--- tags: - mteb - sentence_embedding - feature_extraction - sentence-transformers - transformers - transformers.js model-index: - name: UAE-Large-V1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.55223880597015 - type: ap value: 38.264070815317794 - type: f1 value: 69.40977934769845 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.84267499999999 - type: ap value: 89.57568507997713 - type: f1 value: 92.82590734337774 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.292 - type: f1 value: 47.90257816032778 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 42.105 - type: map_at_10 value: 58.181000000000004 - type: map_at_100 value: 58.653999999999996 - type: map_at_1000 value: 58.657000000000004 - type: map_at_3 value: 54.386 - type: map_at_5 value: 56.757999999999996 - type: mrr_at_1 value: 42.745 - type: mrr_at_10 value: 58.437 - type: mrr_at_100 value: 58.894999999999996 - type: mrr_at_1000 value: 58.897999999999996 - type: mrr_at_3 value: 54.635 - type: mrr_at_5 value: 56.99999999999999 - type: ndcg_at_1 value: 42.105 - type: ndcg_at_10 value: 66.14999999999999 - type: ndcg_at_100 value: 68.048 - type: ndcg_at_1000 value: 68.11399999999999 - type: ndcg_at_3 value: 58.477000000000004 - type: ndcg_at_5 value: 62.768 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 9.110999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.447000000000003 - type: precision_at_5 value: 16.159000000000002 - type: recall_at_1 value: 42.105 - type: recall_at_10 value: 91.11 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 70.341 - type: recall_at_5 value: 80.797 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 49.02580759154173 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.093601280163554 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.19590406875427 - type: mrr value: 77.09547992788991 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.86678362843676 - type: cos_sim_spearman value: 86.1423242570783 - type: euclidean_pearson value: 85.98994198511751 - type: euclidean_spearman value: 86.48209103503942 - type: manhattan_pearson value: 85.6446436316182 - type: manhattan_spearman value: 86.21039809734357 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.69155844155844 - type: f1 value: 87.68109381943547 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.37501687500394 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.23401405155885 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.232 - type: map_at_10 value: 41.404999999999994 - type: map_at_100 value: 42.896 - type: map_at_1000 value: 43.028 - type: map_at_3 value: 37.925 - type: map_at_5 value: 39.865 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.969 - type: mrr_at_100 value: 47.684 - type: mrr_at_1000 value: 47.731 - type: mrr_at_3 value: 44.063 - type: mrr_at_5 value: 45.908 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.887 - type: ndcg_at_100 value: 53.357 - type: ndcg_at_1000 value: 55.376999999999995 - type: ndcg_at_3 value: 42.588 - type: ndcg_at_5 value: 45.132 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.17 - type: precision_at_100 value: 1.4909999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.315 - type: precision_at_5 value: 14.793000000000001 - type: recall_at_1 value: 30.232 - type: recall_at_10 value: 60.67399999999999 - type: recall_at_100 value: 83.628 - type: recall_at_1000 value: 96.209 - type: recall_at_3 value: 45.48 - type: recall_at_5 value: 52.354 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.237 - type: map_at_10 value: 42.829 - type: map_at_100 value: 44.065 - type: map_at_1000 value: 44.199 - type: map_at_3 value: 39.885999999999996 - type: map_at_5 value: 41.55 - type: mrr_at_1 value: 40.064 - type: mrr_at_10 value: 48.611 - type: mrr_at_100 value: 49.245 - type: mrr_at_1000 value: 49.29 - type: mrr_at_3 value: 46.561 - type: mrr_at_5 value: 47.771 - type: ndcg_at_1 value: 40.064 - type: ndcg_at_10 value: 48.388 - type: ndcg_at_100 value: 52.666999999999994 - type: ndcg_at_1000 value: 54.67100000000001 - type: ndcg_at_3 value: 44.504 - type: ndcg_at_5 value: 46.303 - type: precision_at_1 value: 40.064 - type: precision_at_10 value: 9.051 - type: precision_at_100 value: 1.4500000000000002 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 21.444 - type: precision_at_5 value: 15.045 - type: recall_at_1 value: 32.237 - type: recall_at_10 value: 57.943999999999996 - type: recall_at_100 value: 75.98700000000001 - type: recall_at_1000 value: 88.453 - type: recall_at_3 value: 46.268 - type: recall_at_5 value: 51.459999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.797 - type: map_at_10 value: 51.263000000000005 - type: map_at_100 value: 52.333 - type: map_at_1000 value: 52.393 - type: map_at_3 value: 47.936 - type: map_at_5 value: 49.844 - type: mrr_at_1 value: 44.389 - type: mrr_at_10 value: 54.601 - type: mrr_at_100 value: 55.300000000000004 - type: mrr_at_1000 value: 55.333 - type: mrr_at_3 value: 52.068999999999996 - type: mrr_at_5 value: 53.627 - type: ndcg_at_1 value: 44.389 - type: ndcg_at_10 value: 57.193000000000005 - type: ndcg_at_100 value: 61.307 - type: ndcg_at_1000 value: 62.529 - type: ndcg_at_3 value: 51.607 - type: ndcg_at_5 value: 54.409 - type: precision_at_1 value: 44.389 - type: precision_at_10 value: 9.26 - type: precision_at_100 value: 1.222 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 23.03 - type: precision_at_5 value: 15.887 - type: recall_at_1 value: 38.797 - type: recall_at_10 value: 71.449 - type: recall_at_100 value: 88.881 - type: recall_at_1000 value: 97.52 - type: recall_at_3 value: 56.503 - type: recall_at_5 value: 63.392 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.291999999999998 - type: map_at_10 value: 35.65 - type: map_at_100 value: 36.689 - type: map_at_1000 value: 36.753 - type: map_at_3 value: 32.995000000000005 - type: map_at_5 value: 34.409 - type: mrr_at_1 value: 29.04 - type: mrr_at_10 value: 37.486000000000004 - type: mrr_at_100 value: 38.394 - type: mrr_at_1000 value: 38.445 - type: mrr_at_3 value: 35.028 - type: mrr_at_5 value: 36.305 - type: ndcg_at_1 value: 29.04 - type: ndcg_at_10 value: 40.613 - type: ndcg_at_100 value: 45.733000000000004 - type: ndcg_at_1000 value: 47.447 - type: ndcg_at_3 value: 35.339999999999996 - type: ndcg_at_5 value: 37.706 - type: precision_at_1 value: 29.04 - type: precision_at_10 value: 6.192 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 14.802000000000001 - type: precision_at_5 value: 10.305 - type: recall_at_1 value: 27.291999999999998 - type: recall_at_10 value: 54.25299999999999 - type: recall_at_100 value: 77.773 - type: recall_at_1000 value: 90.795 - type: recall_at_3 value: 39.731 - type: recall_at_5 value: 45.403999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.326 - type: map_at_10 value: 26.290999999999997 - type: map_at_100 value: 27.456999999999997 - type: map_at_1000 value: 27.583000000000002 - type: map_at_3 value: 23.578 - type: map_at_5 value: 25.113000000000003 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 31.139 - type: mrr_at_100 value: 32.074999999999996 - type: mrr_at_1000 value: 32.147 - type: mrr_at_3 value: 28.483000000000004 - type: mrr_at_5 value: 29.963 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 31.717000000000002 - type: ndcg_at_100 value: 37.201 - type: ndcg_at_1000 value: 40.088 - type: ndcg_at_3 value: 26.686 - type: ndcg_at_5 value: 29.076999999999998 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 5.7090000000000005 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.894 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 18.326 - type: recall_at_10 value: 43.824999999999996 - type: recall_at_100 value: 67.316 - type: recall_at_1000 value: 87.481 - type: recall_at_3 value: 29.866999999999997 - type: recall_at_5 value: 35.961999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.875 - type: map_at_10 value: 40.458 - type: map_at_100 value: 41.772 - type: map_at_1000 value: 41.882999999999996 - type: map_at_3 value: 37.086999999999996 - type: map_at_5 value: 39.153 - type: mrr_at_1 value: 36.381 - type: mrr_at_10 value: 46.190999999999995 - type: mrr_at_100 value: 46.983999999999995 - type: mrr_at_1000 value: 47.032000000000004 - type: mrr_at_3 value: 43.486999999999995 - type: mrr_at_5 value: 45.249 - type: ndcg_at_1 value: 36.381 - type: ndcg_at_10 value: 46.602 - type: ndcg_at_100 value: 51.885999999999996 - type: ndcg_at_1000 value: 53.895 - type: ndcg_at_3 value: 41.155 - type: ndcg_at_5 value: 44.182 - type: precision_at_1 value: 36.381 - type: precision_at_10 value: 8.402 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 19.346 - type: precision_at_5 value: 14.09 - type: recall_at_1 value: 29.875 - type: recall_at_10 value: 59.065999999999995 - type: recall_at_100 value: 80.923 - type: recall_at_1000 value: 93.927 - type: recall_at_3 value: 44.462 - type: recall_at_5 value: 51.89 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.94 - type: map_at_10 value: 35.125 - type: map_at_100 value: 36.476 - type: map_at_1000 value: 36.579 - type: map_at_3 value: 31.840000000000003 - type: map_at_5 value: 33.647 - type: mrr_at_1 value: 30.936000000000003 - type: mrr_at_10 value: 40.637 - type: mrr_at_100 value: 41.471000000000004 - type: mrr_at_1000 value: 41.525 - type: mrr_at_3 value: 38.013999999999996 - type: mrr_at_5 value: 39.469 - type: ndcg_at_1 value: 30.936000000000003 - type: ndcg_at_10 value: 41.295 - type: ndcg_at_100 value: 46.92 - type: ndcg_at_1000 value: 49.183 - type: ndcg_at_3 value: 35.811 - type: ndcg_at_5 value: 38.306000000000004 - type: precision_at_1 value: 30.936000000000003 - type: precision_at_10 value: 7.728 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.237 - type: precision_at_5 value: 12.42 - type: recall_at_1 value: 24.94 - type: recall_at_10 value: 54.235 - type: recall_at_100 value: 78.314 - type: recall_at_1000 value: 93.973 - type: recall_at_3 value: 38.925 - type: recall_at_5 value: 45.505 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.250833333333333 - type: map_at_10 value: 35.46875 - type: map_at_100 value: 36.667 - type: map_at_1000 value: 36.78025 - type: map_at_3 value: 32.56733333333334 - type: map_at_5 value: 34.20333333333333 - type: mrr_at_1 value: 30.8945 - type: mrr_at_10 value: 39.636833333333335 - type: mrr_at_100 value: 40.46508333333333 - type: mrr_at_1000 value: 40.521249999999995 - type: mrr_at_3 value: 37.140166666666666 - type: mrr_at_5 value: 38.60999999999999 - type: ndcg_at_1 value: 30.8945 - type: ndcg_at_10 value: 40.93441666666667 - type: ndcg_at_100 value: 46.062416666666664 - type: ndcg_at_1000 value: 48.28341666666667 - type: ndcg_at_3 value: 35.97575 - type: ndcg_at_5 value: 38.3785 - type: precision_at_1 value: 30.8945 - type: precision_at_10 value: 7.180250000000001 - type: precision_at_100 value: 1.1468333333333334 - type: precision_at_1000 value: 0.15283333333333332 - type: precision_at_3 value: 16.525583333333334 - type: precision_at_5 value: 11.798333333333332 - type: recall_at_1 value: 26.250833333333333 - type: recall_at_10 value: 52.96108333333333 - type: recall_at_100 value: 75.45908333333334 - type: recall_at_1000 value: 90.73924999999998 - type: recall_at_3 value: 39.25483333333333 - type: recall_at_5 value: 45.37950000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.595 - type: map_at_10 value: 31.747999999999998 - type: map_at_100 value: 32.62 - type: map_at_1000 value: 32.713 - type: map_at_3 value: 29.48 - type: map_at_5 value: 30.635 - type: mrr_at_1 value: 27.607 - type: mrr_at_10 value: 34.449000000000005 - type: mrr_at_100 value: 35.182 - type: mrr_at_1000 value: 35.254000000000005 - type: mrr_at_3 value: 32.413 - type: mrr_at_5 value: 33.372 - type: ndcg_at_1 value: 27.607 - type: ndcg_at_10 value: 36.041000000000004 - type: ndcg_at_100 value: 40.514 - type: ndcg_at_1000 value: 42.851 - type: ndcg_at_3 value: 31.689 - type: ndcg_at_5 value: 33.479 - type: precision_at_1 value: 27.607 - type: precision_at_10 value: 5.66 - type: precision_at_100 value: 0.868 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.264 - type: recall_at_1 value: 24.595 - type: recall_at_10 value: 46.79 - type: recall_at_100 value: 67.413 - type: recall_at_1000 value: 84.753 - type: recall_at_3 value: 34.644999999999996 - type: recall_at_5 value: 39.09 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.333000000000002 - type: map_at_10 value: 24.427 - type: map_at_100 value: 25.576 - type: map_at_1000 value: 25.692999999999998 - type: map_at_3 value: 22.002 - type: map_at_5 value: 23.249 - type: mrr_at_1 value: 20.716 - type: mrr_at_10 value: 28.072000000000003 - type: mrr_at_100 value: 29.067 - type: mrr_at_1000 value: 29.137 - type: mrr_at_3 value: 25.832 - type: mrr_at_5 value: 27.045 - type: ndcg_at_1 value: 20.716 - type: ndcg_at_10 value: 29.109 - type: ndcg_at_100 value: 34.797 - type: ndcg_at_1000 value: 37.503 - type: ndcg_at_3 value: 24.668 - type: ndcg_at_5 value: 26.552999999999997 - type: precision_at_1 value: 20.716 - type: precision_at_10 value: 5.351 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.584999999999999 - type: precision_at_5 value: 8.362 - type: recall_at_1 value: 17.333000000000002 - type: recall_at_10 value: 39.604 - type: recall_at_100 value: 65.525 - type: recall_at_1000 value: 84.651 - type: recall_at_3 value: 27.199 - type: recall_at_5 value: 32.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.342 - type: map_at_10 value: 35.349000000000004 - type: map_at_100 value: 36.443 - type: map_at_1000 value: 36.548 - type: map_at_3 value: 32.307 - type: map_at_5 value: 34.164 - type: mrr_at_1 value: 31.063000000000002 - type: mrr_at_10 value: 39.703 - type: mrr_at_100 value: 40.555 - type: mrr_at_1000 value: 40.614 - type: mrr_at_3 value: 37.141999999999996 - type: mrr_at_5 value: 38.812000000000005 - type: ndcg_at_1 value: 31.063000000000002 - type: ndcg_at_10 value: 40.873 - type: ndcg_at_100 value: 45.896 - type: ndcg_at_1000 value: 48.205999999999996 - type: ndcg_at_3 value: 35.522 - type: ndcg_at_5 value: 38.419 - type: precision_at_1 value: 31.063000000000002 - type: precision_at_10 value: 6.866 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 16.014 - type: precision_at_5 value: 11.604000000000001 - type: recall_at_1 value: 26.342 - type: recall_at_10 value: 53.40200000000001 - type: recall_at_100 value: 75.251 - type: recall_at_1000 value: 91.13799999999999 - type: recall_at_3 value: 39.103 - type: recall_at_5 value: 46.357 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.71 - type: map_at_10 value: 32.153999999999996 - type: map_at_100 value: 33.821 - type: map_at_1000 value: 34.034 - type: map_at_3 value: 29.376 - type: map_at_5 value: 30.878 - type: mrr_at_1 value: 28.458 - type: mrr_at_10 value: 36.775999999999996 - type: mrr_at_100 value: 37.804 - type: mrr_at_1000 value: 37.858999999999995 - type: mrr_at_3 value: 34.123999999999995 - type: mrr_at_5 value: 35.596 - type: ndcg_at_1 value: 28.458 - type: ndcg_at_10 value: 37.858999999999995 - type: ndcg_at_100 value: 44.194 - type: ndcg_at_1000 value: 46.744 - type: ndcg_at_3 value: 33.348 - type: ndcg_at_5 value: 35.448 - type: precision_at_1 value: 28.458 - type: precision_at_10 value: 7.4510000000000005 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.23700000000000002 - type: precision_at_3 value: 15.809999999999999 - type: precision_at_5 value: 11.462 - type: recall_at_1 value: 23.71 - type: recall_at_10 value: 48.272999999999996 - type: recall_at_100 value: 77.134 - type: recall_at_1000 value: 93.001 - type: recall_at_3 value: 35.480000000000004 - type: recall_at_5 value: 41.19 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.331 - type: map_at_10 value: 28.926000000000002 - type: map_at_100 value: 29.855999999999998 - type: map_at_1000 value: 29.957 - type: map_at_3 value: 26.395999999999997 - type: map_at_5 value: 27.933000000000003 - type: mrr_at_1 value: 23.105 - type: mrr_at_10 value: 31.008000000000003 - type: mrr_at_100 value: 31.819999999999997 - type: mrr_at_1000 value: 31.887999999999998 - type: mrr_at_3 value: 28.466 - type: mrr_at_5 value: 30.203000000000003 - type: ndcg_at_1 value: 23.105 - type: ndcg_at_10 value: 33.635999999999996 - type: ndcg_at_100 value: 38.277 - type: ndcg_at_1000 value: 40.907 - type: ndcg_at_3 value: 28.791 - type: ndcg_at_5 value: 31.528 - type: precision_at_1 value: 23.105 - type: precision_at_10 value: 5.323 - type: precision_at_100 value: 0.815 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.384 - type: precision_at_5 value: 9.02 - type: recall_at_1 value: 21.331 - type: recall_at_10 value: 46.018 - type: recall_at_100 value: 67.364 - type: recall_at_1000 value: 86.97 - type: recall_at_3 value: 33.395 - type: recall_at_5 value: 39.931 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 17.011000000000003 - type: map_at_10 value: 28.816999999999997 - type: map_at_100 value: 30.761 - type: map_at_1000 value: 30.958000000000002 - type: map_at_3 value: 24.044999999999998 - type: map_at_5 value: 26.557 - type: mrr_at_1 value: 38.696999999999996 - type: mrr_at_10 value: 50.464 - type: mrr_at_100 value: 51.193999999999996 - type: mrr_at_1000 value: 51.219 - type: mrr_at_3 value: 47.339999999999996 - type: mrr_at_5 value: 49.346000000000004 - type: ndcg_at_1 value: 38.696999999999996 - type: ndcg_at_10 value: 38.53 - type: ndcg_at_100 value: 45.525 - type: ndcg_at_1000 value: 48.685 - type: ndcg_at_3 value: 32.282 - type: ndcg_at_5 value: 34.482 - type: precision_at_1 value: 38.696999999999996 - type: precision_at_10 value: 11.895999999999999 - type: precision_at_100 value: 1.95 - type: precision_at_1000 value: 0.254 - type: precision_at_3 value: 24.038999999999998 - type: precision_at_5 value: 18.332 - type: recall_at_1 value: 17.011000000000003 - type: recall_at_10 value: 44.452999999999996 - type: recall_at_100 value: 68.223 - type: recall_at_1000 value: 85.653 - type: recall_at_3 value: 28.784 - type: recall_at_5 value: 35.66 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.516 - type: map_at_10 value: 21.439 - type: map_at_100 value: 31.517 - type: map_at_1000 value: 33.267 - type: map_at_3 value: 15.004999999999999 - type: map_at_5 value: 17.793999999999997 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 79.071 - type: mrr_at_100 value: 79.325 - type: mrr_at_1000 value: 79.33 - type: mrr_at_3 value: 77.708 - type: mrr_at_5 value: 78.546 - type: ndcg_at_1 value: 58.62500000000001 - type: ndcg_at_10 value: 44.889 - type: ndcg_at_100 value: 50.536 - type: ndcg_at_1000 value: 57.724 - type: ndcg_at_3 value: 49.32 - type: ndcg_at_5 value: 46.775 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 36.175000000000004 - type: precision_at_100 value: 11.940000000000001 - type: precision_at_1000 value: 2.178 - type: precision_at_3 value: 53.583000000000006 - type: precision_at_5 value: 45.550000000000004 - type: recall_at_1 value: 9.516 - type: recall_at_10 value: 27.028000000000002 - type: recall_at_100 value: 57.581 - type: recall_at_1000 value: 80.623 - type: recall_at_3 value: 16.313 - type: recall_at_5 value: 20.674 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.74999999999999 - type: f1 value: 46.46706502669774 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 77.266 - type: map_at_10 value: 84.89999999999999 - type: map_at_100 value: 85.109 - type: map_at_1000 value: 85.123 - type: map_at_3 value: 83.898 - type: map_at_5 value: 84.541 - type: mrr_at_1 value: 83.138 - type: mrr_at_10 value: 89.37 - type: mrr_at_100 value: 89.432 - type: mrr_at_1000 value: 89.43299999999999 - type: mrr_at_3 value: 88.836 - type: mrr_at_5 value: 89.21 - type: ndcg_at_1 value: 83.138 - type: ndcg_at_10 value: 88.244 - type: ndcg_at_100 value: 88.98700000000001 - type: ndcg_at_1000 value: 89.21900000000001 - type: ndcg_at_3 value: 86.825 - type: ndcg_at_5 value: 87.636 - type: precision_at_1 value: 83.138 - type: precision_at_10 value: 10.47 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.933 - type: precision_at_5 value: 20.36 - type: recall_at_1 value: 77.266 - type: recall_at_10 value: 94.063 - type: recall_at_100 value: 96.993 - type: recall_at_1000 value: 98.414 - type: recall_at_3 value: 90.228 - type: recall_at_5 value: 92.328 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.319 - type: map_at_10 value: 36.943 - type: map_at_100 value: 38.951 - type: map_at_1000 value: 39.114 - type: map_at_3 value: 32.82 - type: map_at_5 value: 34.945 - type: mrr_at_1 value: 44.135999999999996 - type: mrr_at_10 value: 53.071999999999996 - type: mrr_at_100 value: 53.87 - type: mrr_at_1000 value: 53.90200000000001 - type: mrr_at_3 value: 50.77199999999999 - type: mrr_at_5 value: 52.129999999999995 - type: ndcg_at_1 value: 44.135999999999996 - type: ndcg_at_10 value: 44.836 - type: ndcg_at_100 value: 51.754 - type: ndcg_at_1000 value: 54.36 - type: ndcg_at_3 value: 41.658 - type: ndcg_at_5 value: 42.354 - type: precision_at_1 value: 44.135999999999996 - type: precision_at_10 value: 12.284 - type: precision_at_100 value: 1.952 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 27.828999999999997 - type: precision_at_5 value: 20.093 - type: recall_at_1 value: 22.319 - type: recall_at_10 value: 51.528 - type: recall_at_100 value: 76.70700000000001 - type: recall_at_1000 value: 92.143 - type: recall_at_3 value: 38.641 - type: recall_at_5 value: 43.653999999999996 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.182 - type: map_at_10 value: 65.146 - type: map_at_100 value: 66.023 - type: map_at_1000 value: 66.078 - type: map_at_3 value: 61.617999999999995 - type: map_at_5 value: 63.82299999999999 - type: mrr_at_1 value: 80.365 - type: mrr_at_10 value: 85.79 - type: mrr_at_100 value: 85.963 - type: mrr_at_1000 value: 85.968 - type: mrr_at_3 value: 84.952 - type: mrr_at_5 value: 85.503 - type: ndcg_at_1 value: 80.365 - type: ndcg_at_10 value: 73.13499999999999 - type: ndcg_at_100 value: 76.133 - type: ndcg_at_1000 value: 77.151 - type: ndcg_at_3 value: 68.255 - type: ndcg_at_5 value: 70.978 - type: precision_at_1 value: 80.365 - type: precision_at_10 value: 15.359 - type: precision_at_100 value: 1.7690000000000001 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 44.024 - type: precision_at_5 value: 28.555999999999997 - type: recall_at_1 value: 40.182 - type: recall_at_10 value: 76.793 - type: recall_at_100 value: 88.474 - type: recall_at_1000 value: 95.159 - type: recall_at_3 value: 66.036 - type: recall_at_5 value: 71.391 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.7796 - type: ap value: 89.24883716810874 - type: f1 value: 92.7706903433313 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.016 - type: map_at_10 value: 34.408 - type: map_at_100 value: 35.592 - type: map_at_1000 value: 35.64 - type: map_at_3 value: 30.459999999999997 - type: map_at_5 value: 32.721000000000004 - type: mrr_at_1 value: 22.593 - type: mrr_at_10 value: 34.993 - type: mrr_at_100 value: 36.113 - type: mrr_at_1000 value: 36.156 - type: mrr_at_3 value: 31.101 - type: mrr_at_5 value: 33.364 - type: ndcg_at_1 value: 22.579 - type: ndcg_at_10 value: 41.404999999999994 - type: ndcg_at_100 value: 47.018 - type: ndcg_at_1000 value: 48.211999999999996 - type: ndcg_at_3 value: 33.389 - type: ndcg_at_5 value: 37.425000000000004 - type: precision_at_1 value: 22.579 - type: precision_at_10 value: 6.59 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.241000000000001 - type: precision_at_5 value: 10.59 - type: recall_at_1 value: 22.016 - type: recall_at_10 value: 62.927 - type: recall_at_100 value: 88.72 - type: recall_at_1000 value: 97.80799999999999 - type: recall_at_3 value: 41.229 - type: recall_at_5 value: 50.88 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.01732786137711 - type: f1 value: 93.76353126402202 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.91746466028272 - type: f1 value: 57.715651682646765 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.5030262273033 - type: f1 value: 74.6693629986121 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.74781439139207 - type: f1 value: 79.96684171018774 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.2156206892017 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.180539484816137 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.51125957874274 - type: mrr value: 33.777037359249995 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.248 - type: map_at_10 value: 15.340000000000002 - type: map_at_100 value: 19.591 - type: map_at_1000 value: 21.187 - type: map_at_3 value: 11.329 - type: map_at_5 value: 13.209999999999999 - type: mrr_at_1 value: 47.678 - type: mrr_at_10 value: 57.493 - type: mrr_at_100 value: 58.038999999999994 - type: mrr_at_1000 value: 58.07 - type: mrr_at_3 value: 55.36600000000001 - type: mrr_at_5 value: 56.635999999999996 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.653999999999996 - type: ndcg_at_100 value: 36.288 - type: ndcg_at_1000 value: 44.765 - type: ndcg_at_3 value: 43.553 - type: ndcg_at_5 value: 41.317 - type: precision_at_1 value: 47.368 - type: precision_at_10 value: 28.669 - type: precision_at_100 value: 9.158 - type: precision_at_1000 value: 2.207 - type: precision_at_3 value: 40.97 - type: precision_at_5 value: 35.604 - type: recall_at_1 value: 7.248 - type: recall_at_10 value: 19.46 - type: recall_at_100 value: 37.214000000000006 - type: recall_at_1000 value: 67.64099999999999 - type: recall_at_3 value: 12.025 - type: recall_at_5 value: 15.443999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.595000000000002 - type: map_at_10 value: 47.815999999999995 - type: map_at_100 value: 48.811 - type: map_at_1000 value: 48.835 - type: map_at_3 value: 43.225 - type: map_at_5 value: 46.017 - type: mrr_at_1 value: 35.689 - type: mrr_at_10 value: 50.341 - type: mrr_at_100 value: 51.044999999999995 - type: mrr_at_1000 value: 51.062 - type: mrr_at_3 value: 46.553 - type: mrr_at_5 value: 48.918 - type: ndcg_at_1 value: 35.66 - type: ndcg_at_10 value: 55.859 - type: ndcg_at_100 value: 59.864 - type: ndcg_at_1000 value: 60.419999999999995 - type: ndcg_at_3 value: 47.371 - type: ndcg_at_5 value: 51.995000000000005 - type: precision_at_1 value: 35.66 - type: precision_at_10 value: 9.27 - type: precision_at_100 value: 1.1520000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.63 - type: precision_at_5 value: 15.655 - type: recall_at_1 value: 31.595000000000002 - type: recall_at_10 value: 77.704 - type: recall_at_100 value: 94.774 - type: recall_at_1000 value: 98.919 - type: recall_at_3 value: 56.052 - type: recall_at_5 value: 66.623 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.489 - type: map_at_10 value: 85.411 - type: map_at_100 value: 86.048 - type: map_at_1000 value: 86.064 - type: map_at_3 value: 82.587 - type: map_at_5 value: 84.339 - type: mrr_at_1 value: 82.28 - type: mrr_at_10 value: 88.27199999999999 - type: mrr_at_100 value: 88.362 - type: mrr_at_1000 value: 88.362 - type: mrr_at_3 value: 87.372 - type: mrr_at_5 value: 87.995 - type: ndcg_at_1 value: 82.27 - type: ndcg_at_10 value: 89.023 - type: ndcg_at_100 value: 90.191 - type: ndcg_at_1000 value: 90.266 - type: ndcg_at_3 value: 86.37 - type: ndcg_at_5 value: 87.804 - type: precision_at_1 value: 82.27 - type: precision_at_10 value: 13.469000000000001 - type: precision_at_100 value: 1.533 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.797 - type: precision_at_5 value: 24.734 - type: recall_at_1 value: 71.489 - type: recall_at_10 value: 95.824 - type: recall_at_100 value: 99.70599999999999 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 88.099 - type: recall_at_5 value: 92.285 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.52398807444541 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.34855891507871 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.188000000000001 - type: map_at_10 value: 13.987 - type: map_at_100 value: 16.438 - type: map_at_1000 value: 16.829 - type: map_at_3 value: 9.767000000000001 - type: map_at_5 value: 11.912 - type: mrr_at_1 value: 25.6 - type: mrr_at_10 value: 37.744 - type: mrr_at_100 value: 38.847 - type: mrr_at_1000 value: 38.894 - type: mrr_at_3 value: 34.166999999999994 - type: mrr_at_5 value: 36.207 - type: ndcg_at_1 value: 25.6 - type: ndcg_at_10 value: 22.980999999999998 - type: ndcg_at_100 value: 32.039 - type: ndcg_at_1000 value: 38.157000000000004 - type: ndcg_at_3 value: 21.567 - type: ndcg_at_5 value: 19.070999999999998 - type: precision_at_1 value: 25.6 - type: precision_at_10 value: 12.02 - type: precision_at_100 value: 2.5100000000000002 - type: precision_at_1000 value: 0.396 - type: precision_at_3 value: 20.333000000000002 - type: precision_at_5 value: 16.98 - type: recall_at_1 value: 5.188000000000001 - type: recall_at_10 value: 24.372 - type: recall_at_100 value: 50.934999999999995 - type: recall_at_1000 value: 80.477 - type: recall_at_3 value: 12.363 - type: recall_at_5 value: 17.203 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 87.24286275535398 - type: cos_sim_spearman value: 82.62333770991818 - type: euclidean_pearson value: 84.60353717637284 - type: euclidean_spearman value: 82.32990108810047 - type: manhattan_pearson value: 84.6089049738196 - type: manhattan_spearman value: 82.33361785438936 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.87428858503165 - type: cos_sim_spearman value: 79.09145886519929 - type: euclidean_pearson value: 86.42669231664036 - type: euclidean_spearman value: 80.03127375435449 - type: manhattan_pearson value: 86.41330338305022 - type: manhattan_spearman value: 80.02492538673368 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 88.67912277322645 - type: cos_sim_spearman value: 89.6171319711762 - type: euclidean_pearson value: 86.56571917398725 - type: euclidean_spearman value: 87.71216907898948 - type: manhattan_pearson value: 86.57459050182473 - type: manhattan_spearman value: 87.71916648349993 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 86.71957379085862 - type: cos_sim_spearman value: 85.01784075851465 - type: euclidean_pearson value: 84.7407848472801 - type: euclidean_spearman value: 84.61063091345538 - type: manhattan_pearson value: 84.71494352494403 - type: manhattan_spearman value: 84.58772077604254 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.40508326325175 - type: cos_sim_spearman value: 89.50912897763186 - type: euclidean_pearson value: 87.82349070086627 - type: euclidean_spearman value: 88.44179162727521 - type: manhattan_pearson value: 87.80181927025595 - type: manhattan_spearman value: 88.43205129636243 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.35846741715478 - type: cos_sim_spearman value: 86.61172476741842 - type: euclidean_pearson value: 84.60123125491637 - type: euclidean_spearman value: 85.3001948141827 - type: manhattan_pearson value: 84.56231142658329 - type: manhattan_spearman value: 85.23579900798813 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.94539129818824 - type: cos_sim_spearman value: 88.99349064256742 - type: euclidean_pearson value: 88.7142444640351 - type: euclidean_spearman value: 88.34120813505011 - type: manhattan_pearson value: 88.70363008238084 - type: manhattan_spearman value: 88.31952816956954 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 68.29910260369893 - type: cos_sim_spearman value: 68.79263346213466 - type: euclidean_pearson value: 68.41627521422252 - type: euclidean_spearman value: 66.61602587398579 - type: manhattan_pearson value: 68.49402183447361 - type: manhattan_spearman value: 66.80157792354453 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.43703906343708 - type: cos_sim_spearman value: 89.06081805093662 - type: euclidean_pearson value: 87.48311456299662 - type: euclidean_spearman value: 88.07417597580013 - type: manhattan_pearson value: 87.48202249768894 - type: manhattan_spearman value: 88.04758031111642 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.49080620485203 - type: mrr value: 96.19145378949301 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.317 - type: map_at_10 value: 69.296 - type: map_at_100 value: 69.738 - type: map_at_1000 value: 69.759 - type: map_at_3 value: 66.12599999999999 - type: map_at_5 value: 67.532 - type: mrr_at_1 value: 62 - type: mrr_at_10 value: 70.176 - type: mrr_at_100 value: 70.565 - type: mrr_at_1000 value: 70.583 - type: mrr_at_3 value: 67.833 - type: mrr_at_5 value: 68.93299999999999 - type: ndcg_at_1 value: 62 - type: ndcg_at_10 value: 74.069 - type: ndcg_at_100 value: 76.037 - type: ndcg_at_1000 value: 76.467 - type: ndcg_at_3 value: 68.628 - type: ndcg_at_5 value: 70.57600000000001 - type: precision_at_1 value: 62 - type: precision_at_10 value: 10 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.667 - type: precision_at_5 value: 17.4 - type: recall_at_1 value: 59.317 - type: recall_at_10 value: 87.822 - type: recall_at_100 value: 96.833 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 73.06099999999999 - type: recall_at_5 value: 77.928 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.88910891089108 - type: cos_sim_ap value: 97.236958456951 - type: cos_sim_f1 value: 94.39999999999999 - type: cos_sim_precision value: 94.39999999999999 - type: cos_sim_recall value: 94.39999999999999 - type: dot_accuracy value: 99.82574257425742 - type: dot_ap value: 94.94344759441888 - type: dot_f1 value: 91.17352056168507 - type: dot_precision value: 91.44869215291752 - type: dot_recall value: 90.9 - type: euclidean_accuracy value: 99.88415841584158 - type: euclidean_ap value: 97.2044250782305 - type: euclidean_f1 value: 94.210786739238 - type: euclidean_precision value: 93.24191968658178 - type: euclidean_recall value: 95.19999999999999 - type: manhattan_accuracy value: 99.88613861386139 - type: manhattan_ap value: 97.20683205497689 - type: manhattan_f1 value: 94.2643391521197 - type: manhattan_precision value: 94.02985074626866 - type: manhattan_recall value: 94.5 - type: max_accuracy value: 99.88910891089108 - type: max_ap value: 97.236958456951 - type: max_f1 value: 94.39999999999999 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.53940781726187 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.71865011295108 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.3218674533331 - type: mrr value: 56.28279910449028 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.723915667479673 - type: cos_sim_spearman value: 32.029070449745234 - type: dot_pearson value: 28.864944212481454 - type: dot_spearman value: 27.939266999596725 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.231 - type: map_at_10 value: 1.949 - type: map_at_100 value: 10.023 - type: map_at_1000 value: 23.485 - type: map_at_3 value: 0.652 - type: map_at_5 value: 1.054 - type: mrr_at_1 value: 86 - type: mrr_at_10 value: 92.067 - type: mrr_at_100 value: 92.067 - type: mrr_at_1000 value: 92.067 - type: mrr_at_3 value: 91.667 - type: mrr_at_5 value: 92.067 - type: ndcg_at_1 value: 83 - type: ndcg_at_10 value: 76.32900000000001 - type: ndcg_at_100 value: 54.662 - type: ndcg_at_1000 value: 48.062 - type: ndcg_at_3 value: 81.827 - type: ndcg_at_5 value: 80.664 - type: precision_at_1 value: 86 - type: precision_at_10 value: 80 - type: precision_at_100 value: 55.48 - type: precision_at_1000 value: 20.938000000000002 - type: precision_at_3 value: 85.333 - type: precision_at_5 value: 84.39999999999999 - type: recall_at_1 value: 0.231 - type: recall_at_10 value: 2.158 - type: recall_at_100 value: 13.344000000000001 - type: recall_at_1000 value: 44.31 - type: recall_at_3 value: 0.6779999999999999 - type: recall_at_5 value: 1.13 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.524 - type: map_at_10 value: 10.183 - type: map_at_100 value: 16.625 - type: map_at_1000 value: 18.017 - type: map_at_3 value: 5.169 - type: map_at_5 value: 6.772 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 47.128 - type: mrr_at_100 value: 48.458 - type: mrr_at_1000 value: 48.473 - type: mrr_at_3 value: 44.897999999999996 - type: mrr_at_5 value: 45.306000000000004 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 24.928 - type: ndcg_at_100 value: 37.613 - type: ndcg_at_1000 value: 48.528 - type: ndcg_at_3 value: 28.829 - type: ndcg_at_5 value: 25.237 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.448999999999998 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 30.612000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.524 - type: recall_at_10 value: 16.38 - type: recall_at_100 value: 49.529 - type: recall_at_1000 value: 83.598 - type: recall_at_3 value: 6.411 - type: recall_at_5 value: 8.932 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.09020000000001 - type: ap value: 14.451710060978993 - type: f1 value: 54.7874410609049 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.745331069609506 - type: f1 value: 60.08387848592697 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.71549485462037 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.39345532574357 - type: cos_sim_ap value: 78.16796549696478 - type: cos_sim_f1 value: 71.27713276123171 - type: cos_sim_precision value: 68.3115626511853 - type: cos_sim_recall value: 74.51187335092348 - type: dot_accuracy value: 85.12248912201228 - type: dot_ap value: 69.26039256107077 - type: dot_f1 value: 65.04294321240867 - type: dot_precision value: 63.251059586138126 - type: dot_recall value: 66.93931398416886 - type: euclidean_accuracy value: 87.07754664123503 - type: euclidean_ap value: 77.7872176038945 - type: euclidean_f1 value: 70.85587801278899 - type: euclidean_precision value: 66.3519115614924 - type: euclidean_recall value: 76.01583113456465 - type: manhattan_accuracy value: 87.07754664123503 - type: manhattan_ap value: 77.7341400185556 - type: manhattan_f1 value: 70.80310880829015 - type: manhattan_precision value: 69.54198473282443 - type: manhattan_recall value: 72.1108179419525 - type: max_accuracy value: 87.39345532574357 - type: max_ap value: 78.16796549696478 - type: max_f1 value: 71.27713276123171 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.09457833663213 - type: cos_sim_ap value: 86.33024314706873 - type: cos_sim_f1 value: 78.59623733719248 - type: cos_sim_precision value: 74.13322413322413 - type: cos_sim_recall value: 83.63104404065291 - type: dot_accuracy value: 88.3086894089339 - type: dot_ap value: 83.92225241805097 - type: dot_f1 value: 76.8721826377781 - type: dot_precision value: 72.8168044077135 - type: dot_recall value: 81.40591315060055 - type: euclidean_accuracy value: 88.77052043311213 - type: euclidean_ap value: 85.7410710218755 - type: euclidean_f1 value: 77.97705489398781 - type: euclidean_precision value: 73.77713657598241 - type: euclidean_recall value: 82.68401601478288 - type: manhattan_accuracy value: 88.73753250281368 - type: manhattan_ap value: 85.72867199072802 - type: manhattan_f1 value: 77.89774182922812 - type: manhattan_precision value: 74.23787931635857 - type: manhattan_recall value: 81.93717277486911 - type: max_accuracy value: 89.09457833663213 - type: max_ap value: 86.33024314706873 - type: max_f1 value: 78.59623733719248 license: mit language: - en --- # [Universal AnglE Embedding](https://github.com/SeanLee97/AnglE) 📢 `WhereIsAI/UAE-Large-V1` **is licensed under MIT. Feel free to use it in any scenario.** If you use it for academic papers, we would greatly appreciate it if you could cite us. 👉 [citation info](#citation). **🤝 Follow us on:** - GitHub: https://github.com/SeanLee97/AnglE. - Arxiv: https://arxiv.org/abs/2309.12871 🔥 Our universal English sentence embedding `WhereIsAI/UAE-Large-V1` achieves **SOTA** on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of 64.64! ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/635cc29de7aef2358a9b03ee/jY3tr0DCMdyJXOihSqJFr.jpeg) **🧑‍🤝‍🧑 Siblings:** - [WhereIsAI/UAE-Code-Large-V1](https://huggingface.co/WhereIsAI/UAE-Code-Large-V1): This model can be used for code or GitHub issue similarity measurement. # Usage ## 1. angle_emb ```bash python -m pip install -U angle-emb ``` 1) Non-Retrieval Tasks There is no need to specify any prompts. ```python from angle_emb import AnglE from scipy import spatial angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda() doc_vecs = angle.encode([ 'The weather is great!', 'The weather is very good!', 'i am going to bed' ]) for i, dv1 in enumerate(doc_vecs): for dv2 in doc_vecs[i+1:]: print(1 - spatial.distance.cosine(dv1, dv2)) ``` 2) Retrieval Tasks For retrieval purposes, please use the prompt `Prompts.C` for query (not for document). ```python from angle_emb import AnglE, Prompts from scipy import spatial angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda() qv = angle.encode(Prompts.C.format(text='what is the weather?')) doc_vecs = angle.encode([ 'The weather is great!', 'it is rainy today.', 'i am going to bed' ]) for dv in doc_vecs: print(1 - spatial.distance.cosine(qv[0], dv)) ``` ## 2. sentence transformer ```python from angle_emb import Prompts from sentence_transformers import SentenceTransformer model = SentenceTransformer("WhereIsAI/UAE-Large-V1").cuda() qv = model.encode(Prompts.C.format(text='what is the weather?')) doc_vecs = model.encode([ 'The weather is great!', 'it is rainy today.', 'i am going to bed' ]) for dv in doc_vecs: print(1 - spatial.distance.cosine(qv, dv)) ``` # Citation If you use our pre-trained models, welcome to support us by citing our work: ``` @article{li2023angle, title={AnglE-optimized Text Embeddings}, author={Li, Xianming and Li, Jing}, journal={arXiv preprint arXiv:2309.12871}, year={2023} } ```