--- tags: - mteb - transformers.js - transformers model-index: - name: mxbai-embed-2d-large-v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.76119402985074 - type: ap value: 37.90611182084586 - type: f1 value: 68.80795400445113 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.255525 - type: ap value: 90.06886124154308 - type: f1 value: 93.24785420201029 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.162000000000006 - type: f1 value: 45.66989189593428 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 37.980000000000004 - type: map_at_10 value: 54.918 - type: map_at_100 value: 55.401 - type: map_at_1000 value: 55.403000000000006 - type: map_at_3 value: 50.249 - type: map_at_5 value: 53.400000000000006 - type: mrr_at_1 value: 38.834 - type: mrr_at_10 value: 55.24 - type: mrr_at_100 value: 55.737 - type: mrr_at_1000 value: 55.738 - type: mrr_at_3 value: 50.580999999999996 - type: mrr_at_5 value: 53.71 - type: ndcg_at_1 value: 37.980000000000004 - type: ndcg_at_10 value: 63.629000000000005 - type: ndcg_at_100 value: 65.567 - type: ndcg_at_1000 value: 65.61399999999999 - type: ndcg_at_3 value: 54.275 - type: ndcg_at_5 value: 59.91 - type: precision_at_1 value: 37.980000000000004 - type: precision_at_10 value: 9.110999999999999 - type: precision_at_100 value: 0.993 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.977 - type: precision_at_5 value: 15.903 - type: recall_at_1 value: 37.980000000000004 - type: recall_at_10 value: 91.11 - type: recall_at_100 value: 99.289 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 65.932 - type: recall_at_5 value: 79.51599999999999 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.28746486562395 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.335244985544165 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.771155681602096 - type: mrr value: 76.55993052807459 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.76152904846916 - type: cos_sim_spearman value: 88.05622328825284 - type: euclidean_pearson value: 88.2821986323439 - type: euclidean_spearman value: 88.05622328825284 - type: manhattan_pearson value: 87.98419111117559 - type: manhattan_spearman value: 87.905617446958 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.65259740259741 - type: f1 value: 86.62044951853902 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.7270855384167 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.95365397158872 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.604 - type: map_at_10 value: 42.126999999999995 - type: map_at_100 value: 43.702999999999996 - type: map_at_1000 value: 43.851 - type: map_at_3 value: 38.663 - type: map_at_5 value: 40.67 - type: mrr_at_1 value: 37.625 - type: mrr_at_10 value: 48.203 - type: mrr_at_100 value: 48.925000000000004 - type: mrr_at_1000 value: 48.979 - type: mrr_at_3 value: 45.494 - type: mrr_at_5 value: 47.288999999999994 - type: ndcg_at_1 value: 37.625 - type: ndcg_at_10 value: 48.649 - type: ndcg_at_100 value: 54.041 - type: ndcg_at_1000 value: 56.233999999999995 - type: ndcg_at_3 value: 43.704 - type: ndcg_at_5 value: 46.172999999999995 - type: precision_at_1 value: 37.625 - type: precision_at_10 value: 9.371 - type: precision_at_100 value: 1.545 - type: precision_at_1000 value: 0.20400000000000001 - type: precision_at_3 value: 21.364 - type: precision_at_5 value: 15.421999999999999 - type: recall_at_1 value: 30.604 - type: recall_at_10 value: 60.94199999999999 - type: recall_at_100 value: 82.893 - type: recall_at_1000 value: 96.887 - type: recall_at_3 value: 46.346 - type: recall_at_5 value: 53.495000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.959000000000003 - type: map_at_10 value: 40.217999999999996 - type: map_at_100 value: 41.337 - type: map_at_1000 value: 41.471999999999994 - type: map_at_3 value: 37.029 - type: map_at_5 value: 38.873000000000005 - type: mrr_at_1 value: 37.325 - type: mrr_at_10 value: 45.637 - type: mrr_at_100 value: 46.243 - type: mrr_at_1000 value: 46.297 - type: mrr_at_3 value: 43.323 - type: mrr_at_5 value: 44.734 - type: ndcg_at_1 value: 37.325 - type: ndcg_at_10 value: 45.864 - type: ndcg_at_100 value: 49.832 - type: ndcg_at_1000 value: 52.056000000000004 - type: ndcg_at_3 value: 41.329 - type: ndcg_at_5 value: 43.547000000000004 - type: precision_at_1 value: 37.325 - type: precision_at_10 value: 8.732 - type: precision_at_100 value: 1.369 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 19.936 - type: precision_at_5 value: 14.306 - type: recall_at_1 value: 29.959000000000003 - type: recall_at_10 value: 56.113 - type: recall_at_100 value: 73.231 - type: recall_at_1000 value: 87.373 - type: recall_at_3 value: 42.88 - type: recall_at_5 value: 49.004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.679 - type: map_at_10 value: 50.696 - type: map_at_100 value: 51.788000000000004 - type: map_at_1000 value: 51.849999999999994 - type: map_at_3 value: 47.414 - type: map_at_5 value: 49.284 - type: mrr_at_1 value: 44.263000000000005 - type: mrr_at_10 value: 54.03 - type: mrr_at_100 value: 54.752 - type: mrr_at_1000 value: 54.784 - type: mrr_at_3 value: 51.661 - type: mrr_at_5 value: 53.047 - type: ndcg_at_1 value: 44.263000000000005 - type: ndcg_at_10 value: 56.452999999999996 - type: ndcg_at_100 value: 60.736999999999995 - type: ndcg_at_1000 value: 61.982000000000006 - type: ndcg_at_3 value: 51.085 - type: ndcg_at_5 value: 53.715999999999994 - type: precision_at_1 value: 44.263000000000005 - type: precision_at_10 value: 9.129 - type: precision_at_100 value: 1.218 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 22.8 - type: precision_at_5 value: 15.674 - type: recall_at_1 value: 38.679 - type: recall_at_10 value: 70.1 - type: recall_at_100 value: 88.649 - type: recall_at_1000 value: 97.48 - type: recall_at_3 value: 55.757999999999996 - type: recall_at_5 value: 62.244 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.796999999999997 - type: map_at_10 value: 34.011 - type: map_at_100 value: 35.103 - type: map_at_1000 value: 35.187000000000005 - type: map_at_3 value: 31.218 - type: map_at_5 value: 32.801 - type: mrr_at_1 value: 28.022999999999996 - type: mrr_at_10 value: 36.108000000000004 - type: mrr_at_100 value: 37.094 - type: mrr_at_1000 value: 37.158 - type: mrr_at_3 value: 33.635 - type: mrr_at_5 value: 35.081 - type: ndcg_at_1 value: 28.022999999999996 - type: ndcg_at_10 value: 38.887 - type: ndcg_at_100 value: 44.159 - type: ndcg_at_1000 value: 46.300000000000004 - type: ndcg_at_3 value: 33.623 - type: ndcg_at_5 value: 36.281 - type: precision_at_1 value: 28.022999999999996 - type: precision_at_10 value: 6.010999999999999 - type: precision_at_100 value: 0.901 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 14.124 - type: precision_at_5 value: 10.034 - type: recall_at_1 value: 25.796999999999997 - type: recall_at_10 value: 51.86300000000001 - type: recall_at_100 value: 75.995 - type: recall_at_1000 value: 91.93299999999999 - type: recall_at_3 value: 37.882 - type: recall_at_5 value: 44.34 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.468000000000002 - type: map_at_10 value: 24.026 - type: map_at_100 value: 25.237 - type: map_at_1000 value: 25.380000000000003 - type: map_at_3 value: 21.342 - type: map_at_5 value: 22.843 - type: mrr_at_1 value: 19.154 - type: mrr_at_10 value: 28.429 - type: mrr_at_100 value: 29.416999999999998 - type: mrr_at_1000 value: 29.491 - type: mrr_at_3 value: 25.746000000000002 - type: mrr_at_5 value: 27.282 - type: ndcg_at_1 value: 19.154 - type: ndcg_at_10 value: 29.512 - type: ndcg_at_100 value: 35.331 - type: ndcg_at_1000 value: 38.435 - type: ndcg_at_3 value: 24.566 - type: ndcg_at_5 value: 26.891 - type: precision_at_1 value: 19.154 - type: precision_at_10 value: 5.647 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.98 - type: recall_at_1 value: 15.468000000000002 - type: recall_at_10 value: 41.908 - type: recall_at_100 value: 67.17 - type: recall_at_1000 value: 89.05499999999999 - type: recall_at_3 value: 28.436 - type: recall_at_5 value: 34.278 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.116000000000003 - type: map_at_10 value: 39.034 - type: map_at_100 value: 40.461000000000006 - type: map_at_1000 value: 40.563 - type: map_at_3 value: 35.742000000000004 - type: map_at_5 value: 37.762 - type: mrr_at_1 value: 34.264 - type: mrr_at_10 value: 44.173 - type: mrr_at_100 value: 45.111000000000004 - type: mrr_at_1000 value: 45.149 - type: mrr_at_3 value: 41.626999999999995 - type: mrr_at_5 value: 43.234 - type: ndcg_at_1 value: 34.264 - type: ndcg_at_10 value: 45.011 - type: ndcg_at_100 value: 50.91 - type: ndcg_at_1000 value: 52.886 - type: ndcg_at_3 value: 39.757999999999996 - type: ndcg_at_5 value: 42.569 - type: precision_at_1 value: 34.264 - type: precision_at_10 value: 8.114 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.864 - type: precision_at_5 value: 13.628000000000002 - type: recall_at_1 value: 28.116000000000003 - type: recall_at_10 value: 57.764 - type: recall_at_100 value: 82.393 - type: recall_at_1000 value: 95.345 - type: recall_at_3 value: 43.35 - type: recall_at_5 value: 50.368 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.557 - type: map_at_10 value: 33.94 - type: map_at_100 value: 35.382000000000005 - type: map_at_1000 value: 35.497 - type: map_at_3 value: 30.635 - type: map_at_5 value: 32.372 - type: mrr_at_1 value: 29.224 - type: mrr_at_10 value: 39.017 - type: mrr_at_100 value: 39.908 - type: mrr_at_1000 value: 39.96 - type: mrr_at_3 value: 36.225 - type: mrr_at_5 value: 37.869 - type: ndcg_at_1 value: 29.224 - type: ndcg_at_10 value: 40.097 - type: ndcg_at_100 value: 46.058 - type: ndcg_at_1000 value: 48.309999999999995 - type: ndcg_at_3 value: 34.551 - type: ndcg_at_5 value: 36.937 - type: precision_at_1 value: 29.224 - type: precision_at_10 value: 7.6259999999999994 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 16.781 - type: precision_at_5 value: 12.26 - type: recall_at_1 value: 23.557 - type: recall_at_10 value: 53.46300000000001 - type: recall_at_100 value: 78.797 - type: recall_at_1000 value: 93.743 - type: recall_at_3 value: 37.95 - type: recall_at_5 value: 44.121 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.81583333333333 - type: map_at_10 value: 34.057833333333335 - type: map_at_100 value: 35.29658333333334 - type: map_at_1000 value: 35.418666666666674 - type: map_at_3 value: 31.16416666666667 - type: map_at_5 value: 32.797 - type: mrr_at_1 value: 29.40216666666667 - type: mrr_at_10 value: 38.11191666666667 - type: mrr_at_100 value: 38.983250000000005 - type: mrr_at_1000 value: 39.043 - type: mrr_at_3 value: 35.663333333333334 - type: mrr_at_5 value: 37.08975 - type: ndcg_at_1 value: 29.40216666666667 - type: ndcg_at_10 value: 39.462416666666655 - type: ndcg_at_100 value: 44.74341666666666 - type: ndcg_at_1000 value: 47.12283333333333 - type: ndcg_at_3 value: 34.57383333333334 - type: ndcg_at_5 value: 36.91816666666667 - type: precision_at_1 value: 29.40216666666667 - type: precision_at_10 value: 7.008416666666667 - type: precision_at_100 value: 1.143333333333333 - type: precision_at_1000 value: 0.15391666666666665 - type: precision_at_3 value: 16.011083333333335 - type: precision_at_5 value: 11.506666666666664 - type: recall_at_1 value: 24.81583333333333 - type: recall_at_10 value: 51.39391666666666 - type: recall_at_100 value: 74.52983333333333 - type: recall_at_1000 value: 91.00650000000002 - type: recall_at_3 value: 37.87458333333334 - type: recall_at_5 value: 43.865833333333335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.04 - type: map_at_10 value: 30.651 - type: map_at_100 value: 31.561 - type: map_at_1000 value: 31.667 - type: map_at_3 value: 28.358 - type: map_at_5 value: 29.644 - type: mrr_at_1 value: 26.840000000000003 - type: mrr_at_10 value: 33.397 - type: mrr_at_100 value: 34.166999999999994 - type: mrr_at_1000 value: 34.252 - type: mrr_at_3 value: 31.339 - type: mrr_at_5 value: 32.451 - type: ndcg_at_1 value: 26.840000000000003 - type: ndcg_at_10 value: 34.821999999999996 - type: ndcg_at_100 value: 39.155 - type: ndcg_at_1000 value: 41.837999999999994 - type: ndcg_at_3 value: 30.55 - type: ndcg_at_5 value: 32.588 - type: precision_at_1 value: 26.840000000000003 - type: precision_at_10 value: 5.383 - type: precision_at_100 value: 0.827 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 9.11 - type: recall_at_1 value: 24.04 - type: recall_at_10 value: 45.133 - type: recall_at_100 value: 64.519 - type: recall_at_1000 value: 84.397 - type: recall_at_3 value: 33.465 - type: recall_at_5 value: 38.504 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.744 - type: map_at_10 value: 22.557 - type: map_at_100 value: 23.705000000000002 - type: map_at_1000 value: 23.833 - type: map_at_3 value: 20.342 - type: map_at_5 value: 21.584 - type: mrr_at_1 value: 19.133 - type: mrr_at_10 value: 26.316 - type: mrr_at_100 value: 27.285999999999998 - type: mrr_at_1000 value: 27.367 - type: mrr_at_3 value: 24.214 - type: mrr_at_5 value: 25.419999999999998 - type: ndcg_at_1 value: 19.133 - type: ndcg_at_10 value: 27.002 - type: ndcg_at_100 value: 32.544000000000004 - type: ndcg_at_1000 value: 35.624 - type: ndcg_at_3 value: 23.015 - type: ndcg_at_5 value: 24.916 - type: precision_at_1 value: 19.133 - type: precision_at_10 value: 4.952 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.908 - type: precision_at_5 value: 8.004 - type: recall_at_1 value: 15.744 - type: recall_at_10 value: 36.63 - type: recall_at_100 value: 61.58 - type: recall_at_1000 value: 83.648 - type: recall_at_3 value: 25.545 - type: recall_at_5 value: 30.392000000000003 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.944 - type: map_at_10 value: 33.611000000000004 - type: map_at_100 value: 34.737 - type: map_at_1000 value: 34.847 - type: map_at_3 value: 30.746000000000002 - type: map_at_5 value: 32.357 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 37.632 - type: mrr_at_100 value: 38.53 - type: mrr_at_1000 value: 38.59 - type: mrr_at_3 value: 35.292 - type: mrr_at_5 value: 36.519 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 38.946999999999996 - type: ndcg_at_100 value: 44.348 - type: ndcg_at_1000 value: 46.787 - type: ndcg_at_3 value: 33.794999999999995 - type: ndcg_at_5 value: 36.166 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 6.595 - type: precision_at_100 value: 1.055 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 15.235999999999999 - type: precision_at_5 value: 10.896 - type: recall_at_1 value: 24.944 - type: recall_at_10 value: 51.284 - type: recall_at_100 value: 75.197 - type: recall_at_1000 value: 92.10000000000001 - type: recall_at_3 value: 37.213 - type: recall_at_5 value: 43.129 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.979000000000003 - type: map_at_10 value: 31.349 - type: map_at_100 value: 32.969 - type: map_at_1000 value: 33.2 - type: map_at_3 value: 28.237000000000002 - type: map_at_5 value: 30.09 - type: mrr_at_1 value: 27.075 - type: mrr_at_10 value: 35.946 - type: mrr_at_100 value: 36.897000000000006 - type: mrr_at_1000 value: 36.951 - type: mrr_at_3 value: 32.971000000000004 - type: mrr_at_5 value: 34.868 - type: ndcg_at_1 value: 27.075 - type: ndcg_at_10 value: 37.317 - type: ndcg_at_100 value: 43.448 - type: ndcg_at_1000 value: 45.940999999999995 - type: ndcg_at_3 value: 32.263 - type: ndcg_at_5 value: 34.981 - type: precision_at_1 value: 27.075 - type: precision_at_10 value: 7.568999999999999 - type: precision_at_100 value: 1.5650000000000002 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 15.547 - type: precision_at_5 value: 11.818 - type: recall_at_1 value: 21.979000000000003 - type: recall_at_10 value: 48.522999999999996 - type: recall_at_100 value: 76.51 - type: recall_at_1000 value: 92.168 - type: recall_at_3 value: 34.499 - type: recall_at_5 value: 41.443999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.903 - type: map_at_10 value: 26.473999999999997 - type: map_at_100 value: 27.576 - type: map_at_1000 value: 27.677000000000003 - type: map_at_3 value: 24.244 - type: map_at_5 value: 25.284000000000002 - type: mrr_at_1 value: 20.702 - type: mrr_at_10 value: 28.455000000000002 - type: mrr_at_100 value: 29.469 - type: mrr_at_1000 value: 29.537999999999997 - type: mrr_at_3 value: 26.433 - type: mrr_at_5 value: 27.283 - type: ndcg_at_1 value: 20.702 - type: ndcg_at_10 value: 30.988 - type: ndcg_at_100 value: 36.358000000000004 - type: ndcg_at_1000 value: 39.080999999999996 - type: ndcg_at_3 value: 26.647 - type: ndcg_at_5 value: 28.253 - type: precision_at_1 value: 20.702 - type: precision_at_10 value: 4.972 - type: precision_at_100 value: 0.823 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 11.522 - type: precision_at_5 value: 7.9479999999999995 - type: recall_at_1 value: 18.903 - type: recall_at_10 value: 43.004 - type: recall_at_100 value: 67.42399999999999 - type: recall_at_1000 value: 87.949 - type: recall_at_3 value: 31.171 - type: recall_at_5 value: 35.071000000000005 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.942 - type: map_at_10 value: 22.017999999999997 - type: map_at_100 value: 23.968 - type: map_at_1000 value: 24.169 - type: map_at_3 value: 18.282 - type: map_at_5 value: 20.191 - type: mrr_at_1 value: 29.121000000000002 - type: mrr_at_10 value: 40.897 - type: mrr_at_100 value: 41.787 - type: mrr_at_1000 value: 41.819 - type: mrr_at_3 value: 37.535000000000004 - type: mrr_at_5 value: 39.626 - type: ndcg_at_1 value: 29.121000000000002 - type: ndcg_at_10 value: 30.728 - type: ndcg_at_100 value: 38.231 - type: ndcg_at_1000 value: 41.735 - type: ndcg_at_3 value: 25.141000000000002 - type: ndcg_at_5 value: 27.093 - type: precision_at_1 value: 29.121000000000002 - type: precision_at_10 value: 9.674000000000001 - type: precision_at_100 value: 1.775 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 18.826999999999998 - type: precision_at_5 value: 14.515 - type: recall_at_1 value: 12.942 - type: recall_at_10 value: 36.692 - type: recall_at_100 value: 62.688 - type: recall_at_1000 value: 82.203 - type: recall_at_3 value: 22.820999999999998 - type: recall_at_5 value: 28.625 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.6 - type: map_at_10 value: 18.672 - type: map_at_100 value: 27.199 - type: map_at_1000 value: 29.032999999999998 - type: map_at_3 value: 13.045000000000002 - type: map_at_5 value: 15.271 - type: mrr_at_1 value: 69 - type: mrr_at_10 value: 75.304 - type: mrr_at_100 value: 75.68 - type: mrr_at_1000 value: 75.688 - type: mrr_at_3 value: 73.708 - type: mrr_at_5 value: 74.333 - type: ndcg_at_1 value: 56.25 - type: ndcg_at_10 value: 40.741 - type: ndcg_at_100 value: 45.933 - type: ndcg_at_1000 value: 53.764 - type: ndcg_at_3 value: 44.664 - type: ndcg_at_5 value: 42.104 - type: precision_at_1 value: 69 - type: precision_at_10 value: 33 - type: precision_at_100 value: 10.75 - type: precision_at_1000 value: 2.1999999999999997 - type: precision_at_3 value: 48.167 - type: precision_at_5 value: 41.099999999999994 - type: recall_at_1 value: 8.6 - type: recall_at_10 value: 24.447 - type: recall_at_100 value: 52.697 - type: recall_at_1000 value: 77.717 - type: recall_at_3 value: 14.13 - type: recall_at_5 value: 17.485999999999997 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.32 - type: f1 value: 43.92815810776849 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 68.987 - type: map_at_10 value: 78.025 - type: map_at_100 value: 78.28500000000001 - type: map_at_1000 value: 78.3 - type: map_at_3 value: 76.735 - type: map_at_5 value: 77.558 - type: mrr_at_1 value: 74.482 - type: mrr_at_10 value: 82.673 - type: mrr_at_100 value: 82.799 - type: mrr_at_1000 value: 82.804 - type: mrr_at_3 value: 81.661 - type: mrr_at_5 value: 82.369 - type: ndcg_at_1 value: 74.482 - type: ndcg_at_10 value: 82.238 - type: ndcg_at_100 value: 83.245 - type: ndcg_at_1000 value: 83.557 - type: ndcg_at_3 value: 80.066 - type: ndcg_at_5 value: 81.316 - type: precision_at_1 value: 74.482 - type: precision_at_10 value: 10.006 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 30.808000000000003 - type: precision_at_5 value: 19.256 - type: recall_at_1 value: 68.987 - type: recall_at_10 value: 90.646 - type: recall_at_100 value: 94.85900000000001 - type: recall_at_1000 value: 96.979 - type: recall_at_3 value: 84.76599999999999 - type: recall_at_5 value: 87.929 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.3 - type: map_at_10 value: 33.499 - type: map_at_100 value: 35.510000000000005 - type: map_at_1000 value: 35.693999999999996 - type: map_at_3 value: 29.083 - type: map_at_5 value: 31.367 - type: mrr_at_1 value: 39.660000000000004 - type: mrr_at_10 value: 49.517 - type: mrr_at_100 value: 50.18899999999999 - type: mrr_at_1000 value: 50.224000000000004 - type: mrr_at_3 value: 46.965 - type: mrr_at_5 value: 48.184 - type: ndcg_at_1 value: 39.660000000000004 - type: ndcg_at_10 value: 41.75 - type: ndcg_at_100 value: 48.477 - type: ndcg_at_1000 value: 51.373999999999995 - type: ndcg_at_3 value: 37.532 - type: ndcg_at_5 value: 38.564 - type: precision_at_1 value: 39.660000000000004 - type: precision_at_10 value: 11.774999999999999 - type: precision_at_100 value: 1.883 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 25.102999999999998 - type: precision_at_5 value: 18.395 - type: recall_at_1 value: 20.3 - type: recall_at_10 value: 49.633 - type: recall_at_100 value: 73.932 - type: recall_at_1000 value: 91.174 - type: recall_at_3 value: 34.516999999999996 - type: recall_at_5 value: 40.217000000000006 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 34.699999999999996 - type: map_at_10 value: 54.400000000000006 - type: map_at_100 value: 55.45 - type: map_at_1000 value: 55.525999999999996 - type: map_at_3 value: 50.99 - type: map_at_5 value: 53.054 - type: mrr_at_1 value: 69.399 - type: mrr_at_10 value: 76.454 - type: mrr_at_100 value: 76.771 - type: mrr_at_1000 value: 76.783 - type: mrr_at_3 value: 75.179 - type: mrr_at_5 value: 75.978 - type: ndcg_at_1 value: 69.399 - type: ndcg_at_10 value: 63.001 - type: ndcg_at_100 value: 66.842 - type: ndcg_at_1000 value: 68.33500000000001 - type: ndcg_at_3 value: 57.961 - type: ndcg_at_5 value: 60.67700000000001 - type: precision_at_1 value: 69.399 - type: precision_at_10 value: 13.4 - type: precision_at_100 value: 1.6420000000000001 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 37.218 - type: precision_at_5 value: 24.478 - type: recall_at_1 value: 34.699999999999996 - type: recall_at_10 value: 67.002 - type: recall_at_100 value: 82.113 - type: recall_at_1000 value: 91.945 - type: recall_at_3 value: 55.827000000000005 - type: recall_at_5 value: 61.195 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.40480000000001 - type: ap value: 86.34472513785936 - type: f1 value: 90.3766943422773 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 19.796 - type: map_at_10 value: 31.344 - type: map_at_100 value: 32.525999999999996 - type: map_at_1000 value: 32.582 - type: map_at_3 value: 27.514 - type: map_at_5 value: 29.683 - type: mrr_at_1 value: 20.358 - type: mrr_at_10 value: 31.924999999999997 - type: mrr_at_100 value: 33.056000000000004 - type: mrr_at_1000 value: 33.105000000000004 - type: mrr_at_3 value: 28.149 - type: mrr_at_5 value: 30.303 - type: ndcg_at_1 value: 20.372 - type: ndcg_at_10 value: 38.025999999999996 - type: ndcg_at_100 value: 43.813 - type: ndcg_at_1000 value: 45.21 - type: ndcg_at_3 value: 30.218 - type: ndcg_at_5 value: 34.088 - type: precision_at_1 value: 20.372 - type: precision_at_10 value: 6.123 - type: precision_at_100 value: 0.903 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 12.918 - type: precision_at_5 value: 9.702 - type: recall_at_1 value: 19.796 - type: recall_at_10 value: 58.644 - type: recall_at_100 value: 85.611 - type: recall_at_1000 value: 96.314 - type: recall_at_3 value: 37.419999999999995 - type: recall_at_5 value: 46.697 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.0984952120383 - type: f1 value: 92.9409029889071 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24441404468764 - type: f1 value: 54.66568676132254 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.86684599865501 - type: f1 value: 72.16086061041996 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.16745124411568 - type: f1 value: 78.76361933295068 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.66329421728342 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.21637418682758 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.85308363141191 - type: mrr value: 33.06713899953772 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.392 - type: map_at_10 value: 14.539 - type: map_at_100 value: 18.811 - type: map_at_1000 value: 20.471 - type: map_at_3 value: 10.26 - type: map_at_5 value: 12.224 - type: mrr_at_1 value: 46.749 - type: mrr_at_10 value: 55.72200000000001 - type: mrr_at_100 value: 56.325 - type: mrr_at_1000 value: 56.35 - type: mrr_at_3 value: 53.30200000000001 - type: mrr_at_5 value: 54.742000000000004 - type: ndcg_at_1 value: 44.891999999999996 - type: ndcg_at_10 value: 37.355 - type: ndcg_at_100 value: 35.285 - type: ndcg_at_1000 value: 44.246 - type: ndcg_at_3 value: 41.291 - type: ndcg_at_5 value: 39.952 - type: precision_at_1 value: 46.749 - type: precision_at_10 value: 28.111000000000004 - type: precision_at_100 value: 9.127 - type: precision_at_1000 value: 2.23 - type: precision_at_3 value: 38.803 - type: precision_at_5 value: 35.046 - type: recall_at_1 value: 6.392 - type: recall_at_10 value: 19.066 - type: recall_at_100 value: 37.105 - type: recall_at_1000 value: 69.37299999999999 - type: recall_at_3 value: 11.213 - type: recall_at_5 value: 14.648 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.387999999999998 - type: map_at_10 value: 47.172 - type: map_at_100 value: 48.158 - type: map_at_1000 value: 48.186 - type: map_at_3 value: 42.952 - type: map_at_5 value: 45.405 - type: mrr_at_1 value: 35.458 - type: mrr_at_10 value: 49.583 - type: mrr_at_100 value: 50.324999999999996 - type: mrr_at_1000 value: 50.344 - type: mrr_at_3 value: 46.195 - type: mrr_at_5 value: 48.258 - type: ndcg_at_1 value: 35.458 - type: ndcg_at_10 value: 54.839000000000006 - type: ndcg_at_100 value: 58.974000000000004 - type: ndcg_at_1000 value: 59.64699999999999 - type: ndcg_at_3 value: 47.012 - type: ndcg_at_5 value: 51.080999999999996 - type: precision_at_1 value: 35.458 - type: precision_at_10 value: 9.056000000000001 - type: precision_at_100 value: 1.137 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.582 - type: precision_at_5 value: 15.295 - type: recall_at_1 value: 31.387999999999998 - type: recall_at_10 value: 75.661 - type: recall_at_100 value: 93.605 - type: recall_at_1000 value: 98.658 - type: recall_at_3 value: 55.492 - type: recall_at_5 value: 64.85600000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.547 - type: map_at_10 value: 84.495 - type: map_at_100 value: 85.14 - type: map_at_1000 value: 85.15599999999999 - type: map_at_3 value: 81.606 - type: map_at_5 value: 83.449 - type: mrr_at_1 value: 81.22 - type: mrr_at_10 value: 87.31 - type: mrr_at_100 value: 87.436 - type: mrr_at_1000 value: 87.437 - type: mrr_at_3 value: 86.363 - type: mrr_at_5 value: 87.06 - type: ndcg_at_1 value: 81.24 - type: ndcg_at_10 value: 88.145 - type: ndcg_at_100 value: 89.423 - type: ndcg_at_1000 value: 89.52799999999999 - type: ndcg_at_3 value: 85.435 - type: ndcg_at_5 value: 87 - type: precision_at_1 value: 81.24 - type: precision_at_10 value: 13.381000000000002 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.44 - type: precision_at_5 value: 24.62 - type: recall_at_1 value: 70.547 - type: recall_at_10 value: 95.083 - type: recall_at_100 value: 99.50099999999999 - type: recall_at_1000 value: 99.982 - type: recall_at_3 value: 87.235 - type: recall_at_5 value: 91.701 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 57.93101384071724 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.46951126228829 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.018000000000001 - type: map_at_10 value: 13.818 - type: map_at_100 value: 16.346 - type: map_at_1000 value: 16.744999999999997 - type: map_at_3 value: 9.456000000000001 - type: map_at_5 value: 11.879000000000001 - type: mrr_at_1 value: 24.8 - type: mrr_at_10 value: 37.092000000000006 - type: mrr_at_100 value: 38.199 - type: mrr_at_1000 value: 38.243 - type: mrr_at_3 value: 33.517 - type: mrr_at_5 value: 35.692 - type: ndcg_at_1 value: 24.8 - type: ndcg_at_10 value: 22.782 - type: ndcg_at_100 value: 32.072 - type: ndcg_at_1000 value: 38.163000000000004 - type: ndcg_at_3 value: 21.046 - type: ndcg_at_5 value: 19.134 - type: precision_at_1 value: 24.8 - type: precision_at_10 value: 12 - type: precision_at_100 value: 2.5420000000000003 - type: precision_at_1000 value: 0.39899999999999997 - type: precision_at_3 value: 20 - type: precision_at_5 value: 17.4 - type: recall_at_1 value: 5.018000000000001 - type: recall_at_10 value: 24.34 - type: recall_at_100 value: 51.613 - type: recall_at_1000 value: 80.95 - type: recall_at_3 value: 12.153 - type: recall_at_5 value: 17.648 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 86.28259142800503 - type: cos_sim_spearman value: 82.04792579356291 - type: euclidean_pearson value: 83.7755858026306 - type: euclidean_spearman value: 82.04789872846196 - type: manhattan_pearson value: 83.79937122515567 - type: manhattan_spearman value: 82.05076966288574 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.37773414195387 - type: cos_sim_spearman value: 78.76929696642694 - type: euclidean_pearson value: 85.75861298616339 - type: euclidean_spearman value: 78.76607739031363 - type: manhattan_pearson value: 85.74412868736295 - type: manhattan_spearman value: 78.74388526796852 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 89.6176449076649 - type: cos_sim_spearman value: 90.39810997063387 - type: euclidean_pearson value: 89.753863994154 - type: euclidean_spearman value: 90.39810989027997 - type: manhattan_pearson value: 89.67750819879801 - type: manhattan_spearman value: 90.3286558059104 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 87.7488246203373 - type: cos_sim_spearman value: 85.44794976383963 - type: euclidean_pearson value: 87.33205836313964 - type: euclidean_spearman value: 85.44793954377185 - type: manhattan_pearson value: 87.30760291906203 - type: manhattan_spearman value: 85.4308413187653 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.6937750952719 - type: cos_sim_spearman value: 90.01162604967037 - type: euclidean_pearson value: 89.35321306629116 - type: euclidean_spearman value: 90.01161406477627 - type: manhattan_pearson value: 89.31351907042307 - type: manhattan_spearman value: 89.97264644642166 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.49107564294891 - type: cos_sim_spearman value: 87.42092493144571 - type: euclidean_pearson value: 86.88112016705634 - type: euclidean_spearman value: 87.42092430260175 - type: manhattan_pearson value: 86.85846210123235 - type: manhattan_spearman value: 87.40059575522972 - 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: 88.71766466521638 - type: cos_sim_spearman value: 88.80244555668372 - type: euclidean_pearson value: 89.59428700746064 - type: euclidean_spearman value: 88.80244555668372 - type: manhattan_pearson value: 89.62272396580352 - type: manhattan_spearman value: 88.77584531534937 - 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: 67.7743776239708 - type: cos_sim_spearman value: 68.79768249749681 - type: euclidean_pearson value: 70.16430919697441 - type: euclidean_spearman value: 68.79768249749681 - type: manhattan_pearson value: 70.17205038967042 - type: manhattan_spearman value: 68.89740094589914 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.9087137484716 - type: cos_sim_spearman value: 89.19783009521629 - type: euclidean_pearson value: 88.89888500166009 - type: euclidean_spearman value: 89.19783009521629 - type: manhattan_pearson value: 88.88400033783687 - type: manhattan_spearman value: 89.16299162200889 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.9799916253683 - type: mrr value: 96.0708200659181 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.928000000000004 - type: map_at_10 value: 69.56400000000001 - type: map_at_100 value: 70.125 - type: map_at_1000 value: 70.148 - type: map_at_3 value: 66.774 - type: map_at_5 value: 68.267 - type: mrr_at_1 value: 62.666999999999994 - type: mrr_at_10 value: 70.448 - type: mrr_at_100 value: 70.94 - type: mrr_at_1000 value: 70.962 - type: mrr_at_3 value: 68.389 - type: mrr_at_5 value: 69.65599999999999 - type: ndcg_at_1 value: 62.666999999999994 - type: ndcg_at_10 value: 74.117 - type: ndcg_at_100 value: 76.248 - type: ndcg_at_1000 value: 76.768 - type: ndcg_at_3 value: 69.358 - type: ndcg_at_5 value: 71.574 - type: precision_at_1 value: 62.666999999999994 - type: precision_at_10 value: 9.933 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.222 - type: precision_at_5 value: 17.867 - type: recall_at_1 value: 59.928000000000004 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.167 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 74.117 - type: recall_at_5 value: 79.80000000000001 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.83762376237624 - type: cos_sim_ap value: 96.05077689253707 - type: cos_sim_f1 value: 91.75879396984925 - type: cos_sim_precision value: 92.22222222222223 - type: cos_sim_recall value: 91.3 - type: dot_accuracy value: 99.83762376237624 - type: dot_ap value: 96.05082513542375 - type: dot_f1 value: 91.75879396984925 - type: dot_precision value: 92.22222222222223 - type: dot_recall value: 91.3 - type: euclidean_accuracy value: 99.83762376237624 - type: euclidean_ap value: 96.05077689253707 - type: euclidean_f1 value: 91.75879396984925 - type: euclidean_precision value: 92.22222222222223 - type: euclidean_recall value: 91.3 - type: manhattan_accuracy value: 99.83861386138614 - type: manhattan_ap value: 96.07646831090695 - type: manhattan_f1 value: 91.86220668996505 - type: manhattan_precision value: 91.72482552342971 - type: manhattan_recall value: 92 - type: max_accuracy value: 99.83861386138614 - type: max_ap value: 96.07646831090695 - type: max_f1 value: 91.86220668996505 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.40672513062134 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.31519237029376 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.15764586446943 - type: mrr value: 53.981596426449364 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.92935724124931 - type: cos_sim_spearman value: 31.54589922149803 - type: dot_pearson value: 30.929365687857675 - type: dot_spearman value: 31.54589922149803 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22100000000000003 - type: map_at_10 value: 1.791 - type: map_at_100 value: 9.404 - type: map_at_1000 value: 22.932 - type: map_at_3 value: 0.601 - type: map_at_5 value: 1.001 - type: mrr_at_1 value: 76 - type: mrr_at_10 value: 85.667 - type: mrr_at_100 value: 85.667 - type: mrr_at_1000 value: 85.667 - type: mrr_at_3 value: 84.667 - type: mrr_at_5 value: 85.667 - type: ndcg_at_1 value: 72 - type: ndcg_at_10 value: 68.637 - type: ndcg_at_100 value: 51.418 - type: ndcg_at_1000 value: 47.75 - type: ndcg_at_3 value: 70.765 - type: ndcg_at_5 value: 71.808 - type: precision_at_1 value: 76 - type: precision_at_10 value: 73.8 - type: precision_at_100 value: 52.68000000000001 - type: precision_at_1000 value: 20.9 - type: precision_at_3 value: 74.667 - type: precision_at_5 value: 78 - type: recall_at_1 value: 0.22100000000000003 - type: recall_at_10 value: 2.027 - type: recall_at_100 value: 12.831000000000001 - type: recall_at_1000 value: 44.996 - type: recall_at_3 value: 0.635 - type: recall_at_5 value: 1.097 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.289 - type: map_at_10 value: 10.475 - type: map_at_100 value: 16.993 - type: map_at_1000 value: 18.598 - type: map_at_3 value: 5.891 - type: map_at_5 value: 7.678999999999999 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 49.475 - type: mrr_at_100 value: 50.483 - type: mrr_at_1000 value: 50.499 - type: mrr_at_3 value: 45.918 - type: mrr_at_5 value: 48.469 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 25.891 - type: ndcg_at_100 value: 38.106 - type: ndcg_at_1000 value: 49.873 - type: ndcg_at_3 value: 29.915999999999997 - type: ndcg_at_5 value: 27.982000000000003 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.448999999999998 - type: precision_at_100 value: 7.837 - type: precision_at_1000 value: 1.5730000000000002 - type: precision_at_3 value: 31.293 - type: precision_at_5 value: 27.755000000000003 - type: recall_at_1 value: 2.289 - type: recall_at_10 value: 16.594 - type: recall_at_100 value: 48.619 - type: recall_at_1000 value: 85.467 - type: recall_at_3 value: 7.144 - type: recall_at_5 value: 10.465 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.5268 - type: ap value: 14.763212211567907 - type: f1 value: 55.200562727472736 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.25297113752123 - type: f1 value: 59.55315247947331 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.47685515092062 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.73183525064076 - type: cos_sim_ap value: 76.08498196190112 - type: cos_sim_f1 value: 69.4834471209584 - type: cos_sim_precision value: 67.88321167883211 - type: cos_sim_recall value: 71.16094986807387 - type: dot_accuracy value: 86.73183525064076 - type: dot_ap value: 76.08503499590553 - type: dot_f1 value: 69.4834471209584 - type: dot_precision value: 67.88321167883211 - type: dot_recall value: 71.16094986807387 - type: euclidean_accuracy value: 86.73183525064076 - type: euclidean_ap value: 76.08500172594562 - type: euclidean_f1 value: 69.4834471209584 - type: euclidean_precision value: 67.88321167883211 - type: euclidean_recall value: 71.16094986807387 - type: manhattan_accuracy value: 86.6960720033379 - type: manhattan_ap value: 76.00885156192993 - type: manhattan_f1 value: 69.24488725747247 - type: manhattan_precision value: 68.8118811881188 - type: manhattan_recall value: 69.68337730870712 - type: max_accuracy value: 86.73183525064076 - type: max_ap value: 76.08503499590553 - type: max_f1 value: 69.4834471209584 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.74529436876625 - type: cos_sim_ap value: 85.53503158777171 - type: cos_sim_f1 value: 77.68167368965773 - type: cos_sim_precision value: 74.70496232048912 - type: cos_sim_recall value: 80.9054511857099 - type: dot_accuracy value: 88.74529436876625 - type: dot_ap value: 85.5350158446314 - type: dot_f1 value: 77.68167368965773 - type: dot_precision value: 74.70496232048912 - type: dot_recall value: 80.9054511857099 - type: euclidean_accuracy value: 88.74529436876625 - type: euclidean_ap value: 85.53503846009764 - type: euclidean_f1 value: 77.68167368965773 - type: euclidean_precision value: 74.70496232048912 - type: euclidean_recall value: 80.9054511857099 - type: manhattan_accuracy value: 88.73753250281368 - type: manhattan_ap value: 85.53197689629393 - type: manhattan_f1 value: 77.58753437213566 - type: manhattan_precision value: 74.06033456988871 - type: manhattan_recall value: 81.46750846935633 - type: max_accuracy value: 88.74529436876625 - type: max_ap value: 85.53503846009764 - type: max_f1 value: 77.68167368965773 license: apache-2.0 language: - en library_name: sentence-transformers ---

The crispy sentence embedding family from mixedbread ai.

# 🪆mxbai-embed-2d-large-v1🪆 This is our [2DMSE](https://arxiv.org/abs/2402.14776) sentence embedding model. It supports the adaptive transformer layer and embedding size. Find out more in our [blog post](https://mixedbread.ai/blog/mxbai-embed-2d-large-v1). TLDR: TLDR: 2D-🪆 allows you to shrink the model and the embeddings layer. Shrinking only the embeddings model yields competetive results to other models like [nomics embeddings model](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). Shrinking the model to ~50% maintains upto 85% of the performance without further training. ## Quickstart Here, we provide several ways to produce sentence embeddings with adaptive layers and embedding sizes. **For this version, it is recommended to set adaptive layers from 20 to 24.** ### sentence-transformers Currently, the best way to use our models is with the most recent version of sentence-transformers. ```bash python -m pip install -U sentence-transformers ``` ```python from sentence_transformers import models, SentenceTransformer from sentence_transformers.util import cos_sim # 1. load model with `cls` pooling model = SentenceTransformer("mixedbread-ai/mxbai-embed-2d-large-v1") # 2. set adaptive layer and embedding size. # it is recommended to set layers from 20 to 24. new_num_layers = 22 # 1D: set layer size model[0].auto_model.encoder.layer = model[0].auto_model.encoder.layer[:new_num_layers] new_embedding_size = 768 # 2D: set embedding size # 3. encode embeddings = model.encode( [ 'Who is german and likes bread?', 'Everybody in Germany.' ] ) # Similarity of the first sentence with the other two similarities = cos_sim(embeddings[0, :new_embedding_size], embeddings[1, :new_embedding_size]) print('similarities:', similarities) ``` ### angle-emb You can also use the lastest `angle-emb` for inference, as follows: ```bash python -m pip install -U angle-emb ``` ```python from angle_emb import AnglE from sentence_transformers.util import cos_sim # 1. load model model = AnglE.from_pretrained("mixedbread-ai/mxbai-embed-2d-large-v1", pooling_strategy='cls').cuda() # 2. set adaptive layer and embedding size. # it is recommended to set layers from 20 to 24. layer_index = 22 # 1d: layer embedding_size = 768 # 2d: embedding size # 3. encode embeddings = model.encode([ 'Who is german and likes bread?', 'Everybody in Germany.' ], layer_index=layer_index, embedding_size=embedding_size) similarities = cos_sim(embeddings[0], embeddings[1:]) print('similarities:', similarities) ``` ### Transformers.js If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: ```bash npm i @xenova/transformers ``` You can then use the model to compute embeddings as follows: ```js import { pipeline, cos_sim } from '@xenova/transformers'; // Create a feature-extraction pipeline const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-2d-large-v1', { quantized: false, // (Optional) remove this line to use the 8-bit quantized model }); // Compute sentence embeddings (with `cls` pooling) const sentences = ['Who is german and likes bread?', 'Everybody in Germany.' ]; const output = await extractor(sentences, { pooling: 'cls' }); // Set embedding size and truncate embeddings const new_embedding_size = 768; const truncated = output.slice(null, [0, new_embedding_size]); // Compute cosine similarity console.log(cos_sim(truncated[0].data, truncated[1].data)); // 0.6979532021425204 ``` ### Using API You’ll be able to use the models through our API as well. The API is coming soon and will have some exciting features. Stay tuned! ## Evaluation Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-2d-large-v1). ## Community Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. ## License Apache 2.0