--- 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: 66.26865671641792 - type: ap value: 28.174006539079688 - type: f1 value: 59.724963358211035 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 75.3691 - type: ap value: 69.64182876373573 - type: f1 value: 75.2906345000088 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 35.806 - type: f1 value: 35.506516495961904 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 27.24 - type: map_at_10 value: 42.832 - type: map_at_100 value: 43.797000000000004 - type: map_at_1000 value: 43.804 - type: map_at_3 value: 38.134 - type: map_at_5 value: 40.744 - type: mrr_at_1 value: 27.951999999999998 - type: mrr_at_10 value: 43.111 - type: mrr_at_100 value: 44.083 - type: mrr_at_1000 value: 44.09 - type: mrr_at_3 value: 38.431 - type: mrr_at_5 value: 41.019 - type: ndcg_at_1 value: 27.24 - type: ndcg_at_10 value: 51.513 - type: ndcg_at_100 value: 55.762 - type: ndcg_at_1000 value: 55.938 - type: ndcg_at_3 value: 41.743 - type: ndcg_at_5 value: 46.454 - type: precision_at_1 value: 27.24 - type: precision_at_10 value: 7.93 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 17.402 - type: precision_at_5 value: 12.731 - type: recall_at_1 value: 27.24 - type: recall_at_10 value: 79.303 - type: recall_at_100 value: 98.151 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 52.205 - type: recall_at_5 value: 63.656 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.59766397469585 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 34.480143023109626 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.09326229984527 - type: mrr value: 72.18429846546191 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.47582391622187 - type: cos_sim_spearman value: 83.41635852964214 - type: euclidean_pearson value: 84.21969728559216 - type: euclidean_spearman value: 83.46575724558684 - type: manhattan_pearson value: 83.83107014910223 - type: manhattan_spearman value: 83.13321954800792 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 80.58116883116882 - type: f1 value: 80.53335622619781 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.13458676004344 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.720429607514898 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.051000000000002 - type: map_at_10 value: 36.291000000000004 - type: map_at_100 value: 37.632 - type: map_at_1000 value: 37.772 - type: map_at_3 value: 33.288000000000004 - type: map_at_5 value: 35.035 - type: mrr_at_1 value: 33.333 - type: mrr_at_10 value: 42.642 - type: mrr_at_100 value: 43.401 - type: mrr_at_1000 value: 43.463 - type: mrr_at_3 value: 40.272000000000006 - type: mrr_at_5 value: 41.753 - type: ndcg_at_1 value: 33.333 - type: ndcg_at_10 value: 42.291000000000004 - type: ndcg_at_100 value: 47.602 - type: ndcg_at_1000 value: 50.109 - type: ndcg_at_3 value: 38.033 - type: ndcg_at_5 value: 40.052 - type: precision_at_1 value: 33.333 - type: precision_at_10 value: 8.254999999999999 - type: precision_at_100 value: 1.353 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 18.884 - type: precision_at_5 value: 13.447999999999999 - type: recall_at_1 value: 26.051000000000002 - type: recall_at_10 value: 53.107000000000006 - type: recall_at_100 value: 76.22 - type: recall_at_1000 value: 92.92399999999999 - type: recall_at_3 value: 40.073 - type: recall_at_5 value: 46.327 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.698999999999998 - type: map_at_10 value: 26.186 - type: map_at_100 value: 27.133000000000003 - type: map_at_1000 value: 27.256999999999998 - type: map_at_3 value: 24.264 - type: map_at_5 value: 25.307000000000002 - type: mrr_at_1 value: 24.712999999999997 - type: mrr_at_10 value: 30.703999999999997 - type: mrr_at_100 value: 31.445 - type: mrr_at_1000 value: 31.517 - type: mrr_at_3 value: 28.992 - type: mrr_at_5 value: 29.963 - type: ndcg_at_1 value: 24.712999999999997 - type: ndcg_at_10 value: 30.198000000000004 - type: ndcg_at_100 value: 34.412 - type: ndcg_at_1000 value: 37.174 - type: ndcg_at_3 value: 27.148 - type: ndcg_at_5 value: 28.464 - type: precision_at_1 value: 24.712999999999997 - type: precision_at_10 value: 5.489999999999999 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 12.803 - type: precision_at_5 value: 8.981 - type: recall_at_1 value: 19.698999999999998 - type: recall_at_10 value: 37.595 - type: recall_at_100 value: 55.962 - type: recall_at_1000 value: 74.836 - type: recall_at_3 value: 28.538999999999998 - type: recall_at_5 value: 32.279 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 34.224 - type: map_at_10 value: 44.867000000000004 - type: map_at_100 value: 45.944 - type: map_at_1000 value: 46.013999999999996 - type: map_at_3 value: 42.009 - type: map_at_5 value: 43.684 - type: mrr_at_1 value: 39.436 - type: mrr_at_10 value: 48.301 - type: mrr_at_100 value: 49.055 - type: mrr_at_1000 value: 49.099 - type: mrr_at_3 value: 45.956 - type: mrr_at_5 value: 47.445 - type: ndcg_at_1 value: 39.436 - type: ndcg_at_10 value: 50.214000000000006 - type: ndcg_at_100 value: 54.63 - type: ndcg_at_1000 value: 56.165 - type: ndcg_at_3 value: 45.272 - type: ndcg_at_5 value: 47.826 - type: precision_at_1 value: 39.436 - type: precision_at_10 value: 8.037999999999998 - type: precision_at_100 value: 1.118 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 20.125 - type: precision_at_5 value: 13.918 - type: recall_at_1 value: 34.224 - type: recall_at_10 value: 62.690999999999995 - type: recall_at_100 value: 81.951 - type: recall_at_1000 value: 92.93299999999999 - type: recall_at_3 value: 49.299 - type: recall_at_5 value: 55.533 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.375 - type: map_at_10 value: 28.366000000000003 - type: map_at_100 value: 29.363 - type: map_at_1000 value: 29.458000000000002 - type: map_at_3 value: 26.247 - type: map_at_5 value: 27.439000000000004 - type: mrr_at_1 value: 22.938 - type: mrr_at_10 value: 30.072 - type: mrr_at_100 value: 30.993 - type: mrr_at_1000 value: 31.070999999999998 - type: mrr_at_3 value: 28.004 - type: mrr_at_5 value: 29.179 - type: ndcg_at_1 value: 22.938 - type: ndcg_at_10 value: 32.516 - type: ndcg_at_100 value: 37.641999999999996 - type: ndcg_at_1000 value: 40.150999999999996 - type: ndcg_at_3 value: 28.341 - type: ndcg_at_5 value: 30.394 - type: precision_at_1 value: 22.938 - type: precision_at_10 value: 5.028 - type: precision_at_100 value: 0.8 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.052999999999999 - type: precision_at_5 value: 8.497 - type: recall_at_1 value: 21.375 - type: recall_at_10 value: 43.682 - type: recall_at_100 value: 67.619 - type: recall_at_1000 value: 86.64699999999999 - type: recall_at_3 value: 32.478 - type: recall_at_5 value: 37.347 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.95 - type: map_at_10 value: 21.417 - type: map_at_100 value: 22.525000000000002 - type: map_at_1000 value: 22.665 - type: map_at_3 value: 18.684 - type: map_at_5 value: 20.275000000000002 - type: mrr_at_1 value: 18.159 - type: mrr_at_10 value: 25.373 - type: mrr_at_100 value: 26.348 - type: mrr_at_1000 value: 26.432 - type: mrr_at_3 value: 22.698999999999998 - type: mrr_at_5 value: 24.254 - type: ndcg_at_1 value: 18.159 - type: ndcg_at_10 value: 26.043 - type: ndcg_at_100 value: 31.491999999999997 - type: ndcg_at_1000 value: 34.818 - type: ndcg_at_3 value: 21.05 - type: ndcg_at_5 value: 23.580000000000002 - type: precision_at_1 value: 18.159 - type: precision_at_10 value: 4.938 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.611999999999999 - type: recall_at_1 value: 14.95 - type: recall_at_10 value: 36.285000000000004 - type: recall_at_100 value: 60.431999999999995 - type: recall_at_1000 value: 84.208 - type: recall_at_3 value: 23.006 - type: recall_at_5 value: 29.304999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.580000000000002 - type: map_at_10 value: 32.906 - type: map_at_100 value: 34.222 - type: map_at_1000 value: 34.346 - type: map_at_3 value: 29.891000000000002 - type: map_at_5 value: 31.679000000000002 - type: mrr_at_1 value: 28.778 - type: mrr_at_10 value: 37.783 - type: mrr_at_100 value: 38.746 - type: mrr_at_1000 value: 38.804 - type: mrr_at_3 value: 35.098 - type: mrr_at_5 value: 36.739 - type: ndcg_at_1 value: 28.778 - type: ndcg_at_10 value: 38.484 - type: ndcg_at_100 value: 44.322 - type: ndcg_at_1000 value: 46.772000000000006 - type: ndcg_at_3 value: 33.586 - type: ndcg_at_5 value: 36.098 - type: precision_at_1 value: 28.778 - type: precision_at_10 value: 7.151000000000001 - type: precision_at_100 value: 1.185 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.105 - type: precision_at_5 value: 11.704 - type: recall_at_1 value: 23.580000000000002 - type: recall_at_10 value: 50.151999999999994 - type: recall_at_100 value: 75.114 - type: recall_at_1000 value: 91.467 - type: recall_at_3 value: 36.552 - type: recall_at_5 value: 43.014 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.669999999999998 - type: map_at_10 value: 28.687 - type: map_at_100 value: 30.061 - type: map_at_1000 value: 30.197000000000003 - type: map_at_3 value: 26.134 - type: map_at_5 value: 27.508 - type: mrr_at_1 value: 26.256 - type: mrr_at_10 value: 34.105999999999995 - type: mrr_at_100 value: 35.137 - type: mrr_at_1000 value: 35.214 - type: mrr_at_3 value: 31.791999999999998 - type: mrr_at_5 value: 33.145 - type: ndcg_at_1 value: 26.256 - type: ndcg_at_10 value: 33.68 - type: ndcg_at_100 value: 39.7 - type: ndcg_at_1000 value: 42.625 - type: ndcg_at_3 value: 29.457 - type: ndcg_at_5 value: 31.355 - type: precision_at_1 value: 26.256 - type: precision_at_10 value: 6.2330000000000005 - type: precision_at_100 value: 1.08 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 14.193 - type: precision_at_5 value: 10.113999999999999 - type: recall_at_1 value: 20.669999999999998 - type: recall_at_10 value: 43.254999999999995 - type: recall_at_100 value: 69.118 - type: recall_at_1000 value: 89.408 - type: recall_at_3 value: 31.135 - type: recall_at_5 value: 36.574 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.488833333333336 - type: map_at_10 value: 29.025416666666665 - type: map_at_100 value: 30.141249999999992 - type: map_at_1000 value: 30.264083333333335 - type: map_at_3 value: 26.599333333333337 - type: map_at_5 value: 28.004666666666665 - type: mrr_at_1 value: 25.515 - type: mrr_at_10 value: 32.8235 - type: mrr_at_100 value: 33.69958333333333 - type: mrr_at_1000 value: 33.77191666666668 - type: mrr_at_3 value: 30.581000000000003 - type: mrr_at_5 value: 31.919666666666668 - type: ndcg_at_1 value: 25.515 - type: ndcg_at_10 value: 33.64241666666666 - type: ndcg_at_100 value: 38.75816666666667 - type: ndcg_at_1000 value: 41.472166666666666 - type: ndcg_at_3 value: 29.435083333333335 - type: ndcg_at_5 value: 31.519083333333338 - type: precision_at_1 value: 25.515 - type: precision_at_10 value: 5.89725 - type: precision_at_100 value: 0.9918333333333335 - type: precision_at_1000 value: 0.14075 - type: precision_at_3 value: 13.504000000000001 - type: precision_at_5 value: 9.6885 - type: recall_at_1 value: 21.488833333333336 - type: recall_at_10 value: 43.60808333333333 - type: recall_at_100 value: 66.5045 - type: recall_at_1000 value: 85.70024999999998 - type: recall_at_3 value: 31.922166666666662 - type: recall_at_5 value: 37.29758333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.781 - type: map_at_10 value: 27.173000000000002 - type: map_at_100 value: 27.967 - type: map_at_1000 value: 28.061999999999998 - type: map_at_3 value: 24.973 - type: map_at_5 value: 26.279999999999998 - type: mrr_at_1 value: 23.773 - type: mrr_at_10 value: 29.849999999999998 - type: mrr_at_100 value: 30.595 - type: mrr_at_1000 value: 30.669 - type: mrr_at_3 value: 27.761000000000003 - type: mrr_at_5 value: 29.003 - type: ndcg_at_1 value: 23.773 - type: ndcg_at_10 value: 31.033 - type: ndcg_at_100 value: 35.174 - type: ndcg_at_1000 value: 37.72 - type: ndcg_at_3 value: 26.927 - type: ndcg_at_5 value: 29.047 - type: precision_at_1 value: 23.773 - type: precision_at_10 value: 4.8469999999999995 - type: precision_at_100 value: 0.75 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 11.452 - type: precision_at_5 value: 8.129 - type: recall_at_1 value: 20.781 - type: recall_at_10 value: 40.463 - type: recall_at_100 value: 59.483 - type: recall_at_1000 value: 78.396 - type: recall_at_3 value: 29.241 - type: recall_at_5 value: 34.544000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.074000000000002 - type: map_at_10 value: 20.757 - type: map_at_100 value: 21.72 - type: map_at_1000 value: 21.844 - type: map_at_3 value: 18.929000000000002 - type: map_at_5 value: 19.894000000000002 - type: mrr_at_1 value: 18.307000000000002 - type: mrr_at_10 value: 24.215 - type: mrr_at_100 value: 25.083 - type: mrr_at_1000 value: 25.168000000000003 - type: mrr_at_3 value: 22.316 - type: mrr_at_5 value: 23.36 - type: ndcg_at_1 value: 18.307000000000002 - type: ndcg_at_10 value: 24.651999999999997 - type: ndcg_at_100 value: 29.296 - type: ndcg_at_1000 value: 32.538 - type: ndcg_at_3 value: 21.243000000000002 - type: ndcg_at_5 value: 22.727 - type: precision_at_1 value: 18.307000000000002 - type: precision_at_10 value: 4.446 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 9.945 - type: precision_at_5 value: 7.123 - type: recall_at_1 value: 15.074000000000002 - type: recall_at_10 value: 33.031 - type: recall_at_100 value: 53.954 - type: recall_at_1000 value: 77.631 - type: recall_at_3 value: 23.253 - type: recall_at_5 value: 27.218999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.04 - type: map_at_10 value: 28.226000000000003 - type: map_at_100 value: 29.337999999999997 - type: map_at_1000 value: 29.448999999999998 - type: map_at_3 value: 25.759 - type: map_at_5 value: 27.226 - type: mrr_at_1 value: 24.067 - type: mrr_at_10 value: 31.646 - type: mrr_at_100 value: 32.592999999999996 - type: mrr_at_1000 value: 32.668 - type: mrr_at_3 value: 29.26 - type: mrr_at_5 value: 30.725 - type: ndcg_at_1 value: 24.067 - type: ndcg_at_10 value: 32.789 - type: ndcg_at_100 value: 38.253 - type: ndcg_at_1000 value: 40.961 - type: ndcg_at_3 value: 28.189999999999998 - type: ndcg_at_5 value: 30.557000000000002 - type: precision_at_1 value: 24.067 - type: precision_at_10 value: 5.532 - type: precision_at_100 value: 0.928 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 12.5 - type: precision_at_5 value: 9.16 - type: recall_at_1 value: 21.04 - type: recall_at_10 value: 43.167 - type: recall_at_100 value: 67.569 - type: recall_at_1000 value: 86.817 - type: recall_at_3 value: 31.178 - type: recall_at_5 value: 36.730000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.439 - type: map_at_10 value: 28.531000000000002 - type: map_at_100 value: 29.953999999999997 - type: map_at_1000 value: 30.171 - type: map_at_3 value: 26.546999999999997 - type: map_at_5 value: 27.71 - type: mrr_at_1 value: 26.087 - type: mrr_at_10 value: 32.635 - type: mrr_at_100 value: 33.629999999999995 - type: mrr_at_1000 value: 33.71 - type: mrr_at_3 value: 30.731 - type: mrr_at_5 value: 31.807999999999996 - type: ndcg_at_1 value: 26.087 - type: ndcg_at_10 value: 32.975 - type: ndcg_at_100 value: 38.853 - type: ndcg_at_1000 value: 42.158 - type: ndcg_at_3 value: 29.894 - type: ndcg_at_5 value: 31.397000000000002 - type: precision_at_1 value: 26.087 - type: precision_at_10 value: 6.2059999999999995 - type: precision_at_100 value: 1.298 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 14.097000000000001 - type: precision_at_5 value: 9.959999999999999 - type: recall_at_1 value: 21.439 - type: recall_at_10 value: 40.519 - type: recall_at_100 value: 68.073 - type: recall_at_1000 value: 89.513 - type: recall_at_3 value: 31.513 - type: recall_at_5 value: 35.702 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.983 - type: map_at_10 value: 24.898 - type: map_at_100 value: 25.836 - type: map_at_1000 value: 25.934 - type: map_at_3 value: 22.467000000000002 - type: map_at_5 value: 24.019 - type: mrr_at_1 value: 20.333000000000002 - type: mrr_at_10 value: 26.555 - type: mrr_at_100 value: 27.369 - type: mrr_at_1000 value: 27.448 - type: mrr_at_3 value: 24.091 - type: mrr_at_5 value: 25.662000000000003 - type: ndcg_at_1 value: 20.333000000000002 - type: ndcg_at_10 value: 28.834 - type: ndcg_at_100 value: 33.722 - type: ndcg_at_1000 value: 36.475 - type: ndcg_at_3 value: 24.08 - type: ndcg_at_5 value: 26.732 - type: precision_at_1 value: 20.333000000000002 - type: precision_at_10 value: 4.603 - type: precision_at_100 value: 0.771 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 9.982000000000001 - type: precision_at_5 value: 7.6160000000000005 - type: recall_at_1 value: 18.983 - type: recall_at_10 value: 39.35 - type: recall_at_100 value: 62.559 - type: recall_at_1000 value: 83.623 - type: recall_at_3 value: 26.799 - type: recall_at_5 value: 32.997 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.621 - type: map_at_10 value: 17.298 - type: map_at_100 value: 18.983 - type: map_at_1000 value: 19.182 - type: map_at_3 value: 14.552999999999999 - type: map_at_5 value: 15.912 - type: mrr_at_1 value: 23.453 - type: mrr_at_10 value: 33.932 - type: mrr_at_100 value: 34.891 - type: mrr_at_1000 value: 34.943000000000005 - type: mrr_at_3 value: 30.770999999999997 - type: mrr_at_5 value: 32.556000000000004 - type: ndcg_at_1 value: 23.453 - type: ndcg_at_10 value: 24.771 - type: ndcg_at_100 value: 31.738 - type: ndcg_at_1000 value: 35.419 - type: ndcg_at_3 value: 20.22 - type: ndcg_at_5 value: 21.698999999999998 - type: precision_at_1 value: 23.453 - type: precision_at_10 value: 7.785 - type: precision_at_100 value: 1.5270000000000001 - type: precision_at_1000 value: 0.22 - type: precision_at_3 value: 14.962 - type: precision_at_5 value: 11.401 - type: recall_at_1 value: 10.621 - type: recall_at_10 value: 29.726000000000003 - type: recall_at_100 value: 53.996 - type: recall_at_1000 value: 74.878 - type: recall_at_3 value: 18.572 - type: recall_at_5 value: 22.994999999999997 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.819 - type: map_at_10 value: 14.188 - type: map_at_100 value: 19.627 - type: map_at_1000 value: 20.757 - type: map_at_3 value: 10.352 - type: map_at_5 value: 12.096 - type: mrr_at_1 value: 54.25 - type: mrr_at_10 value: 63.798 - type: mrr_at_100 value: 64.25 - type: mrr_at_1000 value: 64.268 - type: mrr_at_3 value: 61.667 - type: mrr_at_5 value: 63.153999999999996 - type: ndcg_at_1 value: 39.5 - type: ndcg_at_10 value: 31.064999999999998 - type: ndcg_at_100 value: 34.701 - type: ndcg_at_1000 value: 41.687000000000005 - type: ndcg_at_3 value: 34.455999999999996 - type: ndcg_at_5 value: 32.919 - type: precision_at_1 value: 54.25 - type: precision_at_10 value: 25.4 - type: precision_at_100 value: 7.79 - type: precision_at_1000 value: 1.577 - type: precision_at_3 value: 39.333 - type: precision_at_5 value: 33.6 - type: recall_at_1 value: 6.819 - type: recall_at_10 value: 19.134 - type: recall_at_100 value: 41.191 - type: recall_at_1000 value: 64.699 - type: recall_at_3 value: 11.637 - type: recall_at_5 value: 14.807 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 42.474999999999994 - type: f1 value: 37.79154895614037 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 53.187 - type: map_at_10 value: 64.031 - type: map_at_100 value: 64.507 - type: map_at_1000 value: 64.526 - type: map_at_3 value: 61.926 - type: map_at_5 value: 63.278999999999996 - type: mrr_at_1 value: 57.396 - type: mrr_at_10 value: 68.296 - type: mrr_at_100 value: 68.679 - type: mrr_at_1000 value: 68.688 - type: mrr_at_3 value: 66.289 - type: mrr_at_5 value: 67.593 - type: ndcg_at_1 value: 57.396 - type: ndcg_at_10 value: 69.64 - type: ndcg_at_100 value: 71.75399999999999 - type: ndcg_at_1000 value: 72.179 - type: ndcg_at_3 value: 65.66199999999999 - type: ndcg_at_5 value: 67.932 - type: precision_at_1 value: 57.396 - type: precision_at_10 value: 9.073 - type: precision_at_100 value: 1.024 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 26.133 - type: precision_at_5 value: 16.943 - type: recall_at_1 value: 53.187 - type: recall_at_10 value: 82.839 - type: recall_at_100 value: 92.231 - type: recall_at_1000 value: 95.249 - type: recall_at_3 value: 72.077 - type: recall_at_5 value: 77.667 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 10.957 - type: map_at_10 value: 18.427 - type: map_at_100 value: 19.885 - type: map_at_1000 value: 20.088 - type: map_at_3 value: 15.709000000000001 - type: map_at_5 value: 17.153 - type: mrr_at_1 value: 22.377 - type: mrr_at_10 value: 30.076999999999998 - type: mrr_at_100 value: 31.233 - type: mrr_at_1000 value: 31.311 - type: mrr_at_3 value: 27.521 - type: mrr_at_5 value: 29.025000000000002 - type: ndcg_at_1 value: 22.377 - type: ndcg_at_10 value: 24.367 - type: ndcg_at_100 value: 31.04 - type: ndcg_at_1000 value: 35.106 - type: ndcg_at_3 value: 21.051000000000002 - type: ndcg_at_5 value: 22.231 - type: precision_at_1 value: 22.377 - type: precision_at_10 value: 7.005999999999999 - type: precision_at_100 value: 1.3599999999999999 - type: precision_at_1000 value: 0.208 - type: precision_at_3 value: 13.991999999999999 - type: precision_at_5 value: 10.833 - type: recall_at_1 value: 10.957 - type: recall_at_10 value: 30.274 - type: recall_at_100 value: 55.982 - type: recall_at_1000 value: 80.757 - type: recall_at_3 value: 19.55 - type: recall_at_5 value: 24.105999999999998 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 29.526999999999997 - type: map_at_10 value: 40.714 - type: map_at_100 value: 41.655 - type: map_at_1000 value: 41.744 - type: map_at_3 value: 38.171 - type: map_at_5 value: 39.646 - type: mrr_at_1 value: 59.055 - type: mrr_at_10 value: 66.411 - type: mrr_at_100 value: 66.85900000000001 - type: mrr_at_1000 value: 66.88300000000001 - type: mrr_at_3 value: 64.846 - type: mrr_at_5 value: 65.824 - type: ndcg_at_1 value: 59.055 - type: ndcg_at_10 value: 49.732 - type: ndcg_at_100 value: 53.441 - type: ndcg_at_1000 value: 55.354000000000006 - type: ndcg_at_3 value: 45.551 - type: ndcg_at_5 value: 47.719 - type: precision_at_1 value: 59.055 - type: precision_at_10 value: 10.366 - type: precision_at_100 value: 1.328 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 28.322999999999997 - type: precision_at_5 value: 18.709 - type: recall_at_1 value: 29.526999999999997 - type: recall_at_10 value: 51.83 - type: recall_at_100 value: 66.42099999999999 - type: recall_at_1000 value: 79.176 - type: recall_at_3 value: 42.485 - type: recall_at_5 value: 46.772000000000006 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.69959999999999 - type: ap value: 64.95539314492567 - type: f1 value: 70.5554935943308 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 13.153 - type: map_at_10 value: 22.277 - type: map_at_100 value: 23.462 - type: map_at_1000 value: 23.546 - type: map_at_3 value: 19.026 - type: map_at_5 value: 20.825 - type: mrr_at_1 value: 13.539000000000001 - type: mrr_at_10 value: 22.753 - type: mrr_at_100 value: 23.906 - type: mrr_at_1000 value: 23.982999999999997 - type: mrr_at_3 value: 19.484 - type: mrr_at_5 value: 21.306 - type: ndcg_at_1 value: 13.553 - type: ndcg_at_10 value: 27.848 - type: ndcg_at_100 value: 33.900999999999996 - type: ndcg_at_1000 value: 36.155 - type: ndcg_at_3 value: 21.116 - type: ndcg_at_5 value: 24.349999999999998 - type: precision_at_1 value: 13.553 - type: precision_at_10 value: 4.695 - type: precision_at_100 value: 0.7779999999999999 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 9.207 - type: precision_at_5 value: 7.155 - type: recall_at_1 value: 13.153 - type: recall_at_10 value: 45.205 - type: recall_at_100 value: 73.978 - type: recall_at_1000 value: 91.541 - type: recall_at_3 value: 26.735 - type: recall_at_5 value: 34.493 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.2530779753762 - type: f1 value: 89.59402328284126 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.95029639762883 - type: f1 value: 48.99988836758662 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.77740416946874 - type: f1 value: 66.21341120969817 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.03631472763955 - type: f1 value: 72.5779336237941 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.98182669158824 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 29.259462874407582 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.29342377286548 - type: mrr value: 32.32805799117226 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 4.692 - type: map_at_10 value: 10.559000000000001 - type: map_at_100 value: 13.665 - type: map_at_1000 value: 15.082 - type: map_at_3 value: 7.68 - type: map_at_5 value: 8.844000000000001 - type: mrr_at_1 value: 38.7 - type: mrr_at_10 value: 47.864000000000004 - type: mrr_at_100 value: 48.583999999999996 - type: mrr_at_1000 value: 48.636 - type: mrr_at_3 value: 45.975 - type: mrr_at_5 value: 47.074 - type: ndcg_at_1 value: 36.378 - type: ndcg_at_10 value: 30.038999999999998 - type: ndcg_at_100 value: 28.226000000000003 - type: ndcg_at_1000 value: 36.958 - type: ndcg_at_3 value: 33.469 - type: ndcg_at_5 value: 32.096999999999994 - type: precision_at_1 value: 38.080000000000005 - type: precision_at_10 value: 22.941 - type: precision_at_100 value: 7.632 - type: precision_at_1000 value: 2.0420000000000003 - type: precision_at_3 value: 31.579 - type: precision_at_5 value: 28.235 - type: recall_at_1 value: 4.692 - type: recall_at_10 value: 14.496 - type: recall_at_100 value: 29.69 - type: recall_at_1000 value: 61.229 - type: recall_at_3 value: 8.871 - type: recall_at_5 value: 10.825999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 13.120000000000001 - type: map_at_10 value: 24.092 - type: map_at_100 value: 25.485999999999997 - type: map_at_1000 value: 25.557999999999996 - type: map_at_3 value: 20.076 - type: map_at_5 value: 22.368 - type: mrr_at_1 value: 15.093 - type: mrr_at_10 value: 26.142 - type: mrr_at_100 value: 27.301 - type: mrr_at_1000 value: 27.357 - type: mrr_at_3 value: 22.364 - type: mrr_at_5 value: 24.564 - type: ndcg_at_1 value: 15.093 - type: ndcg_at_10 value: 30.734 - type: ndcg_at_100 value: 37.147999999999996 - type: ndcg_at_1000 value: 38.997 - type: ndcg_at_3 value: 22.82 - type: ndcg_at_5 value: 26.806 - type: precision_at_1 value: 15.093 - type: precision_at_10 value: 5.863 - type: precision_at_100 value: 0.942 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 11.047 - type: precision_at_5 value: 8.863999999999999 - type: recall_at_1 value: 13.120000000000001 - type: recall_at_10 value: 49.189 - type: recall_at_100 value: 78.032 - type: recall_at_1000 value: 92.034 - type: recall_at_3 value: 28.483000000000004 - type: recall_at_5 value: 37.756 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 67.765 - type: map_at_10 value: 81.069 - type: map_at_100 value: 81.757 - type: map_at_1000 value: 81.782 - type: map_at_3 value: 78.148 - type: map_at_5 value: 79.95400000000001 - type: mrr_at_1 value: 77.8 - type: mrr_at_10 value: 84.639 - type: mrr_at_100 value: 84.789 - type: mrr_at_1000 value: 84.79100000000001 - type: mrr_at_3 value: 83.467 - type: mrr_at_5 value: 84.251 - type: ndcg_at_1 value: 77.82 - type: ndcg_at_10 value: 85.286 - type: ndcg_at_100 value: 86.86500000000001 - type: ndcg_at_1000 value: 87.062 - type: ndcg_at_3 value: 82.116 - type: ndcg_at_5 value: 83.811 - type: precision_at_1 value: 77.82 - type: precision_at_10 value: 12.867999999999999 - type: precision_at_100 value: 1.498 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.723 - type: precision_at_5 value: 23.52 - type: recall_at_1 value: 67.765 - type: recall_at_10 value: 93.381 - type: recall_at_100 value: 98.901 - type: recall_at_1000 value: 99.864 - type: recall_at_3 value: 84.301 - type: recall_at_5 value: 89.049 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.27190981742137 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 54.47444004585028 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.213 - type: map_at_10 value: 10.166 - type: map_at_100 value: 11.987 - type: map_at_1000 value: 12.285 - type: map_at_3 value: 7.538 - type: map_at_5 value: 8.606 - type: mrr_at_1 value: 20.8 - type: mrr_at_10 value: 30.066 - type: mrr_at_100 value: 31.290000000000003 - type: mrr_at_1000 value: 31.357000000000003 - type: mrr_at_3 value: 27.083000000000002 - type: mrr_at_5 value: 28.748 - type: ndcg_at_1 value: 20.8 - type: ndcg_at_10 value: 17.258000000000003 - type: ndcg_at_100 value: 24.801000000000002 - type: ndcg_at_1000 value: 30.348999999999997 - type: ndcg_at_3 value: 16.719 - type: ndcg_at_5 value: 14.145 - type: precision_at_1 value: 20.8 - type: precision_at_10 value: 8.88 - type: precision_at_100 value: 1.9789999999999999 - type: precision_at_1000 value: 0.332 - type: precision_at_3 value: 15.5 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 4.213 - type: recall_at_10 value: 17.983 - type: recall_at_100 value: 40.167 - type: recall_at_1000 value: 67.43 - type: recall_at_3 value: 9.433 - type: recall_at_5 value: 12.267999999999999 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 80.36742239848913 - type: cos_sim_spearman value: 72.39470010828755 - type: euclidean_pearson value: 77.26919895870947 - type: euclidean_spearman value: 72.26534999077315 - type: manhattan_pearson value: 77.04066349814258 - type: manhattan_spearman value: 72.0072248699278 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 80.26991474037257 - type: cos_sim_spearman value: 71.90287122017716 - type: euclidean_pearson value: 76.68006075912453 - type: euclidean_spearman value: 71.69301858764365 - type: manhattan_pearson value: 76.72277285842371 - type: manhattan_spearman value: 71.73265239703795 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.74371413317881 - type: cos_sim_spearman value: 80.9279612820358 - type: euclidean_pearson value: 80.6417435294782 - type: euclidean_spearman value: 81.17460969254459 - type: manhattan_pearson value: 80.51820155178402 - type: manhattan_spearman value: 81.08028700017084 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.37085777051112 - type: cos_sim_spearman value: 76.60308382518285 - type: euclidean_pearson value: 79.59684787227351 - type: euclidean_spearman value: 76.8769048249242 - type: manhattan_pearson value: 79.55617632538295 - type: manhattan_spearman value: 76.90186497973124 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 83.99513105301321 - type: cos_sim_spearman value: 84.92034548133665 - type: euclidean_pearson value: 84.70872540095195 - type: euclidean_spearman value: 85.14591726040749 - type: manhattan_pearson value: 84.65707417430595 - type: manhattan_spearman value: 85.10407163865375 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 79.40758449150897 - type: cos_sim_spearman value: 80.71692246880549 - type: euclidean_pearson value: 80.51658552062683 - type: euclidean_spearman value: 80.87118389043233 - type: manhattan_pearson value: 80.41534690825016 - type: manhattan_spearman value: 80.73925282537256 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.93617076910748 - type: cos_sim_spearman value: 85.61118538966805 - type: euclidean_pearson value: 85.56187558635287 - type: euclidean_spearman value: 85.21910090757267 - type: manhattan_pearson value: 85.29916699037645 - type: manhattan_spearman value: 84.96820527868671 - 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: 64.22294088543077 - type: cos_sim_spearman value: 65.89748502901078 - type: euclidean_pearson value: 66.15637850660805 - type: euclidean_spearman value: 65.86095841381278 - type: manhattan_pearson value: 66.80966197857856 - type: manhattan_spearman value: 66.48325202219692 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 81.75298158703048 - type: cos_sim_spearman value: 81.32168373072322 - type: euclidean_pearson value: 82.3251793712207 - type: euclidean_spearman value: 81.31655163330606 - type: manhattan_pearson value: 82.14136865023298 - type: manhattan_spearman value: 81.13410964028606 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.77937068780793 - type: mrr value: 93.334709952357 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - 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type: recall_at_1000 value: 99.0 - type: recall_at_3 value: 65.328 - type: recall_at_5 value: 72.583 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82178217821782 - type: cos_sim_ap value: 95.30078788098801 - type: cos_sim_f1 value: 91.11549851924975 - type: cos_sim_precision value: 89.96101364522417 - type: cos_sim_recall value: 92.30000000000001 - type: dot_accuracy value: 99.74851485148515 - type: dot_ap value: 93.12383012680787 - type: dot_f1 value: 87.17171717171716 - type: dot_precision value: 88.06122448979592 - type: dot_recall value: 86.3 - type: euclidean_accuracy value: 99.82673267326733 - type: euclidean_ap value: 95.29507269622621 - type: euclidean_f1 value: 91.3151364764268 - type: euclidean_precision value: 90.64039408866995 - type: euclidean_recall value: 92.0 - type: manhattan_accuracy value: 99.82178217821782 - type: manhattan_ap value: 95.34300712110257 - type: manhattan_f1 value: 91.05367793240556 - type: manhattan_precision value: 90.51383399209486 - type: manhattan_recall value: 91.60000000000001 - type: max_accuracy value: 99.82673267326733 - type: max_ap value: 95.34300712110257 - type: max_f1 value: 91.3151364764268 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 53.10993894014712 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.67216071080345 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 48.96344255085851 - type: mrr value: 49.816123419064596 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.580410074992177 - type: cos_sim_spearman value: 31.155995112739966 - type: dot_pearson value: 31.112094423048998 - type: dot_spearman value: 31.29974829801922 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.17700000000000002 - type: map_at_10 value: 1.22 - type: map_at_100 value: 6.2170000000000005 - type: map_at_1000 value: 15.406 - type: map_at_3 value: 0.483 - type: map_at_5 value: 0.729 - type: mrr_at_1 value: 64.0 - type: mrr_at_10 value: 76.333 - type: mrr_at_100 value: 76.47 - type: mrr_at_1000 value: 76.47 - type: mrr_at_3 value: 75.0 - type: mrr_at_5 value: 76.0 - type: ndcg_at_1 value: 59.0 - type: ndcg_at_10 value: 52.62 - type: ndcg_at_100 value: 39.932 - type: ndcg_at_1000 value: 37.317 - type: ndcg_at_3 value: 57.123000000000005 - type: ndcg_at_5 value: 56.376000000000005 - type: precision_at_1 value: 64.0 - type: precision_at_10 value: 55.800000000000004 - type: precision_at_100 value: 41.04 - type: precision_at_1000 value: 17.124 - type: precision_at_3 value: 63.333 - type: precision_at_5 value: 62.0 - type: recall_at_1 value: 0.17700000000000002 - type: recall_at_10 value: 1.46 - type: recall_at_100 value: 9.472999999999999 - type: recall_at_1000 value: 35.661 - type: recall_at_3 value: 0.527 - type: recall_at_5 value: 0.8250000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.539 - type: map_at_10 value: 7.178 - type: map_at_100 value: 12.543000000000001 - type: map_at_1000 value: 14.126 - type: map_at_3 value: 3.09 - type: map_at_5 value: 5.008 - type: mrr_at_1 value: 18.367 - type: mrr_at_10 value: 32.933 - type: mrr_at_100 value: 34.176 - type: mrr_at_1000 value: 34.176 - type: mrr_at_3 value: 27.551 - type: mrr_at_5 value: 30.714000000000002 - type: ndcg_at_1 value: 15.306000000000001 - type: ndcg_at_10 value: 18.343 - type: ndcg_at_100 value: 30.076000000000004 - type: ndcg_at_1000 value: 42.266999999999996 - type: ndcg_at_3 value: 17.233999999999998 - type: ndcg_at_5 value: 18.677 - type: precision_at_1 value: 18.367 - type: precision_at_10 value: 18.367 - type: precision_at_100 value: 6.837 - type: precision_at_1000 value: 1.467 - type: precision_at_3 value: 19.048000000000002 - type: precision_at_5 value: 21.224 - type: recall_at_1 value: 1.539 - type: recall_at_10 value: 13.289000000000001 - type: recall_at_100 value: 42.480000000000004 - type: recall_at_1000 value: 79.463 - type: recall_at_3 value: 4.202999999999999 - type: recall_at_5 value: 7.9030000000000005 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.2056 - type: ap value: 13.564165903349778 - type: f1 value: 53.303385089202656 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.71477079796264 - type: f1 value: 57.01563439439609 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 39.373040570976514 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.44757703999524 - type: cos_sim_ap value: 65.78689843625949 - type: cos_sim_f1 value: 62.25549384206713 - type: cos_sim_precision value: 57.39091718610864 - type: cos_sim_recall value: 68.02110817941951 - type: dot_accuracy value: 81.3971508612982 - type: dot_ap value: 58.42933051967154 - type: dot_f1 value: 57.85580214198962 - type: dot_precision value: 49.74368710841086 - type: dot_recall value: 69.12928759894459 - type: euclidean_accuracy value: 83.54294569946951 - type: euclidean_ap value: 66.10612585693795 - type: euclidean_f1 value: 62.66666666666667 - type: euclidean_precision value: 58.88631090487239 - type: euclidean_recall value: 66.96569920844327 - type: manhattan_accuracy value: 83.43565595756095 - type: manhattan_ap value: 65.88532290329134 - type: manhattan_f1 value: 62.58408721874276 - type: manhattan_precision value: 55.836092715231786 - type: manhattan_recall value: 71.18733509234828 - type: max_accuracy value: 83.54294569946951 - type: max_ap value: 66.10612585693795 - type: max_f1 value: 62.66666666666667 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.02344083517679 - type: cos_sim_ap value: 84.21589190889944 - type: cos_sim_f1 value: 76.36723039754007 - type: cos_sim_precision value: 72.79134682484299 - type: cos_sim_recall value: 80.31259624268556 - type: dot_accuracy value: 87.43353902278108 - type: dot_ap value: 82.08962394120071 - type: dot_f1 value: 74.97709923664122 - type: dot_precision value: 74.34150772025431 - type: dot_recall value: 75.62365260240222 - type: euclidean_accuracy value: 87.97686963946133 - type: euclidean_ap value: 84.20578083922416 - type: euclidean_f1 value: 76.4299182903834 - type: euclidean_precision value: 73.51874244256348 - type: euclidean_recall value: 79.58115183246073 - type: manhattan_accuracy value: 88.00209570380719 - type: manhattan_ap value: 84.14700304263556 - type: manhattan_f1 value: 76.36429345861944 - type: manhattan_precision value: 71.95886119057349 - type: manhattan_recall value: 81.34431783184478 - type: max_accuracy value: 88.02344083517679 - type: max_ap value: 84.21589190889944 - type: max_f1 value: 76.4299182903834 --- # bge-micro 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. It is distilled from [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5/blob/main/config.json), with 1/4 the non-embedding parameters. It has 1/2 the parameters of the smallest commonly-used embedding model, all-MiniLM-L6-v2, with similar performance. ## 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