diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,3074 @@ +--- +tags: +- sentence-transformers +- feature-extraction +- sentence-similarity +- transformers +- mteb +model-index: +- name: bge-small-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: 73.79104477611939 + - type: ap + value: 37.21923821573361 + - type: f1 + value: 68.0914945617093 + - task: + type: Classification + dataset: + type: mteb/amazon_polarity + name: MTEB AmazonPolarityClassification + config: default + split: test + revision: e2d317d38cd51312af73b3d32a06d1a08b442046 + metrics: + - type: accuracy + value: 92.75377499999999 + - type: ap + value: 89.46766124546022 + - type: f1 + value: 92.73884001331487 + - task: + type: Classification + dataset: + type: mteb/amazon_reviews_multi + name: MTEB AmazonReviewsClassification (en) + config: en + split: test + revision: 1399c76144fd37290681b995c656ef9b2e06e26d + metrics: + - type: accuracy + value: 46.986 + - type: f1 + value: 46.55936786727896 + - task: + type: Retrieval + dataset: + type: arguana + name: MTEB ArguAna + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 35.846000000000004 + - type: map_at_10 + value: 51.388 + - type: map_at_100 + value: 52.132999999999996 + - type: map_at_1000 + value: 52.141000000000005 + - type: map_at_3 + value: 47.037 + - type: map_at_5 + value: 49.579 + - type: mrr_at_1 + value: 36.558 + - type: mrr_at_10 + value: 51.658 + - type: mrr_at_100 + value: 52.402 + - type: mrr_at_1000 + value: 52.410000000000004 + - type: mrr_at_3 + value: 47.345 + - type: mrr_at_5 + value: 49.797999999999995 + - type: ndcg_at_1 + value: 35.846000000000004 + - type: ndcg_at_10 + value: 59.550000000000004 + - type: ndcg_at_100 + value: 62.596 + - type: ndcg_at_1000 + value: 62.759 + - type: ndcg_at_3 + value: 50.666999999999994 + - type: ndcg_at_5 + value: 55.228 + - type: precision_at_1 + value: 35.846000000000004 + - type: precision_at_10 + value: 8.542 + - type: precision_at_100 + value: 0.984 + - type: precision_at_1000 + value: 0.1 + - type: precision_at_3 + value: 20.389 + - type: precision_at_5 + value: 14.438 + - type: recall_at_1 + value: 35.846000000000004 + - type: recall_at_10 + value: 85.42 + - type: recall_at_100 + value: 98.43499999999999 + - type: recall_at_1000 + value: 99.644 + - type: recall_at_3 + value: 61.166 + - type: recall_at_5 + value: 72.191 + - task: + type: Clustering + dataset: + type: mteb/arxiv-clustering-p2p + name: MTEB ArxivClusteringP2P + config: default + split: test + revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d + metrics: + - type: v_measure + value: 47.402770198163594 + - task: + type: Clustering + dataset: + type: mteb/arxiv-clustering-s2s + name: MTEB ArxivClusteringS2S + config: default + split: test + revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 + metrics: + - type: v_measure + value: 40.01545436974177 + - task: + type: Reranking + dataset: + type: mteb/askubuntudupquestions-reranking + name: MTEB AskUbuntuDupQuestions + config: default + split: test + revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 + metrics: + - type: map + value: 62.586465273207196 + - type: mrr + value: 74.42169019038825 + - task: + type: STS + dataset: + type: mteb/biosses-sts + name: MTEB BIOSSES + config: default + split: test + revision: d3fb88f8f02e40887cd149695127462bbcf29b4a + metrics: + - type: cos_sim_pearson + value: 85.1891186537969 + - type: cos_sim_spearman + value: 83.75492046087288 + - type: euclidean_pearson + value: 84.11766204805357 + - type: euclidean_spearman + value: 84.01456493126516 + - type: manhattan_pearson + value: 84.2132950502772 + - type: manhattan_spearman + value: 83.89227298813377 + - task: + type: Classification + dataset: + type: mteb/banking77 + name: MTEB Banking77Classification + config: default + split: test + revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 + metrics: + - type: accuracy + value: 85.74025974025975 + - type: f1 + value: 85.71493566466381 + - task: + type: Clustering + dataset: + type: mteb/biorxiv-clustering-p2p + name: MTEB BiorxivClusteringP2P + config: default + split: test + revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 + metrics: + - type: v_measure + value: 38.467181385006434 + - task: + type: Clustering + dataset: + type: mteb/biorxiv-clustering-s2s + name: MTEB BiorxivClusteringS2S + config: default + split: test + revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 + metrics: + - type: v_measure + value: 34.719496037339056 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackAndroidRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 29.587000000000003 + - type: map_at_10 + value: 41.114 + - type: map_at_100 + value: 42.532 + - type: map_at_1000 + value: 42.661 + - type: map_at_3 + value: 37.483 + - type: map_at_5 + value: 39.652 + - type: mrr_at_1 + value: 36.338 + - type: mrr_at_10 + value: 46.763 + - type: mrr_at_100 + value: 47.393 + - type: mrr_at_1000 + value: 47.445 + - type: mrr_at_3 + value: 43.538 + - type: mrr_at_5 + value: 45.556000000000004 + - type: ndcg_at_1 + value: 36.338 + - type: ndcg_at_10 + value: 47.658 + - type: ndcg_at_100 + value: 52.824000000000005 + - type: ndcg_at_1000 + value: 54.913999999999994 + - type: ndcg_at_3 + value: 41.989 + - type: ndcg_at_5 + value: 44.944 + - type: precision_at_1 + value: 36.338 + - type: precision_at_10 + value: 9.156 + - type: precision_at_100 + value: 1.4789999999999999 + - type: precision_at_1000 + value: 0.196 + - type: precision_at_3 + value: 20.076 + - type: precision_at_5 + value: 14.85 + - type: recall_at_1 + value: 29.587000000000003 + - type: recall_at_10 + value: 60.746 + - type: recall_at_100 + value: 82.157 + - type: recall_at_1000 + value: 95.645 + - type: recall_at_3 + value: 44.821 + - type: recall_at_5 + value: 52.819 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackEnglishRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 30.239 + - type: map_at_10 + value: 39.989000000000004 + - type: map_at_100 + value: 41.196 + - type: map_at_1000 + value: 41.325 + - type: map_at_3 + value: 37.261 + - type: map_at_5 + value: 38.833 + - type: mrr_at_1 + value: 37.516 + - type: mrr_at_10 + value: 46.177 + - type: mrr_at_100 + value: 46.806 + - type: mrr_at_1000 + value: 46.849000000000004 + - type: mrr_at_3 + value: 44.002 + - type: mrr_at_5 + value: 45.34 + - type: ndcg_at_1 + value: 37.516 + - type: ndcg_at_10 + value: 45.586 + - type: ndcg_at_100 + value: 49.897000000000006 + - type: ndcg_at_1000 + value: 51.955 + - type: ndcg_at_3 + value: 41.684 + - type: ndcg_at_5 + value: 43.617 + - type: precision_at_1 + value: 37.516 + - type: precision_at_10 + value: 8.522 + - type: precision_at_100 + value: 1.374 + - type: precision_at_1000 + value: 0.184 + - type: precision_at_3 + value: 20.105999999999998 + - type: precision_at_5 + value: 14.152999999999999 + - type: recall_at_1 + value: 30.239 + - type: recall_at_10 + value: 55.03 + - type: recall_at_100 + value: 73.375 + - type: recall_at_1000 + value: 86.29599999999999 + - type: recall_at_3 + value: 43.269000000000005 + - type: recall_at_5 + value: 48.878 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackGamingRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 38.338 + - type: map_at_10 + value: 50.468999999999994 + - type: map_at_100 + value: 51.553000000000004 + - type: map_at_1000 + value: 51.608 + - type: map_at_3 + value: 47.107 + - type: map_at_5 + value: 49.101 + - type: mrr_at_1 + value: 44.201 + - type: mrr_at_10 + value: 54.057 + - type: mrr_at_100 + value: 54.764 + - type: mrr_at_1000 + value: 54.791000000000004 + - type: mrr_at_3 + value: 51.56699999999999 + - type: mrr_at_5 + value: 53.05 + - type: ndcg_at_1 + value: 44.201 + - type: ndcg_at_10 + value: 56.379000000000005 + - type: ndcg_at_100 + value: 60.645 + - type: ndcg_at_1000 + value: 61.73499999999999 + - type: ndcg_at_3 + value: 50.726000000000006 + - type: ndcg_at_5 + value: 53.58500000000001 + - type: precision_at_1 + value: 44.201 + - type: precision_at_10 + value: 9.141 + - type: precision_at_100 + value: 1.216 + - type: precision_at_1000 + value: 0.135 + - type: precision_at_3 + value: 22.654 + - type: precision_at_5 + value: 15.723999999999998 + - type: recall_at_1 + value: 38.338 + - type: recall_at_10 + value: 70.30499999999999 + - type: recall_at_100 + value: 88.77199999999999 + - type: recall_at_1000 + value: 96.49799999999999 + - type: recall_at_3 + value: 55.218 + - type: recall_at_5 + value: 62.104000000000006 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackGisRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 25.682 + - type: map_at_10 + value: 33.498 + - type: map_at_100 + value: 34.461000000000006 + - type: map_at_1000 + value: 34.544000000000004 + - type: map_at_3 + value: 30.503999999999998 + - type: map_at_5 + value: 32.216 + - type: mrr_at_1 + value: 27.683999999999997 + - type: mrr_at_10 + value: 35.467999999999996 + - type: mrr_at_100 + value: 36.32 + - type: mrr_at_1000 + value: 36.386 + - type: mrr_at_3 + value: 32.618 + - type: mrr_at_5 + value: 34.262 + - type: ndcg_at_1 + value: 27.683999999999997 + - type: ndcg_at_10 + value: 38.378 + - type: ndcg_at_100 + value: 43.288 + - type: ndcg_at_1000 + value: 45.413 + - type: ndcg_at_3 + value: 32.586 + - type: ndcg_at_5 + value: 35.499 + - type: precision_at_1 + value: 27.683999999999997 + - type: precision_at_10 + value: 5.864 + - type: precision_at_100 + value: 0.882 + - type: precision_at_1000 + value: 0.11 + - type: precision_at_3 + value: 13.446 + - type: precision_at_5 + value: 9.718 + - type: recall_at_1 + value: 25.682 + - type: recall_at_10 + value: 51.712 + - type: recall_at_100 + value: 74.446 + - type: recall_at_1000 + value: 90.472 + - type: recall_at_3 + value: 36.236000000000004 + - type: recall_at_5 + value: 43.234 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackMathematicaRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 16.073999999999998 + - type: map_at_10 + value: 24.352999999999998 + - type: map_at_100 + value: 25.438 + - type: map_at_1000 + value: 25.545 + - type: map_at_3 + value: 21.614 + - type: map_at_5 + value: 23.104 + - type: mrr_at_1 + value: 19.776 + - type: mrr_at_10 + value: 28.837000000000003 + - type: mrr_at_100 + value: 29.755 + - type: mrr_at_1000 + value: 29.817 + - type: mrr_at_3 + value: 26.201999999999998 + - type: mrr_at_5 + value: 27.714 + - type: ndcg_at_1 + value: 19.776 + - type: ndcg_at_10 + value: 29.701 + - type: ndcg_at_100 + value: 35.307 + - type: ndcg_at_1000 + value: 37.942 + - type: ndcg_at_3 + value: 24.764 + - type: ndcg_at_5 + value: 27.025 + - type: precision_at_1 + value: 19.776 + - type: precision_at_10 + value: 5.659 + - type: precision_at_100 + value: 0.971 + - type: precision_at_1000 + value: 0.133 + - type: precision_at_3 + value: 12.065 + - type: precision_at_5 + value: 8.905000000000001 + - type: recall_at_1 + value: 16.073999999999998 + - type: recall_at_10 + value: 41.647 + - type: recall_at_100 + value: 66.884 + - type: recall_at_1000 + value: 85.91499999999999 + - type: recall_at_3 + value: 27.916 + - type: recall_at_5 + value: 33.729 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackPhysicsRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 28.444999999999997 + - type: map_at_10 + value: 38.218999999999994 + - type: map_at_100 + value: 39.595 + - type: map_at_1000 + value: 39.709 + - type: map_at_3 + value: 35.586 + - type: map_at_5 + value: 36.895 + - type: mrr_at_1 + value: 34.841 + - type: mrr_at_10 + value: 44.106 + - type: mrr_at_100 + value: 44.98 + - type: mrr_at_1000 + value: 45.03 + - type: mrr_at_3 + value: 41.979 + - type: mrr_at_5 + value: 43.047999999999995 + - type: ndcg_at_1 + value: 34.841 + - type: ndcg_at_10 + value: 43.922 + - type: ndcg_at_100 + value: 49.504999999999995 + - type: ndcg_at_1000 + value: 51.675000000000004 + - type: ndcg_at_3 + value: 39.858 + - type: ndcg_at_5 + value: 41.408 + - type: precision_at_1 + value: 34.841 + - type: precision_at_10 + value: 7.872999999999999 + - type: precision_at_100 + value: 1.2449999999999999 + - type: precision_at_1000 + value: 0.161 + - type: precision_at_3 + value: 18.993 + - type: precision_at_5 + value: 13.032 + - type: recall_at_1 + value: 28.444999999999997 + - type: recall_at_10 + value: 54.984 + - type: recall_at_100 + value: 78.342 + - type: recall_at_1000 + value: 92.77 + - type: recall_at_3 + value: 42.842999999999996 + - type: recall_at_5 + value: 47.247 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackProgrammersRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 23.072 + - type: map_at_10 + value: 32.354 + - type: map_at_100 + value: 33.800000000000004 + - type: map_at_1000 + value: 33.908 + - type: map_at_3 + value: 29.232000000000003 + - type: map_at_5 + value: 31.049 + - type: mrr_at_1 + value: 29.110000000000003 + - type: mrr_at_10 + value: 38.03 + - type: mrr_at_100 + value: 39.032 + - type: mrr_at_1000 + value: 39.086999999999996 + - type: mrr_at_3 + value: 35.407 + - type: mrr_at_5 + value: 36.76 + - type: ndcg_at_1 + value: 29.110000000000003 + - type: ndcg_at_10 + value: 38.231 + - type: ndcg_at_100 + value: 44.425 + - type: ndcg_at_1000 + value: 46.771 + - type: ndcg_at_3 + value: 33.095 + - type: ndcg_at_5 + value: 35.459 + - type: precision_at_1 + value: 29.110000000000003 + - type: precision_at_10 + value: 7.215000000000001 + - type: precision_at_100 + value: 1.2109999999999999 + - type: precision_at_1000 + value: 0.157 + - type: precision_at_3 + value: 16.058 + - type: precision_at_5 + value: 11.644 + - type: recall_at_1 + value: 23.072 + - type: recall_at_10 + value: 50.285999999999994 + - type: recall_at_100 + value: 76.596 + - type: recall_at_1000 + value: 92.861 + - type: recall_at_3 + value: 35.702 + - type: recall_at_5 + value: 42.152 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 24.937916666666666 + - type: map_at_10 + value: 33.755250000000004 + - type: map_at_100 + value: 34.955999999999996 + - type: map_at_1000 + value: 35.070499999999996 + - type: map_at_3 + value: 30.98708333333333 + - type: map_at_5 + value: 32.51491666666666 + - type: mrr_at_1 + value: 29.48708333333333 + - type: mrr_at_10 + value: 37.92183333333334 + - type: mrr_at_100 + value: 38.76583333333333 + - type: mrr_at_1000 + value: 38.82466666666667 + - type: mrr_at_3 + value: 35.45125 + - type: mrr_at_5 + value: 36.827000000000005 + - type: ndcg_at_1 + value: 29.48708333333333 + - type: ndcg_at_10 + value: 39.05225 + - type: ndcg_at_100 + value: 44.25983333333334 + - type: ndcg_at_1000 + value: 46.568333333333335 + - type: ndcg_at_3 + value: 34.271583333333325 + - type: ndcg_at_5 + value: 36.483916666666666 + - type: precision_at_1 + value: 29.48708333333333 + - type: precision_at_10 + value: 6.865749999999999 + - type: precision_at_100 + value: 1.1195833333333332 + - type: precision_at_1000 + value: 0.15058333333333335 + - type: precision_at_3 + value: 15.742083333333333 + - type: precision_at_5 + value: 11.221916666666667 + - type: recall_at_1 + value: 24.937916666666666 + - type: recall_at_10 + value: 50.650416666666665 + - type: recall_at_100 + value: 73.55383333333334 + - type: recall_at_1000 + value: 89.61691666666667 + - type: recall_at_3 + value: 37.27808333333334 + - type: recall_at_5 + value: 42.99475 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackStatsRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 23.947 + - type: map_at_10 + value: 30.575000000000003 + - type: map_at_100 + value: 31.465 + - type: map_at_1000 + value: 31.558000000000003 + - type: map_at_3 + value: 28.814 + - type: map_at_5 + value: 29.738999999999997 + - type: mrr_at_1 + value: 26.994 + - type: mrr_at_10 + value: 33.415 + - type: mrr_at_100 + value: 34.18 + - type: mrr_at_1000 + value: 34.245 + - type: mrr_at_3 + value: 31.621 + - type: mrr_at_5 + value: 32.549 + - type: ndcg_at_1 + value: 26.994 + - type: ndcg_at_10 + value: 34.482 + - type: ndcg_at_100 + value: 38.915 + - type: ndcg_at_1000 + value: 41.355 + - type: ndcg_at_3 + value: 31.139 + - type: ndcg_at_5 + value: 32.589 + - type: precision_at_1 + value: 26.994 + - type: precision_at_10 + value: 5.322 + - type: precision_at_100 + value: 0.8160000000000001 + - type: precision_at_1000 + value: 0.11100000000000002 + - type: precision_at_3 + value: 13.344000000000001 + - type: precision_at_5 + value: 8.988 + - type: recall_at_1 + value: 23.947 + - type: recall_at_10 + value: 43.647999999999996 + - type: recall_at_100 + value: 63.851 + - type: recall_at_1000 + value: 82.0 + - type: recall_at_3 + value: 34.288000000000004 + - type: recall_at_5 + value: 38.117000000000004 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackTexRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 16.197 + - type: map_at_10 + value: 22.968 + - type: map_at_100 + value: 24.095 + - type: map_at_1000 + value: 24.217 + - type: map_at_3 + value: 20.771 + - type: map_at_5 + value: 21.995 + - type: mrr_at_1 + value: 19.511 + - type: mrr_at_10 + value: 26.55 + - type: mrr_at_100 + value: 27.500999999999998 + - type: mrr_at_1000 + value: 27.578999999999997 + - type: mrr_at_3 + value: 24.421 + - type: mrr_at_5 + value: 25.604 + - type: ndcg_at_1 + value: 19.511 + - type: ndcg_at_10 + value: 27.386 + - type: ndcg_at_100 + value: 32.828 + - type: ndcg_at_1000 + value: 35.739 + - type: ndcg_at_3 + value: 23.405 + - type: ndcg_at_5 + value: 25.255 + - type: precision_at_1 + value: 19.511 + - type: precision_at_10 + value: 5.017 + - type: precision_at_100 + value: 0.91 + - type: precision_at_1000 + value: 0.133 + - type: precision_at_3 + value: 11.023 + - type: precision_at_5 + value: 8.025 + - type: recall_at_1 + value: 16.197 + - type: recall_at_10 + value: 37.09 + - type: recall_at_100 + value: 61.778 + - type: recall_at_1000 + value: 82.56599999999999 + - type: recall_at_3 + value: 26.034000000000002 + - type: recall_at_5 + value: 30.762 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackUnixRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 25.41 + - type: map_at_10 + value: 33.655 + - type: map_at_100 + value: 34.892 + - type: map_at_1000 + value: 34.995 + - type: map_at_3 + value: 30.94 + - type: map_at_5 + value: 32.303 + - type: mrr_at_1 + value: 29.477999999999998 + - type: mrr_at_10 + value: 37.443 + - type: mrr_at_100 + value: 38.383 + - type: mrr_at_1000 + value: 38.440000000000005 + - type: mrr_at_3 + value: 34.949999999999996 + - type: mrr_at_5 + value: 36.228 + - type: ndcg_at_1 + value: 29.477999999999998 + - type: ndcg_at_10 + value: 38.769 + - type: ndcg_at_100 + value: 44.245000000000005 + - type: ndcg_at_1000 + value: 46.593 + - type: ndcg_at_3 + value: 33.623 + - type: ndcg_at_5 + value: 35.766 + - type: precision_at_1 + value: 29.477999999999998 + - type: precision_at_10 + value: 6.455 + - type: precision_at_100 + value: 1.032 + - type: precision_at_1000 + value: 0.135 + - type: precision_at_3 + value: 14.893999999999998 + - type: precision_at_5 + value: 10.485 + - type: recall_at_1 + value: 25.41 + - type: recall_at_10 + value: 50.669 + - type: recall_at_100 + value: 74.084 + - type: recall_at_1000 + value: 90.435 + - type: recall_at_3 + value: 36.679 + - type: recall_at_5 + value: 41.94 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackWebmastersRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 23.339 + - type: map_at_10 + value: 31.852000000000004 + - type: map_at_100 + value: 33.411 + - type: map_at_1000 + value: 33.62 + - type: map_at_3 + value: 28.929 + - type: map_at_5 + value: 30.542 + - type: mrr_at_1 + value: 28.063 + - type: mrr_at_10 + value: 36.301 + - type: mrr_at_100 + value: 37.288 + - type: mrr_at_1000 + value: 37.349 + - type: mrr_at_3 + value: 33.663 + - type: mrr_at_5 + value: 35.165 + - type: ndcg_at_1 + value: 28.063 + - type: ndcg_at_10 + value: 37.462 + - type: ndcg_at_100 + value: 43.620999999999995 + - type: ndcg_at_1000 + value: 46.211 + - type: ndcg_at_3 + value: 32.68 + - type: ndcg_at_5 + value: 34.981 + - type: precision_at_1 + value: 28.063 + - type: precision_at_10 + value: 7.1739999999999995 + - type: precision_at_100 + value: 1.486 + - type: precision_at_1000 + value: 0.23500000000000001 + - type: precision_at_3 + value: 15.217 + - type: precision_at_5 + value: 11.265 + - type: recall_at_1 + value: 23.339 + - type: recall_at_10 + value: 48.376999999999995 + - type: recall_at_100 + value: 76.053 + - type: recall_at_1000 + value: 92.455 + - type: recall_at_3 + value: 34.735 + - type: recall_at_5 + value: 40.71 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackWordpressRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 18.925 + - type: map_at_10 + value: 26.017000000000003 + - type: map_at_100 + value: 27.034000000000002 + - type: map_at_1000 + value: 27.156000000000002 + - type: map_at_3 + value: 23.604 + - type: map_at_5 + value: 24.75 + - type: mrr_at_1 + value: 20.333000000000002 + - type: mrr_at_10 + value: 27.915 + - type: mrr_at_100 + value: 28.788000000000004 + - type: mrr_at_1000 + value: 28.877999999999997 + - type: mrr_at_3 + value: 25.446999999999996 + - type: mrr_at_5 + value: 26.648 + - type: ndcg_at_1 + value: 20.333000000000002 + - type: ndcg_at_10 + value: 30.673000000000002 + - type: ndcg_at_100 + value: 35.618 + - type: ndcg_at_1000 + value: 38.517 + - type: ndcg_at_3 + value: 25.71 + - type: ndcg_at_5 + value: 27.679 + - type: precision_at_1 + value: 20.333000000000002 + - type: precision_at_10 + value: 4.9910000000000005 + - type: precision_at_100 + value: 0.8130000000000001 + - type: precision_at_1000 + value: 0.117 + - type: precision_at_3 + value: 11.029 + - type: precision_at_5 + value: 7.8740000000000006 + - type: recall_at_1 + value: 18.925 + - type: recall_at_10 + value: 43.311 + - type: recall_at_100 + value: 66.308 + - type: recall_at_1000 + value: 87.49 + - type: recall_at_3 + value: 29.596 + - type: recall_at_5 + value: 34.245 + - task: + type: Retrieval + dataset: + type: climate-fever + name: MTEB ClimateFEVER + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 13.714 + - type: map_at_10 + value: 23.194 + - type: map_at_100 + value: 24.976000000000003 + - type: map_at_1000 + value: 25.166 + - type: map_at_3 + value: 19.709 + - type: map_at_5 + value: 21.523999999999997 + - type: mrr_at_1 + value: 30.619000000000003 + - type: mrr_at_10 + value: 42.563 + - type: mrr_at_100 + value: 43.386 + - type: mrr_at_1000 + value: 43.423 + - type: mrr_at_3 + value: 39.555 + - type: mrr_at_5 + value: 41.268 + - type: ndcg_at_1 + value: 30.619000000000003 + - type: ndcg_at_10 + value: 31.836 + - type: ndcg_at_100 + value: 38.652 + - type: ndcg_at_1000 + value: 42.088 + - type: ndcg_at_3 + value: 26.733 + - type: ndcg_at_5 + value: 28.435 + - type: precision_at_1 + value: 30.619000000000003 + - type: precision_at_10 + value: 9.751999999999999 + - type: precision_at_100 + value: 1.71 + - type: precision_at_1000 + value: 0.23500000000000001 + - type: precision_at_3 + value: 19.935 + - type: precision_at_5 + value: 14.984 + - type: recall_at_1 + value: 13.714 + - type: recall_at_10 + value: 37.26 + - type: recall_at_100 + value: 60.546 + - type: recall_at_1000 + value: 79.899 + - type: recall_at_3 + value: 24.325 + - type: recall_at_5 + value: 29.725 + - task: + type: Retrieval + dataset: + type: dbpedia-entity + name: MTEB DBPedia + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 8.462 + - type: map_at_10 + value: 18.637 + - type: map_at_100 + value: 26.131999999999998 + - type: map_at_1000 + value: 27.607 + - type: map_at_3 + value: 13.333 + - type: map_at_5 + value: 15.654000000000002 + - type: mrr_at_1 + value: 66.25 + - type: mrr_at_10 + value: 74.32600000000001 + - type: mrr_at_100 + value: 74.60900000000001 + - type: mrr_at_1000 + value: 74.62 + - type: mrr_at_3 + value: 72.667 + - type: mrr_at_5 + value: 73.817 + - type: ndcg_at_1 + value: 53.87499999999999 + - type: ndcg_at_10 + value: 40.028999999999996 + - type: ndcg_at_100 + value: 44.199 + - type: ndcg_at_1000 + value: 51.629999999999995 + - type: ndcg_at_3 + value: 44.113 + - type: ndcg_at_5 + value: 41.731 + - type: precision_at_1 + value: 66.25 + - type: precision_at_10 + value: 31.900000000000002 + - type: precision_at_100 + value: 10.043000000000001 + - type: precision_at_1000 + value: 1.926 + - type: precision_at_3 + value: 47.417 + - type: precision_at_5 + value: 40.65 + - type: recall_at_1 + value: 8.462 + - type: recall_at_10 + value: 24.293 + - type: recall_at_100 + value: 50.146 + - type: recall_at_1000 + value: 74.034 + - type: recall_at_3 + value: 14.967 + - type: recall_at_5 + value: 18.682000000000002 + - task: + type: Classification + dataset: + type: mteb/emotion + name: MTEB EmotionClassification + config: default + split: test + revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 + metrics: + - type: accuracy + value: 47.84499999999999 + - type: f1 + value: 42.48106691979349 + - task: + type: Retrieval + dataset: + type: fever + name: MTEB FEVER + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 74.034 + - type: map_at_10 + value: 82.76 + - type: map_at_100 + value: 82.968 + - type: map_at_1000 + value: 82.98299999999999 + - type: map_at_3 + value: 81.768 + - type: map_at_5 + value: 82.418 + - type: mrr_at_1 + value: 80.048 + - type: mrr_at_10 + value: 87.64999999999999 + - type: mrr_at_100 + value: 87.712 + - type: mrr_at_1000 + value: 87.713 + - type: mrr_at_3 + value: 87.01100000000001 + - type: mrr_at_5 + value: 87.466 + - type: ndcg_at_1 + value: 80.048 + - type: ndcg_at_10 + value: 86.643 + - type: ndcg_at_100 + value: 87.361 + - type: ndcg_at_1000 + value: 87.606 + - type: ndcg_at_3 + value: 85.137 + - type: ndcg_at_5 + value: 86.016 + - type: precision_at_1 + value: 80.048 + - type: precision_at_10 + value: 10.372 + - type: precision_at_100 + value: 1.093 + - type: precision_at_1000 + value: 0.11299999999999999 + - type: precision_at_3 + value: 32.638 + - type: precision_at_5 + value: 20.177 + - type: recall_at_1 + value: 74.034 + - type: recall_at_10 + value: 93.769 + - type: recall_at_100 + value: 96.569 + - type: recall_at_1000 + value: 98.039 + - type: recall_at_3 + value: 89.581 + - type: recall_at_5 + value: 91.906 + - task: + type: Retrieval + dataset: + type: fiqa + name: MTEB FiQA2018 + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 20.5 + - type: map_at_10 + value: 32.857 + - type: map_at_100 + value: 34.589 + - type: map_at_1000 + value: 34.778 + - type: map_at_3 + value: 29.160999999999998 + - type: map_at_5 + value: 31.033 + - type: mrr_at_1 + value: 40.123 + - type: mrr_at_10 + value: 48.776 + - type: mrr_at_100 + value: 49.495 + - type: mrr_at_1000 + value: 49.539 + - type: mrr_at_3 + value: 46.605000000000004 + - type: mrr_at_5 + value: 47.654 + - type: ndcg_at_1 + value: 40.123 + - type: ndcg_at_10 + value: 40.343 + - type: ndcg_at_100 + value: 46.56 + - type: ndcg_at_1000 + value: 49.777 + - type: ndcg_at_3 + value: 37.322 + - type: ndcg_at_5 + value: 37.791000000000004 + - type: precision_at_1 + value: 40.123 + - type: precision_at_10 + value: 11.08 + - type: precision_at_100 + value: 1.752 + - type: precision_at_1000 + value: 0.232 + - type: precision_at_3 + value: 24.897 + - type: precision_at_5 + value: 17.809 + - type: recall_at_1 + value: 20.5 + - type: recall_at_10 + value: 46.388 + - type: recall_at_100 + value: 69.552 + - type: recall_at_1000 + value: 89.011 + - type: recall_at_3 + value: 33.617999999999995 + - type: recall_at_5 + value: 38.211 + - task: + type: Retrieval + dataset: + type: hotpotqa + name: MTEB HotpotQA + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 39.135999999999996 + - type: map_at_10 + value: 61.673 + - type: map_at_100 + value: 62.562 + - type: map_at_1000 + value: 62.62 + - type: map_at_3 + value: 58.467999999999996 + - type: map_at_5 + value: 60.463 + - type: mrr_at_1 + value: 78.271 + - type: mrr_at_10 + value: 84.119 + - type: mrr_at_100 + value: 84.29299999999999 + - type: mrr_at_1000 + value: 84.299 + - type: mrr_at_3 + value: 83.18900000000001 + - type: mrr_at_5 + value: 83.786 + - type: ndcg_at_1 + value: 78.271 + - type: ndcg_at_10 + value: 69.935 + - type: ndcg_at_100 + value: 73.01299999999999 + - type: ndcg_at_1000 + value: 74.126 + - type: ndcg_at_3 + value: 65.388 + - type: ndcg_at_5 + value: 67.906 + - type: precision_at_1 + value: 78.271 + - type: precision_at_10 + value: 14.562 + - type: precision_at_100 + value: 1.6969999999999998 + - type: precision_at_1000 + value: 0.184 + - type: precision_at_3 + value: 41.841 + - type: precision_at_5 + value: 27.087 + - type: recall_at_1 + value: 39.135999999999996 + - type: recall_at_10 + value: 72.809 + - type: recall_at_100 + value: 84.86200000000001 + - type: recall_at_1000 + value: 92.208 + - type: recall_at_3 + value: 62.76199999999999 + - type: recall_at_5 + value: 67.718 + - task: + type: Classification + dataset: + type: mteb/imdb + name: MTEB ImdbClassification + config: default + split: test + revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 + metrics: + - type: accuracy + value: 90.60600000000001 + - type: ap + value: 86.6579587804335 + - type: f1 + value: 90.5938853929307 + - task: + type: Retrieval + dataset: + type: msmarco + name: MTEB MSMARCO + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 21.852 + - type: map_at_10 + value: 33.982 + - type: map_at_100 + value: 35.116 + - type: map_at_1000 + value: 35.167 + - type: map_at_3 + value: 30.134 + - type: map_at_5 + value: 32.340999999999994 + - type: mrr_at_1 + value: 22.479 + - type: mrr_at_10 + value: 34.594 + - type: mrr_at_100 + value: 35.672 + - type: mrr_at_1000 + value: 35.716 + - type: mrr_at_3 + value: 30.84 + - type: mrr_at_5 + value: 32.998 + - type: ndcg_at_1 + value: 22.493 + - type: ndcg_at_10 + value: 40.833000000000006 + - type: ndcg_at_100 + value: 46.357 + - type: ndcg_at_1000 + value: 47.637 + - type: ndcg_at_3 + value: 32.995999999999995 + - type: ndcg_at_5 + value: 36.919000000000004 + - type: precision_at_1 + value: 22.493 + - type: precision_at_10 + value: 6.465999999999999 + - type: precision_at_100 + value: 0.9249999999999999 + - type: precision_at_1000 + value: 0.104 + - type: precision_at_3 + value: 14.030999999999999 + - type: precision_at_5 + value: 10.413 + - type: recall_at_1 + value: 21.852 + - type: recall_at_10 + value: 61.934999999999995 + - type: recall_at_100 + value: 87.611 + - type: recall_at_1000 + value: 97.441 + - type: recall_at_3 + value: 40.583999999999996 + - type: recall_at_5 + value: 49.992999999999995 + - task: + type: Classification + dataset: + type: mteb/mtop_domain + name: MTEB MTOPDomainClassification (en) + config: en + split: test + revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf + metrics: + - type: accuracy + value: 93.36069311445507 + - type: f1 + value: 93.16456330371453 + - task: + type: Classification + dataset: + type: mteb/mtop_intent + name: MTEB MTOPIntentClassification (en) + config: en + split: test + revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba + metrics: + - type: accuracy + value: 74.74692202462381 + - type: f1 + value: 58.17903579421599 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_intent + name: MTEB MassiveIntentClassification (en) + config: en + split: test + revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 + metrics: + - type: accuracy + value: 74.80833893745796 + - type: f1 + value: 72.70786592684664 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_scenario + name: MTEB MassiveScenarioClassification (en) + config: en + split: test + revision: 7d571f92784cd94a019292a1f45445077d0ef634 + metrics: + - type: accuracy + value: 78.69872225958305 + - type: f1 + value: 78.61626934504731 + - task: + type: Clustering + dataset: + type: mteb/medrxiv-clustering-p2p + name: MTEB MedrxivClusteringP2P + config: default + split: test + revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 + metrics: + - type: v_measure + value: 33.058658628717694 + - task: + type: Clustering + dataset: + type: mteb/medrxiv-clustering-s2s + name: MTEB MedrxivClusteringS2S + config: default + split: test + revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 + metrics: + - type: v_measure + value: 30.85561739360599 + - task: + type: Reranking + dataset: + type: mteb/mind_small + name: MTEB MindSmallReranking + config: default + split: test + revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 + metrics: + - type: map + value: 31.290259910144385 + - type: mrr + value: 32.44223046102856 + - task: + type: Retrieval + dataset: + type: nfcorpus + name: MTEB NFCorpus + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 5.288 + - type: map_at_10 + value: 12.267999999999999 + - type: map_at_100 + value: 15.557000000000002 + - type: map_at_1000 + value: 16.98 + - type: map_at_3 + value: 8.866 + - type: map_at_5 + value: 10.418 + - type: mrr_at_1 + value: 43.653 + - type: mrr_at_10 + value: 52.681 + - type: mrr_at_100 + value: 53.315999999999995 + - type: mrr_at_1000 + value: 53.357 + - type: mrr_at_3 + value: 51.393 + - type: mrr_at_5 + value: 51.903999999999996 + - type: ndcg_at_1 + value: 42.415000000000006 + - type: ndcg_at_10 + value: 34.305 + - type: ndcg_at_100 + value: 30.825999999999997 + - type: ndcg_at_1000 + value: 39.393 + - type: ndcg_at_3 + value: 39.931 + - type: ndcg_at_5 + value: 37.519999999999996 + - type: precision_at_1 + value: 43.653 + - type: precision_at_10 + value: 25.728 + - type: precision_at_100 + value: 7.932 + - type: precision_at_1000 + value: 2.07 + - type: precision_at_3 + value: 38.184000000000005 + - type: precision_at_5 + value: 32.879000000000005 + - type: recall_at_1 + value: 5.288 + - type: recall_at_10 + value: 16.195 + - type: recall_at_100 + value: 31.135 + - type: recall_at_1000 + value: 61.531000000000006 + - type: recall_at_3 + value: 10.313 + - type: recall_at_5 + value: 12.754999999999999 + - task: + type: Retrieval + dataset: + type: nq + name: MTEB NQ + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 28.216 + - type: map_at_10 + value: 42.588 + - type: map_at_100 + value: 43.702999999999996 + - type: map_at_1000 + value: 43.739 + - type: map_at_3 + value: 38.177 + - type: map_at_5 + value: 40.754000000000005 + - type: mrr_at_1 + value: 31.866 + - type: mrr_at_10 + value: 45.189 + - type: mrr_at_100 + value: 46.056000000000004 + - type: mrr_at_1000 + value: 46.081 + - type: mrr_at_3 + value: 41.526999999999994 + - type: mrr_at_5 + value: 43.704 + - type: ndcg_at_1 + value: 31.837 + - type: ndcg_at_10 + value: 50.178 + - type: ndcg_at_100 + value: 54.98800000000001 + - type: ndcg_at_1000 + value: 55.812 + - type: ndcg_at_3 + value: 41.853 + - type: ndcg_at_5 + value: 46.153 + - type: precision_at_1 + value: 31.837 + - type: precision_at_10 + value: 8.43 + - type: precision_at_100 + value: 1.1119999999999999 + - type: precision_at_1000 + value: 0.11900000000000001 + - type: precision_at_3 + value: 19.023 + - type: precision_at_5 + value: 13.911000000000001 + - type: recall_at_1 + value: 28.216 + - type: recall_at_10 + value: 70.8 + - type: recall_at_100 + value: 91.857 + - type: recall_at_1000 + value: 97.941 + - type: recall_at_3 + value: 49.196 + - type: recall_at_5 + value: 59.072 + - task: + type: Retrieval + dataset: + type: quora + name: MTEB QuoraRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 71.22800000000001 + - type: map_at_10 + value: 85.115 + - type: map_at_100 + value: 85.72 + - type: map_at_1000 + value: 85.737 + - type: map_at_3 + value: 82.149 + - type: map_at_5 + value: 84.029 + - type: mrr_at_1 + value: 81.96 + - type: mrr_at_10 + value: 88.00200000000001 + - type: mrr_at_100 + value: 88.088 + - type: mrr_at_1000 + value: 88.089 + - type: mrr_at_3 + value: 87.055 + - type: mrr_at_5 + value: 87.715 + - type: ndcg_at_1 + value: 82.01 + - type: ndcg_at_10 + value: 88.78 + - type: ndcg_at_100 + value: 89.91 + - type: ndcg_at_1000 + value: 90.013 + - type: ndcg_at_3 + value: 85.957 + - type: ndcg_at_5 + value: 87.56 + - type: precision_at_1 + value: 82.01 + - type: precision_at_10 + value: 13.462 + - type: precision_at_100 + value: 1.528 + - type: precision_at_1000 + value: 0.157 + - type: precision_at_3 + value: 37.553 + - type: precision_at_5 + value: 24.732000000000003 + - type: recall_at_1 + value: 71.22800000000001 + - type: recall_at_10 + value: 95.69 + - type: recall_at_100 + value: 99.531 + - type: recall_at_1000 + value: 99.98 + - type: recall_at_3 + value: 87.632 + - type: recall_at_5 + value: 92.117 + - task: + type: Clustering + dataset: + type: mteb/reddit-clustering + name: MTEB RedditClustering + config: default + split: test + revision: 24640382cdbf8abc73003fb0fa6d111a705499eb + metrics: + - type: v_measure + value: 52.31768034366916 + - task: + type: Clustering + dataset: + type: mteb/reddit-clustering-p2p + name: MTEB RedditClusteringP2P + config: default + split: test + revision: 282350215ef01743dc01b456c7f5241fa8937f16 + metrics: + - type: v_measure + value: 60.640266772723606 + - task: + type: Retrieval + dataset: + type: scidocs + name: MTEB SCIDOCS + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 4.7780000000000005 + - type: map_at_10 + value: 12.299 + - type: map_at_100 + value: 14.363000000000001 + - type: map_at_1000 + value: 14.71 + - type: map_at_3 + value: 8.738999999999999 + - type: map_at_5 + value: 10.397 + - type: mrr_at_1 + value: 23.599999999999998 + - type: mrr_at_10 + value: 34.845 + - type: mrr_at_100 + value: 35.916 + - type: mrr_at_1000 + value: 35.973 + - type: mrr_at_3 + value: 31.7 + - type: mrr_at_5 + value: 33.535 + - type: ndcg_at_1 + value: 23.599999999999998 + - type: ndcg_at_10 + value: 20.522000000000002 + - type: ndcg_at_100 + value: 28.737000000000002 + - type: ndcg_at_1000 + value: 34.596 + - type: ndcg_at_3 + value: 19.542 + - type: ndcg_at_5 + value: 16.958000000000002 + - type: precision_at_1 + value: 23.599999999999998 + - type: precision_at_10 + value: 10.67 + - type: precision_at_100 + value: 2.259 + - type: precision_at_1000 + value: 0.367 + - type: precision_at_3 + value: 18.333 + - type: precision_at_5 + value: 14.879999999999999 + - type: recall_at_1 + value: 4.7780000000000005 + - type: recall_at_10 + value: 21.617 + - type: recall_at_100 + value: 45.905 + - type: recall_at_1000 + value: 74.42 + - type: recall_at_3 + value: 11.148 + - type: recall_at_5 + value: 15.082999999999998 + - task: + type: STS + dataset: + type: mteb/sickr-sts + name: MTEB SICK-R + config: default + split: test + revision: a6ea5a8cab320b040a23452cc28066d9beae2cee + metrics: + - type: cos_sim_pearson + value: 83.22372750297885 + - type: cos_sim_spearman + value: 79.40972617119405 + - type: euclidean_pearson + value: 80.6101072020434 + - type: euclidean_spearman + value: 79.53844217225202 + - type: manhattan_pearson + value: 80.57265975286111 + - type: manhattan_spearman + value: 79.46335611792958 + - task: + type: STS + dataset: + type: mteb/sts12-sts + name: MTEB STS12 + config: default + split: test + revision: a0d554a64d88156834ff5ae9920b964011b16384 + metrics: + - type: cos_sim_pearson + value: 85.43713315520749 + - type: cos_sim_spearman + value: 77.44128693329532 + - type: euclidean_pearson + value: 81.63869928101123 + - type: euclidean_spearman + value: 77.29512977961515 + - type: manhattan_pearson + value: 81.63704185566183 + - type: manhattan_spearman + value: 77.29909412738657 + - task: + type: STS + dataset: + type: mteb/sts13-sts + name: MTEB STS13 + config: default + split: test + revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca + metrics: + - type: cos_sim_pearson + value: 81.59451537860527 + - type: cos_sim_spearman + value: 82.97994638856723 + - type: euclidean_pearson + value: 82.89478688288412 + - type: euclidean_spearman + value: 83.58740751053104 + - type: manhattan_pearson + value: 82.69140840941608 + - type: manhattan_spearman + value: 83.33665956040555 + - task: + type: STS + dataset: + type: mteb/sts14-sts + name: MTEB STS14 + config: default + split: test + revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 + metrics: + - type: cos_sim_pearson + value: 82.00756527711764 + - type: cos_sim_spearman + value: 81.83560996841379 + - type: euclidean_pearson + value: 82.07684151976518 + - type: euclidean_spearman + value: 82.00913052060511 + - type: manhattan_pearson + value: 82.05690778488794 + - type: manhattan_spearman + value: 82.02260252019525 + - task: + type: STS + dataset: + type: mteb/sts15-sts + name: MTEB STS15 + config: default + split: test + revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 + metrics: + - type: cos_sim_pearson + value: 86.13710262895447 + - type: cos_sim_spearman + value: 87.26412811156248 + - type: euclidean_pearson + value: 86.94151453230228 + - type: euclidean_spearman + value: 87.5363796699571 + - type: manhattan_pearson + value: 86.86989424083748 + - type: manhattan_spearman + value: 87.47315940781353 + - task: + type: STS + dataset: + type: mteb/sts16-sts + name: MTEB STS16 + config: default + split: test + revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 + metrics: + - type: cos_sim_pearson + value: 83.0230597603627 + - type: cos_sim_spearman + value: 84.93344499318864 + - type: euclidean_pearson + value: 84.23754743431141 + - type: euclidean_spearman + value: 85.09707376597099 + - type: manhattan_pearson + value: 84.04325160987763 + - type: manhattan_spearman + value: 84.89353071339909 + - 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: 86.75620824563921 + - type: cos_sim_spearman + value: 87.15065513706398 + - type: euclidean_pearson + value: 88.26281533633521 + - type: euclidean_spearman + value: 87.51963738643983 + - type: manhattan_pearson + value: 88.25599267618065 + - type: manhattan_spearman + value: 87.58048736047483 + - 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: 64.74645319195137 + - type: cos_sim_spearman + value: 65.29996325037214 + - type: euclidean_pearson + value: 67.04297794086443 + - type: euclidean_spearman + value: 65.43841726694343 + - type: manhattan_pearson + value: 67.39459955690904 + - type: manhattan_spearman + value: 65.92864704413651 + - task: + type: STS + dataset: + type: mteb/stsbenchmark-sts + name: MTEB STSBenchmark + config: default + split: test + revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 + metrics: + - type: cos_sim_pearson + value: 84.31291020270801 + - type: cos_sim_spearman + value: 85.86473738688068 + - type: euclidean_pearson + value: 85.65537275064152 + - type: euclidean_spearman + value: 86.13087454209642 + - type: manhattan_pearson + value: 85.43946955047609 + - type: manhattan_spearman + value: 85.91568175344916 + - task: + type: Reranking + dataset: + type: mteb/scidocs-reranking + name: MTEB SciDocsRR + config: default + split: test + revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab + metrics: + - type: map + value: 85.93798118350695 + - type: mrr + value: 95.93536274908824 + - task: + type: Retrieval + dataset: + type: scifact + name: MTEB SciFact + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 57.594 + - type: map_at_10 + value: 66.81899999999999 + - type: map_at_100 + value: 67.368 + - type: map_at_1000 + value: 67.4 + - type: map_at_3 + value: 64.061 + - type: map_at_5 + value: 65.47 + - type: mrr_at_1 + value: 60.667 + - type: mrr_at_10 + value: 68.219 + - type: mrr_at_100 + value: 68.655 + - type: mrr_at_1000 + value: 68.684 + - type: mrr_at_3 + value: 66.22200000000001 + - type: mrr_at_5 + value: 67.289 + - type: ndcg_at_1 + value: 60.667 + - type: ndcg_at_10 + value: 71.275 + - type: ndcg_at_100 + value: 73.642 + - type: ndcg_at_1000 + value: 74.373 + - type: ndcg_at_3 + value: 66.521 + - type: ndcg_at_5 + value: 68.581 + - type: precision_at_1 + value: 60.667 + - type: precision_at_10 + value: 9.433 + - type: precision_at_100 + value: 1.0699999999999998 + - type: precision_at_1000 + value: 0.11299999999999999 + - type: precision_at_3 + value: 25.556 + - type: precision_at_5 + value: 16.8 + - type: recall_at_1 + value: 57.594 + - type: recall_at_10 + value: 83.622 + - type: recall_at_100 + value: 94.167 + - type: recall_at_1000 + value: 99.667 + - type: recall_at_3 + value: 70.64399999999999 + - type: recall_at_5 + value: 75.983 + - task: + type: PairClassification + dataset: + type: mteb/sprintduplicatequestions-pairclassification + name: MTEB SprintDuplicateQuestions + config: default + split: test + revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 + metrics: + - type: cos_sim_accuracy + value: 99.85841584158416 + - type: cos_sim_ap + value: 96.66996142314342 + - type: cos_sim_f1 + value: 92.83208020050125 + - type: cos_sim_precision + value: 93.06532663316584 + - type: cos_sim_recall + value: 92.60000000000001 + - type: dot_accuracy + value: 99.85841584158416 + - type: dot_ap + value: 96.6775307676576 + - type: dot_f1 + value: 92.69289729177312 + - type: dot_precision + value: 94.77533960292581 + - type: dot_recall + value: 90.7 + - type: euclidean_accuracy + value: 99.86138613861387 + - type: euclidean_ap + value: 96.6338454403108 + - type: euclidean_f1 + value: 92.92214357937311 + - type: euclidean_precision + value: 93.96728016359918 + - type: euclidean_recall + value: 91.9 + - type: manhattan_accuracy + value: 99.86237623762376 + - type: manhattan_ap + value: 96.60370449645053 + - type: manhattan_f1 + value: 92.91177970423253 + - type: manhattan_precision + value: 94.7970863683663 + - type: manhattan_recall + value: 91.10000000000001 + - type: max_accuracy + value: 99.86237623762376 + - type: max_ap + value: 96.6775307676576 + - type: max_f1 + value: 92.92214357937311 + - task: + type: Clustering + dataset: + type: mteb/stackexchange-clustering + name: MTEB StackExchangeClustering + config: default + split: test + revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 + metrics: + - type: v_measure + value: 60.77977058695198 + - task: + type: Clustering + dataset: + type: mteb/stackexchange-clustering-p2p + name: MTEB StackExchangeClusteringP2P + config: default + split: test + revision: 815ca46b2622cec33ccafc3735d572c266efdb44 + metrics: + - type: v_measure + value: 35.2725272535638 + - task: + type: Reranking + dataset: + type: mteb/stackoverflowdupquestions-reranking + name: MTEB StackOverflowDupQuestions + config: default + split: test + revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 + metrics: + - type: map + value: 53.64052466362125 + - type: mrr + value: 54.533067014684654 + - task: + type: Summarization + dataset: + type: mteb/summeval + name: MTEB SummEval + config: default + split: test + revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c + metrics: + - type: cos_sim_pearson + value: 30.677624219206578 + - type: cos_sim_spearman + value: 30.121368518123447 + - type: dot_pearson + value: 30.69870088041608 + - type: dot_spearman + value: 29.61284927093751 + - 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: 1.855 + - type: map_at_100 + value: 9.885 + - type: map_at_1000 + value: 23.416999999999998 + - type: map_at_3 + value: 0.637 + - type: map_at_5 + value: 1.024 + - type: mrr_at_1 + value: 88.0 + - type: mrr_at_10 + value: 93.067 + - type: mrr_at_100 + value: 93.067 + - type: mrr_at_1000 + value: 93.067 + - type: mrr_at_3 + value: 92.667 + - type: mrr_at_5 + value: 93.067 + - type: ndcg_at_1 + value: 82.0 + - type: ndcg_at_10 + value: 75.899 + - type: ndcg_at_100 + value: 55.115 + - type: ndcg_at_1000 + value: 48.368 + - type: ndcg_at_3 + value: 79.704 + - type: ndcg_at_5 + value: 78.39699999999999 + - type: precision_at_1 + value: 88.0 + - type: precision_at_10 + value: 79.60000000000001 + - type: precision_at_100 + value: 56.06 + - type: precision_at_1000 + value: 21.206 + - type: precision_at_3 + value: 84.667 + - type: precision_at_5 + value: 83.2 + - type: recall_at_1 + value: 0.22 + - type: recall_at_10 + value: 2.078 + - type: recall_at_100 + value: 13.297 + - type: recall_at_1000 + value: 44.979 + - type: recall_at_3 + value: 0.6689999999999999 + - type: recall_at_5 + value: 1.106 + - task: + type: Retrieval + dataset: + type: webis-touche2020 + name: MTEB Touche2020 + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 2.258 + - type: map_at_10 + value: 10.439 + - type: map_at_100 + value: 16.89 + - type: map_at_1000 + value: 18.407999999999998 + - type: map_at_3 + value: 5.668 + - type: map_at_5 + value: 7.718 + - type: mrr_at_1 + value: 32.653 + - type: mrr_at_10 + value: 51.159 + - type: mrr_at_100 + value: 51.714000000000006 + - type: mrr_at_1000 + value: 51.714000000000006 + - type: mrr_at_3 + value: 47.959 + - type: mrr_at_5 + value: 50.407999999999994 + - type: ndcg_at_1 + value: 29.592000000000002 + - type: ndcg_at_10 + value: 26.037 + - type: ndcg_at_100 + value: 37.924 + - type: ndcg_at_1000 + value: 49.126999999999995 + - type: ndcg_at_3 + value: 30.631999999999998 + - type: ndcg_at_5 + value: 28.571 + - type: precision_at_1 + value: 32.653 + - type: precision_at_10 + value: 22.857 + - type: precision_at_100 + value: 7.754999999999999 + - type: precision_at_1000 + value: 1.529 + - type: precision_at_3 + value: 34.014 + - type: precision_at_5 + value: 29.796 + - type: recall_at_1 + value: 2.258 + - type: recall_at_10 + value: 16.554 + - type: recall_at_100 + value: 48.439 + - type: recall_at_1000 + value: 82.80499999999999 + - type: recall_at_3 + value: 7.283 + - type: recall_at_5 + value: 10.732 + - task: + type: Classification + dataset: + type: mteb/toxic_conversations_50k + name: MTEB ToxicConversationsClassification + config: default + split: test + revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c + metrics: + - type: accuracy + value: 69.8858 + - type: ap + value: 13.835684144362109 + - type: f1 + value: 53.803351693244586 + - task: + type: Classification + dataset: + type: mteb/tweet_sentiment_extraction + name: MTEB TweetSentimentExtractionClassification + config: default + split: test + revision: d604517c81ca91fe16a244d1248fc021f9ecee7a + metrics: + - type: accuracy + value: 60.50650820599886 + - type: f1 + value: 60.84357825979259 + - task: + type: Clustering + dataset: + type: mteb/twentynewsgroups-clustering + name: MTEB TwentyNewsgroupsClustering + config: default + split: test + revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 + metrics: + - type: v_measure + value: 48.52131044852134 + - task: + type: PairClassification + dataset: + type: mteb/twittersemeval2015-pairclassification + name: MTEB TwitterSemEval2015 + config: default + split: test + revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 + metrics: + - type: cos_sim_accuracy + value: 85.59337187816654 + - type: cos_sim_ap + value: 73.23925826533437 + - type: cos_sim_f1 + value: 67.34693877551021 + - type: cos_sim_precision + value: 62.40432237730752 + - type: cos_sim_recall + value: 73.13984168865434 + - type: dot_accuracy + value: 85.31322644096085 + - type: dot_ap + value: 72.30723963807422 + - type: dot_f1 + value: 66.47051612112296 + - type: dot_precision + value: 62.0792305930845 + - type: dot_recall + value: 71.53034300791556 + - type: euclidean_accuracy + value: 85.61125350181797 + - type: euclidean_ap + value: 73.32843720487845 + - type: euclidean_f1 + value: 67.36549633745895 + - type: euclidean_precision + value: 64.60755813953489 + - type: euclidean_recall + value: 70.36939313984169 + - type: manhattan_accuracy + value: 85.63509566668654 + - type: manhattan_ap + value: 73.16658488311325 + - type: manhattan_f1 + value: 67.20597386434349 + - type: manhattan_precision + value: 63.60424028268551 + - type: manhattan_recall + value: 71.2401055408971 + - type: max_accuracy + value: 85.63509566668654 + - type: max_ap + value: 73.32843720487845 + - type: max_f1 + value: 67.36549633745895 + - task: + type: PairClassification + dataset: + type: mteb/twitterurlcorpus-pairclassification + name: MTEB TwitterURLCorpus + config: default + split: test + revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf + metrics: + - type: cos_sim_accuracy + value: 88.33779640625606 + - type: cos_sim_ap + value: 84.83868375898157 + - type: cos_sim_f1 + value: 77.16506154017773 + - type: cos_sim_precision + value: 74.62064005753327 + - type: cos_sim_recall + value: 79.88912842623961 + - type: dot_accuracy + value: 88.02732176815307 + - type: dot_ap + value: 83.95089283763002 + - type: dot_f1 + value: 76.29635101196631 + - type: dot_precision + value: 73.31771720613288 + - type: dot_recall + value: 79.52725592854944 + - type: euclidean_accuracy + value: 88.44452206310397 + - type: euclidean_ap + value: 84.98384576824827 + - type: euclidean_f1 + value: 77.29311047696697 + - type: euclidean_precision + value: 74.51232583065381 + - type: euclidean_recall + value: 80.28949799815214 + - type: manhattan_accuracy + value: 88.47362906042613 + - type: manhattan_ap + value: 84.91421462218432 + - type: manhattan_f1 + value: 77.05107637204792 + - type: manhattan_precision + value: 74.74484256243214 + - type: manhattan_recall + value: 79.50415768401602 + - type: max_accuracy + value: 88.47362906042613 + - type: max_ap + value: 84.98384576824827 + - type: max_f1 + value: 77.29311047696697 +license: mit +language: +- en +--- + + +

FlagEmbedding

+ + +

+

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

+

+ +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) of BGE has been released +- 09/15/2023: The [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 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) +``` + +#### 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-small-en-v1.5') +model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5') +model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', 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. +Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. + +```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-small-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()) +``` + + +## 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. +