--- library_name: transformers tags: - sentence-transformers - gte - mteb - transformers.js license: apache-2.0 language: - en model-index: - name: gte-base-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.7910447761194 - type: ap value: 37.053785713650626 - type: f1 value: 68.51101510998551 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.016875 - type: ap value: 89.17750268426342 - type: f1 value: 92.9970977240524 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 53.312000000000005 - type: f1 value: 52.98175784163017 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 38.193 - type: map_at_10 value: 54.848 - type: map_at_100 value: 55.388000000000005 - type: map_at_1000 value: 55.388999999999996 - type: map_at_3 value: 50.427 - type: map_at_5 value: 53.105000000000004 - type: mrr_at_1 value: 39.047 - type: mrr_at_10 value: 55.153 - type: mrr_at_100 value: 55.686 - type: mrr_at_1000 value: 55.688 - type: mrr_at_3 value: 50.676 - type: mrr_at_5 value: 53.417 - type: ndcg_at_1 value: 38.193 - type: ndcg_at_10 value: 63.486 - type: ndcg_at_100 value: 65.58 - type: ndcg_at_1000 value: 65.61 - type: ndcg_at_3 value: 54.494 - type: ndcg_at_5 value: 59.339 - type: precision_at_1 value: 38.193 - type: precision_at_10 value: 9.075 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.096 - type: precision_at_5 value: 15.619 - type: recall_at_1 value: 38.193 - type: recall_at_10 value: 90.754 - type: recall_at_100 value: 99.431 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.28699999999999 - type: recall_at_5 value: 78.094 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.508221208908964 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.04668382560096 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.828759903716815 - type: mrr value: 74.37343358395991 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.03673698773017 - type: cos_sim_spearman value: 83.6470866785058 - type: euclidean_pearson value: 82.64048673096565 - type: euclidean_spearman value: 83.63142367101115 - type: manhattan_pearson value: 82.71493099760228 - type: manhattan_spearman value: 83.60491704294326 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.73376623376623 - type: f1 value: 86.70294049278262 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.31923804167062 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.552547125348454 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 30.567 - type: map_at_10 value: 41.269 - type: map_at_100 value: 42.689 - type: map_at_1000 value: 42.84 - type: map_at_3 value: 37.567 - type: map_at_5 value: 39.706 - type: mrr_at_1 value: 37.053000000000004 - type: mrr_at_10 value: 46.900999999999996 - type: mrr_at_100 value: 47.662 - type: mrr_at_1000 value: 47.713 - type: mrr_at_3 value: 43.801 - type: mrr_at_5 value: 45.689 - type: ndcg_at_1 value: 37.053000000000004 - type: ndcg_at_10 value: 47.73 - type: ndcg_at_100 value: 53.128 - type: ndcg_at_1000 value: 55.300000000000004 - type: ndcg_at_3 value: 42.046 - type: ndcg_at_5 value: 44.782 - type: precision_at_1 value: 37.053000000000004 - type: precision_at_10 value: 9.142 - type: precision_at_100 value: 1.485 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.535 - type: recall_at_1 value: 30.567 - type: recall_at_10 value: 60.602999999999994 - type: recall_at_100 value: 83.22800000000001 - type: recall_at_1000 value: 96.696 - type: recall_at_3 value: 44.336999999999996 - type: recall_at_5 value: 51.949 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 28.538000000000004 - type: map_at_10 value: 38.757999999999996 - type: map_at_100 value: 40.129 - type: map_at_1000 value: 40.262 - type: map_at_3 value: 35.866 - type: map_at_5 value: 37.417 - type: mrr_at_1 value: 36.051 - type: mrr_at_10 value: 44.868 - type: mrr_at_100 value: 45.568999999999996 - type: mrr_at_1000 value: 45.615 - type: mrr_at_3 value: 42.558 - type: mrr_at_5 value: 43.883 - type: ndcg_at_1 value: 36.051 - type: ndcg_at_10 value: 44.584 - type: ndcg_at_100 value: 49.356 - type: ndcg_at_1000 value: 51.39 - type: ndcg_at_3 value: 40.389 - type: ndcg_at_5 value: 42.14 - type: precision_at_1 value: 36.051 - type: precision_at_10 value: 8.446 - type: precision_at_100 value: 1.411 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 19.639 - type: precision_at_5 value: 13.796 - type: recall_at_1 value: 28.538000000000004 - type: recall_at_10 value: 54.99000000000001 - type: recall_at_100 value: 75.098 - type: recall_at_1000 value: 87.848 - type: recall_at_3 value: 42.236000000000004 - type: recall_at_5 value: 47.377 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 37.188 - type: map_at_10 value: 50.861000000000004 - type: map_at_100 value: 51.917 - type: map_at_1000 value: 51.964999999999996 - type: map_at_3 value: 47.144000000000005 - type: map_at_5 value: 49.417 - type: mrr_at_1 value: 42.571 - type: mrr_at_10 value: 54.086999999999996 - type: mrr_at_100 value: 54.739000000000004 - type: mrr_at_1000 value: 54.762 - type: mrr_at_3 value: 51.285000000000004 - type: mrr_at_5 value: 53.0 - type: ndcg_at_1 value: 42.571 - type: ndcg_at_10 value: 57.282 - type: ndcg_at_100 value: 61.477000000000004 - type: ndcg_at_1000 value: 62.426 - type: ndcg_at_3 value: 51.0 - type: ndcg_at_5 value: 54.346000000000004 - type: precision_at_1 value: 42.571 - type: precision_at_10 value: 9.467 - type: precision_at_100 value: 1.2550000000000001 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.114 - type: precision_at_5 value: 16.250999999999998 - type: recall_at_1 value: 37.188 - type: recall_at_10 value: 73.068 - type: recall_at_100 value: 91.203 - type: recall_at_1000 value: 97.916 - type: recall_at_3 value: 56.552 - type: recall_at_5 value: 64.567 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 25.041000000000004 - type: map_at_10 value: 33.86 - type: map_at_100 value: 34.988 - type: map_at_1000 value: 35.064 - type: map_at_3 value: 31.049 - type: map_at_5 value: 32.845 - type: mrr_at_1 value: 26.893 - type: mrr_at_10 value: 35.594 - type: mrr_at_100 value: 36.617 - type: mrr_at_1000 value: 36.671 - type: mrr_at_3 value: 33.051 - type: mrr_at_5 value: 34.61 - type: ndcg_at_1 value: 26.893 - type: ndcg_at_10 value: 38.674 - type: ndcg_at_100 value: 44.178 - type: ndcg_at_1000 value: 46.089999999999996 - type: ndcg_at_3 value: 33.485 - type: ndcg_at_5 value: 36.402 - type: precision_at_1 value: 26.893 - type: precision_at_10 value: 5.989 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 14.2 - type: precision_at_5 value: 10.26 - type: recall_at_1 value: 25.041000000000004 - type: recall_at_10 value: 51.666000000000004 - type: recall_at_100 value: 76.896 - type: recall_at_1000 value: 91.243 - type: recall_at_3 value: 38.035999999999994 - type: recall_at_5 value: 44.999 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 15.909999999999998 - type: map_at_10 value: 23.901 - type: map_at_100 value: 25.165 - type: map_at_1000 value: 25.291000000000004 - type: map_at_3 value: 21.356 - type: map_at_5 value: 22.816 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 28.382 - type: mrr_at_100 value: 29.465000000000003 - type: mrr_at_1000 value: 29.535 - type: mrr_at_3 value: 25.933 - type: mrr_at_5 value: 27.332 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 29.099000000000004 - type: ndcg_at_100 value: 35.127 - type: ndcg_at_1000 value: 38.096000000000004 - type: ndcg_at_3 value: 24.464 - type: ndcg_at_5 value: 26.709 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 5.398 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.774 - type: precision_at_5 value: 8.632 - type: recall_at_1 value: 15.909999999999998 - type: recall_at_10 value: 40.672000000000004 - type: recall_at_100 value: 66.855 - type: recall_at_1000 value: 87.922 - type: recall_at_3 value: 28.069 - type: recall_at_5 value: 33.812 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 30.175 - type: map_at_10 value: 41.36 - type: map_at_100 value: 42.701 - type: map_at_1000 value: 42.817 - type: map_at_3 value: 37.931 - type: map_at_5 value: 39.943 - type: mrr_at_1 value: 35.611 - type: mrr_at_10 value: 46.346 - type: mrr_at_100 value: 47.160000000000004 - type: mrr_at_1000 value: 47.203 - type: mrr_at_3 value: 43.712 - type: mrr_at_5 value: 45.367000000000004 - type: ndcg_at_1 value: 35.611 - type: ndcg_at_10 value: 47.532000000000004 - type: ndcg_at_100 value: 53.003 - type: ndcg_at_1000 value: 55.007 - type: ndcg_at_3 value: 42.043 - type: ndcg_at_5 value: 44.86 - type: precision_at_1 value: 35.611 - type: precision_at_10 value: 8.624 - type: precision_at_100 value: 1.332 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 20.083000000000002 - type: precision_at_5 value: 14.437 - type: recall_at_1 value: 30.175 - type: recall_at_10 value: 60.5 - type: recall_at_100 value: 83.399 - type: recall_at_1000 value: 96.255 - type: recall_at_3 value: 45.448 - type: recall_at_5 value: 52.432 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 22.467000000000002 - type: map_at_10 value: 33.812999999999995 - type: map_at_100 value: 35.248000000000005 - type: map_at_1000 value: 35.359 - type: map_at_3 value: 30.316 - type: map_at_5 value: 32.233000000000004 - type: mrr_at_1 value: 28.310999999999996 - type: mrr_at_10 value: 38.979 - type: mrr_at_100 value: 39.937 - type: mrr_at_1000 value: 39.989999999999995 - type: mrr_at_3 value: 36.244 - type: mrr_at_5 value: 37.871 - type: ndcg_at_1 value: 28.310999999999996 - type: ndcg_at_10 value: 40.282000000000004 - type: ndcg_at_100 value: 46.22 - type: ndcg_at_1000 value: 48.507 - type: ndcg_at_3 value: 34.596 - type: ndcg_at_5 value: 37.267 - type: precision_at_1 value: 28.310999999999996 - type: precision_at_10 value: 7.831 - type: precision_at_100 value: 1.257 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.275 - type: precision_at_5 value: 12.556999999999999 - type: recall_at_1 value: 22.467000000000002 - type: recall_at_10 value: 54.14099999999999 - type: recall_at_100 value: 79.593 - type: recall_at_1000 value: 95.063 - type: recall_at_3 value: 38.539 - type: recall_at_5 value: 45.403 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 24.18591666666667 - type: map_at_10 value: 33.84258333333333 - type: map_at_100 value: 35.11391666666666 - type: map_at_1000 value: 35.23258333333333 - type: map_at_3 value: 30.764249999999997 - type: map_at_5 value: 32.52333333333334 - type: mrr_at_1 value: 28.54733333333333 - type: mrr_at_10 value: 37.81725 - type: mrr_at_100 value: 38.716499999999996 - type: mrr_at_1000 value: 38.77458333333333 - type: mrr_at_3 value: 35.157833333333336 - type: mrr_at_5 value: 36.69816666666667 - type: ndcg_at_1 value: 28.54733333333333 - type: ndcg_at_10 value: 39.51508333333334 - type: ndcg_at_100 value: 44.95316666666666 - type: ndcg_at_1000 value: 47.257083333333334 - type: ndcg_at_3 value: 34.205833333333324 - type: ndcg_at_5 value: 36.78266666666667 - type: precision_at_1 value: 28.54733333333333 - type: precision_at_10 value: 7.082583333333334 - type: precision_at_100 value: 1.1590833333333332 - type: precision_at_1000 value: 0.15516666666666662 - type: precision_at_3 value: 15.908750000000001 - type: precision_at_5 value: 11.505416666666669 - type: recall_at_1 value: 24.18591666666667 - type: recall_at_10 value: 52.38758333333333 - type: recall_at_100 value: 76.13666666666667 - type: recall_at_1000 value: 91.99066666666667 - type: recall_at_3 value: 37.78333333333334 - type: recall_at_5 value: 44.30141666666666 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 21.975 - type: map_at_10 value: 29.781000000000002 - type: map_at_100 value: 30.847 - type: map_at_1000 value: 30.94 - type: map_at_3 value: 27.167 - type: map_at_5 value: 28.633999999999997 - type: mrr_at_1 value: 24.387 - type: mrr_at_10 value: 32.476 - type: mrr_at_100 value: 33.337 - type: mrr_at_1000 value: 33.403 - type: mrr_at_3 value: 29.881999999999998 - type: mrr_at_5 value: 31.339 - type: ndcg_at_1 value: 24.387 - type: ndcg_at_10 value: 34.596 - type: ndcg_at_100 value: 39.635 - type: ndcg_at_1000 value: 42.079 - type: ndcg_at_3 value: 29.516 - type: ndcg_at_5 value: 31.959 - type: precision_at_1 value: 24.387 - type: precision_at_10 value: 5.6129999999999995 - type: precision_at_100 value: 0.8909999999999999 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.73 - type: precision_at_5 value: 9.171999999999999 - type: recall_at_1 value: 21.975 - type: recall_at_10 value: 46.826 - type: recall_at_100 value: 69.554 - type: recall_at_1000 value: 87.749 - type: recall_at_3 value: 33.016 - type: recall_at_5 value: 38.97 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 15.614 - type: map_at_10 value: 22.927 - type: map_at_100 value: 24.185000000000002 - type: map_at_1000 value: 24.319 - type: map_at_3 value: 20.596 - type: map_at_5 value: 21.854000000000003 - type: mrr_at_1 value: 18.858 - type: mrr_at_10 value: 26.535999999999998 - type: mrr_at_100 value: 27.582 - type: mrr_at_1000 value: 27.665 - type: mrr_at_3 value: 24.295 - type: mrr_at_5 value: 25.532 - type: ndcg_at_1 value: 18.858 - type: ndcg_at_10 value: 27.583000000000002 - type: ndcg_at_100 value: 33.635 - type: ndcg_at_1000 value: 36.647 - type: ndcg_at_3 value: 23.348 - type: ndcg_at_5 value: 25.257 - type: precision_at_1 value: 18.858 - type: precision_at_10 value: 5.158 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 11.092 - type: precision_at_5 value: 8.1 - type: recall_at_1 value: 15.614 - type: recall_at_10 value: 37.916 - type: recall_at_100 value: 65.205 - type: recall_at_1000 value: 86.453 - type: recall_at_3 value: 26.137 - type: recall_at_5 value: 31.087999999999997 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 23.078000000000003 - type: map_at_10 value: 31.941999999999997 - type: map_at_100 value: 33.196999999999996 - type: map_at_1000 value: 33.303 - type: map_at_3 value: 28.927000000000003 - type: map_at_5 value: 30.707 - type: mrr_at_1 value: 26.866 - type: mrr_at_10 value: 35.557 - type: mrr_at_100 value: 36.569 - type: mrr_at_1000 value: 36.632 - type: mrr_at_3 value: 32.897999999999996 - type: mrr_at_5 value: 34.437 - type: ndcg_at_1 value: 26.866 - type: ndcg_at_10 value: 37.372 - type: ndcg_at_100 value: 43.248 - type: ndcg_at_1000 value: 45.632 - type: ndcg_at_3 value: 31.852999999999998 - type: ndcg_at_5 value: 34.582 - type: precision_at_1 value: 26.866 - type: precision_at_10 value: 6.511 - type: precision_at_100 value: 1.078 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 14.582999999999998 - type: precision_at_5 value: 10.634 - type: recall_at_1 value: 23.078000000000003 - type: recall_at_10 value: 50.334 - type: recall_at_100 value: 75.787 - type: recall_at_1000 value: 92.485 - type: recall_at_3 value: 35.386 - type: recall_at_5 value: 42.225 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 22.203999999999997 - type: map_at_10 value: 31.276 - type: map_at_100 value: 32.844 - type: map_at_1000 value: 33.062999999999995 - type: map_at_3 value: 27.733999999999998 - type: map_at_5 value: 29.64 - type: mrr_at_1 value: 27.272999999999996 - type: mrr_at_10 value: 36.083 - type: mrr_at_100 value: 37.008 - type: mrr_at_1000 value: 37.076 - type: mrr_at_3 value: 33.004 - type: mrr_at_5 value: 34.664 - type: ndcg_at_1 value: 27.272999999999996 - type: ndcg_at_10 value: 37.763000000000005 - type: ndcg_at_100 value: 43.566 - type: ndcg_at_1000 value: 46.356 - type: ndcg_at_3 value: 31.673000000000002 - type: ndcg_at_5 value: 34.501 - type: precision_at_1 value: 27.272999999999996 - type: precision_at_10 value: 7.470000000000001 - type: precision_at_100 value: 1.502 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 14.756 - type: precision_at_5 value: 11.225 - type: recall_at_1 value: 22.203999999999997 - type: recall_at_10 value: 51.437999999999995 - type: recall_at_100 value: 76.845 - type: recall_at_1000 value: 94.38600000000001 - type: recall_at_3 value: 34.258 - type: recall_at_5 value: 41.512 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 17.474 - type: map_at_10 value: 26.362999999999996 - type: map_at_100 value: 27.456999999999997 - type: map_at_1000 value: 27.567999999999998 - type: map_at_3 value: 23.518 - type: map_at_5 value: 25.068 - type: mrr_at_1 value: 18.669 - type: mrr_at_10 value: 27.998 - type: mrr_at_100 value: 28.953 - type: mrr_at_1000 value: 29.03 - type: mrr_at_3 value: 25.230999999999998 - type: mrr_at_5 value: 26.654 - type: ndcg_at_1 value: 18.669 - type: ndcg_at_10 value: 31.684 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.555 - type: ndcg_at_3 value: 26.057000000000002 - type: ndcg_at_5 value: 28.587 - type: precision_at_1 value: 18.669 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.847 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 11.583 - type: precision_at_5 value: 8.466 - type: recall_at_1 value: 17.474 - type: recall_at_10 value: 46.497 - type: recall_at_100 value: 69.977 - type: recall_at_1000 value: 89.872 - type: recall_at_3 value: 31.385999999999996 - type: recall_at_5 value: 37.283 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 17.173 - type: map_at_10 value: 30.407 - type: map_at_100 value: 32.528 - type: map_at_1000 value: 32.698 - type: map_at_3 value: 25.523 - type: map_at_5 value: 28.038 - type: mrr_at_1 value: 38.958 - type: mrr_at_10 value: 51.515 - type: mrr_at_100 value: 52.214000000000006 - type: mrr_at_1000 value: 52.237 - type: mrr_at_3 value: 48.502 - type: mrr_at_5 value: 50.251000000000005 - type: ndcg_at_1 value: 38.958 - type: ndcg_at_10 value: 40.355000000000004 - type: ndcg_at_100 value: 47.68 - type: ndcg_at_1000 value: 50.370000000000005 - type: ndcg_at_3 value: 33.946 - type: ndcg_at_5 value: 36.057 - type: precision_at_1 value: 38.958 - type: precision_at_10 value: 12.508 - type: precision_at_100 value: 2.054 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 25.581 - type: precision_at_5 value: 19.256999999999998 - type: recall_at_1 value: 17.173 - type: recall_at_10 value: 46.967 - type: recall_at_100 value: 71.47200000000001 - type: recall_at_1000 value: 86.238 - type: recall_at_3 value: 30.961 - type: recall_at_5 value: 37.539 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 8.999 - type: map_at_10 value: 18.989 - type: map_at_100 value: 26.133 - type: map_at_1000 value: 27.666 - type: map_at_3 value: 13.918 - type: map_at_5 value: 16.473 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.161 - type: mrr_at_100 value: 74.516 - type: mrr_at_1000 value: 74.524 - type: mrr_at_3 value: 72.875 - type: mrr_at_5 value: 73.613 - type: ndcg_at_1 value: 54.37499999999999 - type: ndcg_at_10 value: 39.902 - type: ndcg_at_100 value: 44.212 - type: ndcg_at_1000 value: 51.62 - type: ndcg_at_3 value: 45.193 - type: ndcg_at_5 value: 42.541000000000004 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 30.425 - type: precision_at_100 value: 9.754999999999999 - type: precision_at_1000 value: 2.043 - type: precision_at_3 value: 48.25 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.999 - type: recall_at_10 value: 24.133 - type: recall_at_100 value: 49.138999999999996 - type: recall_at_1000 value: 72.639 - type: recall_at_3 value: 15.287999999999998 - type: recall_at_5 value: 19.415 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.38999999999999 - type: f1 value: 41.444205512055234 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 87.35000000000001 - type: map_at_10 value: 92.837 - type: map_at_100 value: 92.996 - type: map_at_1000 value: 93.006 - type: map_at_3 value: 92.187 - type: map_at_5 value: 92.595 - type: mrr_at_1 value: 93.864 - type: mrr_at_10 value: 96.723 - type: mrr_at_100 value: 96.72500000000001 - type: mrr_at_1000 value: 96.72500000000001 - type: mrr_at_3 value: 96.64 - type: mrr_at_5 value: 96.71499999999999 - type: ndcg_at_1 value: 93.864 - type: ndcg_at_10 value: 94.813 - type: ndcg_at_100 value: 95.243 - type: ndcg_at_1000 value: 95.38600000000001 - type: ndcg_at_3 value: 94.196 - type: ndcg_at_5 value: 94.521 - type: precision_at_1 value: 93.864 - type: precision_at_10 value: 10.951 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 35.114000000000004 - type: precision_at_5 value: 21.476 - type: recall_at_1 value: 87.35000000000001 - type: recall_at_10 value: 96.941 - type: recall_at_100 value: 98.397 - type: recall_at_1000 value: 99.21600000000001 - type: recall_at_3 value: 95.149 - type: recall_at_5 value: 96.131 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 24.476 - type: map_at_10 value: 40.11 - type: map_at_100 value: 42.229 - type: map_at_1000 value: 42.378 - type: map_at_3 value: 34.512 - type: map_at_5 value: 38.037 - type: mrr_at_1 value: 47.839999999999996 - type: mrr_at_10 value: 57.053 - type: mrr_at_100 value: 57.772 - type: mrr_at_1000 value: 57.799 - type: mrr_at_3 value: 54.552 - type: mrr_at_5 value: 56.011 - type: ndcg_at_1 value: 47.839999999999996 - type: ndcg_at_10 value: 48.650999999999996 - type: ndcg_at_100 value: 55.681000000000004 - type: ndcg_at_1000 value: 57.979 - type: ndcg_at_3 value: 43.923 - type: ndcg_at_5 value: 46.037 - type: precision_at_1 value: 47.839999999999996 - type: precision_at_10 value: 13.395000000000001 - type: precision_at_100 value: 2.0660000000000003 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 29.064 - type: precision_at_5 value: 22.006 - type: recall_at_1 value: 24.476 - type: recall_at_10 value: 56.216 - type: recall_at_100 value: 81.798 - type: recall_at_1000 value: 95.48299999999999 - type: recall_at_3 value: 39.357 - type: recall_at_5 value: 47.802 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 42.728 - type: map_at_10 value: 57.737 - type: map_at_100 value: 58.531 - type: map_at_1000 value: 58.594 - type: map_at_3 value: 54.869 - type: map_at_5 value: 56.55 - type: mrr_at_1 value: 85.456 - type: mrr_at_10 value: 90.062 - type: mrr_at_100 value: 90.159 - type: mrr_at_1000 value: 90.16 - type: mrr_at_3 value: 89.37899999999999 - type: mrr_at_5 value: 89.81 - type: ndcg_at_1 value: 85.456 - type: ndcg_at_10 value: 67.755 - type: ndcg_at_100 value: 70.341 - type: ndcg_at_1000 value: 71.538 - type: ndcg_at_3 value: 63.735 - type: ndcg_at_5 value: 65.823 - type: precision_at_1 value: 85.456 - type: precision_at_10 value: 13.450000000000001 - type: precision_at_100 value: 1.545 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 38.861000000000004 - type: precision_at_5 value: 24.964 - type: recall_at_1 value: 42.728 - type: recall_at_10 value: 67.252 - type: recall_at_100 value: 77.265 - type: recall_at_1000 value: 85.246 - type: recall_at_3 value: 58.292 - type: recall_at_5 value: 62.41100000000001 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 87.4836 - type: ap value: 82.29552224030336 - type: f1 value: 87.42791432227448 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 23.015 - type: map_at_10 value: 35.621 - type: map_at_100 value: 36.809 - type: map_at_1000 value: 36.853 - type: map_at_3 value: 31.832 - type: map_at_5 value: 34.006 - type: mrr_at_1 value: 23.738999999999997 - type: mrr_at_10 value: 36.309999999999995 - type: mrr_at_100 value: 37.422 - type: mrr_at_1000 value: 37.461 - type: mrr_at_3 value: 32.592999999999996 - type: mrr_at_5 value: 34.736 - type: ndcg_at_1 value: 23.724999999999998 - type: ndcg_at_10 value: 42.617 - type: ndcg_at_100 value: 48.217999999999996 - type: ndcg_at_1000 value: 49.309 - type: ndcg_at_3 value: 34.905 - type: ndcg_at_5 value: 38.769 - type: precision_at_1 value: 23.724999999999998 - type: precision_at_10 value: 6.689 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.89 - type: precision_at_5 value: 10.897 - type: recall_at_1 value: 23.015 - type: recall_at_10 value: 64.041 - type: recall_at_100 value: 89.724 - type: recall_at_1000 value: 98.00999999999999 - type: recall_at_3 value: 43.064 - type: recall_at_5 value: 52.31099999999999 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.49794801641588 - type: f1 value: 96.28931114498003 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 82.81121751025992 - type: f1 value: 63.18740125901853 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.66644250168123 - type: f1 value: 74.93211186867839 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 81.77202420981843 - type: f1 value: 81.63681969283554 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.596687684870645 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.26965660101405 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.33619694846802 - type: mrr value: 32.53719657720334 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 6.0729999999999995 - type: map_at_10 value: 13.245999999999999 - type: map_at_100 value: 16.747999999999998 - type: map_at_1000 value: 18.163 - type: map_at_3 value: 10.064 - type: map_at_5 value: 11.513 - type: mrr_at_1 value: 49.536 - type: mrr_at_10 value: 58.092 - type: mrr_at_100 value: 58.752 - type: mrr_at_1000 value: 58.78 - type: mrr_at_3 value: 56.398 - type: mrr_at_5 value: 57.389 - type: ndcg_at_1 value: 47.059 - type: ndcg_at_10 value: 35.881 - type: ndcg_at_100 value: 32.751999999999995 - type: ndcg_at_1000 value: 41.498000000000005 - type: ndcg_at_3 value: 42.518 - type: ndcg_at_5 value: 39.550999999999995 - type: precision_at_1 value: 49.536 - type: precision_at_10 value: 26.316 - type: precision_at_100 value: 8.084 - type: precision_at_1000 value: 2.081 - type: precision_at_3 value: 39.938 - type: precision_at_5 value: 34.056 - type: recall_at_1 value: 6.0729999999999995 - type: recall_at_10 value: 16.593 - type: recall_at_100 value: 32.883 - type: recall_at_1000 value: 64.654 - type: recall_at_3 value: 11.174000000000001 - type: recall_at_5 value: 13.528 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 30.043 - type: map_at_10 value: 45.318999999999996 - type: map_at_100 value: 46.381 - type: map_at_1000 value: 46.412 - type: map_at_3 value: 40.941 - type: map_at_5 value: 43.662 - type: mrr_at_1 value: 33.98 - type: mrr_at_10 value: 47.870000000000005 - type: mrr_at_100 value: 48.681999999999995 - type: mrr_at_1000 value: 48.703 - type: mrr_at_3 value: 44.341 - type: mrr_at_5 value: 46.547 - type: ndcg_at_1 value: 33.98 - type: ndcg_at_10 value: 52.957 - type: ndcg_at_100 value: 57.434 - type: ndcg_at_1000 value: 58.103 - type: ndcg_at_3 value: 44.896 - type: ndcg_at_5 value: 49.353 - type: precision_at_1 value: 33.98 - type: precision_at_10 value: 8.786 - type: precision_at_100 value: 1.1280000000000001 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 20.577 - type: precision_at_5 value: 14.942 - type: recall_at_1 value: 30.043 - type: recall_at_10 value: 73.593 - type: recall_at_100 value: 93.026 - type: recall_at_1000 value: 97.943 - type: recall_at_3 value: 52.955 - type: recall_at_5 value: 63.132 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.808 - type: map_at_10 value: 84.675 - type: map_at_100 value: 85.322 - type: map_at_1000 value: 85.33800000000001 - type: map_at_3 value: 81.68900000000001 - type: map_at_5 value: 83.543 - type: mrr_at_1 value: 81.5 - type: mrr_at_10 value: 87.59700000000001 - type: mrr_at_100 value: 87.705 - type: mrr_at_1000 value: 87.70599999999999 - type: mrr_at_3 value: 86.607 - type: mrr_at_5 value: 87.289 - type: ndcg_at_1 value: 81.51 - type: ndcg_at_10 value: 88.41799999999999 - type: ndcg_at_100 value: 89.644 - type: ndcg_at_1000 value: 89.725 - type: ndcg_at_3 value: 85.49900000000001 - type: ndcg_at_5 value: 87.078 - type: precision_at_1 value: 81.51 - type: precision_at_10 value: 13.438 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.363 - type: precision_at_5 value: 24.57 - type: recall_at_1 value: 70.808 - type: recall_at_10 value: 95.575 - type: recall_at_100 value: 99.667 - type: recall_at_1000 value: 99.98899999999999 - type: recall_at_3 value: 87.223 - type: recall_at_5 value: 91.682 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 58.614831329137715 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 66.86580408560826 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.093 - type: map_at_10 value: 13.014000000000001 - type: map_at_100 value: 15.412999999999998 - type: map_at_1000 value: 15.756999999999998 - type: map_at_3 value: 9.216000000000001 - type: map_at_5 value: 11.036999999999999 - type: mrr_at_1 value: 25.1 - type: mrr_at_10 value: 37.133 - type: mrr_at_100 value: 38.165 - type: mrr_at_1000 value: 38.198 - type: mrr_at_3 value: 33.217 - type: mrr_at_5 value: 35.732 - type: ndcg_at_1 value: 25.1 - type: ndcg_at_10 value: 21.918000000000003 - type: ndcg_at_100 value: 30.983 - type: ndcg_at_1000 value: 36.629 - type: ndcg_at_3 value: 20.544999999999998 - type: ndcg_at_5 value: 18.192 - type: precision_at_1 value: 25.1 - type: precision_at_10 value: 11.44 - type: precision_at_100 value: 2.459 - type: precision_at_1000 value: 0.381 - type: precision_at_3 value: 19.267 - type: precision_at_5 value: 16.16 - type: recall_at_1 value: 5.093 - type: recall_at_10 value: 23.215 - type: recall_at_100 value: 49.902 - type: recall_at_1000 value: 77.403 - type: recall_at_3 value: 11.733 - type: recall_at_5 value: 16.372999999999998 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.9365442977452 - type: cos_sim_spearman value: 79.36960687383745 - type: euclidean_pearson value: 79.6045204840714 - type: euclidean_spearman value: 79.26382712751337 - type: manhattan_pearson value: 79.4805084789529 - type: manhattan_spearman value: 79.21847863209523 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - 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type: euclidean_pearson value: 81.51346991046043 - type: euclidean_spearman value: 80.08678485109435 - type: manhattan_pearson value: 81.57058914661894 - type: manhattan_spearman value: 80.1516230725106 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.40310839662533 - type: cos_sim_spearman value: 87.16293477217867 - type: euclidean_pearson value: 86.50688711184775 - type: euclidean_spearman value: 87.08651444923031 - type: manhattan_pearson value: 86.54674677557857 - type: manhattan_spearman value: 87.15079017870971 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.32886275207817 - type: cos_sim_spearman value: 85.0190460590732 - type: euclidean_pearson value: 84.42553652784679 - type: euclidean_spearman value: 85.20027364279328 - 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task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.93190546593995 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 37.602808534760655 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.29214480978073 - type: mrr value: 53.123169722434426 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.967800769650022 - type: cos_sim_spearman value: 31.168490040206926 - 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type: precision_at_3 value: 77.333 - type: precision_at_5 value: 78.0 - type: recall_at_1 value: 0.22300000000000003 - type: recall_at_10 value: 2.049 - type: recall_at_100 value: 13.553 - type: recall_at_1000 value: 46.367999999999995 - type: recall_at_3 value: 0.604 - type: recall_at_5 value: 1.015 - task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 3.0380000000000003 - type: map_at_10 value: 10.188 - type: map_at_100 value: 16.395 - type: map_at_1000 value: 18.024 - type: map_at_3 value: 6.236 - type: map_at_5 value: 7.276000000000001 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 46.292 - type: mrr_at_100 value: 47.446 - type: mrr_at_1000 value: 47.446 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 44.32 - type: ndcg_at_1 value: 32.653 - type: ndcg_at_10 value: 25.219 - type: ndcg_at_100 value: 37.802 - type: ndcg_at_1000 value: 49.274 - 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type: euclidean_ap value: 75.51438087434647 - type: euclidean_f1 value: 68.02370523061066 - type: euclidean_precision value: 66.46525679758308 - type: euclidean_recall value: 69.65699208443272 - type: manhattan_accuracy value: 86.46361089586935 - type: manhattan_ap value: 75.50800785730111 - type: manhattan_f1 value: 67.9220437187253 - type: manhattan_precision value: 67.79705573080967 - type: manhattan_recall value: 68.04749340369392 - type: max_accuracy value: 86.46361089586935 - type: max_ap value: 75.53126388573047 - type: max_f1 value: 68.02370523061066 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.80350836341057 - type: cos_sim_ap value: 85.51101933260743 - type: cos_sim_f1 value: 77.9152271629704 - type: cos_sim_precision value: 75.27815662910056 - type: cos_sim_recall value: 80.74376347397599 - type: dot_accuracy value: 88.84425815966158 - type: dot_ap value: 85.49726945962519 - type: dot_f1 value: 77.94445269567801 - type: dot_precision value: 75.27251864601261 - type: dot_recall value: 80.81305820757623 - type: euclidean_accuracy value: 88.80350836341057 - type: euclidean_ap value: 85.4882880790211 - type: euclidean_f1 value: 77.87063284615103 - type: euclidean_precision value: 74.61022927689595 - type: euclidean_recall value: 81.42901139513397 - type: manhattan_accuracy value: 88.7161873714441 - type: manhattan_ap value: 85.45753871906821 - type: manhattan_f1 value: 77.8686401480111 - type: manhattan_precision value: 74.95903683123174 - type: manhattan_recall value: 81.01324299353249 - type: max_accuracy value: 88.84425815966158 - type: max_ap value: 85.51101933260743 - type: max_f1 value: 77.94445269567801 --- # gte-base-en-v1.5 We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)). We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct), a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB. - **Developed by:** Institute for Intelligent Computing, Alibaba Group - **Model type:** Text Embeddings - **Paper:** Coming soon. ### Model list | Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo | |:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: | |[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 | |[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 | |[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 | ## How to Get Started with the Model Use the code below to get started with the model. ```python # Requires transformers>=4.36.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] model_path = 'Alibaba-NLP/gte-base-en-v1.5' tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModel.from_pretrained(model_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** Use with `sentence-transformers`: ```python # Requires sentence_transformers>=2.7.0 from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True) embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` Use with `transformers.js`: ```js // npm i @xenova/transformers import { pipeline, dot } from '@xenova/transformers'; // Create feature extraction pipeline const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', { quantized: false, // Comment out this line to use the quantized version }); // Generate sentence embeddings const sentences = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); // Compute similarity scores const [source_embeddings, ...document_embeddings ] = output.tolist(); const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x)); console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362] ``` ## Training Details ### Training Data - Masked language modeling (MLM): `c4-en` - Weak-supervised contrastive (WSC) pre-training: [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data - Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data ### Training Procedure To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training. The entire training process is as follows: - MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000 - MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000 - WSC: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000 - Fine-tuning: TODO ## Evaluation ### MTEB The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2). | Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | | [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | ### LoCo | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | | [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | | [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | ## Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```