--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge_micro results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 67.76119402985074 - type: ap value: 29.637849284211114 - type: f1 value: 61.31181187111905 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 79.7547 - type: ap value: 74.21401629809145 - type: f1 value: 79.65319615433783 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.452000000000005 - type: f1 value: 37.0245198854966 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 31.152 - type: map_at_10 value: 46.702 - type: map_at_100 value: 47.563 - type: map_at_1000 value: 47.567 - type: map_at_3 value: 42.058 - type: map_at_5 value: 44.608 - type: mrr_at_1 value: 32.006 - type: mrr_at_10 value: 47.064 - type: mrr_at_100 value: 47.910000000000004 - type: mrr_at_1000 value: 47.915 - type: mrr_at_3 value: 42.283 - type: mrr_at_5 value: 44.968 - type: ndcg_at_1 value: 31.152 - type: ndcg_at_10 value: 55.308 - type: ndcg_at_100 value: 58.965 - type: ndcg_at_1000 value: 59.067 - type: ndcg_at_3 value: 45.698 - type: ndcg_at_5 value: 50.296 - type: precision_at_1 value: 31.152 - type: precision_at_10 value: 8.279 - type: precision_at_100 value: 0.987 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.753 - type: precision_at_5 value: 13.485 - type: recall_at_1 value: 31.152 - type: recall_at_10 value: 82.788 - type: recall_at_100 value: 98.72 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 56.259 - type: recall_at_5 value: 67.425 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.52692241938116 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 33.245710292773595 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.08493637155168 - type: mrr value: 71.94378490084861 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.1602804378326 - type: cos_sim_spearman value: 82.92478106365587 - type: euclidean_pearson value: 82.27930167277077 - type: euclidean_spearman value: 82.18560759458093 - type: manhattan_pearson value: 82.34277425888187 - type: manhattan_spearman value: 81.72776583704467 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 81.17207792207792 - type: f1 value: 81.09893836310513 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 36.109308463095516 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 28.06048212317168 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.233999999999998 - type: map_at_10 value: 38.092999999999996 - type: map_at_100 value: 39.473 - type: map_at_1000 value: 39.614 - type: map_at_3 value: 34.839 - type: map_at_5 value: 36.523 - type: mrr_at_1 value: 35.193000000000005 - type: mrr_at_10 value: 44.089 - type: mrr_at_100 value: 44.927 - type: mrr_at_1000 value: 44.988 - type: mrr_at_3 value: 41.559000000000005 - type: mrr_at_5 value: 43.162 - type: ndcg_at_1 value: 35.193000000000005 - type: ndcg_at_10 value: 44.04 - type: ndcg_at_100 value: 49.262 - type: ndcg_at_1000 value: 51.847 - type: ndcg_at_3 value: 39.248 - type: ndcg_at_5 value: 41.298 - type: precision_at_1 value: 35.193000000000005 - type: precision_at_10 value: 8.555 - type: precision_at_100 value: 1.3820000000000001 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 19.123 - type: precision_at_5 value: 13.648 - type: recall_at_1 value: 28.233999999999998 - type: recall_at_10 value: 55.094 - type: recall_at_100 value: 76.85300000000001 - type: recall_at_1000 value: 94.163 - type: recall_at_3 value: 40.782000000000004 - type: recall_at_5 value: 46.796 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.538 - type: map_at_10 value: 28.449 - type: map_at_100 value: 29.471000000000004 - type: map_at_1000 value: 29.599999999999998 - type: map_at_3 value: 26.371 - type: map_at_5 value: 27.58 - type: mrr_at_1 value: 26.815 - type: mrr_at_10 value: 33.331 - type: mrr_at_100 value: 34.114 - type: mrr_at_1000 value: 34.182 - type: mrr_at_3 value: 31.561 - type: mrr_at_5 value: 32.608 - type: ndcg_at_1 value: 26.815 - type: ndcg_at_10 value: 32.67 - type: ndcg_at_100 value: 37.039 - type: ndcg_at_1000 value: 39.769 - type: ndcg_at_3 value: 29.523 - type: ndcg_at_5 value: 31.048 - type: precision_at_1 value: 26.815 - type: precision_at_10 value: 5.955 - type: precision_at_100 value: 1.02 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 14.033999999999999 - type: precision_at_5 value: 9.911 - type: recall_at_1 value: 21.538 - type: recall_at_10 value: 40.186 - type: recall_at_100 value: 58.948 - type: recall_at_1000 value: 77.158 - type: recall_at_3 value: 30.951 - type: recall_at_5 value: 35.276 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.211999999999996 - type: map_at_10 value: 46.562 - type: map_at_100 value: 47.579 - type: map_at_1000 value: 47.646 - type: map_at_3 value: 43.485 - type: map_at_5 value: 45.206 - type: mrr_at_1 value: 40.627 - type: mrr_at_10 value: 49.928 - type: mrr_at_100 value: 50.647 - type: mrr_at_1000 value: 50.685 - type: mrr_at_3 value: 47.513 - type: mrr_at_5 value: 48.958 - type: ndcg_at_1 value: 40.627 - type: ndcg_at_10 value: 52.217 - type: ndcg_at_100 value: 56.423 - type: ndcg_at_1000 value: 57.821999999999996 - type: ndcg_at_3 value: 46.949000000000005 - type: ndcg_at_5 value: 49.534 - type: precision_at_1 value: 40.627 - type: precision_at_10 value: 8.476 - type: precision_at_100 value: 1.15 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 21.003 - type: precision_at_5 value: 14.469999999999999 - type: recall_at_1 value: 35.211999999999996 - type: recall_at_10 value: 65.692 - type: recall_at_100 value: 84.011 - type: recall_at_1000 value: 94.03099999999999 - type: recall_at_3 value: 51.404 - type: recall_at_5 value: 57.882 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.09 - type: map_at_10 value: 29.516 - type: map_at_100 value: 30.462 - type: map_at_1000 value: 30.56 - type: map_at_3 value: 26.945000000000004 - type: map_at_5 value: 28.421999999999997 - type: mrr_at_1 value: 23.616 - type: mrr_at_10 value: 31.221 - type: mrr_at_100 value: 32.057 - type: mrr_at_1000 value: 32.137 - type: mrr_at_3 value: 28.738000000000003 - type: mrr_at_5 value: 30.156 - type: ndcg_at_1 value: 23.616 - type: ndcg_at_10 value: 33.97 - type: ndcg_at_100 value: 38.806000000000004 - type: ndcg_at_1000 value: 41.393 - type: ndcg_at_3 value: 28.908 - type: ndcg_at_5 value: 31.433 - type: precision_at_1 value: 23.616 - type: precision_at_10 value: 5.299 - type: precision_at_100 value: 0.812 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 12.015 - type: precision_at_5 value: 8.701 - type: recall_at_1 value: 22.09 - type: recall_at_10 value: 46.089999999999996 - type: recall_at_100 value: 68.729 - type: recall_at_1000 value: 88.435 - type: recall_at_3 value: 32.584999999999994 - type: recall_at_5 value: 38.550000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.469 - type: map_at_10 value: 22.436 - type: map_at_100 value: 23.465 - type: map_at_1000 value: 23.608999999999998 - type: map_at_3 value: 19.716 - type: map_at_5 value: 21.182000000000002 - type: mrr_at_1 value: 18.905 - type: mrr_at_10 value: 26.55 - type: mrr_at_100 value: 27.46 - type: mrr_at_1000 value: 27.553 - type: mrr_at_3 value: 23.921999999999997 - type: mrr_at_5 value: 25.302999999999997 - type: ndcg_at_1 value: 18.905 - type: ndcg_at_10 value: 27.437 - type: ndcg_at_100 value: 32.555 - type: ndcg_at_1000 value: 35.885 - type: ndcg_at_3 value: 22.439 - type: ndcg_at_5 value: 24.666 - type: precision_at_1 value: 18.905 - type: precision_at_10 value: 5.2490000000000006 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 10.862 - type: precision_at_5 value: 8.085 - type: recall_at_1 value: 15.469 - type: recall_at_10 value: 38.706 - type: recall_at_100 value: 61.242 - type: recall_at_1000 value: 84.84 - type: recall_at_3 value: 24.973 - type: recall_at_5 value: 30.603 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.918000000000003 - type: map_at_10 value: 34.296 - type: map_at_100 value: 35.632000000000005 - type: map_at_1000 value: 35.748999999999995 - type: map_at_3 value: 31.304 - type: map_at_5 value: 33.166000000000004 - type: mrr_at_1 value: 30.703000000000003 - type: mrr_at_10 value: 39.655 - type: mrr_at_100 value: 40.569 - type: mrr_at_1000 value: 40.621 - type: mrr_at_3 value: 37.023 - type: mrr_at_5 value: 38.664 - type: ndcg_at_1 value: 30.703000000000003 - type: ndcg_at_10 value: 39.897 - type: ndcg_at_100 value: 45.777 - type: ndcg_at_1000 value: 48.082 - type: ndcg_at_3 value: 35.122 - type: ndcg_at_5 value: 37.691 - type: precision_at_1 value: 30.703000000000003 - type: precision_at_10 value: 7.305000000000001 - type: precision_at_100 value: 1.208 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 16.811 - type: precision_at_5 value: 12.203999999999999 - type: recall_at_1 value: 24.918000000000003 - type: recall_at_10 value: 51.31 - type: recall_at_100 value: 76.534 - type: recall_at_1000 value: 91.911 - type: recall_at_3 value: 37.855 - type: recall_at_5 value: 44.493 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.416 - type: map_at_10 value: 30.474 - type: map_at_100 value: 31.759999999999998 - type: map_at_1000 value: 31.891000000000002 - type: map_at_3 value: 27.728 - type: map_at_5 value: 29.247 - type: mrr_at_1 value: 28.881 - type: mrr_at_10 value: 36.418 - type: mrr_at_100 value: 37.347 - type: mrr_at_1000 value: 37.415 - type: mrr_at_3 value: 33.942 - type: mrr_at_5 value: 35.386 - type: ndcg_at_1 value: 28.881 - type: ndcg_at_10 value: 35.812 - type: ndcg_at_100 value: 41.574 - type: ndcg_at_1000 value: 44.289 - type: ndcg_at_3 value: 31.239 - type: ndcg_at_5 value: 33.302 - type: precision_at_1 value: 28.881 - type: precision_at_10 value: 6.598 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 14.954 - type: precision_at_5 value: 10.776 - type: recall_at_1 value: 22.416 - type: recall_at_10 value: 46.243 - type: recall_at_100 value: 71.352 - type: recall_at_1000 value: 90.034 - type: recall_at_3 value: 32.873000000000005 - type: recall_at_5 value: 38.632 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.528166666666667 - type: map_at_10 value: 30.317833333333333 - type: map_at_100 value: 31.44108333333333 - type: map_at_1000 value: 31.566666666666666 - type: map_at_3 value: 27.84425 - type: map_at_5 value: 29.233333333333334 - type: mrr_at_1 value: 26.75733333333333 - type: mrr_at_10 value: 34.24425 - type: mrr_at_100 value: 35.11375 - type: mrr_at_1000 value: 35.184333333333335 - type: mrr_at_3 value: 32.01225 - type: mrr_at_5 value: 33.31225 - type: ndcg_at_1 value: 26.75733333333333 - type: ndcg_at_10 value: 35.072583333333334 - type: ndcg_at_100 value: 40.13358333333334 - type: ndcg_at_1000 value: 42.81825 - type: ndcg_at_3 value: 30.79275000000001 - type: ndcg_at_5 value: 32.822 - type: precision_at_1 value: 26.75733333333333 - type: precision_at_10 value: 6.128083333333334 - type: precision_at_100 value: 1.019 - type: precision_at_1000 value: 0.14391666666666664 - type: precision_at_3 value: 14.129916666666665 - type: precision_at_5 value: 10.087416666666668 - type: recall_at_1 value: 22.528166666666667 - type: recall_at_10 value: 45.38341666666667 - type: recall_at_100 value: 67.81791666666668 - type: recall_at_1000 value: 86.71716666666666 - type: recall_at_3 value: 33.38741666666667 - type: recall_at_5 value: 38.62041666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.975 - type: map_at_10 value: 28.144999999999996 - type: map_at_100 value: 28.994999999999997 - type: map_at_1000 value: 29.086000000000002 - type: map_at_3 value: 25.968999999999998 - type: map_at_5 value: 27.321 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 30.822 - type: mrr_at_100 value: 31.647 - type: mrr_at_1000 value: 31.712 - type: mrr_at_3 value: 28.860000000000003 - type: mrr_at_5 value: 30.041 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 31.929999999999996 - type: ndcg_at_100 value: 36.258 - type: ndcg_at_1000 value: 38.682 - type: ndcg_at_3 value: 27.972 - type: ndcg_at_5 value: 30.089 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 4.923 - type: precision_at_100 value: 0.767 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 11.860999999999999 - type: precision_at_5 value: 8.466 - type: recall_at_1 value: 21.975 - type: recall_at_10 value: 41.102 - type: recall_at_100 value: 60.866 - type: recall_at_1000 value: 78.781 - type: recall_at_3 value: 30.268 - type: recall_at_5 value: 35.552 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.845999999999998 - type: map_at_10 value: 21.861 - type: map_at_100 value: 22.798 - type: map_at_1000 value: 22.925 - type: map_at_3 value: 19.922 - type: map_at_5 value: 21.054000000000002 - type: mrr_at_1 value: 19.098000000000003 - type: mrr_at_10 value: 25.397 - type: mrr_at_100 value: 26.246000000000002 - type: mrr_at_1000 value: 26.33 - type: mrr_at_3 value: 23.469 - type: mrr_at_5 value: 24.646 - type: ndcg_at_1 value: 19.098000000000003 - type: ndcg_at_10 value: 25.807999999999996 - type: ndcg_at_100 value: 30.445 - type: ndcg_at_1000 value: 33.666000000000004 - type: ndcg_at_3 value: 22.292 - type: ndcg_at_5 value: 24.075 - type: precision_at_1 value: 19.098000000000003 - type: precision_at_10 value: 4.58 - type: precision_at_100 value: 0.8099999999999999 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 10.346 - type: precision_at_5 value: 7.542999999999999 - type: recall_at_1 value: 15.845999999999998 - type: recall_at_10 value: 34.172999999999995 - type: recall_at_100 value: 55.24099999999999 - type: recall_at_1000 value: 78.644 - type: recall_at_3 value: 24.401 - type: recall_at_5 value: 28.938000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.974 - type: map_at_10 value: 30.108 - type: map_at_100 value: 31.208000000000002 - type: map_at_1000 value: 31.330999999999996 - type: map_at_3 value: 27.889999999999997 - type: map_at_5 value: 29.023 - type: mrr_at_1 value: 26.493 - type: mrr_at_10 value: 33.726 - type: mrr_at_100 value: 34.622 - type: mrr_at_1000 value: 34.703 - type: mrr_at_3 value: 31.575999999999997 - type: mrr_at_5 value: 32.690999999999995 - type: ndcg_at_1 value: 26.493 - type: ndcg_at_10 value: 34.664 - type: ndcg_at_100 value: 39.725 - type: ndcg_at_1000 value: 42.648 - type: ndcg_at_3 value: 30.447999999999997 - type: ndcg_at_5 value: 32.145 - type: precision_at_1 value: 26.493 - type: precision_at_10 value: 5.7090000000000005 - type: precision_at_100 value: 0.9199999999999999 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 13.464 - type: precision_at_5 value: 9.384 - type: recall_at_1 value: 22.974 - type: recall_at_10 value: 45.097 - type: recall_at_100 value: 66.908 - type: recall_at_1000 value: 87.495 - type: recall_at_3 value: 33.338 - type: recall_at_5 value: 37.499 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.408 - type: map_at_10 value: 29.580000000000002 - type: map_at_100 value: 31.145 - type: map_at_1000 value: 31.369000000000003 - type: map_at_3 value: 27.634999999999998 - type: map_at_5 value: 28.766000000000002 - type: mrr_at_1 value: 27.272999999999996 - type: mrr_at_10 value: 33.93 - type: mrr_at_100 value: 34.963 - type: mrr_at_1000 value: 35.031 - type: mrr_at_3 value: 32.016 - type: mrr_at_5 value: 33.221000000000004 - type: ndcg_at_1 value: 27.272999999999996 - type: ndcg_at_10 value: 33.993 - type: ndcg_at_100 value: 40.333999999999996 - type: ndcg_at_1000 value: 43.361 - type: ndcg_at_3 value: 30.918 - type: ndcg_at_5 value: 32.552 - type: precision_at_1 value: 27.272999999999996 - type: precision_at_10 value: 6.285 - type: precision_at_100 value: 1.389 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 14.427000000000001 - type: precision_at_5 value: 10.356 - type: recall_at_1 value: 22.408 - type: recall_at_10 value: 41.318 - type: recall_at_100 value: 70.539 - type: recall_at_1000 value: 90.197 - type: recall_at_3 value: 32.513 - type: recall_at_5 value: 37.0 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.258000000000003 - type: map_at_10 value: 24.294 - type: map_at_100 value: 25.305 - type: map_at_1000 value: 25.419999999999998 - type: map_at_3 value: 22.326999999999998 - type: map_at_5 value: 23.31 - type: mrr_at_1 value: 18.484 - type: mrr_at_10 value: 25.863999999999997 - type: mrr_at_100 value: 26.766000000000002 - type: mrr_at_1000 value: 26.855 - type: mrr_at_3 value: 23.968 - type: mrr_at_5 value: 24.911 - type: ndcg_at_1 value: 18.484 - type: ndcg_at_10 value: 28.433000000000003 - type: ndcg_at_100 value: 33.405 - type: ndcg_at_1000 value: 36.375 - type: ndcg_at_3 value: 24.455 - type: ndcg_at_5 value: 26.031 - type: precision_at_1 value: 18.484 - type: precision_at_10 value: 4.603 - type: precision_at_100 value: 0.773 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 10.659 - type: precision_at_5 value: 7.505000000000001 - type: recall_at_1 value: 17.258000000000003 - type: recall_at_10 value: 39.589999999999996 - type: recall_at_100 value: 62.592000000000006 - type: recall_at_1000 value: 84.917 - type: recall_at_3 value: 28.706 - type: recall_at_5 value: 32.224000000000004 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.578999999999999 - type: map_at_10 value: 17.642 - type: map_at_100 value: 19.451 - type: map_at_1000 value: 19.647000000000002 - type: map_at_3 value: 14.618 - type: map_at_5 value: 16.145 - type: mrr_at_1 value: 23.322000000000003 - type: mrr_at_10 value: 34.204 - type: mrr_at_100 value: 35.185 - type: mrr_at_1000 value: 35.235 - type: mrr_at_3 value: 30.847 - type: mrr_at_5 value: 32.824 - type: ndcg_at_1 value: 23.322000000000003 - type: ndcg_at_10 value: 25.352999999999998 - type: ndcg_at_100 value: 32.574 - type: ndcg_at_1000 value: 36.073 - type: ndcg_at_3 value: 20.318 - type: ndcg_at_5 value: 22.111 - type: precision_at_1 value: 23.322000000000003 - type: precision_at_10 value: 8.02 - type: precision_at_100 value: 1.5730000000000002 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 15.049000000000001 - type: precision_at_5 value: 11.87 - type: recall_at_1 value: 10.578999999999999 - type: recall_at_10 value: 30.964999999999996 - type: recall_at_100 value: 55.986000000000004 - type: recall_at_1000 value: 75.565 - type: recall_at_3 value: 18.686 - type: recall_at_5 value: 23.629 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 7.327 - type: map_at_10 value: 14.904 - type: map_at_100 value: 20.29 - type: map_at_1000 value: 21.42 - type: map_at_3 value: 10.911 - type: map_at_5 value: 12.791 - type: mrr_at_1 value: 57.25 - type: mrr_at_10 value: 66.62700000000001 - type: mrr_at_100 value: 67.035 - type: mrr_at_1000 value: 67.052 - type: mrr_at_3 value: 64.833 - type: mrr_at_5 value: 65.908 - type: ndcg_at_1 value: 43.75 - type: ndcg_at_10 value: 32.246 - type: ndcg_at_100 value: 35.774 - type: ndcg_at_1000 value: 42.872 - type: ndcg_at_3 value: 36.64 - type: ndcg_at_5 value: 34.487 - type: precision_at_1 value: 57.25 - type: precision_at_10 value: 25.924999999999997 - type: precision_at_100 value: 7.670000000000001 - type: precision_at_1000 value: 1.599 - type: precision_at_3 value: 41.167 - type: precision_at_5 value: 34.65 - type: recall_at_1 value: 7.327 - type: recall_at_10 value: 19.625 - type: recall_at_100 value: 41.601 - type: recall_at_1000 value: 65.117 - type: recall_at_3 value: 12.308 - type: recall_at_5 value: 15.437999999999999 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.53 - type: f1 value: 39.39884255816736 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 58.913000000000004 - type: map_at_10 value: 69.592 - type: map_at_100 value: 69.95599999999999 - type: map_at_1000 value: 69.973 - type: map_at_3 value: 67.716 - type: map_at_5 value: 68.899 - type: mrr_at_1 value: 63.561 - type: mrr_at_10 value: 74.2 - type: mrr_at_100 value: 74.468 - type: mrr_at_1000 value: 74.47500000000001 - type: mrr_at_3 value: 72.442 - type: mrr_at_5 value: 73.58 - type: ndcg_at_1 value: 63.561 - type: ndcg_at_10 value: 74.988 - type: ndcg_at_100 value: 76.52799999999999 - type: ndcg_at_1000 value: 76.88000000000001 - type: ndcg_at_3 value: 71.455 - type: ndcg_at_5 value: 73.42699999999999 - type: precision_at_1 value: 63.561 - type: precision_at_10 value: 9.547 - type: precision_at_100 value: 1.044 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 28.143 - type: precision_at_5 value: 18.008 - type: recall_at_1 value: 58.913000000000004 - type: recall_at_10 value: 87.18 - type: recall_at_100 value: 93.852 - type: recall_at_1000 value: 96.256 - type: recall_at_3 value: 77.55199999999999 - type: recall_at_5 value: 82.42399999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 11.761000000000001 - type: map_at_10 value: 19.564999999999998 - type: map_at_100 value: 21.099 - type: map_at_1000 value: 21.288999999999998 - type: map_at_3 value: 16.683999999999997 - type: map_at_5 value: 18.307000000000002 - type: mrr_at_1 value: 23.302 - type: mrr_at_10 value: 30.979 - type: mrr_at_100 value: 32.121 - type: mrr_at_1000 value: 32.186 - type: mrr_at_3 value: 28.549000000000003 - type: mrr_at_5 value: 30.038999999999998 - type: ndcg_at_1 value: 23.302 - type: ndcg_at_10 value: 25.592 - type: ndcg_at_100 value: 32.416 - type: ndcg_at_1000 value: 36.277 - type: ndcg_at_3 value: 22.151 - type: ndcg_at_5 value: 23.483999999999998 - type: precision_at_1 value: 23.302 - type: precision_at_10 value: 7.377000000000001 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 14.712 - type: precision_at_5 value: 11.358 - type: recall_at_1 value: 11.761000000000001 - type: recall_at_10 value: 31.696 - type: recall_at_100 value: 58.01500000000001 - type: recall_at_1000 value: 81.572 - type: recall_at_3 value: 20.742 - type: recall_at_5 value: 25.707 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 32.275 - type: map_at_10 value: 44.712 - type: map_at_100 value: 45.621 - type: map_at_1000 value: 45.698 - type: map_at_3 value: 42.016999999999996 - type: map_at_5 value: 43.659 - type: mrr_at_1 value: 64.551 - type: mrr_at_10 value: 71.58099999999999 - type: mrr_at_100 value: 71.952 - type: mrr_at_1000 value: 71.96900000000001 - type: mrr_at_3 value: 70.236 - type: mrr_at_5 value: 71.051 - type: ndcg_at_1 value: 64.551 - type: ndcg_at_10 value: 53.913999999999994 - type: ndcg_at_100 value: 57.421 - type: ndcg_at_1000 value: 59.06 - type: ndcg_at_3 value: 49.716 - type: ndcg_at_5 value: 51.971999999999994 - type: precision_at_1 value: 64.551 - type: precision_at_10 value: 11.110000000000001 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 30.822 - type: precision_at_5 value: 20.273 - type: recall_at_1 value: 32.275 - type: recall_at_10 value: 55.55 - type: recall_at_100 value: 69.38600000000001 - type: recall_at_1000 value: 80.35799999999999 - type: recall_at_3 value: 46.232 - type: recall_at_5 value: 50.682 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 76.4604 - type: ap value: 70.40498168422701 - type: f1 value: 76.38572688476046 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 15.065999999999999 - type: map_at_10 value: 25.058000000000003 - type: map_at_100 value: 26.268 - type: map_at_1000 value: 26.344 - type: map_at_3 value: 21.626 - type: map_at_5 value: 23.513 - type: mrr_at_1 value: 15.501000000000001 - type: mrr_at_10 value: 25.548 - type: mrr_at_100 value: 26.723000000000003 - type: mrr_at_1000 value: 26.793 - type: mrr_at_3 value: 22.142 - type: mrr_at_5 value: 24.024 - type: ndcg_at_1 value: 15.501000000000001 - type: ndcg_at_10 value: 31.008000000000003 - type: ndcg_at_100 value: 37.08 - type: ndcg_at_1000 value: 39.102 - type: ndcg_at_3 value: 23.921999999999997 - type: ndcg_at_5 value: 27.307 - type: precision_at_1 value: 15.501000000000001 - type: precision_at_10 value: 5.155 - type: precision_at_100 value: 0.822 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 10.363 - type: precision_at_5 value: 7.917000000000001 - type: recall_at_1 value: 15.065999999999999 - type: recall_at_10 value: 49.507 - type: recall_at_100 value: 78.118 - type: recall_at_1000 value: 93.881 - type: recall_at_3 value: 30.075000000000003 - type: recall_at_5 value: 38.222 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.6703146374829 - type: f1 value: 90.1258004293966 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 68.29229366165072 - type: f1 value: 50.016194478997875 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.57767316745124 - type: f1 value: 67.16194062146954 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.92064559515804 - type: f1 value: 73.6680729569968 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.56335607367883 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.131807833734268 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.07390328719844 - type: mrr value: 32.117370992867905 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.274 - type: map_at_10 value: 11.489 - type: map_at_100 value: 14.518 - type: map_at_1000 value: 15.914 - type: map_at_3 value: 8.399 - type: map_at_5 value: 9.889000000000001 - type: mrr_at_1 value: 42.724000000000004 - type: mrr_at_10 value: 51.486 - type: mrr_at_100 value: 51.941 - type: mrr_at_1000 value: 51.99 - type: mrr_at_3 value: 49.278 - type: mrr_at_5 value: 50.485 - type: ndcg_at_1 value: 39.938 - type: ndcg_at_10 value: 31.862000000000002 - type: ndcg_at_100 value: 29.235 - type: ndcg_at_1000 value: 37.802 - type: ndcg_at_3 value: 35.754999999999995 - type: ndcg_at_5 value: 34.447 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 23.901 - type: precision_at_100 value: 7.715 - type: precision_at_1000 value: 2.045 - type: precision_at_3 value: 33.437 - type: precision_at_5 value: 29.782999999999998 - type: recall_at_1 value: 5.274 - type: recall_at_10 value: 15.351 - type: recall_at_100 value: 29.791 - type: recall_at_1000 value: 60.722 - type: recall_at_3 value: 9.411 - type: recall_at_5 value: 12.171999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 16.099 - type: map_at_10 value: 27.913 - type: map_at_100 value: 29.281000000000002 - type: map_at_1000 value: 29.343999999999998 - type: map_at_3 value: 23.791 - type: map_at_5 value: 26.049 - type: mrr_at_1 value: 18.337 - type: mrr_at_10 value: 29.953999999999997 - type: mrr_at_100 value: 31.080999999999996 - type: mrr_at_1000 value: 31.130000000000003 - type: mrr_at_3 value: 26.168000000000003 - type: mrr_at_5 value: 28.277 - type: ndcg_at_1 value: 18.308 - type: ndcg_at_10 value: 34.938 - type: ndcg_at_100 value: 41.125 - type: ndcg_at_1000 value: 42.708 - type: ndcg_at_3 value: 26.805 - type: ndcg_at_5 value: 30.686999999999998 - type: precision_at_1 value: 18.308 - type: precision_at_10 value: 6.476999999999999 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.784999999999998 - type: precision_at_5 value: 9.878 - type: recall_at_1 value: 16.099 - type: recall_at_10 value: 54.63 - type: recall_at_100 value: 82.24900000000001 - type: recall_at_1000 value: 94.242 - type: recall_at_3 value: 33.174 - type: recall_at_5 value: 42.164 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 67.947 - type: map_at_10 value: 81.499 - type: map_at_100 value: 82.17 - type: map_at_1000 value: 82.194 - type: map_at_3 value: 78.567 - type: map_at_5 value: 80.34400000000001 - type: mrr_at_1 value: 78.18 - type: mrr_at_10 value: 85.05 - type: mrr_at_100 value: 85.179 - type: mrr_at_1000 value: 85.181 - type: mrr_at_3 value: 83.91 - type: mrr_at_5 value: 84.638 - type: ndcg_at_1 value: 78.2 - type: ndcg_at_10 value: 85.715 - type: ndcg_at_100 value: 87.2 - type: ndcg_at_1000 value: 87.39 - type: ndcg_at_3 value: 82.572 - type: ndcg_at_5 value: 84.176 - type: precision_at_1 value: 78.2 - type: precision_at_10 value: 12.973 - type: precision_at_100 value: 1.5010000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.949999999999996 - type: precision_at_5 value: 23.62 - type: recall_at_1 value: 67.947 - type: recall_at_10 value: 93.804 - type: recall_at_100 value: 98.971 - type: recall_at_1000 value: 99.91600000000001 - type: recall_at_3 value: 84.75399999999999 - type: recall_at_5 value: 89.32 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.457201684255104 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 55.162226937477875 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.173 - type: map_at_10 value: 10.463000000000001 - type: map_at_100 value: 12.278 - type: map_at_1000 value: 12.572 - type: map_at_3 value: 7.528 - type: map_at_5 value: 8.863 - type: mrr_at_1 value: 20.599999999999998 - type: mrr_at_10 value: 30.422 - type: mrr_at_100 value: 31.6 - type: mrr_at_1000 value: 31.663000000000004 - type: mrr_at_3 value: 27.400000000000002 - type: mrr_at_5 value: 29.065 - type: ndcg_at_1 value: 20.599999999999998 - type: ndcg_at_10 value: 17.687 - type: ndcg_at_100 value: 25.172 - type: ndcg_at_1000 value: 30.617 - type: ndcg_at_3 value: 16.81 - type: ndcg_at_5 value: 14.499 - type: precision_at_1 value: 20.599999999999998 - type: precision_at_10 value: 9.17 - type: precision_at_100 value: 2.004 - type: precision_at_1000 value: 0.332 - type: precision_at_3 value: 15.6 - type: precision_at_5 value: 12.58 - type: recall_at_1 value: 4.173 - type: recall_at_10 value: 18.575 - type: recall_at_100 value: 40.692 - type: recall_at_1000 value: 67.467 - type: recall_at_3 value: 9.488000000000001 - type: recall_at_5 value: 12.738 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 81.12603499315416 - type: cos_sim_spearman value: 73.62060290948378 - type: euclidean_pearson value: 78.14083565781135 - type: euclidean_spearman value: 73.16840437541543 - type: manhattan_pearson value: 77.92017261109734 - type: manhattan_spearman value: 72.8805059949965 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 79.75955377133172 - type: cos_sim_spearman value: 71.8872633964069 - type: euclidean_pearson value: 76.31922068538256 - type: euclidean_spearman value: 70.86449661855376 - type: manhattan_pearson value: 76.47852229730407 - type: manhattan_spearman value: 70.99367421984789 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.80762722908158 - type: cos_sim_spearman value: 79.84588978756372 - type: euclidean_pearson value: 79.8216849781164 - type: euclidean_spearman value: 80.22647061695481 - type: manhattan_pearson value: 79.56604194112572 - type: manhattan_spearman value: 79.96495189862462 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.1012718092742 - type: cos_sim_spearman value: 76.86011381793661 - type: euclidean_pearson value: 79.94426039862019 - type: euclidean_spearman value: 77.36751135465131 - type: manhattan_pearson value: 79.87959373304288 - type: manhattan_spearman value: 77.37717129004746 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 83.90618420346104 - type: cos_sim_spearman value: 84.77290791243722 - type: euclidean_pearson value: 84.64732258073293 - type: euclidean_spearman value: 85.21053649543357 - type: manhattan_pearson value: 84.61616883522647 - type: manhattan_spearman value: 85.19803126766931 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 80.52192114059063 - type: cos_sim_spearman value: 81.9103244827937 - type: euclidean_pearson value: 80.99375176138985 - type: euclidean_spearman value: 81.540250641079 - type: manhattan_pearson value: 80.84979573396426 - type: manhattan_spearman value: 81.3742591621492 - 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: 85.82166001234197 - type: cos_sim_spearman value: 86.81857495659123 - type: euclidean_pearson value: 85.72798403202849 - type: euclidean_spearman value: 85.70482438950965 - type: manhattan_pearson value: 85.51579093130357 - type: manhattan_spearman value: 85.41233705379751 - 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: 64.48071151079803 - type: cos_sim_spearman value: 65.37838108084044 - type: euclidean_pearson value: 64.67378947096257 - type: euclidean_spearman value: 65.39187147219869 - type: manhattan_pearson value: 65.35487466133208 - type: manhattan_spearman value: 65.51328499442272 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.64702367823314 - type: cos_sim_spearman value: 82.49732953181818 - type: euclidean_pearson value: 83.05996062475664 - type: euclidean_spearman value: 82.28159546751176 - type: manhattan_pearson value: 82.98305503664952 - type: manhattan_spearman value: 82.18405771943928 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.5744649318696 - type: mrr value: 93.35386291268645 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 52.093999999999994 - type: map_at_10 value: 61.646 - type: map_at_100 value: 62.197 - type: map_at_1000 value: 62.22800000000001 - type: map_at_3 value: 58.411 - type: map_at_5 value: 60.585 - type: mrr_at_1 value: 55.00000000000001 - type: mrr_at_10 value: 62.690999999999995 - type: mrr_at_100 value: 63.139 - type: mrr_at_1000 value: 63.166999999999994 - type: mrr_at_3 value: 60.111000000000004 - type: mrr_at_5 value: 61.778 - type: ndcg_at_1 value: 55.00000000000001 - type: ndcg_at_10 value: 66.271 - type: ndcg_at_100 value: 68.879 - type: ndcg_at_1000 value: 69.722 - type: ndcg_at_3 value: 60.672000000000004 - type: ndcg_at_5 value: 63.929 - type: precision_at_1 value: 55.00000000000001 - type: precision_at_10 value: 9.0 - type: precision_at_100 value: 1.043 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.555999999999997 - type: precision_at_5 value: 16.2 - type: recall_at_1 value: 52.093999999999994 - type: recall_at_10 value: 79.567 - type: recall_at_100 value: 91.60000000000001 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 64.633 - type: recall_at_5 value: 72.68299999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.83267326732673 - type: cos_sim_ap value: 95.77995366495178 - type: cos_sim_f1 value: 91.51180311401306 - type: cos_sim_precision value: 91.92734611503532 - type: cos_sim_recall value: 91.10000000000001 - type: dot_accuracy value: 99.63366336633663 - type: dot_ap value: 88.53996286967461 - type: dot_f1 value: 81.06537530266343 - type: dot_precision value: 78.59154929577464 - type: dot_recall value: 83.7 - type: euclidean_accuracy value: 99.82376237623762 - type: euclidean_ap value: 95.53192209281187 - type: euclidean_f1 value: 91.19683481701286 - type: euclidean_precision value: 90.21526418786692 - type: euclidean_recall value: 92.2 - type: manhattan_accuracy value: 99.82376237623762 - type: manhattan_ap value: 95.55642082191741 - type: manhattan_f1 value: 91.16186693147964 - type: manhattan_precision value: 90.53254437869822 - type: manhattan_recall value: 91.8 - type: max_accuracy value: 99.83267326732673 - type: max_ap value: 95.77995366495178 - type: max_f1 value: 91.51180311401306 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 54.508462134213474 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.06549765184959 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.43129549466616 - type: mrr value: 50.20613169510227 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.069516173193044 - type: cos_sim_spearman value: 29.872498354017353 - type: dot_pearson value: 28.80761257516063 - type: dot_spearman value: 28.397422678527708 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.169 - type: map_at_10 value: 1.208 - type: map_at_100 value: 5.925 - type: map_at_1000 value: 14.427000000000001 - type: map_at_3 value: 0.457 - type: map_at_5 value: 0.716 - type: mrr_at_1 value: 64.0 - type: mrr_at_10 value: 74.075 - type: mrr_at_100 value: 74.303 - type: mrr_at_1000 value: 74.303 - type: mrr_at_3 value: 71.0 - type: mrr_at_5 value: 72.89999999999999 - type: ndcg_at_1 value: 57.99999999999999 - type: ndcg_at_10 value: 50.376 - type: ndcg_at_100 value: 38.582 - type: ndcg_at_1000 value: 35.663 - type: ndcg_at_3 value: 55.592 - type: ndcg_at_5 value: 53.647999999999996 - type: precision_at_1 value: 64.0 - type: precision_at_10 value: 53.2 - type: precision_at_100 value: 39.6 - type: precision_at_1000 value: 16.218 - type: precision_at_3 value: 59.333000000000006 - type: precision_at_5 value: 57.599999999999994 - type: recall_at_1 value: 0.169 - type: recall_at_10 value: 1.423 - type: recall_at_100 value: 9.049999999999999 - type: recall_at_1000 value: 34.056999999999995 - type: recall_at_3 value: 0.48700000000000004 - type: recall_at_5 value: 0.792 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.319 - type: map_at_10 value: 7.112 - type: map_at_100 value: 12.588 - type: map_at_1000 value: 14.056 - type: map_at_3 value: 2.8049999999999997 - type: map_at_5 value: 4.68 - type: mrr_at_1 value: 18.367 - type: mrr_at_10 value: 33.94 - type: mrr_at_100 value: 35.193000000000005 - type: mrr_at_1000 value: 35.193000000000005 - type: mrr_at_3 value: 29.932 - type: mrr_at_5 value: 32.279 - type: ndcg_at_1 value: 15.306000000000001 - type: ndcg_at_10 value: 18.096 - type: ndcg_at_100 value: 30.512 - type: ndcg_at_1000 value: 42.148 - type: ndcg_at_3 value: 17.034 - type: ndcg_at_5 value: 18.509 - type: precision_at_1 value: 18.367 - type: precision_at_10 value: 18.776 - type: precision_at_100 value: 7.02 - type: precision_at_1000 value: 1.467 - type: precision_at_3 value: 19.048000000000002 - type: precision_at_5 value: 22.041 - type: recall_at_1 value: 1.319 - type: recall_at_10 value: 13.748 - type: recall_at_100 value: 43.972 - type: recall_at_1000 value: 79.557 - type: recall_at_3 value: 4.042 - type: recall_at_5 value: 7.742 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.2282 - type: ap value: 13.995763859570426 - type: f1 value: 54.08126256731344 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.64006791171477 - type: f1 value: 57.95841320748957 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 40.19267841788564 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.96614412588663 - type: cos_sim_ap value: 67.75985678572738 - type: cos_sim_f1 value: 64.04661542276222 - type: cos_sim_precision value: 60.406922357343305 - type: cos_sim_recall value: 68.15303430079156 - type: dot_accuracy value: 79.5732252488526 - type: dot_ap value: 51.30562107572645 - type: dot_f1 value: 53.120759837177744 - type: dot_precision value: 46.478037198258804 - type: dot_recall value: 61.97889182058047 - type: euclidean_accuracy value: 84.00786791440663 - type: euclidean_ap value: 67.58930214486998 - type: euclidean_f1 value: 64.424821579775 - type: euclidean_precision value: 59.4817958454322 - type: euclidean_recall value: 70.26385224274406 - type: manhattan_accuracy value: 83.87673600762949 - type: manhattan_ap value: 67.4250981523309 - type: manhattan_f1 value: 64.10286658015808 - type: manhattan_precision value: 57.96885001066781 - type: manhattan_recall value: 71.68865435356201 - type: max_accuracy value: 84.00786791440663 - type: max_ap value: 67.75985678572738 - type: max_f1 value: 64.424821579775 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.41347459929368 - type: cos_sim_ap value: 84.89261930113058 - type: cos_sim_f1 value: 77.13677607258877 - type: cos_sim_precision value: 74.88581164358733 - type: cos_sim_recall value: 79.52725592854944 - type: dot_accuracy value: 86.32359219156285 - type: dot_ap value: 79.29794992131094 - type: dot_f1 value: 72.84356337679777 - type: dot_precision value: 67.31761478675462 - type: dot_recall value: 79.35786880197105 - type: euclidean_accuracy value: 88.33585593976791 - type: euclidean_ap value: 84.73257641312746 - type: euclidean_f1 value: 76.83529582788195 - type: euclidean_precision value: 72.76294052863436 - type: euclidean_recall value: 81.3905143209116 - type: manhattan_accuracy value: 88.3086894089339 - type: manhattan_ap value: 84.66304891729399 - type: manhattan_f1 value: 76.8181650632165 - type: manhattan_precision value: 73.6864436744219 - type: manhattan_recall value: 80.22790267939637 - type: max_accuracy value: 88.41347459929368 - type: max_ap value: 84.89261930113058 - type: max_f1 value: 77.13677607258877 --- # bge-micro-v2 > Forked from https://huggingface.co/TaylorAI/bge-micro-v2 purely to ensure it remains available. See also [license](LICENSE). This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Distilled in a 2-step training process (bge-micro was step 1) from `BAAI/bge-small-en-v1.5`. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors