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"description": "--- 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 --- **Recommend switching to newest BAAI/bge-base-en-v1.5, which has more reasonable similarity distribution and same method of usage.** <h1 align=\"center\">FlagEmbedding</h1> <h4 align=\"center\"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href=\"#evaluation\">Evaluation</a> | <a href=\"#train\">Train</a> | <a href=\"#contact\">Contact</a> | <a href=\"#citation\">Citation</a> | <a href=\"#license\">License</a> <p> </h4> More details please refer to our Github: FlagEmbedding. English | δΈζ 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. ************* π**Updates**π ************* - 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper :fire: - 09/15/2023: The technical report of BGE has been released - 09/15/2023: The masive training data of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models and , which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like this; C-MTEB **leaderboard** is available. - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size π€** - 08/02/2023: Release (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 (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List is short for . | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | BAAI/llm-embedder | English | Inference Fine-tune | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See README | | BAAI/bge-reranker-large | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | | | BAAI/bge-reranker-base | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | | | BAAI/bge-large-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | | | BAAI/bge-base-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | | | BAAI/bge-small-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | | | BAAI/bge-large-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | | | BAAI/bge-base-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | | | BAAI/bge-small-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | | | BAAI/bge-large-en | English | Inference Fine-tune | :trophy: rank **1st** in MTEB leaderboard | | | BAAI/bge-base-en | English | Inference Fine-tune | a base-scale model but with similar ability to | | | BAAI/bge-small-en | English | Inference Fine-tune |a small-scale model but with competitive performance | | | BAAI/bge-large-zh | Chinese | Inference Fine-tune | :trophy: rank **1st** in C-MTEB benchmark | | | BAAI/bge-base-zh | Chinese | Inference Fine-tune | a base-scale model but with similar ability to | | | BAAI/bge-small-zh | Chinese | Inference Fine-tune | a small-scale model but with competitive performance | | [1\\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at If you cannot open the Huggingface Hub, you also can download the models at . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this example to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this example, which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \\[0.6, 1\\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the , we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using models with FlagEmbedding, Sentence-Transformers, Langchain, or Huggingface Transformers. #### Using FlagEmbedding If it doesn't work for you, you can see FlagEmbedding for more methods to install FlagEmbedding. For the value of the argument , see Model List. By default, FlagModel will use all available GPUs when encoding. Please set to select specific GPUs. You also can set to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the models with sentence-transformers: For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see Model List). But the instruction is not needed for passages. #### Using Langchain You can use in langchain like this: #### Using HuggingFace Transformers With the 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 the first token (i.e., [CLS]) as the sentence embedding. ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding Get relevance scores (higher scores indicate more relevance): #### Using Huggingface transformers ## Evaluation models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our scripts. - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | BAAI/bge-large-en-v1.5 | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | BAAI/bge-base-en-v1.5 | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | BAAI/bge-small-en-v1.5 | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | bge-large-en | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | bge-base-en | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | gte-large | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | gte-base | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | e5-large-v2 | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | bge-small-en | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | instructor-xl | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | e5-base-v2 | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | gte-small | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | text-embedding-ada-002 | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | e5-small-v2 | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | 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 | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | sgpt-bloom-7b1-msmarco | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to C_MTEB for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | **BAAI/bge-large-zh-v1.5** | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | BAAI/bge-base-zh-v1.5 | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | BAAI/bge-small-zh-v1.5 | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | BAAI/bge-large-zh | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | bge-large-zh-noinstruct | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | BAAI/bge-base-zh | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | multilingual-e5-large | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | BAAI/bge-small-zh | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | m3e-base | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | m3e-large | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | multilingual-e5-base | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | multilingual-e5-small | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | text-embedding-ada-002(OpenAI) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | luotuo | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | text2vec-base | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | text2vec-large | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See C_MTEB for evaluation script. | Model | T2Reranking | T2RerankingZh2En\\* | T2RerankingEn2Zh\\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | BAAI/bge-reranker-base | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | BAAI/bge-reranker-large | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using retromae and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our examples.** We also provide a pre-train example. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see baai_general_embedding. ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our example. More details please refer to ./FlagEmbedding/reranker/README.md ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). ## Citation If you find this repository useful, please consider giving a star :star: and citation ## License FlagEmbedding is licensed under the MIT License. The released models can be used for commercial purposes free of charge.", |
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"model_explanation_gemini": "\"BAAI_bge-base-en is a versatile model excelling in classification, retrieval, clustering, reranking, and semantic textual similarity (STS) tasks, as demonstrated by its performance across multiple MTEB benchmarks.\"\n\nModel Features: \n- Classification (e.g., Amazon reviews, Banking77) \n- Retrieval (e.g., ArguAna, CQADupstack) \n- Clustering (e.g., Arxiv/Biorxiv papers) \n- Reranking (e", |
