| "description": "--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-base-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.14925373134328 - type: ap value: 39.32336517995478 - type: f1 value: 70.16902252611425 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.386825 - type: ap value: 90.21276917991995 - type: f1 value: 93.37741030006174 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.846000000000004 - type: f1 value: 48.14646269778261 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 40.754000000000005 - type: map_at_10 value: 55.761 - type: map_at_100 value: 56.330999999999996 - type: map_at_1000 value: 56.333999999999996 - type: map_at_3 value: 51.92 - type: map_at_5 value: 54.010999999999996 - type: mrr_at_1 value: 41.181 - type: mrr_at_10 value: 55.967999999999996 - type: mrr_at_100 value: 56.538 - type: mrr_at_1000 value: 56.542 - type: mrr_at_3 value: 51.980000000000004 - type: mrr_at_5 value: 54.208999999999996 - type: ndcg_at_1 value: 40.754000000000005 - type: ndcg_at_10 value: 63.605000000000004 - type: ndcg_at_100 value: 66.05199999999999 - type: ndcg_at_1000 value: 66.12 - type: ndcg_at_3 value: 55.708 - type: ndcg_at_5 value: 59.452000000000005 - type: precision_at_1 value: 40.754000000000005 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.238 - type: precision_at_5 value: 15.149000000000001 - type: recall_at_1 value: 40.754000000000005 - type: recall_at_10 value: 88.407 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.714 - type: recall_at_5 value: 75.747 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.74884539679369 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.8075893810716 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.128470519187736 - type: mrr value: 74.28065778481289 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.24629081484655 - type: cos_sim_spearman value: 86.93752309911496 - type: euclidean_pearson value: 87.58589628573816 - type: euclidean_spearman value: 88.05622328825284 - type: manhattan_pearson value: 87.5594959805773 - type: manhattan_spearman value: 88.19658793233961 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.9512987012987 - type: f1 value: 86.92515357973708 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10263762928872 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.69711517426737 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.327 - type: map_at_10 value: 44.099 - type: map_at_100 value: 45.525 - type: map_at_1000 value: 45.641999999999996 - type: map_at_3 value: 40.47 - type: map_at_5 value: 42.36 - type: mrr_at_1 value: 39.199 - type: mrr_at_10 value: 49.651 - type: mrr_at_100 value: 50.29 - type: mrr_at_1000 value: 50.329 - type: mrr_at_3 value: 46.924 - type: mrr_at_5 value: 48.548 - type: ndcg_at_1 value: 39.199 - type: ndcg_at_10 value: 50.773 - type: ndcg_at_100 value: 55.67999999999999 - type: ndcg_at_1000 value: 57.495 - type: ndcg_at_3 value: 45.513999999999996 - type: ndcg_at_5 value: 47.703 - type: precision_at_1 value: 39.199 - type: precision_at_10 value: 9.914000000000001 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 21.984 - type: precision_at_5 value: 15.737000000000002 - type: recall_at_1 value: 32.327 - type: recall_at_10 value: 63.743 - type: recall_at_100 value: 84.538 - type: recall_at_1000 value: 96.089 - type: recall_at_3 value: 48.065000000000005 - type: recall_at_5 value: 54.519 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.671 - type: map_at_10 value: 42.954 - type: map_at_100 value: 44.151 - type: map_at_1000 value: 44.287 - type: map_at_3 value: 39.912 - type: map_at_5 value: 41.798 - type: mrr_at_1 value: 41.465 - type: mrr_at_10 value: 49.351 - type: mrr_at_100 value: 49.980000000000004 - type: mrr_at_1000 value: 50.016000000000005 - type: mrr_at_3 value: 47.144000000000005 - type: mrr_at_5 value: 48.592999999999996 - type: ndcg_at_1 value: 41.465 - type: ndcg_at_10 value: 48.565999999999995 - type: ndcg_at_100 value: 52.76499999999999 - type: ndcg_at_1000 value: 54.749 - type: ndcg_at_3 value: 44.57 - type: ndcg_at_5 value: 46.759 - type: precision_at_1 value: 41.465 - type: precision_at_10 value: 9.107999999999999 - type: precision_at_100 value: 1.433 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 21.423000000000002 - type: precision_at_5 value: 15.414 - type: recall_at_1 value: 32.671 - type: recall_at_10 value: 57.738 - type: recall_at_100 value: 75.86500000000001 - type: recall_at_1000 value: 88.36 - type: recall_at_3 value: 45.626 - type: recall_at_5 value: 51.812000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 41.185 - type: map_at_10 value: 53.929 - type: map_at_100 value: 54.92 - type: map_at_1000 value: 54.967999999999996 - type: map_at_3 value: 50.70400000000001 - type: map_at_5 value: 52.673 - type: mrr_at_1 value: 47.398 - type: mrr_at_10 value: 57.303000000000004 - type: mrr_at_100 value: 57.959 - type: mrr_at_1000 value: 57.985 - type: mrr_at_3 value: 54.932 - type: mrr_at_5 value: 56.464999999999996 - type: ndcg_at_1 value: 47.398 - type: ndcg_at_10 value: 59.653 - type: ndcg_at_100 value: 63.627 - type: ndcg_at_1000 value: 64.596 - type: ndcg_at_3 value: 54.455 - type: ndcg_at_5 value: 57.245000000000005 - type: precision_at_1 value: 47.398 - type: precision_at_10 value: 9.524000000000001 - type: precision_at_100 value: 1.243 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 24.389 - type: precision_at_5 value: 16.752 - type: recall_at_1 value: 41.185 - type: recall_at_10 value: 73.193 - type: recall_at_100 value: 90.357 - type: recall_at_1000 value: 97.253 - type: recall_at_3 value: 59.199999999999996 - type: recall_at_5 value: 66.118 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.27 - type: map_at_10 value: 36.223 - type: map_at_100 value: 37.218 - type: map_at_1000 value: 37.293 - type: map_at_3 value: 33.503 - type: map_at_5 value: 35.097 - type: mrr_at_1 value: 29.492 - type: mrr_at_10 value: 38.352000000000004 - type: mrr_at_100 value: 39.188 - type: mrr_at_1000 value: 39.247 - type: mrr_at_3 value: 35.876000000000005 - type: mrr_at_5 value: 37.401 - type: ndcg_at_1 value: 29.492 - type: ndcg_at_10 value: 41.239 - type: ndcg_at_100 value: 46.066 - type: ndcg_at_1000 value: 47.992000000000004 - type: ndcg_at_3 value: 36.11 - type: ndcg_at_5 value: 38.772 - type: precision_at_1 value: 29.492 - type: precision_at_10 value: 6.260000000000001 - type: precision_at_100 value: 0.914 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 15.104000000000001 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 27.27 - type: recall_at_10 value: 54.589 - type: recall_at_100 value: 76.70700000000001 - type: recall_at_1000 value: 91.158 - type: recall_at_3 value: 40.974 - type: recall_at_5 value: 47.327000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.848 - type: map_at_10 value: 26.207 - type: map_at_100 value: 27.478 - type: map_at_1000 value: 27.602 - type: map_at_3 value: 23.405 - type: map_at_5 value: 24.98 - type: mrr_at_1 value: 21.891 - type: mrr_at_10 value: 31.041999999999998 - type: mrr_at_100 value: 32.092 - type: mrr_at_1000 value: 32.151999999999994 - type: mrr_at_3 value: 28.358 - type: mrr_at_5 value: 29.969 - type: ndcg_at_1 value: 21.891 - type: ndcg_at_10 value: 31.585 - type: ndcg_at_100 value: 37.531 - type: ndcg_at_1000 value: 40.256 - type: ndcg_at_3 value: 26.508 - type: ndcg_at_5 value: 28.894 - type: precision_at_1 value: 21.891 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.769 - type: precision_at_5 value: 9.279 - type: recall_at_1 value: 17.848 - type: recall_at_10 value: 43.452 - type: recall_at_100 value: 69.216 - type: recall_at_1000 value: 88.102 - type: recall_at_3 value: 29.18 - type: recall_at_5 value: 35.347 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.94 - type: map_at_10 value: 41.248000000000005 - type: map_at_100 value: 42.495 - type: map_at_1000 value: 42.602000000000004 - type: map_at_3 value: 37.939 - type: map_at_5 value: 39.924 - type: mrr_at_1 value: 37.824999999999996 - type: mrr_at_10 value: 47.041 - type: mrr_at_100 value: 47.83 - type: mrr_at_1000 value: 47.878 - type: mrr_at_3 value: 44.466 - type: mrr_at_5 value: 46.111999999999995 - type: ndcg_at_1 value: 37.824999999999996 - type: ndcg_at_10 value: 47.223 - type: ndcg_at_100 value: 52.394 - type: ndcg_at_1000 value: 54.432 - type: ndcg_at_3 value: 42.032000000000004 - type: ndcg_at_5 value: 44.772 - type: precision_at_1 value: 37.824999999999996 - type: precision_at_10 value: 8.393 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 19.698 - type: precision_at_5 value: 14.013 - type: recall_at_1 value: 30.94 - type: recall_at_10 value: 59.316 - type: recall_at_100 value: 80.783 - type: recall_at_1000 value: 94.15400000000001 - type: recall_at_3 value: 44.712 - type: recall_at_5 value: 51.932 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.104 - type: map_at_10 value: 36.675999999999995 - type: map_at_100 value: 38.076 - type: map_at_1000 value: 38.189 - type: map_at_3 value: 33.733999999999995 - type: map_at_5 value: 35.287 - type: mrr_at_1 value: 33.904 - type: mrr_at_10 value: 42.55 - type: mrr_at_100 value: 43.434 - type: mrr_at_1000 value: 43.494 - type: mrr_at_3 value: 40.126 - type: mrr_at_5 value: 41.473 - type: ndcg_at_1 value: 33.904 - type: ndcg_at_10 value: 42.414 - type: ndcg_at_100 value: 48.203 - type: ndcg_at_1000 value: 50.437 - type: ndcg_at_3 value: 37.633 - type: ndcg_at_5 value: 39.67 - type: precision_at_1 value: 33.904 - type: precision_at_10 value: 7.82 - type: precision_at_100 value: 1.2409999999999999 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 17.884 - type: precision_at_5 value: 12.648000000000001 - type: recall_at_1 value: 27.104 - type: recall_at_10 value: 53.563 - type: recall_at_100 value: 78.557 - type: recall_at_1000 value: 93.533 - type: recall_at_3 value: 39.92 - type: recall_at_5 value: 45.457 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.707749999999997 - type: map_at_10 value: 36.961 - type: map_at_100 value: 38.158833333333334 - type: map_at_1000 value: 38.270333333333326 - type: map_at_3 value: 34.07183333333334 - type: map_at_5 value: 35.69533333333334 - type: mrr_at_1 value: 32.81875 - type: mrr_at_10 value: 41.293 - type: mrr_at_100 value: 42.116499999999995 - type: mrr_at_1000 value: 42.170249999999996 - type: mrr_at_3 value: 38.83983333333333 - type: mrr_at_5 value: 40.29775 - type: ndcg_at_1 value: 32.81875 - type: ndcg_at_10 value: 42.355 - type: ndcg_at_100 value: 47.41374999999999 - type: ndcg_at_1000 value: 49.5805 - type: ndcg_at_3 value: 37.52825 - type: ndcg_at_5 value: 39.83266666666667 - type: precision_at_1 value: 32.81875 - type: precision_at_10 value: 7.382416666666666 - type: precision_at_100 value: 1.1640833333333334 - type: precision_at_1000 value: 0.15383333333333335 - type: precision_at_3 value: 17.134166666666665 - type: precision_at_5 value: 12.174833333333336 - type: recall_at_1 value: 27.707749999999997 - type: recall_at_10 value: 53.945 - type: recall_at_100 value: 76.191 - type: recall_at_1000 value: 91.101 - type: recall_at_3 value: 40.39083333333334 - type: recall_at_5 value: 46.40083333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.482 - type: map_at_10 value: 33.201 - type: map_at_100 value: 34.107 - type: map_at_1000 value: 34.197 - type: map_at_3 value: 31.174000000000003 - type: map_at_5 value: 32.279 - type: mrr_at_1 value: 29.908 - type: mrr_at_10 value: 36.235 - type: mrr_at_100 value: 37.04 - type: mrr_at_1000 value: 37.105 - type: mrr_at_3 value: 34.355999999999995 - type: mrr_at_5 value: 35.382999999999996 - type: ndcg_at_1 value: 29.908 - type: ndcg_at_10 value: 37.325 - type: ndcg_at_100 value: 41.795 - type: ndcg_at_1000 value: 44.105 - type: ndcg_at_3 value: 33.555 - type: ndcg_at_5 value: 35.266999999999996 - type: precision_at_1 value: 29.908 - type: precision_at_10 value: 5.721 - type: precision_at_100 value: 0.8630000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 14.008000000000001 - type: precision_at_5 value: 9.754999999999999 - type: recall_at_1 value: 26.482 - type: recall_at_10 value: 47.072 - type: recall_at_100 value: 67.27 - type: recall_at_1000 value: 84.371 - type: recall_at_3 value: 36.65 - type: recall_at_5 value: 40.774 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.815 - type: map_at_10 value: 26.369999999999997 - type: map_at_100 value: 27.458 - type: map_at_1000 value: 27.588 - type: map_at_3 value: 23.990000000000002 - type: map_at_5 value: 25.345000000000002 - type: mrr_at_1 value: 22.953000000000003 - type: mrr_at_10 value: 30.342999999999996 - type: mrr_at_100 value: 31.241000000000003 - type: mrr_at_1000 value: 31.319000000000003 - type: mrr_at_3 value: 28.16 - type: mrr_at_5 value: 29.406 - type: ndcg_at_1 value: 22.953000000000003 - type: ndcg_at_10 value: 31.151 - type: ndcg_at_100 value: 36.309000000000005 - type: ndcg_at_1000 value: 39.227000000000004 - type: ndcg_at_3 value: 26.921 - type: ndcg_at_5 value: 28.938000000000002 - type: precision_at_1 value: 22.953000000000003 - type: precision_at_10 value: 5.602 - type: precision_at_100 value: 0.9530000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.606 - type: precision_at_5 value: 9.119 - type: recall_at_1 value: 18.815 - type: recall_at_10 value: 41.574 - type: recall_at_100 value: 64.84400000000001 - type: recall_at_1000 value: 85.406 - type: recall_at_3 value: 29.694 - type: recall_at_5 value: 34.935 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.840999999999998 - type: map_at_10 value: 36.797999999999995 - type: map_at_100 value: 37.993 - type: map_at_1000 value: 38.086999999999996 - type: map_at_3 value: 34.050999999999995 - type: map_at_5 value: 35.379 - type: mrr_at_1 value: 32.649 - type: mrr_at_10 value: 41.025 - type: mrr_at_100 value: 41.878 - type: mrr_at_1000 value: 41.929 - type: mrr_at_3 value: 38.573 - type: mrr_at_5 value: 39.715 - type: ndcg_at_1 value: 32.649 - type: ndcg_at_10 value: 42.142 - type: ndcg_at_100 value: 47.558 - type: ndcg_at_1000 value: 49.643 - type: ndcg_at_3 value: 37.12 - type: ndcg_at_5 value: 38.983000000000004 - type: precision_at_1 value: 32.649 - type: precision_at_10 value: 7.08 - type: precision_at_100 value: 1.1039999999999999 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.698 - type: precision_at_5 value: 11.511000000000001 - type: recall_at_1 value: 27.840999999999998 - type: recall_at_10 value: 54.245 - type: recall_at_100 value: 77.947 - type: recall_at_1000 value: 92.36999999999999 - type: recall_at_3 value: 40.146 - type: recall_at_5 value: 44.951 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.529000000000003 - type: map_at_10 value: 35.010000000000005 - type: map_at_100 value: 36.647 - type: map_at_1000 value: 36.857 - type: map_at_3 value: 31.968000000000004 - type: map_at_5 value: 33.554 - type: mrr_at_1 value: 31.818 - type: mrr_at_10 value: 39.550999999999995 - type: mrr_at_100 value: 40.54 - type: mrr_at_1000 value: 40.596 - type: mrr_at_3 value: 36.726 - type: mrr_at_5 value: 38.416 - type: ndcg_at_1 value: 31.818 - type: ndcg_at_10 value: 40.675 - type: ndcg_at_100 value: 46.548 - type: ndcg_at_1000 value: 49.126 - type: ndcg_at_3 value: 35.829 - type: ndcg_at_5 value: 38.0 - type: precision_at_1 value: 31.818 - type: precision_at_10 value: 7.826 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 16.601 - type: precision_at_5 value: 12.095 - type: recall_at_1 value: 26.529000000000003 - type: recall_at_10 value: 51.03 - type: recall_at_100 value: 77.556 - type: recall_at_1000 value: 93.804 - type: recall_at_3 value: 36.986000000000004 - type: recall_at_5 value: 43.096000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.480999999999998 - type: map_at_10 value: 30.817 - type: map_at_100 value: 31.838 - type: map_at_1000 value: 31.932 - type: map_at_3 value: 28.011999999999997 - type: map_at_5 value: 29.668 - type: mrr_at_1 value: 25.323 - type: mrr_at_10 value: 33.072 - type: mrr_at_100 value: 33.926 - type: mrr_at_1000 value: 33.993 - type: mrr_at_3 value: 30.436999999999998 - type: mrr_at_5 value: 32.092 - type: ndcg_at_1 value: 25.323 - type: ndcg_at_10 value: 35.514 - type: ndcg_at_100 value: 40.489000000000004 - type: ndcg_at_1000 value: 42.908 - type: ndcg_at_3 value: 30.092000000000002 - type: ndcg_at_5 value: 32.989000000000004 - type: precision_at_1 value: 25.323 - type: precision_at_10 value: 5.545 - type: precision_at_100 value: 0.861 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.446 - type: precision_at_5 value: 9.131 - type: recall_at_1 value: 23.480999999999998 - type: recall_at_10 value: 47.825 - type: recall_at_100 value: 70.652 - type: recall_at_1000 value: 88.612 - type: recall_at_3 value: 33.537 - type: recall_at_5 value: 40.542 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.333999999999998 - type: map_at_10 value: 22.524 - type: map_at_100 value: 24.506 - type: map_at_1000 value: 24.715 - type: map_at_3 value: 19.022 - type: map_at_5 value: 20.693 - type: mrr_at_1 value: 29.186 - type: mrr_at_10 value: 41.22 - type: mrr_at_100 value: 42.16 - type: mrr_at_1000 value: 42.192 - type: mrr_at_3 value: 38.013000000000005 - type: mrr_at_5 value: 39.704 - type: ndcg_at_1 value: 29.186 - type: ndcg_at_10 value: 31.167 - type: ndcg_at_100 value: 38.879000000000005 - type: ndcg_at_1000 value: 42.376000000000005 - type: ndcg_at_3 value: 25.817 - type: ndcg_at_5 value: 27.377000000000002 - type: precision_at_1 value: 29.186 - type: precision_at_10 value: 9.693999999999999 - type: precision_at_100 value: 1.8030000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 19.11 - type: precision_at_5 value: 14.344999999999999 - type: recall_at_1 value: 13.333999999999998 - type: recall_at_10 value: 37.092000000000006 - type: recall_at_100 value: 63.651 - type: recall_at_1000 value: 83.05 - type: recall_at_3 value: 23.74 - type: recall_at_5 value: 28.655 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.151 - type: map_at_10 value: 19.653000000000002 - type: map_at_100 value: 28.053 - type: map_at_1000 value: 29.709000000000003 - type: map_at_3 value: 14.191 - type: map_at_5 value: 16.456 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.4 - type: mrr_at_100 value: 74.715 - type: mrr_at_1000 value: 74.726 - type: mrr_at_3 value: 72.417 - type: mrr_at_5 value: 73.667 - type: ndcg_at_1 value: 54.25 - type: ndcg_at_10 value: 40.77 - type: ndcg_at_100 value: 46.359 - type: ndcg_at_1000 value: 54.193000000000005 - type: ndcg_at_3 value: 44.832 - type: ndcg_at_5 value: 42.63 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 32.175 - type: precision_at_100 value: 10.668 - type: precision_at_1000 value: 2.067 - type: precision_at_3 value: 47.667 - type: precision_at_5 value: 41.3 - type: recall_at_1 value: 9.151 - type: recall_at_10 value: 25.003999999999998 - type: recall_at_100 value: 52.976 - type: recall_at_1000 value: 78.315 - type: recall_at_3 value: 15.487 - type: recall_at_5 value: 18.999 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.89999999999999 - type: f1 value: 46.47777925067403 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 73.706 - type: map_at_10 value: 82.423 - type: map_at_100 value: 82.67999999999999 - type: map_at_1000 value: 82.694 - type: map_at_3 value: 81.328 - type: map_at_5 value: 82.001 - type: mrr_at_1 value: 79.613 - type: mrr_at_10 value: 87.07000000000001 - type: mrr_at_100 value: 87.169 - type: mrr_at_1000 value: 87.17 - type: mrr_at_3 value: 86.404 - type: mrr_at_5 value: 86.856 - type: ndcg_at_1 value: 79.613 - type: ndcg_at_10 value: 86.289 - type: ndcg_at_100 value: 87.201 - type: ndcg_at_1000 value: 87.428 - type: ndcg_at_3 value: 84.625 - type: ndcg_at_5 value: 85.53699999999999 - type: precision_at_1 value: 79.613 - type: precision_at_10 value: 10.399 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.473 - type: precision_at_5 value: 20.132 - type: recall_at_1 value: 73.706 - type: recall_at_10 value: 93.559 - type: recall_at_100 value: 97.188 - type: recall_at_1000 value: 98.555 - type: recall_at_3 value: 88.98700000000001 - type: recall_at_5 value: 91.373 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.841 - type: map_at_10 value: 32.643 - type: map_at_100 value: 34.575 - type: map_at_1000 value: 34.736 - type: map_at_3 value: 28.317999999999998 - type: map_at_5 value: 30.964000000000002 - type: mrr_at_1 value: 39.660000000000004 - type: mrr_at_10 value: 48.620000000000005 - type: mrr_at_100 value: 49.384 - type: mrr_at_1000 value: 49.415 - type: mrr_at_3 value: 45.988 - type: mrr_at_5 value: 47.361 - type: ndcg_at_1 value: 39.660000000000004 - type: ndcg_at_10 value: 40.646 - type: ndcg_at_100 value: 47.657 - type: ndcg_at_1000 value: 50.428 - type: ndcg_at_3 value: 36.689 - type: ndcg_at_5 value: 38.211 - type: precision_at_1 value: 39.660000000000004 - type: precision_at_10 value: 11.235000000000001 - type: precision_at_100 value: 1.8530000000000002 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.587999999999997 - type: precision_at_5 value: 18.395 - type: recall_at_1 value: 19.841 - type: recall_at_10 value: 48.135 - type: recall_at_100 value: 74.224 - type: recall_at_1000 value: 90.826 - type: recall_at_3 value: 33.536 - type: recall_at_5 value: 40.311 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.358 - type: map_at_10 value: 64.497 - type: map_at_100 value: 65.362 - type: map_at_1000 value: 65.41900000000001 - type: map_at_3 value: 61.06700000000001 - type: map_at_5 value: 63.317 - type: mrr_at_1 value: 80.716 - type: mrr_at_10 value: 86.10799999999999 - type: mrr_at_100 value: 86.265 - type: mrr_at_1000 value: 86.27 - type: mrr_at_3 value: 85.271 - type: mrr_at_5 value: 85.82499999999999 - type: ndcg_at_1 value: 80.716 - type: ndcg_at_10 value: 72.597 - type: ndcg_at_100 value: 75.549 - type: ndcg_at_1000 value: 76.61 - type: ndcg_at_3 value: 67.874 - type: ndcg_at_5 value: 70.655 - type: precision_at_1 value: 80.716 - type: precision_at_10 value: 15.148 - type: precision_at_100 value: 1.745 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 43.597 - type: precision_at_5 value: 28.351 - type: recall_at_1 value: 40.358 - type: recall_at_10 value: 75.739 - type: recall_at_100 value: 87.259 - type: recall_at_1000 value: 94.234 - type: recall_at_3 value: 65.39500000000001 - type: recall_at_5 value: 70.878 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.80799999999998 - type: ap value: 86.81350378180757 - type: f1 value: 90.79901248314215 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.096 - type: map_at_10 value: 34.384 - type: map_at_100 value: 35.541 - type: map_at_1000 value: 35.589999999999996 - type: map_at_3 value: 30.496000000000002 - type: map_at_5 value: 32.718 - type: mrr_at_1 value: 22.750999999999998 - type: mrr_at_10 value: 35.024 - type: mrr_at_100 value: 36.125 - type: mrr_at_1000 value: 36.168 - type: mrr_at_3 value: 31.225 - type: mrr_at_5 value: 33.416000000000004 - type: ndcg_at_1 value: 22.750999999999998 - type: ndcg_at_10 value: 41.351 - type: ndcg_at_100 value: 46.92 - type: ndcg_at_1000 value: 48.111 - type: ndcg_at_3 value: 33.439 - type: ndcg_at_5 value: 37.407000000000004 - type: precision_at_1 value: 22.750999999999998 - type: precision_at_10 value: 6.564 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.288 - type: precision_at_5 value: 10.581999999999999 - type: recall_at_1 value: 22.096 - type: recall_at_10 value: 62.771 - type: recall_at_100 value: 88.529 - type: recall_at_1000 value: 97.55 - type: recall_at_3 value: 41.245 - type: recall_at_5 value: 50.788 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.16780665754673 - type: f1 value: 93.96331194859894 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.90606475148198 - type: f1 value: 58.58344986604187 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.14660390047075 - type: f1 value: 74.31533923533614 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.16139878950908 - type: f1 value: 80.18532656824924 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.949880906135085 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.56300351524862 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.196521894371315 - type: mrr value: 32.22644231694389 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.783 - type: map_at_10 value: 14.549000000000001 - type: map_at_100 value: 18.433 - type: map_at_1000 value: 19.949 - type: map_at_3 value: 10.936 - type: map_at_5 value: 12.514 - type: mrr_at_1 value: 47.368 - type: mrr_at_10 value: 56.42 - type: mrr_at_100 value: 56.908 - type: mrr_at_1000 value: 56.95 - type: mrr_at_3 value: 54.283 - type: mrr_at_5 value: 55.568 - type: ndcg_at_1 value: 45.666000000000004 - type: ndcg_at_10 value: 37.389 - type: ndcg_at_100 value: 34.253 - type: ndcg_at_1000 value: 43.059999999999995 - type: ndcg_at_3 value: 42.725 - type: ndcg_at_5 value: 40.193 - type: precision_at_1 value: 47.368 - type: precision_at_10 value: 27.988000000000003 - type: precision_at_100 value: 8.672 - type: precision_at_1000 value: 2.164 - type: precision_at_3 value: 40.248 - type: precision_at_5 value: 34.737 - type: recall_at_1 value: 6.783 - type: recall_at_10 value: 17.838 - type: recall_at_100 value: 33.672000000000004 - type: recall_at_1000 value: 66.166 - type: recall_at_3 value: 11.849 - type: recall_at_5 value: 14.205000000000002 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.698999999999998 - type: map_at_10 value: 46.556 - type: map_at_100 value: 47.652 - type: map_at_1000 value: 47.68 - type: map_at_3 value: 42.492000000000004 - type: map_at_5 value: 44.763999999999996 - type: mrr_at_1 value: 35.747 - type: mrr_at_10 value: 49.242999999999995 - type: mrr_at_100 value: 50.052 - type: mrr_at_1000 value: 50.068 - type: mrr_at_3 value: 45.867000000000004 - type: mrr_at_5 value: 47.778999999999996 - type: ndcg_at_1 value: 35.717999999999996 - type: ndcg_at_10 value: 54.14600000000001 - type: ndcg_at_100 value: 58.672999999999995 - type: ndcg_at_1000 value: 59.279 - type: ndcg_at_3 value: 46.407 - type: ndcg_at_5 value: 50.181 - type: precision_at_1 value: 35.717999999999996 - type: precision_at_10 value: 8.844000000000001 - type: precision_at_100 value: 1.139 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.993000000000002 - type: precision_at_5 value: 14.791000000000002 - type: recall_at_1 value: 31.698999999999998 - type: recall_at_10 value: 74.693 - type: recall_at_100 value: 94.15299999999999 - type: recall_at_1000 value: 98.585 - type: recall_at_3 value: 54.388999999999996 - type: recall_at_5 value: 63.08200000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.283 - type: map_at_10 value: 85.24000000000001 - type: map_at_100 value: 85.882 - type: map_at_1000 value: 85.897 - type: map_at_3 value: 82.326 - type: map_at_5 value: 84.177 - type: mrr_at_1 value: 82.21000000000001 - type: mrr_at_10 value: 88.228 - type: mrr_at_100 value: 88.32 - type: mrr_at_1000 value: 88.32 - type: mrr_at_3 value: 87.323 - type: mrr_at_5 value: 87.94800000000001 - type: ndcg_at_1 value: 82.17999999999999 - type: ndcg_at_10 value: 88.9 - type: ndcg_at_100 value: 90.079 - type: ndcg_at_1000 value: 90.158 - type: ndcg_at_3 value: 86.18299999999999 - type: ndcg_at_5 value: 87.71799999999999 - type: precision_at_1 value: 82.17999999999999 - type: precision_at_10 value: 13.464 - type: precision_at_100 value: 1.533 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.693 - type: precision_at_5 value: 24.792 - type: recall_at_1 value: 71.283 - type: recall_at_10 value: 95.742 - type: recall_at_100 value: 99.67200000000001 - type: recall_at_1000 value: 99.981 - type: recall_at_3 value: 87.888 - type: recall_at_5 value: 92.24 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.24267063669042 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.88056988932578 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.903 - type: map_at_10 value: 13.202 - type: map_at_100 value: 15.5 - type: map_at_1000 value: 15.870999999999999 - type: map_at_3 value: 9.407 - type: map_at_5 value: 11.238 - type: mrr_at_1 value: 24.2 - type: mrr_at_10 value: 35.867 - type: mrr_at_100 value: 37.001 - type: mrr_at_1000 value: 37.043 - type: mrr_at_3 value: 32.5 - type: mrr_at_5 value: 34.35 - type: ndcg_at_1 value: 24.2 - type: ndcg_at_10 value: 21.731 - type: ndcg_at_100 value: 30.7 - type: ndcg_at_1000 value: 36.618 - type: ndcg_at_3 value: 20.72 - type: ndcg_at_5 value: 17.954 - type: precision_at_1 value: 24.2 - type: precision_at_10 value: 11.33 - type: precision_at_100 value: 2.4410000000000003 - type: precision_at_1000 value: 0.386 - type: precision_at_3 value: 19.667 - type: precision_at_5 value: 15.86 - type: recall_at_1 value: 4.903 - type: recall_at_10 value: 22.962 - type: recall_at_100 value: 49.563 - type: recall_at_1000 value: 78.238 - type: recall_at_3 value: 11.953 - type: recall_at_5 value: 16.067999999999998 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.12694254604078 - type: cos_sim_spearman value: 80.30141815181918 - type: euclidean_pearson value: 81.34015449877128 - type: euclidean_spearman value: 80.13984197010849 - type: manhattan_pearson value: 81.31767068124086 - type: manhattan_spearman value: 80.11720513114103 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.13112984010417 - type: cos_sim_spearman value: 78.03063573402875 - type: euclidean_pearson value: 83.51928418844804 - type: euclidean_spearman value: 78.4045235411144 - type: manhattan_pearson value: 83.49981637388689 - type: manhattan_spearman value: 78.4042575139372 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.50327987379504 - type: cos_sim_spearman value: 84.18556767756205 - type: euclidean_pearson value: 82.69684424327679 - type: euclidean_spearman value: 83.5368106038335 - type: manhattan_pearson value: 82.57967581007374 - type: manhattan_spearman value: 83.43009053133697 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.50756863007814 - type: cos_sim_spearman value: 82.27204331279108 - type: euclidean_pearson value: 81.39535251429741 - type: euclidean_spearman value: 81.84386626336239 - type: manhattan_pearson value: 81.34281737280695 - type: manhattan_spearman value: 81.81149375673166 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.8727714856726 - type: cos_sim_spearman value: 87.95738287792312 - type: euclidean_pearson value: 86.62920602795887 - type: euclidean_spearman value: 87.05207355381243 - type: manhattan_pearson value: 86.53587918472225 - type: manhattan_spearman value: 86.95382961029586 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.52240359769479 - type: cos_sim_spearman value: 85.47685776238286 - type: euclidean_pearson value: 84.25815333483058 - type: euclidean_spearman value: 85.27415639683198 - type: manhattan_pearson value: 84.29127757025637 - type: manhattan_spearman value: 85.30226224917351 - 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: 86.42501708915708 - type: cos_sim_spearman value: 86.42276182795041 - type: euclidean_pearson value: 86.5408207354761 - type: euclidean_spearman value: 85.46096321750838 - type: manhattan_pearson value: 86.54177303026881 - type: manhattan_spearman value: 85.50313151916117 - 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: 64.86521089250766 - type: cos_sim_spearman value: 65.94868540323003 - type: euclidean_pearson value: 67.16569626533084 - type: euclidean_spearman value: 66.37667004134917 - type: manhattan_pearson value: 67.1482365102333 - type: manhattan_spearman value: 66.53240122580029 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.64746265365318 - type: cos_sim_spearman value: 86.41888825906786 - type: euclidean_pearson value: 85.27453642725811 - type: euclidean_spearman value: 85.94095796602544 - type: manhattan_pearson value: 85.28643660505334 - type: manhattan_spearman value: 85.95028003260744 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.48903153618527 - type: mrr value: 96.41081503826601 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 58.594 - type: map_at_10 value: 69.296 - type: map_at_100 value: 69.782 - type: map_at_1000 value: 69.795 - type: map_at_3 value: 66.23 - type: map_at_5 value: 68.293 - type: mrr_at_1 value: 61.667 - type: mrr_at_10 value: 70.339 - type: mrr_at_100 value: 70.708 - type: mrr_at_1000 value: 70.722 - type: mrr_at_3 value: 68.0 - type: mrr_at_5 value: 69.56700000000001 - type: ndcg_at_1 value: 61.667 - type: ndcg_at_10 value: 74.039 - type: ndcg_at_100 value: 76.103 - type: ndcg_at_1000 value: 76.47800000000001 - type: ndcg_at_3 value: 68.967 - type: ndcg_at_5 value: 71.96900000000001 - type: precision_at_1 value: 61.667 - type: precision_at_10 value: 9.866999999999999 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.111 - type: precision_at_5 value: 18.2 - type: recall_at_1 value: 58.594 - type: recall_at_10 value: 87.422 - type: recall_at_100 value: 96.667 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 74.217 - type: recall_at_5 value: 81.539 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85049504950496 - type: cos_sim_ap value: 96.33111544137081 - type: cos_sim_f1 value: 92.35443037974684 - type: cos_sim_precision value: 93.53846153846153 - type: cos_sim_recall value: 91.2 - type: dot_accuracy value: 99.82376237623762 - type: dot_ap value: 95.38082527310888 - type: dot_f1 value: 90.90909090909092 - type: dot_precision value: 92.90187891440502 - type: dot_recall value: 89.0 - type: euclidean_accuracy value: 99.84851485148515 - type: euclidean_ap value: 96.32316003996347 - type: euclidean_f1 value: 92.2071392659628 - type: euclidean_precision value: 92.71991911021233 - type: euclidean_recall value: 91.7 - type: manhattan_accuracy value: 99.84851485148515 - type: manhattan_ap value: 96.3655668249217 - type: manhattan_f1 value: 92.18356026222895 - type: manhattan_precision value: 92.98067141403867 - type: manhattan_recall value: 91.4 - type: max_accuracy value: 99.85049504950496 - type: max_ap value: 96.3655668249217 - type: max_f1 value: 92.35443037974684 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.94861371629051 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.009430451385 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 54.61164066427969 - type: mrr value: 55.49710603938544 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.622620124907662 - type: cos_sim_spearman value: 31.0678351356163 - type: dot_pearson value: 30.863727693306814 - type: dot_spearman value: 31.230306567021255 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 2.011 - type: map_at_100 value: 10.974 - type: map_at_1000 value: 25.819 - type: map_at_3 value: 0.6649999999999999 - type: map_at_5 value: 1.076 - type: mrr_at_1 value: 86.0 - type: mrr_at_10 value: 91.8 - type: mrr_at_100 value: 91.8 - type: mrr_at_1000 value: 91.8 - type: mrr_at_3 value: 91.0 - type: mrr_at_5 value: 91.8 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 78.07300000000001 - type: ndcg_at_100 value: 58.231 - type: ndcg_at_1000 value: 51.153000000000006 - type: ndcg_at_3 value: 81.123 - type: ndcg_at_5 value: 81.059 - type: precision_at_1 value: 86.0 - type: precision_at_10 value: 83.0 - type: precision_at_100 value: 59.38 - type: precision_at_1000 value: 22.55 - type: precision_at_3 value: 87.333 - type: precision_at_5 value: 86.8 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.2079999999999997 - type: recall_at_100 value: 14.069 - type: recall_at_1000 value: 47.678 - type: recall_at_3 value: 0.7040000000000001 - type: recall_at_5 value: 1.161 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.809 - type: map_at_10 value: 10.394 - type: map_at_100 value: 16.598 - type: map_at_1000 value: 18.142 - type: map_at_3 value: 5.572 - type: map_at_5 value: 7.1370000000000005 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 46.564 - type: mrr_at_100 value: 47.469 - type: mrr_at_1000 value: 47.469 - type: mrr_at_3 value: 42.177 - type: mrr_at_5 value: 44.524 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 25.701 - type: ndcg_at_100 value: 37.532 - type: ndcg_at_1000 value: 48.757 - type: ndcg_at_3 value: 28.199999999999996 - type: ndcg_at_5 value: 25.987 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 23.469 - type: precision_at_100 value: 7.9799999999999995 - type: precision_at_1000 value: 1.5350000000000001 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.809 - type: recall_at_10 value: 16.887 - type: recall_at_100 value: 48.67 - type: recall_at_1000 value: 82.89699999999999 - type: recall_at_3 value: 6.521000000000001 - type: recall_at_5 value: 9.609 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.57860000000001 - type: ap value: 13.82629211536393 - type: f1 value: 54.59860966183956 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.38030560271647 - type: f1 value: 59.69685552567865 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.4736717043405 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.92853311080646 - type: cos_sim_ap value: 77.67872502591382 - type: cos_sim_f1 value: 70.33941236068895 - type: cos_sim_precision value: 67.63273258645884 - type: cos_sim_recall value: 73.27176781002639 - type: dot_accuracy value: 85.79603027954938 - type: dot_ap value: 73.73786190233379 - type: dot_f1 value: 67.3437901774235 - type: dot_precision value: 65.67201604814443 - type: dot_recall value: 69.10290237467018 - type: euclidean_accuracy value: 86.94045419324074 - type: euclidean_ap value: 77.6687791535167 - type: euclidean_f1 value: 70.47209214023542 - type: euclidean_precision value: 67.7207492094381 - type: euclidean_recall value: 73.45646437994723 - type: manhattan_accuracy value: 86.87488823985218 - type: manhattan_ap value: 77.63373392430728 - type: manhattan_f1 value: 70.40920716112532 - type: manhattan_precision value: 68.31265508684864 - type: manhattan_recall value: 72.63852242744063 - type: max_accuracy value: 86.94045419324074 - type: max_ap value: 77.67872502591382 - type: max_f1 value: 70.47209214023542 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.67155664221679 - type: cos_sim_ap value: 85.64591703003417 - type: cos_sim_f1 value: 77.59531005352656 - type: cos_sim_precision value: 73.60967184801382 - type: cos_sim_recall value: 82.03726516784724 - type: dot_accuracy value: 88.41541506578181 - type: dot_ap value: 84.6482788957769 - type: dot_f1 value: 77.04748541466657 - type: dot_precision value: 74.02440754931176 - type: dot_recall value: 80.3279950723745 - type: euclidean_accuracy value: 88.63080684596576 - type: euclidean_ap value: 85.44570045321562 - type: euclidean_f1 value: 77.28769403336106 - type: euclidean_precision value: 72.90600040958427 - type: euclidean_recall value: 82.22975053895904 - type: manhattan_accuracy value: 88.59393798269105 - type: manhattan_ap value: 85.40271361038187 - type: manhattan_f1 value: 77.17606419344392 - type: manhattan_precision value: 72.4447747078295 - type: manhattan_recall value: 82.5685247921158 - type: max_accuracy value: 88.67155664221679 - type: max_ap value: 85.64591703003417 - type: max_f1 value: 77.59531005352656 license: mit language: - en --- <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> For more details please refer to our Github: FlagEmbedding. If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. English | 中文 FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: Activation Beacon - **Fine-tuning of LM** : LM-Cocktail - **Dense Retrieval**: BGE-M3, LLM Embedder, BGE Embedding - **Reranker Model**: BGE Reranker - **Benchmark**: C-MTEB ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical Report and Code. :fire: - 1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. Technical Report :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. Technical Report :fire: - 11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report :fire: - 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report - 09/15/2023: The technical report and massive 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 🤗** - 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/bge-m3 | Multilingual | Inference Fine-tune | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | 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 of the ONNX files #### Usage via infinity Its also possible to deploy the onnx files with the infinity_emb pip package. ### 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.", |
