim_spearman value: 76.55983344071117 - type: euclidean_pearson value: 79.75266678300143 - type: euclidean_spearman value: 75.34516823467025 - type: manhattan_pearson value: 79.75959151517357 - type: manhattan_spearman value: 75.42330344141912 task: type: STS - dataset: config: default name: MTEB STS13 revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cos_sim_pearson value: 76.48898993209346 - type: cos_sim_spearman value: 76.96954120323366 - type: euclidean_pearson value: 76.94139109279668 - type: euclidean_spearman value: 76.85860283201711 - type: manhattan_pearson value: 76.6944095091912 - type: manhattan_spearman value: 76.61096912972553 task: type: STS - dataset: config: default name: MTEB STS14 revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cos_sim_pearson value: 77.85082366246944 - type: cos_sim_spearman value: 75.52053350101731 - type: euclidean_pearson value: 77.1165845070926 - type: euclidean_spearman value: 75.31216065884388 - type: manhattan_pearson value: 77.06193941833494 - type: manhattan_spearman value: 75.31003701700112 task: type: STS - dataset: config: default name: MTEB STS15 revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cos_sim_pearson value: 86.36305246526497 - type: cos_sim_spearman value: 87.11704613927415 - type: euclidean_pearson value: 86.04199125810939 - type: euclidean_spearman value: 86.51117572414263 - type: manhattan_pearson value: 86.0805106816633 - type: manhattan_spearman value: 86.52798366512229 task: type: STS - dataset: config: default name: MTEB STS16 revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cos_sim_pearson value: 82.18536255599724 - type: cos_sim_spearman value: 83.63377151025418 - type: euclidean_pearson value: 83.24657467993141 - type: euclidean_spearman value: 84.02751481993825 - type: manhattan_pearson value: 83.11941806582371 - type: manhattan_spearman value: 83.84251281019304 task: type: STS - dataset: config: ko-ko name: MTEB STS17 (ko-ko) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 78.95816528475514 - type: cos_sim_spearman value: 78.86607380120462 - type: euclidean_pearson value: 78.51268699230545 - type: euclidean_spearman value: 79.11649316502229 - type: manhattan_pearson value: 78.32367302808157 - type: manhattan_spearman value: 78.90277699624637 task: type: STS - dataset: config: ar-ar name: MTEB STS17 (ar-ar) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 72.89126914997624 - type: cos_sim_spearman value: 73.0296921832678 - type: euclidean_pearson value: 71.50385903677738 - type: euclidean_spearman value: 73.13368899716289 - type: manhattan_pearson value: 71.47421463379519 - type: manhattan_spearman value: 73.03383242946575 task: type: STS - dataset: config: en-ar name: MTEB STS17 (en-ar) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 59.22923684492637 - type: cos_sim_spearman value: 57.41013211368396 - type: euclidean_pearson value: 61.21107388080905 - type: euclidean_spearman value: 60.07620768697254 - type: manhattan_pearson value: 59.60157142786555 - type: manhattan_spearman value: 59.14069604103739 task: type: STS - dataset: config: en-de name: MTEB STS17 (en-de) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 76.24345978774299 - type: cos_sim_spearman value: 77.24225743830719 - type: euclidean_pearson value: 76.66226095469165 - type: euclidean_spearman value: 77.60708820493146 - type: manhattan_pearson value: 76.05303324760429 - type: manhattan_spearman value: 76.96353149912348 task: type: STS - dataset: config: en-en name: MTEB STS17 (en-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 85.50879160160852 - type: cos_sim_spearman value: 86.43594662965224 - type: euclidean_pearson value: 86.06846012826577 - type: euclidean_spearman value: 86.02041395794136 - type: manhattan_pearson value: 86.10916255616904 - type: manhattan_spearman value: 86.07346068198953 task: type: STS - dataset: config: en-tr name: MTEB STS17 (en-tr) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 58.39803698977196 - type: cos_sim_spearman value: 55.96910950423142 - type: euclidean_pearson value: 58.17941175613059 - type: euclidean_spearman value: 55.03019330522745 - type: manhattan_pearson value: 57.333358138183286 - type: manhattan_spearman value: 54.04614023149965 task: type: STS - dataset: config: es-en name: MTEB STS17 (es-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 70.98304089637197 - type: cos_sim_spearman value: 72.44071656215888 - type: euclidean_pearson value: 72.19224359033983 - type: euclidean_spearman value: 73.89871188913025 - type: manhattan_pearson value: 71.21098311547406 - type: manhattan_spearman value: 72.93405764824821 task: type: STS - dataset: config: es-es name: MTEB STS17 (es-es) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 85.99792397466308 - type: cos_sim_spearman value: 84.83824377879495 - type: euclidean_pearson value: 85.70043288694438 - type: euclidean_spearman value: 84.70627558703686 - type: manhattan_pearson value: 85.89570850150801 - type: manhattan_spearman value: 84.95806105313007 task: type: STS - dataset: config: fr-en name: MTEB STS17 (fr-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 72.21850322994712 - type: cos_sim_spearman value: 72.28669398117248 - type: euclidean_pearson value: 73.40082510412948 - type: euclidean_spearman value: 73.0326539281865 - type: manhattan_pearson value: 71.8659633964841 - type: manhattan_spearman value: 71.57817425823303 task: type: STS - dataset: config: it-en name: MTEB STS17 (it-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 75.80921368595645 - type: cos_sim_spearman value: 77.33209091229315 - type: euclidean_pearson value: 76.53159540154829 - type: euclidean_spearman value: 78.17960842810093 - type: manhattan_pearson value: 76.13530186637601 - type: manhattan_spearman value: 78.00701437666875 task: type: STS - dataset: config: nl-en name: MTEB STS17 (nl-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 74.74980608267349 - type: cos_sim_spearman value: 75.37597374318821 - type: euclidean_pearson value: 74.90506081911661 - type: euclidean_spearman value: 75.30151613124521 - type: manhattan_pearson value: 74.62642745918002 - type: manhattan_spearman value: 75.18619716592303 task: type: STS - dataset: config: en name: MTEB STS22 (en) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 59.632662289205584 - type: cos_sim_spearman value: 60.938543391610914 - type: euclidean_pearson value: 62.113200529767056 - type: euclidean_spearman value: 61.410312633261164 - type: manhattan_pearson value: 61.75494698945686 - type: manhattan_spearman value: 60.92726195322362 task: type: STS - dataset: config: de name: MTEB STS22 (de) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 45.283470551557244 - type: cos_sim_spearman value: 53.44833015864201 - type: euclidean_pearson value: 41.17892011120893 - type: euclidean_spearman value: 53.81441383126767 - type: manhattan_pearson value: 41.17482200420659 - type: manhattan_spearman value: 53.82180269276363 task: type: STS - dataset: config: es name: MTEB STS22 (es) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 60.5069165306236 - type: cos_sim_spearman value: 66.87803259033826 - type: euclidean_pearson value: 63.5428979418236 - type: euclidean_spearman value: 66.9293576586897 - type: manhattan_pearson value: 63.59789526178922 - type: manhattan_spearman value: 66.86555009875066 task: type: STS - dataset: config: pl name: MTEB STS22 (pl) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 28.23026196280264 - type: cos_sim_spearman value: 35.79397812652861 - type: euclidean_pearson value: 17.828102102767353 - type: euclidean_spearman value: 35.721501145568894 - type: manhattan_pearson value: 17.77134274219677 - type: manhattan_spearman value: 35.98107902846267 task: type: STS - dataset: config: tr name: MTEB STS22 (tr) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 56.51946541393812 - type: cos_sim_spearman value: 63.714686006214485 - type: euclidean_pearson value: 58.32104651305898 - type: euclidean_spearman value: 62.237110895702216 - type: manhattan_pearson value: 58.579416468759185 - type: manhattan_spearman value: 62.459738981727 task: type: STS - dataset: config: ar name: MTEB STS22 (ar) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 48.76009839569795 - type: cos_sim_spearman value: 56.65188431953149 - type: euclidean_pearson value: 50.997682160915595 - type: euclidean_spearman value: 55.99910008818135 - type: manhattan_pearson value: 50.76220659606342 - type: manhattan_spearman value: 55.517347595391456 task: type: STS - dataset: config: ru name: MTEB STS22 (ru) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 50.724322379215934 - type: cosine_spearman value: 59.90449732164651 - type: euclidean_pearson value: 50.227545226784024 - type: euclidean_spearman value: 59.898906527601085 - type: main_score value: 59.90449732164651 - type: manhattan_pearson value: 50.21762139819405 - type: manhattan_spearman value: 59.761039813759 - type: pearson value: 50.724322379215934 - type: spearman value: 59.90449732164651 task: type: STS - dataset: config: zh name: MTEB STS22 (zh) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 54.717524559088005 - type: cos_sim_spearman value: 66.83570886252286 - type: euclidean_pearson value: 58.41338625505467 - type: euclidean_spearman value: 66.68991427704938 - type: manhattan_pearson value: 58.78638572916807 - type: manhattan_spearman value: 66.58684161046335 task: type: STS - dataset: config: fr name: MTEB STS22 (fr) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 73.2962042954962 - type: cos_sim_spearman value: 76.58255504852025 - type: euclidean_pearson value: 75.70983192778257 - type: euclidean_spearman value: 77.4547684870542 - type: manhattan_pearson value: 75.75565853870485 - type: manhattan_spearman value: 76.90208974949428 task: type: STS - dataset: config: de-en name: MTEB STS22 (de-en) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 54.47396266924846 - type: cos_sim_spearman value: 56.492267162048606 - type: euclidean_pearson value: 55.998505203070195 - type: euclidean_spearman value: 56.46447012960222 - type: manhattan_pearson value: 54.873172394430995 - type: manhattan_spearman value: 56.58111534551218 task: type: STS - dataset: config: es-en name: MTEB STS22 (es-en) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 69.87177267688686 - type: cos_sim_spearman value: 74.57160943395763 - type: euclidean_pearson value: 70.88330406826788 - type: euclidean_spearman value: 74.29767636038422 - type: manhattan_pearson value: 71.38245248369536 - type: manhattan_spearman value: 74.53102232732175 task: type: STS - dataset: config: it name: MTEB STS22 (it) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 72.80225656959544 - type: cos_sim_spearman value: 76.52646173725735 - type: euclidean_pearson value: 73.95710720200799 - type: euclidean_spearman value: 76.54040031984111 - type: manhattan_pearson value: 73.89679971946774 - type: manhattan_spearman value: 76.60886958161574 task: type: STS - dataset: config: pl-en name: MTEB STS22 (pl-en) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 70.70844249898789 - type: cos_sim_spearman value: 72.68571783670241 - type: euclidean_pearson value: 72.38800772441031 - type: euclidean_spearman value: 72.86804422703312 - type: manhattan_pearson value: 71.29840508203515 - type: manhattan_spearman value: 71.86264441749513 task: type: STS - dataset: config: zh-en name: MTEB STS22 (zh-en) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 58.647478923935694 - type: cos_sim_spearman value: 63.74453623540931 - type: euclidean_pearson value: 59.60138032437505 - type: euclidean_spearman value: 63.947930832166065 - type: manhattan_pearson value: 58.59735509491861 - type: manhattan_spearman value: 62.082503844627404 task: type: STS - dataset: config: es-it name: MTEB STS22 (es-it) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 65.8722516867162 - type: cos_sim_spearman value: 71.81208592523012 - type: euclidean_pearson value: 67.95315252165956 - type: euclidean_spearman value: 73.00749822046009 - type: manhattan_pearson value: 68.07884688638924 - type: manhattan_spearman value: 72.34210325803069 task: type: STS - dataset: config: de-fr name: MTEB STS22 (de-fr) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 54.5405814240949 - type: cos_sim_spearman value: 60.56838649023775 - type: euclidean_pearson value: 53.011731611314104 - type: euclidean_spearman value: 58.533194841668426 - type: manhattan_pearson value: 53.623067729338494 - type: manhattan_spearman value: 58.018756154446926 task: type: STS - dataset: config: de-pl name: MTEB STS22 (de-pl) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 13.611046866216112 - type: cos_sim_spearman value: 28.238192909158492 - type: euclidean_pearson value: 22.16189199885129 - type: euclidean_spearman value: 35.012895679076564 - type: manhattan_pearson value: 21.969771178698387 - type: manhattan_spearman value: 32.456985088607475 task: type: STS - dataset: config: fr-pl name: MTEB STS22 (fr-pl) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 74.58077407011655 - type: cos_sim_spearman value: 84.51542547285167 - type: euclidean_pearson value: 74.64613843596234 - type: euclidean_spearman value: 84.51542547285167 - type: manhattan_pearson value: 75.15335973101396 - type: manhattan_spearman value: 84.51542547285167 task: type: STS - dataset: config: default name: MTEB STSBenchmark revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cos_sim_pearson value: 82.0739825531578 - type: cos_sim_spearman value: 84.01057479311115 - type: euclidean_pearson value: 83.85453227433344 - type: euclidean_spearman value: 84.01630226898655 - type: manhattan_pearson value: 83.75323603028978 - type: manhattan_spearman value: 83.89677983727685 task: type: STS - dataset: config: default name: MTEB SciDocsRR revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics: - type: map value: 78.12945623123957 - type: mrr value: 93.87738713719106 task: type: Reranking - dataset: config: default name: MTEB SciFact revision: None split: test type: scifact metrics: - type: map_at_1 value: 52.983000000000004 - type: map_at_10 value: 62.946000000000005 - type: map_at_100 value: 63.514 - type: map_at_1000 value: 63.554 - type: map_at_3 value: 60.183 - type: map_at_5 value: 61.672000000000004 - type: mrr_at_1 value: 55.667 - type: mrr_at_10 value: 64.522 - type: mrr_at_100 value: 64.957 - type: mrr_at_1000 value: 64.995 - type: mrr_at_3 value: 62.388999999999996 - type: mrr_at_5 value: 63.639 - type: ndcg_at_1 value: 55.667 - type: ndcg_at_10 value: 67.704 - type: ndcg_at_100 value: 70.299 - type: ndcg_at_1000 value: 71.241 - type: ndcg_at_3 value: 62.866 - type: ndcg_at_5 value: 65.16999999999999 - type: precision_at_1 value: 55.667 - type: precision_at_10 value: 9.033 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 24.444 - type: precision_at_5 value: 16.133 - type: recall_at_1 value: 52.983000000000004 - type: recall_at_10 value: 80.656 - type: recall_at_100 value: 92.5 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 67.744 - type: recall_at_5 value: 73.433 task: type: Retrieval - dataset: config: default name: MTEB SprintDuplicateQuestions revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics: - type: cos_sim_accuracy value: 99.72772277227723 - type: cos_sim_ap value: 92.17845897992215 - type: cos_sim_f1 value: 85.9746835443038 - type: cos_sim_precision value: 87.07692307692308 - type: cos_sim_recall value: 84.89999999999999 - type: dot_accuracy value: 99.3039603960396 - type: dot_ap value: 60.70244020124878 - type: dot_f1 value: 59.92742353551063 - type: dot_precision value: 62.21743810548978 - type: dot_recall value: 57.8 - type: euclidean_accuracy value: 99.71683168316832 - type: euclidean_ap value: 91.53997039964659 - type: euclidean_f1 value: 84.88372093023257 - type: euclidean_precision value: 90.02242152466367 - type: euclidean_recall value: 80.30000000000001 - type: manhattan_accuracy value: 99.72376237623763 - type: manhattan_ap value: 91.80756777790289 - type: manhattan_f1 value: 85.48468106479157 - type: manhattan_precision value: 85.8728557013118 - type: manhattan_recall value: 85.1 - type: max_accuracy value: 99.72772277227723 - type: max_ap value: 92.17845897992215 - type: max_f1 value: 85.9746835443038 task: type: PairClassification - dataset: config: default name: MTEB StackExchangeClustering revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: v_measure value: 53.52464042600003 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - type: v_measure value: 32.071631948736 task: type: Clustering - dataset: config: default name: MTEB StackOverflowDupQuestions revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics: - type: map value: 49.19552407604654 - type: mrr value: 49.95269130379425 task: type: Reranking - dataset: config: default name: MTEB SummEval revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cos_sim_pearson value: 29.345293033095427 - type: cos_sim_spearman value: 29.976931423258403 - type: dot_pearson value: 27.047078008958408 - type: dot_spearman value: 27.75894368380218 task: type: Summarization - dataset: config: default name: MTEB TRECCOVID revision: None split: test type: trec-covid metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.706 - type: map_at_100 value: 9.634 - type: map_at_1000 value: 23.665 - type: map_at_3 value: 0.5950000000000001 - type: map_at_5 value: 0.95 - type: mrr_at_1 value: 86.0 - type: mrr_at_10 value: 91.8 - type: mrr_at_100 value: 91.8 - type: mrr_at_1000 value: 91.8 - type: mrr_at_3 value: 91.0 - type: mrr_at_5 value: 91.8 - type: ndcg_at_1 value: 80.0 - type: ndcg_at_10 value: 72.573 - type: ndcg_at_100 value: 53.954 - type: ndcg_at_1000 value: 47.760999999999996 - type: ndcg_at_3 value: 76.173 - type: ndcg_at_5 value: 75.264 - type: precision_at_1 value: 86.0 - type: precision_at_10 value: 76.4 - type: precision_at_100 value: 55.50000000000001 - type: precision_at_1000 value: 21.802 - type: precision_at_3 value: 81.333 - type: precision_at_5 value: 80.4 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 1.925 - type: recall_at_100 value: 12.762 - type: recall_at_1000 value: 44.946000000000005 - type: recall_at_3 value: 0.634 - type: recall_at_5 value: 1.051 task: type: Retrieval - dataset: config: sqi-eng name: MTEB Tatoeba (sqi-eng) revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 split: test type: mteb/tatoeba-bitext-mining metrics: - type: accuracy value: 91.0 - type: f1 value: 88.55666666666666 - type: precision value: 87.46166666666667 - type: recall value: 91.0 task: type: BitextMining - dataset: config: fry-eng name: MTEB Tatoeba (fry-eng) revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 split: test type: mteb/tatoeba-bitext-mining metrics: - type: accuracy value: 57.22543352601156 - type: f1 value: 51.03220478943021 - type: precision value: 48.8150289017341 - type: recall value: 57.22543352601156 task: type: BitextMining - dataset: config: kur-eng name: MTEB Tatoeba (kur-eng) revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 split: test type: mteb/tatoeba-bitext-mining metrics: - type: accuracy value: 46.58536585365854 - type: f1 value: 39.66870798578116 - type: precision value: 37.416085946573745 - type: recall value: 46.58536585365854 task: type: BitextMining - dataset: config: tur-eng name: MTEB Tatoeba (tur-eng) revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 split: test type: mteb/tatoeba-bitext-mining metrics: - type: accuracy value: 89.7 - type: f1 value: 86.77999999999999 - type: precision value: 85.45333333333332 - type: recall value: 89.7 task: type: BitextMining - dataset: config: deu-eng name: MTEB Tatoeba (deu-eng) revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 split: test type: mteb/tatoeba-bitext-mining metrics: - type: accuracy value: 97.39999999999999 - 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The only difference from the original model is `pad_token_id` in `config.json` which is corrected to `1`. [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small') model = AutoModel.from_pretrained('intfloat/multilingual-e5-small') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-small') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.