--- tags: - mteb model-index: - name: bge-base-en results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.73134328358209 - type: ap value: 38.97277232632892 - type: f1 value: 69.81740361139785 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.56522500000001 - type: ap value: 88.88821771869553 - type: f1 value: 92.54817512659696 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.91 - type: f1 value: 46.28536394320311 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.834 - type: map_at_10 value: 53.564 - type: map_at_100 value: 54.230000000000004 - type: map_at_1000 value: 54.235 - type: map_at_3 value: 49.49 - type: map_at_5 value: 51.784 - type: mrr_at_1 value: 39.26 - type: mrr_at_10 value: 53.744 - type: mrr_at_100 value: 54.410000000000004 - type: mrr_at_1000 value: 54.415 - type: mrr_at_3 value: 49.656 - type: mrr_at_5 value: 52.018 - type: ndcg_at_1 value: 38.834 - type: ndcg_at_10 value: 61.487 - type: ndcg_at_100 value: 64.303 - type: ndcg_at_1000 value: 64.408 - type: ndcg_at_3 value: 53.116 - type: ndcg_at_5 value: 57.248 - type: precision_at_1 value: 38.834 - type: precision_at_10 value: 8.663 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.218999999999998 - type: precision_at_5 value: 14.737 - type: recall_at_1 value: 38.834 - type: recall_at_10 value: 86.629 - type: recall_at_100 value: 98.86200000000001 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 63.656 - type: recall_at_5 value: 73.68400000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.88475477433035 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.85053138403176 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.23221013208242 - type: mrr value: 74.64857318735436 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.4403443247284 - type: cos_sim_spearman value: 85.5326718115169 - type: euclidean_pearson value: 86.0114007449595 - type: euclidean_spearman value: 86.05979225604875 - type: manhattan_pearson value: 86.05423806568598 - type: manhattan_spearman value: 86.02485170086835 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.44480519480518 - type: f1 value: 86.41301900941988 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.17547250880036 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.74514172687293 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.096000000000004 - type: map_at_10 value: 43.345 - type: map_at_100 value: 44.73 - type: map_at_1000 value: 44.85 - type: map_at_3 value: 39.956 - type: map_at_5 value: 41.727 - type: mrr_at_1 value: 38.769999999999996 - type: mrr_at_10 value: 48.742000000000004 - type: mrr_at_100 value: 49.474000000000004 - type: mrr_at_1000 value: 49.513 - type: mrr_at_3 value: 46.161 - type: mrr_at_5 value: 47.721000000000004 - type: ndcg_at_1 value: 38.769999999999996 - type: ndcg_at_10 value: 49.464999999999996 - type: ndcg_at_100 value: 54.632000000000005 - type: ndcg_at_1000 value: 56.52 - type: ndcg_at_3 value: 44.687 - type: ndcg_at_5 value: 46.814 - type: precision_at_1 value: 38.769999999999996 - type: precision_at_10 value: 9.471 - type: precision_at_100 value: 1.4909999999999999 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 15.079 - type: recall_at_1 value: 32.096000000000004 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 83.075 - type: recall_at_1000 value: 95.178 - type: recall_at_3 value: 47.009 - type: recall_at_5 value: 53.348 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.588 - type: map_at_10 value: 42.251 - type: map_at_100 value: 43.478 - type: map_at_1000 value: 43.617 - type: map_at_3 value: 39.381 - type: map_at_5 value: 41.141 - type: mrr_at_1 value: 41.21 - type: mrr_at_10 value: 48.765 - type: mrr_at_100 value: 49.403000000000006 - type: mrr_at_1000 value: 49.451 - type: mrr_at_3 value: 46.73 - type: mrr_at_5 value: 47.965999999999994 - type: ndcg_at_1 value: 41.21 - type: ndcg_at_10 value: 47.704 - type: ndcg_at_100 value: 51.916 - type: ndcg_at_1000 value: 54.013999999999996 - type: ndcg_at_3 value: 44.007000000000005 - type: ndcg_at_5 value: 45.936 - type: precision_at_1 value: 41.21 - type: precision_at_10 value: 8.885 - type: precision_at_100 value: 1.409 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 21.274 - type: precision_at_5 value: 15.045 - type: recall_at_1 value: 32.588 - type: recall_at_10 value: 56.333 - type: recall_at_100 value: 74.251 - type: recall_at_1000 value: 87.518 - type: recall_at_3 value: 44.962 - type: recall_at_5 value: 50.609 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.308 - type: map_at_10 value: 53.12 - type: map_at_100 value: 54.123 - type: map_at_1000 value: 54.173 - type: map_at_3 value: 50.017999999999994 - type: map_at_5 value: 51.902 - type: mrr_at_1 value: 46.394999999999996 - type: mrr_at_10 value: 56.531 - type: mrr_at_100 value: 57.19800000000001 - type: mrr_at_1000 value: 57.225 - type: mrr_at_3 value: 54.368 - type: mrr_at_5 value: 55.713 - type: ndcg_at_1 value: 46.394999999999996 - type: ndcg_at_10 value: 58.811 - type: ndcg_at_100 value: 62.834 - type: ndcg_at_1000 value: 63.849999999999994 - type: ndcg_at_3 value: 53.88699999999999 - type: ndcg_at_5 value: 56.477999999999994 - type: precision_at_1 value: 46.394999999999996 - type: precision_at_10 value: 9.398 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 24.221999999999998 - type: precision_at_5 value: 16.539 - type: recall_at_1 value: 40.308 - type: recall_at_10 value: 72.146 - type: recall_at_100 value: 89.60900000000001 - type: recall_at_1000 value: 96.733 - type: recall_at_3 value: 58.91499999999999 - type: recall_at_5 value: 65.34299999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.383000000000003 - type: map_at_10 value: 35.802 - type: map_at_100 value: 36.756 - type: map_at_1000 value: 36.826 - type: map_at_3 value: 32.923 - type: map_at_5 value: 34.577999999999996 - type: mrr_at_1 value: 29.604999999999997 - type: mrr_at_10 value: 37.918 - type: mrr_at_100 value: 38.732 - type: mrr_at_1000 value: 38.786 - type: mrr_at_3 value: 35.198 - type: mrr_at_5 value: 36.808 - type: ndcg_at_1 value: 29.604999999999997 - type: ndcg_at_10 value: 40.836 - type: ndcg_at_100 value: 45.622 - type: ndcg_at_1000 value: 47.427 - type: ndcg_at_3 value: 35.208 - type: ndcg_at_5 value: 38.066 - type: precision_at_1 value: 29.604999999999997 - type: precision_at_10 value: 6.226 - type: precision_at_100 value: 0.9079999999999999 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 14.463000000000001 - type: precision_at_5 value: 10.35 - type: recall_at_1 value: 27.383000000000003 - type: recall_at_10 value: 54.434000000000005 - type: recall_at_100 value: 76.632 - type: recall_at_1000 value: 90.25 - type: recall_at_3 value: 39.275 - type: recall_at_5 value: 46.225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.885 - type: map_at_10 value: 25.724000000000004 - type: map_at_100 value: 26.992 - type: map_at_1000 value: 27.107999999999997 - type: map_at_3 value: 23.04 - type: map_at_5 value: 24.529 - type: mrr_at_1 value: 22.264 - type: mrr_at_10 value: 30.548 - type: mrr_at_100 value: 31.593 - type: mrr_at_1000 value: 31.657999999999998 - type: mrr_at_3 value: 27.756999999999998 - type: mrr_at_5 value: 29.398999999999997 - type: ndcg_at_1 value: 22.264 - type: ndcg_at_10 value: 30.902 - type: ndcg_at_100 value: 36.918 - type: ndcg_at_1000 value: 39.735 - type: ndcg_at_3 value: 25.915 - type: ndcg_at_5 value: 28.255999999999997 - type: precision_at_1 value: 22.264 - type: precision_at_10 value: 5.634 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.055 - type: recall_at_1 value: 17.885 - type: recall_at_10 value: 42.237 - type: recall_at_100 value: 68.489 - type: recall_at_1000 value: 88.721 - type: recall_at_3 value: 28.283 - type: recall_at_5 value: 34.300000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.737000000000002 - type: map_at_10 value: 39.757 - type: map_at_100 value: 40.992 - type: map_at_1000 value: 41.102 - type: map_at_3 value: 36.612 - type: map_at_5 value: 38.413000000000004 - type: mrr_at_1 value: 35.804 - type: mrr_at_10 value: 45.178000000000004 - type: mrr_at_100 value: 45.975 - type: mrr_at_1000 value: 46.021 - type: mrr_at_3 value: 42.541000000000004 - type: mrr_at_5 value: 44.167 - type: ndcg_at_1 value: 35.804 - type: ndcg_at_10 value: 45.608 - type: ndcg_at_100 value: 50.746 - type: ndcg_at_1000 value: 52.839999999999996 - type: ndcg_at_3 value: 40.52 - type: ndcg_at_5 value: 43.051 - type: precision_at_1 value: 35.804 - type: precision_at_10 value: 8.104 - type: precision_at_100 value: 1.256 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 19.121 - type: precision_at_5 value: 13.532 - type: recall_at_1 value: 29.737000000000002 - type: recall_at_10 value: 57.66 - type: recall_at_100 value: 79.121 - type: recall_at_1000 value: 93.023 - type: recall_at_3 value: 43.13 - type: recall_at_5 value: 49.836000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.299 - type: map_at_10 value: 35.617 - type: map_at_100 value: 36.972 - type: map_at_1000 value: 37.096000000000004 - type: map_at_3 value: 32.653999999999996 - type: map_at_5 value: 34.363 - type: mrr_at_1 value: 32.877 - type: mrr_at_10 value: 41.423 - type: mrr_at_100 value: 42.333999999999996 - type: mrr_at_1000 value: 42.398 - type: mrr_at_3 value: 39.193 - type: mrr_at_5 value: 40.426 - type: ndcg_at_1 value: 32.877 - type: ndcg_at_10 value: 41.271 - type: ndcg_at_100 value: 46.843 - type: ndcg_at_1000 value: 49.366 - type: ndcg_at_3 value: 36.735 - type: ndcg_at_5 value: 38.775999999999996 - type: precision_at_1 value: 32.877 - type: precision_at_10 value: 7.580000000000001 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.541999999999998 - type: precision_at_5 value: 12.443 - type: recall_at_1 value: 26.299 - type: recall_at_10 value: 52.256 - type: recall_at_100 value: 75.919 - type: recall_at_1000 value: 93.185 - type: recall_at_3 value: 39.271 - type: recall_at_5 value: 44.901 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.05741666666667 - type: map_at_10 value: 36.086416666666665 - type: map_at_100 value: 37.26916666666667 - type: map_at_1000 value: 37.38191666666666 - type: map_at_3 value: 33.34225 - type: map_at_5 value: 34.86425 - type: mrr_at_1 value: 32.06008333333333 - type: mrr_at_10 value: 40.36658333333333 - type: mrr_at_100 value: 41.206500000000005 - type: mrr_at_1000 value: 41.261083333333325 - type: mrr_at_3 value: 38.01208333333334 - type: mrr_at_5 value: 39.36858333333333 - type: ndcg_at_1 value: 32.06008333333333 - type: ndcg_at_10 value: 41.3535 - type: ndcg_at_100 value: 46.42066666666666 - type: ndcg_at_1000 value: 48.655166666666666 - type: ndcg_at_3 value: 36.78041666666667 - type: ndcg_at_5 value: 38.91783333333334 - type: precision_at_1 value: 32.06008333333333 - type: precision_at_10 value: 7.169833333333332 - type: precision_at_100 value: 1.1395 - type: precision_at_1000 value: 0.15158333333333332 - type: precision_at_3 value: 16.852 - type: precision_at_5 value: 11.8645 - type: recall_at_1 value: 27.05741666666667 - type: recall_at_10 value: 52.64491666666666 - type: recall_at_100 value: 74.99791666666667 - type: recall_at_1000 value: 90.50524999999999 - type: recall_at_3 value: 39.684000000000005 - type: recall_at_5 value: 45.37225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.607999999999997 - type: map_at_10 value: 32.28 - type: map_at_100 value: 33.261 - type: map_at_1000 value: 33.346 - type: map_at_3 value: 30.514999999999997 - type: map_at_5 value: 31.415 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.384 - type: mrr_at_100 value: 36.24 - type: mrr_at_1000 value: 36.299 - type: mrr_at_3 value: 33.717000000000006 - type: mrr_at_5 value: 34.507 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 36.248000000000005 - type: ndcg_at_100 value: 41.034 - type: ndcg_at_1000 value: 43.35 - type: ndcg_at_3 value: 32.987 - type: ndcg_at_5 value: 34.333999999999996 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.506 - type: precision_at_100 value: 0.853 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.11 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 25.607999999999997 - type: recall_at_10 value: 45.344 - type: recall_at_100 value: 67.132 - type: recall_at_1000 value: 84.676 - type: recall_at_3 value: 36.02 - type: recall_at_5 value: 39.613 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.44 - type: map_at_10 value: 25.651000000000003 - type: map_at_100 value: 26.735 - type: map_at_1000 value: 26.86 - type: map_at_3 value: 23.409 - type: map_at_5 value: 24.604 - type: mrr_at_1 value: 22.195 - type: mrr_at_10 value: 29.482000000000003 - type: mrr_at_100 value: 30.395 - type: mrr_at_1000 value: 30.471999999999998 - type: mrr_at_3 value: 27.409 - type: mrr_at_5 value: 28.553 - type: ndcg_at_1 value: 22.195 - type: ndcg_at_10 value: 30.242 - type: ndcg_at_100 value: 35.397 - type: ndcg_at_1000 value: 38.287 - type: ndcg_at_3 value: 26.201 - type: ndcg_at_5 value: 28.008 - type: precision_at_1 value: 22.195 - type: precision_at_10 value: 5.372 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 12.228 - type: precision_at_5 value: 8.727 - type: recall_at_1 value: 18.44 - type: recall_at_10 value: 40.325 - type: recall_at_100 value: 63.504000000000005 - type: recall_at_1000 value: 83.909 - type: recall_at_3 value: 28.925 - type: recall_at_5 value: 33.641 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.535999999999998 - type: map_at_10 value: 35.358000000000004 - type: map_at_100 value: 36.498999999999995 - type: map_at_1000 value: 36.597 - type: map_at_3 value: 32.598 - type: map_at_5 value: 34.185 - type: mrr_at_1 value: 31.25 - type: mrr_at_10 value: 39.593 - type: mrr_at_100 value: 40.443 - type: mrr_at_1000 value: 40.498 - type: mrr_at_3 value: 37.018 - type: mrr_at_5 value: 38.492 - type: ndcg_at_1 value: 31.25 - type: ndcg_at_10 value: 40.71 - type: ndcg_at_100 value: 46.079 - type: ndcg_at_1000 value: 48.287 - type: ndcg_at_3 value: 35.667 - type: ndcg_at_5 value: 38.080000000000005 - type: precision_at_1 value: 31.25 - type: precision_at_10 value: 6.847 - type: precision_at_100 value: 1.079 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 16.262 - type: precision_at_5 value: 11.455 - type: recall_at_1 value: 26.535999999999998 - type: recall_at_10 value: 52.92099999999999 - type: recall_at_100 value: 76.669 - type: recall_at_1000 value: 92.096 - type: recall_at_3 value: 38.956 - type: recall_at_5 value: 45.239000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.691 - type: map_at_10 value: 33.417 - type: map_at_100 value: 35.036 - type: map_at_1000 value: 35.251 - type: map_at_3 value: 30.646 - type: map_at_5 value: 32.177 - type: mrr_at_1 value: 30.04 - type: mrr_at_10 value: 37.905 - type: mrr_at_100 value: 38.929 - type: mrr_at_1000 value: 38.983000000000004 - type: mrr_at_3 value: 35.276999999999994 - type: mrr_at_5 value: 36.897000000000006 - type: ndcg_at_1 value: 30.04 - type: ndcg_at_10 value: 39.037 - type: ndcg_at_100 value: 44.944 - type: ndcg_at_1000 value: 47.644 - type: ndcg_at_3 value: 34.833999999999996 - type: ndcg_at_5 value: 36.83 - type: precision_at_1 value: 30.04 - type: precision_at_10 value: 7.4510000000000005 - type: precision_at_100 value: 1.492 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 11.897 - type: recall_at_1 value: 24.691 - type: recall_at_10 value: 49.303999999999995 - type: recall_at_100 value: 76.20400000000001 - type: recall_at_1000 value: 93.30000000000001 - type: recall_at_3 value: 36.594 - type: recall_at_5 value: 42.41 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.118 - type: map_at_10 value: 30.714999999999996 - type: map_at_100 value: 31.656000000000002 - type: map_at_1000 value: 31.757 - type: map_at_3 value: 28.355000000000004 - type: map_at_5 value: 29.337000000000003 - type: mrr_at_1 value: 25.323 - type: mrr_at_10 value: 32.93 - type: mrr_at_100 value: 33.762 - type: mrr_at_1000 value: 33.829 - type: mrr_at_3 value: 30.775999999999996 - type: mrr_at_5 value: 31.774 - type: ndcg_at_1 value: 25.323 - type: ndcg_at_10 value: 35.408 - type: ndcg_at_100 value: 40.083 - type: ndcg_at_1000 value: 42.542 - type: ndcg_at_3 value: 30.717 - type: ndcg_at_5 value: 32.385000000000005 - type: precision_at_1 value: 25.323 - type: precision_at_10 value: 5.564 - type: precision_at_100 value: 0.843 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 13.001 - type: precision_at_5 value: 8.834999999999999 - type: recall_at_1 value: 23.118 - type: recall_at_10 value: 47.788000000000004 - type: recall_at_100 value: 69.37 - type: recall_at_1000 value: 87.47399999999999 - type: recall_at_3 value: 34.868 - type: recall_at_5 value: 39.001999999999995 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 14.288 - type: map_at_10 value: 23.256 - type: map_at_100 value: 25.115 - type: map_at_1000 value: 25.319000000000003 - type: map_at_3 value: 20.005 - type: map_at_5 value: 21.529999999999998 - type: mrr_at_1 value: 31.401 - type: mrr_at_10 value: 42.251 - type: mrr_at_100 value: 43.236999999999995 - type: mrr_at_1000 value: 43.272 - type: mrr_at_3 value: 39.164 - type: mrr_at_5 value: 40.881 - type: ndcg_at_1 value: 31.401 - type: ndcg_at_10 value: 31.615 - type: ndcg_at_100 value: 38.982 - type: ndcg_at_1000 value: 42.496 - type: ndcg_at_3 value: 26.608999999999998 - type: ndcg_at_5 value: 28.048000000000002 - type: precision_at_1 value: 31.401 - type: precision_at_10 value: 9.536999999999999 - type: precision_at_100 value: 1.763 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 19.153000000000002 - type: precision_at_5 value: 14.228 - type: recall_at_1 value: 14.288 - type: recall_at_10 value: 36.717 - type: recall_at_100 value: 61.9 - type: recall_at_1000 value: 81.676 - type: recall_at_3 value: 24.203 - type: recall_at_5 value: 28.793999999999997 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.019 - type: map_at_10 value: 19.963 - type: map_at_100 value: 28.834 - type: map_at_1000 value: 30.537999999999997 - type: map_at_3 value: 14.45 - type: map_at_5 value: 16.817999999999998 - type: mrr_at_1 value: 65.75 - type: mrr_at_10 value: 74.646 - type: mrr_at_100 value: 74.946 - type: mrr_at_1000 value: 74.95100000000001 - type: mrr_at_3 value: 72.625 - type: mrr_at_5 value: 74.012 - type: ndcg_at_1 value: 54 - type: ndcg_at_10 value: 42.014 - type: ndcg_at_100 value: 47.527 - type: ndcg_at_1000 value: 54.911 - type: ndcg_at_3 value: 46.586 - type: ndcg_at_5 value: 43.836999999999996 - type: precision_at_1 value: 65.75 - type: precision_at_10 value: 33.475 - type: precision_at_100 value: 11.16 - type: precision_at_1000 value: 2.145 - type: precision_at_3 value: 50.083 - type: precision_at_5 value: 42.55 - type: recall_at_1 value: 9.019 - type: recall_at_10 value: 25.558999999999997 - type: recall_at_100 value: 53.937999999999995 - type: recall_at_1000 value: 77.67399999999999 - type: recall_at_3 value: 15.456 - type: recall_at_5 value: 19.259 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.635 - type: f1 value: 47.692783881403926 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 76.893 - type: map_at_10 value: 84.897 - type: map_at_100 value: 85.122 - type: map_at_1000 value: 85.135 - type: map_at_3 value: 83.88 - type: map_at_5 value: 84.565 - type: mrr_at_1 value: 83.003 - type: mrr_at_10 value: 89.506 - type: mrr_at_100 value: 89.574 - type: mrr_at_1000 value: 89.575 - type: mrr_at_3 value: 88.991 - type: mrr_at_5 value: 89.349 - type: ndcg_at_1 value: 83.003 - type: ndcg_at_10 value: 88.351 - type: ndcg_at_100 value: 89.128 - type: ndcg_at_1000 value: 89.34100000000001 - type: ndcg_at_3 value: 86.92 - type: ndcg_at_5 value: 87.78200000000001 - type: precision_at_1 value: 83.003 - type: precision_at_10 value: 10.517999999999999 - type: precision_at_100 value: 1.115 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 33.062999999999995 - type: precision_at_5 value: 20.498 - type: recall_at_1 value: 76.893 - type: recall_at_10 value: 94.374 - type: recall_at_100 value: 97.409 - type: recall_at_1000 value: 98.687 - type: recall_at_3 value: 90.513 - type: recall_at_5 value: 92.709 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.829 - type: map_at_10 value: 32.86 - type: map_at_100 value: 34.838 - type: map_at_1000 value: 35.006 - type: map_at_3 value: 28.597 - type: map_at_5 value: 31.056 - type: mrr_at_1 value: 41.358 - type: mrr_at_10 value: 49.542 - type: mrr_at_100 value: 50.29900000000001 - type: mrr_at_1000 value: 50.334999999999994 - type: mrr_at_3 value: 46.579 - type: mrr_at_5 value: 48.408 - type: ndcg_at_1 value: 41.358 - type: ndcg_at_10 value: 40.758 - type: ndcg_at_100 value: 47.799 - type: ndcg_at_1000 value: 50.589 - type: ndcg_at_3 value: 36.695 - type: ndcg_at_5 value: 38.193 - type: precision_at_1 value: 41.358 - type: precision_at_10 value: 11.142000000000001 - type: precision_at_100 value: 1.8350000000000002 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 24.023 - type: precision_at_5 value: 17.963 - type: recall_at_1 value: 20.829 - type: recall_at_10 value: 47.467999999999996 - type: recall_at_100 value: 73.593 - type: recall_at_1000 value: 90.122 - type: recall_at_3 value: 32.74 - type: recall_at_5 value: 39.608 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.324 - type: map_at_10 value: 64.183 - type: map_at_100 value: 65.037 - type: map_at_1000 value: 65.094 - type: map_at_3 value: 60.663 - type: map_at_5 value: 62.951 - type: mrr_at_1 value: 80.648 - type: mrr_at_10 value: 86.005 - type: mrr_at_100 value: 86.157 - type: mrr_at_1000 value: 86.162 - type: mrr_at_3 value: 85.116 - type: mrr_at_5 value: 85.703 - type: ndcg_at_1 value: 80.648 - type: ndcg_at_10 value: 72.351 - type: ndcg_at_100 value: 75.279 - type: ndcg_at_1000 value: 76.357 - type: ndcg_at_3 value: 67.484 - type: ndcg_at_5 value: 70.31500000000001 - type: precision_at_1 value: 80.648 - type: precision_at_10 value: 15.103 - type: precision_at_100 value: 1.7399999999999998 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 43.232 - type: precision_at_5 value: 28.165000000000003 - type: recall_at_1 value: 40.324 - type: recall_at_10 value: 75.517 - type: recall_at_100 value: 86.982 - type: recall_at_1000 value: 94.072 - type: recall_at_3 value: 64.848 - type: recall_at_5 value: 70.41199999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.4 - type: ap value: 87.4422032289312 - type: f1 value: 91.39249564302281 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.03 - type: map_at_10 value: 34.402 - type: map_at_100 value: 35.599 - type: map_at_1000 value: 35.648 - type: map_at_3 value: 30.603 - type: map_at_5 value: 32.889 - type: mrr_at_1 value: 22.679 - type: mrr_at_10 value: 35.021 - type: mrr_at_100 value: 36.162 - type: mrr_at_1000 value: 36.205 - type: mrr_at_3 value: 31.319999999999997 - type: mrr_at_5 value: 33.562 - type: ndcg_at_1 value: 22.692999999999998 - type: ndcg_at_10 value: 41.258 - type: ndcg_at_100 value: 46.967 - type: ndcg_at_1000 value: 48.175000000000004 - type: ndcg_at_3 value: 33.611000000000004 - type: ndcg_at_5 value: 37.675 - type: precision_at_1 value: 22.692999999999998 - type: precision_at_10 value: 6.5089999999999995 - type: precision_at_100 value: 0.936 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.413 - type: precision_at_5 value: 10.702 - type: recall_at_1 value: 22.03 - type: recall_at_10 value: 62.248000000000005 - type: recall_at_100 value: 88.524 - type: recall_at_1000 value: 97.714 - type: recall_at_3 value: 41.617 - type: recall_at_5 value: 51.359 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.36844505243957 - type: f1 value: 94.12408743818202 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.43410852713177 - type: f1 value: 58.501855709435624 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.04909213180902 - type: f1 value: 74.1800860395823 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.76126429051781 - type: f1 value: 79.85705217473232 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.70119520292863 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.33544316467486 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.75499243990726 - type: mrr value: 31.70602251821063 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.451999999999999 - type: map_at_10 value: 13.918 - type: map_at_100 value: 17.316000000000003 - type: map_at_1000 value: 18.747 - type: map_at_3 value: 10.471 - type: map_at_5 value: 12.104 - type: mrr_at_1 value: 46.749 - type: mrr_at_10 value: 55.717000000000006 - type: mrr_at_100 value: 56.249 - type: mrr_at_1000 value: 56.288000000000004 - type: mrr_at_3 value: 53.818 - type: mrr_at_5 value: 55.103 - type: ndcg_at_1 value: 45.201 - type: ndcg_at_10 value: 35.539 - type: ndcg_at_100 value: 32.586 - type: ndcg_at_1000 value: 41.486000000000004 - type: ndcg_at_3 value: 41.174 - type: ndcg_at_5 value: 38.939 - type: precision_at_1 value: 46.749 - type: precision_at_10 value: 25.944 - type: precision_at_100 value: 8.084 - type: precision_at_1000 value: 2.076 - type: precision_at_3 value: 38.7 - type: precision_at_5 value: 33.56 - type: recall_at_1 value: 6.451999999999999 - type: recall_at_10 value: 17.302 - type: recall_at_100 value: 32.14 - type: recall_at_1000 value: 64.12 - type: recall_at_3 value: 11.219 - type: recall_at_5 value: 13.993 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 32.037 - type: map_at_10 value: 46.565 - type: map_at_100 value: 47.606 - type: map_at_1000 value: 47.636 - type: map_at_3 value: 42.459 - type: map_at_5 value: 44.762 - type: mrr_at_1 value: 36.181999999999995 - type: mrr_at_10 value: 49.291000000000004 - type: mrr_at_100 value: 50.059 - type: mrr_at_1000 value: 50.078 - type: mrr_at_3 value: 45.829 - type: mrr_at_5 value: 47.797 - type: ndcg_at_1 value: 36.153 - type: ndcg_at_10 value: 53.983000000000004 - type: ndcg_at_100 value: 58.347 - type: ndcg_at_1000 value: 59.058 - type: ndcg_at_3 value: 46.198 - type: ndcg_at_5 value: 50.022 - type: precision_at_1 value: 36.153 - type: precision_at_10 value: 8.763 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 20.751 - type: precision_at_5 value: 14.646999999999998 - type: recall_at_1 value: 32.037 - type: recall_at_10 value: 74.008 - type: recall_at_100 value: 92.893 - type: recall_at_1000 value: 98.16 - type: recall_at_3 value: 53.705999999999996 - type: recall_at_5 value: 62.495 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.152 - type: map_at_10 value: 85.104 - type: map_at_100 value: 85.745 - type: map_at_1000 value: 85.761 - type: map_at_3 value: 82.175 - type: map_at_5 value: 84.066 - type: mrr_at_1 value: 82.03 - type: mrr_at_10 value: 88.115 - type: mrr_at_100 value: 88.21 - type: mrr_at_1000 value: 88.211 - type: mrr_at_3 value: 87.19200000000001 - type: mrr_at_5 value: 87.85 - type: ndcg_at_1 value: 82.03 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.96300000000001 - type: ndcg_at_1000 value: 90.056 - type: ndcg_at_3 value: 86.051 - type: ndcg_at_5 value: 87.63499999999999 - type: precision_at_1 value: 82.03 - type: precision_at_10 value: 13.450000000000001 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.627 - type: precision_at_5 value: 24.784 - type: recall_at_1 value: 71.152 - type: recall_at_10 value: 95.649 - type: recall_at_100 value: 99.58200000000001 - type: recall_at_1000 value: 99.981 - type: recall_at_3 value: 87.767 - type: recall_at_5 value: 92.233 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.48713646277477 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.394940772438545 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.043 - type: map_at_10 value: 12.949 - type: map_at_100 value: 15.146 - type: map_at_1000 value: 15.495000000000001 - type: map_at_3 value: 9.333 - type: map_at_5 value: 11.312999999999999 - type: mrr_at_1 value: 24.9 - type: mrr_at_10 value: 35.958 - type: mrr_at_100 value: 37.152 - type: mrr_at_1000 value: 37.201 - type: mrr_at_3 value: 32.667 - type: mrr_at_5 value: 34.567 - type: ndcg_at_1 value: 24.9 - type: ndcg_at_10 value: 21.298000000000002 - type: ndcg_at_100 value: 29.849999999999998 - type: ndcg_at_1000 value: 35.506 - type: ndcg_at_3 value: 20.548 - type: ndcg_at_5 value: 18.064 - type: precision_at_1 value: 24.9 - type: precision_at_10 value: 10.9 - type: precision_at_100 value: 2.331 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 19.267 - type: precision_at_5 value: 15.939999999999998 - type: recall_at_1 value: 5.043 - type: recall_at_10 value: 22.092 - type: recall_at_100 value: 47.323 - type: recall_at_1000 value: 74.553 - type: recall_at_3 value: 11.728 - type: recall_at_5 value: 16.188 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.7007085938325 - type: cos_sim_spearman value: 80.0171084446234 - type: euclidean_pearson value: 81.28133218355893 - type: euclidean_spearman value: 79.99291731740131 - type: manhattan_pearson value: 81.22926922327846 - type: manhattan_spearman value: 79.94444878127038 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.7411883252923 - type: cos_sim_spearman value: 77.93462937801245 - type: euclidean_pearson value: 83.00858563882404 - type: euclidean_spearman value: 77.82717362433257 - type: manhattan_pearson value: 82.92887645790769 - type: manhattan_spearman value: 77.78807488222115 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.04222459361023 - type: cos_sim_spearman value: 83.85931509330395 - type: euclidean_pearson value: 83.26916063876055 - type: euclidean_spearman value: 83.98621985648353 - type: manhattan_pearson value: 83.14935679184327 - type: manhattan_spearman value: 83.87938828586304 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.41136639535318 - type: cos_sim_spearman value: 81.51200091040481 - type: euclidean_pearson value: 81.45382456114775 - type: euclidean_spearman value: 81.46201181707931 - type: manhattan_pearson value: 81.37243088439584 - type: manhattan_spearman value: 81.39828421893426 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.71942451732227 - type: cos_sim_spearman value: 87.33044482064973 - type: euclidean_pearson value: 86.58580899365178 - type: euclidean_spearman value: 87.09206723832895 - type: manhattan_pearson value: 86.47460784157013 - type: manhattan_spearman value: 86.98367656583076 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.55868078863449 - type: cos_sim_spearman value: 85.38299230074065 - type: euclidean_pearson value: 84.64715256244595 - type: euclidean_spearman value: 85.49112229604047 - type: manhattan_pearson value: 84.60814346792462 - type: manhattan_spearman value: 85.44886026766822 - 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: 84.99292526370614 - type: cos_sim_spearman value: 85.58139465695983 - type: euclidean_pearson value: 86.51325066734084 - type: euclidean_spearman value: 85.56736418284562 - type: manhattan_pearson value: 86.48190836601357 - type: manhattan_spearman value: 85.51616256224258 - 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: 64.54124715078807 - type: cos_sim_spearman value: 65.32134275948374 - type: euclidean_pearson value: 67.09791698300816 - type: euclidean_spearman value: 65.79468982468465 - type: manhattan_pearson value: 67.13304723693966 - type: manhattan_spearman value: 65.68439995849283 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 83.4231099581624 - type: cos_sim_spearman value: 85.95475815226862 - type: euclidean_pearson value: 85.00339401999706 - type: euclidean_spearman value: 85.74133081802971 - type: manhattan_pearson value: 85.00407987181666 - type: manhattan_spearman value: 85.77509596397363 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.25666719585716 - type: mrr value: 96.32769917083642 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.828 - type: map_at_10 value: 68.369 - type: map_at_100 value: 68.83399999999999 - type: map_at_1000 value: 68.856 - type: map_at_3 value: 65.38000000000001 - type: map_at_5 value: 67.06299999999999 - type: mrr_at_1 value: 61 - type: mrr_at_10 value: 69.45400000000001 - type: mrr_at_100 value: 69.785 - type: mrr_at_1000 value: 69.807 - type: mrr_at_3 value: 67 - type: mrr_at_5 value: 68.43299999999999 - type: ndcg_at_1 value: 61 - type: ndcg_at_10 value: 73.258 - type: ndcg_at_100 value: 75.173 - type: ndcg_at_1000 value: 75.696 - type: ndcg_at_3 value: 68.162 - type: ndcg_at_5 value: 70.53399999999999 - type: precision_at_1 value: 61 - type: precision_at_10 value: 9.8 - type: precision_at_100 value: 1.087 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27 - type: precision_at_5 value: 17.666999999999998 - type: recall_at_1 value: 57.828 - type: recall_at_10 value: 87.122 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 73.139 - type: recall_at_5 value: 79.361 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85247524752475 - type: cos_sim_ap value: 96.25640197639723 - type: cos_sim_f1 value: 92.37851662404091 - type: cos_sim_precision value: 94.55497382198953 - type: cos_sim_recall value: 90.3 - type: dot_accuracy value: 99.76138613861386 - type: dot_ap value: 93.40295864389073 - type: dot_f1 value: 87.64267990074441 - type: dot_precision value: 86.99507389162562 - type: dot_recall value: 88.3 - type: euclidean_accuracy value: 99.85049504950496 - type: euclidean_ap value: 96.24254350525462 - type: euclidean_f1 value: 92.32323232323232 - type: euclidean_precision value: 93.26530612244898 - type: euclidean_recall value: 91.4 - type: manhattan_accuracy value: 99.85346534653465 - type: manhattan_ap value: 96.2635334753325 - type: manhattan_f1 value: 92.37899073120495 - type: manhattan_precision value: 95.22292993630573 - type: manhattan_recall value: 89.7 - type: max_accuracy value: 99.85346534653465 - type: max_ap value: 96.2635334753325 - type: max_f1 value: 92.37899073120495 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.83905786483794 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.031896152126436 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 54.551326709447146 - type: mrr value: 55.43758222986165 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.305688567308874 - type: cos_sim_spearman value: 29.27135743434515 - type: dot_pearson value: 30.336741878796563 - type: dot_spearman value: 30.513365725895937 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.245 - type: map_at_10 value: 1.92 - type: map_at_100 value: 10.519 - type: map_at_1000 value: 23.874000000000002 - type: map_at_3 value: 0.629 - type: map_at_5 value: 1.0290000000000001 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 93.5 - type: mrr_at_100 value: 93.5 - type: mrr_at_1000 value: 93.5 - type: mrr_at_3 value: 93 - type: mrr_at_5 value: 93.5 - type: ndcg_at_1 value: 84 - type: ndcg_at_10 value: 76.447 - type: ndcg_at_100 value: 56.516 - type: ndcg_at_1000 value: 48.583999999999996 - type: ndcg_at_3 value: 78.877 - type: ndcg_at_5 value: 79.174 - type: precision_at_1 value: 88 - type: precision_at_10 value: 80.60000000000001 - type: precision_at_100 value: 57.64 - type: precision_at_1000 value: 21.227999999999998 - type: precision_at_3 value: 82 - type: precision_at_5 value: 83.6 - type: recall_at_1 value: 0.245 - type: recall_at_10 value: 2.128 - type: recall_at_100 value: 13.767 - type: recall_at_1000 value: 44.958 - type: recall_at_3 value: 0.654 - type: recall_at_5 value: 1.111 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.5170000000000003 - type: map_at_10 value: 10.915 - type: map_at_100 value: 17.535 - type: map_at_1000 value: 19.042 - type: map_at_3 value: 5.689 - type: map_at_5 value: 7.837 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 49.547999999999995 - type: mrr_at_100 value: 50.653000000000006 - type: mrr_at_1000 value: 50.653000000000006 - type: mrr_at_3 value: 44.558 - type: mrr_at_5 value: 48.333 - type: ndcg_at_1 value: 32.653 - type: ndcg_at_10 value: 26.543 - type: ndcg_at_100 value: 38.946 - type: ndcg_at_1000 value: 49.406 - type: ndcg_at_3 value: 29.903000000000002 - type: ndcg_at_5 value: 29.231 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 23.265 - type: precision_at_100 value: 8.102 - type: precision_at_1000 value: 1.5 - type: precision_at_3 value: 31.293 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.5170000000000003 - type: recall_at_10 value: 16.88 - type: recall_at_100 value: 49.381 - type: recall_at_1000 value: 81.23899999999999 - type: recall_at_3 value: 6.965000000000001 - type: recall_at_5 value: 10.847999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.5942 - type: ap value: 13.92074156956546 - type: f1 value: 54.671999698839066 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.39728353140916 - type: f1 value: 59.68980496759517 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.11181870104935 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.46957143708649 - type: cos_sim_ap value: 76.16120197845457 - type: cos_sim_f1 value: 69.69919295671315 - type: cos_sim_precision value: 64.94986326344576 - type: cos_sim_recall value: 75.19788918205805 - type: dot_accuracy value: 83.0780234845324 - type: dot_ap value: 64.21717343541934 - type: dot_f1 value: 59.48375497624245 - type: dot_precision value: 57.94345759319489 - type: dot_recall value: 61.108179419525065 - type: euclidean_accuracy value: 86.6543482148179 - type: euclidean_ap value: 76.4527555010203 - type: euclidean_f1 value: 70.10156056477584 - type: euclidean_precision value: 66.05975723622782 - type: euclidean_recall value: 74.67018469656992 - type: manhattan_accuracy value: 86.66030875603504 - type: manhattan_ap value: 76.40304567255436 - type: manhattan_f1 value: 70.05275426328058 - type: manhattan_precision value: 65.4666360926393 - type: manhattan_recall value: 75.32981530343008 - type: max_accuracy value: 86.66030875603504 - type: max_ap value: 76.4527555010203 - type: max_f1 value: 70.10156056477584 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.42123646524624 - type: cos_sim_ap value: 85.15431437761646 - type: cos_sim_f1 value: 76.98069301530742 - type: cos_sim_precision value: 72.9314502239063 - type: cos_sim_recall value: 81.50600554357868 - type: dot_accuracy value: 86.70974502270346 - type: dot_ap value: 80.77621563599457 - type: dot_f1 value: 73.87058697285117 - type: dot_precision value: 68.98256396552877 - type: dot_recall value: 79.50415768401602 - type: euclidean_accuracy value: 88.46392672798541 - type: euclidean_ap value: 85.20370297495491 - type: euclidean_f1 value: 77.01372369624886 - type: euclidean_precision value: 73.39052800446397 - type: euclidean_recall value: 81.01324299353249 - type: manhattan_accuracy value: 88.43481973066325 - type: manhattan_ap value: 85.16318289864545 - type: manhattan_f1 value: 76.90884877182597 - type: manhattan_precision value: 74.01737396753062 - type: manhattan_recall value: 80.03541730828458 - type: max_accuracy value: 88.46392672798541 - type: max_ap value: 85.20370297495491 - type: max_f1 value: 77.01372369624886 license: mit language: - en pipeline_tag: sentence-similarity ---

FlagEmbedding

Model List | Usage | Evaluation | Train | License

For more details please refer to our GitHub repo: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. ************* 🌟**Updates**🌟 ************* - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | Description | query instruction for retrieval | |:-------------------------------|:--------:| :--------:| :--------:| | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章:` | ## Usage * **Using FlagEmbedding** ``` pip install FlagEmbedding ``` See [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences = ["样例数据-1", "样例数据-2"] model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章:") embeddings = model.encode(sentences) print(embeddings) # for retrieval task, please use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus() queries = ['query_1', 'query_2'] passages = ["样例段落-1", "样例段落-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU. * **Using Sentence-Transformers** Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences = ["样例数据-1", "样例数据-2"] model = SentenceTransformer('BAAI/bge-large-zh') embeddings = model.encode(sentences, normalize_embeddings=True) print(embeddings) ``` For retrieval task, each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). ```python from sentence_transformers import SentenceTransformer queries = ["手机开不了机怎么办?"] passages = ["样例段落-1", "样例段落-2"] instruction = "为这个句子生成表示以用于检索相关文章:" model = SentenceTransformer('BAAI/bge-large-zh') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` * **Using HuggingFace Transformers** With transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh') model = AutoModel.from_pretrained('BAAI/bge-large-zh') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for retrieval task, add an instruction to query # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**bge-large-en**](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | **63.98** | **53.9** | **46.98** | 85.8 | **59.48** | 81.56 | 32.06 | **76.21** | | [**bge-base-en**](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [**bge-small-en**](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 384 | 512 | 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 768 | 512 | 56.00 | 41.88 | 41.1 | 82.54 | 53.14 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 768 | 512 | 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 | - **C-MTEB**: We create a benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 | | [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** | | [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 | | [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | | [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 | ## Train This section will introduce the way we used to train the general embedding. The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md). **1. RetroMAE Pre-train** We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. In retromae, the mask ratio of encoder and decoder are 0.3, and 0.5 respectively. We used the AdamW optimizer and the learning rate is 2e-5. **Pre-training data**: - English: - [Pile](https://pile.eleuther.ai/) - [wikipedia](https://huggingface.co/datasets/wikipedia) - [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus) - Chinese: - Subset of [wudao](https://github.com/BAAI-WuDao/Data) - [baidu-baike](https://baike.baidu.com/) **2. Finetune** We fine-tune the model using a contrastive objective. The format of input data is a triple`(query, positive, negative)`. Besides the negative in the triple, we also adopt in-batch negatives strategy. We employ the cross-device negatives sharing method to share negatives among different GPUs, which can dramatically **increase the number of negatives**. We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). We used the AdamW optimizer and the learning rate is 1e-5. The temperature for contrastive loss is 0.01. For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training. For english, the instruction is `Represent this sentence for searching relevant passages: `; For chinese, the instruction is `为这个句子生成表示以用于检索相关文章:`. In the evaluation, the instruction should be added for sentence to passages retrieval task, not be added for other tasks. The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). You can easily finetune your model with it. **Training data**: - For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on. - For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on. **The data collection is to be released in the future.** We will continually update the embedding models and training codes, hoping to promote the development of the embedding model community. ## License FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.