diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,3073 @@ +--- +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 +--- + + +

FlagEmbedding

+ + +

+

+ Model List | + FAQ | + Usage | + Evaluation | + Train | + Contact | + Citation | + License +

+

+ + +For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). + +If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). + + +[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) + +FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: + +- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) +- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) +- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) +- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) +- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/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](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: +- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :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](https://arxiv.org/abs/2312.15503) :fire: +- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: +- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) +- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released +- 09/12/2023: New models: + - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, 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 `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. + + +
+ More + + +- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): 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](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). +- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** +- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: +- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. + +
+ + +## Model List + +`bge` is short for `BAAI general embedding`. + +| Model | Language | | Description | query instruction for retrieval [1] | +|:-------------------------------|:--------:| :--------:| :--------:|:--------:| +| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | +| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | +| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | +| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | +| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | + + +[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 https://huggingface.co/BAAI. +If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . + + +## Frequently asked questions + +
+ 1. How to fine-tune bge embedding model? + + +Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. +Some suggestions: +- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), 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. + + +
+ +
+ 2. 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). + +
+ +
+ 3. When does the query instruction need to be used + + + +For the `bge-*-v1.5`, 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. + +
+ + +## Usage + +### Usage for Embedding Model + +Here are some examples for using `bge` models with +[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). + +#### Using FlagEmbedding +``` +pip install -U FlagEmbedding +``` +If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. + +```python +from FlagEmbedding import FlagModel +sentences_1 = ["样例数据-1", "样例数据-2"] +sentences_2 = ["样例数据-3", "样例数据-4"] +model = FlagModel('BAAI/bge-large-zh-v1.5', + query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", + use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation +embeddings_1 = model.encode(sentences_1) +embeddings_2 = model.encode(sentences_2) +similarity = embeddings_1 @ embeddings_2.T +print(similarity) + +# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query +# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction +queries = ['query_1', 'query_2'] +passages = ["样例文档-1", "样例文档-2"] +q_embeddings = model.encode_queries(queries) +p_embeddings = model.encode(passages) +scores = q_embeddings @ p_embeddings.T +``` +For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). + +By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. +You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. + + +#### Using Sentence-Transformers + +You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): + +``` +pip install -U sentence-transformers +``` +```python +from sentence_transformers import SentenceTransformer +sentences_1 = ["样例数据-1", "样例数据-2"] +sentences_2 = ["样例数据-3", "样例数据-4"] +model = SentenceTransformer('BAAI/bge-large-zh-v1.5') +embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) +embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) +similarity = embeddings_1 @ embeddings_2.T +print(similarity) +``` +For s2p(short query to long passage) retrieval task, +each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). +But the instruction is not needed for passages. +```python +from sentence_transformers import SentenceTransformer +queries = ['query_1', 'query_2'] +passages = ["样例文档-1", "样例文档-2"] +instruction = "为这个句子生成表示以用于检索相关文章：" + +model = SentenceTransformer('BAAI/bge-large-zh-v1.5') +q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) +p_embeddings = model.encode(passages, normalize_embeddings=True) +scores = q_embeddings @ p_embeddings.T +``` + +#### Using Langchain + +You can use `bge` in langchain like this: +```python +from langchain.embeddings import HuggingFaceBgeEmbeddings +model_name = "BAAI/bge-large-en-v1.5" +model_kwargs = {'device': 'cuda'} +encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity +model = HuggingFaceBgeEmbeddings( + model_name=model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs, + query_instruction="为这个句子生成表示以用于检索相关文章：" +) +model.query_instruction = "为这个句子生成表示以用于检索相关文章：" +``` + + +#### 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. + +```python +from transformers import AutoTokenizer, AutoModel +import torch +# Sentences we want sentence embeddings for +sentences = ["样例数据-1", "样例数据-2"] + +# Load model from HuggingFace Hub +tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') +model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') +model.eval() + +# Tokenize sentences +encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') +# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) +# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') + +# Compute token embeddings +with torch.no_grad(): + model_output = model(**encoded_input) + # Perform pooling. In this case, cls pooling. + sentence_embeddings = model_output[0][:, 0] +# normalize embeddings +sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) +print("Sentence embeddings:", sentence_embeddings) +``` + + +#### Usage of the ONNX files + +```python +from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore + +import torch +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5') +model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13") +model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx") + +# Sentences we want sentence embeddings for +sentences = ["样例数据-1", "样例数据-2"] + +# Tokenize sentences +encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') +# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) +# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') + +model_output_ort = model_ort(**encoded_input) +# Compute token embeddings +with torch.no_grad(): + model_output = model(**encoded_input) + +# model_output and model_output_ort are identical + +``` + +#### Usage via infinity +Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. +```python +import asyncio +from infinity_emb import AsyncEmbeddingEngine, EngineArgs + +sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] +engine = AsyncEmbeddingEngine.from_args( + EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch" +)) + +async def main(): + async with engine: + embeddings, usage = await engine.embed(sentences=sentences) +asyncio.run(main()) +``` + +### 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 +``` +pip install -U FlagEmbedding +``` + +Get relevance scores (higher scores indicate more relevance): +```python +from FlagEmbedding import FlagReranker +reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation + +score = reranker.compute_score(['query', 'passage']) +print(score) + +scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) +print(scores) +``` + + +#### Using Huggingface transformers + +```python +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') +model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') +model.eval() + +pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] +with torch.no_grad(): + inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) + scores = model(**inputs, return_dict=True).logits.view(-1, ).float() + print(scores) +``` + +## Evaluation + +`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** +For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). + +- **MTEB**: + +| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | +|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| [BAAI/bge-large-en-v1.5](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | +| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | +| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | +| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | +| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | +| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | +| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | +| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | +| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | +| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | +| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | +| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | +| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | +| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | + + + +- **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](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. + +| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | +|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| +| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | +| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | +| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | +| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | +| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | +| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | +| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | +| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | +| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | +| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | +| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | + + +- **Reranking**: +See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/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](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | +| [BAAI/bge-reranker-large](https://huggingface.co/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](https://github.com/staoxiao/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](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** +We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). +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](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). + + + +### 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](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). +More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) + + +## 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 + +``` +@misc{bge_embedding, + title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, + author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, + year={2023}, + eprint={2309.07597}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + +## License +FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. +