--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb language: en license: apache-2.0 datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - MS Marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers model-index: - name: all-MiniLM-L12-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test metrics: - type: accuracy value: 65.28358208955224 - type: ap value: 28.02247873560022 - type: f1 value: 59.09977445939425 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test metrics: - type: accuracy value: 57.09850107066381 - type: ap value: 73.38224986285773 - type: f1 value: 55.183322516223434 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test metrics: - type: accuracy value: 67.24137931034483 - type: ap value: 17.93337056203553 - type: f1 value: 55.200711090858846 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test metrics: - type: accuracy value: 59.91434689507494 - type: ap value: 13.610920446878454 - type: f1 value: 48.70464699796398 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test metrics: - type: accuracy value: 62.984899999999996 - type: ap value: 58.19701547898307 - type: f1 value: 62.704020410756144 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test metrics: - type: accuracy value: 30.792 - type: f1 value: 30.254565315575437 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test metrics: - type: accuracy value: 25.907999999999998 - type: f1 value: 25.538149526380543 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test metrics: - type: accuracy value: 27.634000000000004 - type: f1 value: 27.287076320171728 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test metrics: - type: accuracy value: 27.540000000000003 - type: f1 value: 27.21486019130574 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test metrics: - type: accuracy value: 23.566000000000003 - type: f1 value: 23.3492650771905 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test metrics: - type: accuracy value: 22.99 - type: f1 value: 22.47175043426865 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test metrics: - type: map_at_1 value: 23.257 - type: map_at_10 value: 38.083 - type: map_at_100 value: 39.263999999999996 - type: map_at_1000 value: 39.273 - type: map_at_3 value: 32.574999999999996 - type: map_at_5 value: 35.669000000000004 - type: mrr_at_1 value: 23.613 - type: mrr_at_10 value: 38.243 - type: mrr_at_100 value: 39.410000000000004 - type: mrr_at_1000 value: 39.419 - type: mrr_at_3 value: 32.883 - type: mrr_at_5 value: 35.766999999999996 - type: ndcg_at_1 value: 23.257 - type: ndcg_at_10 value: 47.128 - type: ndcg_at_100 value: 52.093 - type: ndcg_at_1000 value: 52.315999999999995 - type: ndcg_at_3 value: 35.794 - type: ndcg_at_5 value: 41.364000000000004 - type: precision_at_1 value: 23.257 - type: precision_at_10 value: 7.632 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.055 - type: precision_at_5 value: 11.735 - type: recall_at_1 value: 23.257 - type: recall_at_10 value: 76.31599999999999 - type: recall_at_100 value: 97.866 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 45.164 - type: recall_at_5 value: 58.677 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test metrics: - type: v_measure value: 46.06982724111873 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test metrics: - type: v_measure value: 37.501829188148264 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test metrics: - type: map value: 64.06160552465775 - type: mrr value: 77.40029899309677 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test metrics: - type: cos_sim_pearson value: 86.73300462416691 - type: cos_sim_spearman value: 83.56756679430214 - type: euclidean_pearson value: 84.35153960397948 - type: euclidean_spearman value: 83.56756679430214 - type: manhattan_pearson value: 84.10087673223914 - type: manhattan_spearman value: 83.58383222516198 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test metrics: - type: accuracy value: 80.40259740259741 - type: f1 value: 79.7932665380276 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test metrics: - type: v_measure value: 36.985834019439366 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test metrics: - type: v_measure value: 33.207831360185644 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test metrics: - type: map_at_1 value: 34.975 - type: map_at_10 value: 47.227999999999994 - type: map_at_100 value: 48.91 - type: map_at_1000 value: 49.016 - type: map_at_3 value: 43.334 - type: map_at_5 value: 45.353 - type: mrr_at_1 value: 43.348 - type: mrr_at_10 value: 53.744 - type: mrr_at_100 value: 54.432 - type: mrr_at_1000 value: 54.458 - type: mrr_at_3 value: 51.359 - type: mrr_at_5 value: 52.825 - type: ndcg_at_1 value: 43.348 - type: ndcg_at_10 value: 54.118 - type: ndcg_at_100 value: 59.496 - type: ndcg_at_1000 value: 60.846999999999994 - type: ndcg_at_3 value: 49.001 - type: ndcg_at_5 value: 51.245 - type: precision_at_1 value: 43.348 - type: precision_at_10 value: 10.658 - type: precision_at_100 value: 1.701 - type: precision_at_1000 value: 0.214 - type: precision_at_3 value: 23.701 - type: precision_at_5 value: 17.082 - type: recall_at_1 value: 34.975 - type: recall_at_10 value: 66.291 - type: recall_at_100 value: 88.727 - type: recall_at_1000 value: 97.26700000000001 - type: recall_at_3 value: 51.505 - type: recall_at_5 value: 57.833 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test metrics: - type: map_at_1 value: 31.509999999999998 - type: map_at_10 value: 43.401 - type: map_at_100 value: 44.762 - type: map_at_1000 value: 44.906 - type: map_at_3 value: 39.83 - type: map_at_5 value: 41.784 - type: mrr_at_1 value: 39.936 - type: mrr_at_10 value: 49.534 - type: mrr_at_100 value: 50.126000000000005 - type: mrr_at_1000 value: 50.163999999999994 - type: mrr_at_3 value: 46.996 - type: mrr_at_5 value: 48.508 - type: ndcg_at_1 value: 39.936 - type: ndcg_at_10 value: 49.845 - type: ndcg_at_100 value: 54.25600000000001 - type: ndcg_at_1000 value: 56.227000000000004 - type: ndcg_at_3 value: 44.982 - type: ndcg_at_5 value: 47.187 - type: precision_at_1 value: 39.936 - type: precision_at_10 value: 9.771 - type: precision_at_100 value: 1.575 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 22.314 - type: precision_at_5 value: 15.975 - type: recall_at_1 value: 31.509999999999998 - type: recall_at_10 value: 61.468 - type: recall_at_100 value: 80.023 - type: recall_at_1000 value: 92.267 - type: recall_at_3 value: 46.698 - type: recall_at_5 value: 53.03600000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test metrics: - type: map_at_1 value: 38.577 - type: map_at_10 value: 51.041000000000004 - type: map_at_100 value: 52.141000000000005 - type: map_at_1000 value: 52.190000000000005 - type: map_at_3 value: 47.904 - type: map_at_5 value: 49.645 - type: mrr_at_1 value: 44.138 - type: mrr_at_10 value: 54.36 - type: mrr_at_100 value: 55.05799999999999 - type: mrr_at_1000 value: 55.084 - type: mrr_at_3 value: 52.017 - type: mrr_at_5 value: 53.321 - type: ndcg_at_1 value: 44.138 - type: ndcg_at_10 value: 56.855999999999995 - type: ndcg_at_100 value: 61.133 - type: ndcg_at_1000 value: 62.17399999999999 - type: ndcg_at_3 value: 51.624 - type: ndcg_at_5 value: 54.108999999999995 - type: precision_at_1 value: 44.138 - type: precision_at_10 value: 9.16 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 23.156 - type: precision_at_5 value: 15.762 - type: recall_at_1 value: 38.577 - type: recall_at_10 value: 70.638 - type: recall_at_100 value: 89.01 - type: recall_at_1000 value: 96.53699999999999 - type: recall_at_3 value: 56.635000000000005 - type: recall_at_5 value: 62.731 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test metrics: - type: map_at_1 value: 27.038 - type: map_at_10 value: 36.108000000000004 - type: map_at_100 value: 37.316 - type: map_at_1000 value: 37.396 - type: map_at_3 value: 33.206 - type: map_at_5 value: 34.674 - type: mrr_at_1 value: 29.04 - type: mrr_at_10 value: 37.979 - type: mrr_at_100 value: 39.056000000000004 - type: mrr_at_1000 value: 39.11 - type: mrr_at_3 value: 35.348 - type: mrr_at_5 value: 36.675999999999995 - type: ndcg_at_1 value: 29.04 - type: ndcg_at_10 value: 41.408 - type: ndcg_at_100 value: 46.918 - type: ndcg_at_1000 value: 48.827 - type: ndcg_at_3 value: 35.699999999999996 - type: ndcg_at_5 value: 38.112 - type: precision_at_1 value: 29.04 - type: precision_at_10 value: 6.463000000000001 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 15.104000000000001 - type: precision_at_5 value: 10.508000000000001 - type: recall_at_1 value: 27.038 - type: recall_at_10 value: 55.989 - type: recall_at_100 value: 80.418 - type: recall_at_1000 value: 94.506 - type: recall_at_3 value: 40.388000000000005 - type: recall_at_5 value: 46.085 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test metrics: - type: map_at_1 value: 17.264 - type: map_at_10 value: 26.157000000000004 - type: map_at_100 value: 27.503 - type: map_at_1000 value: 27.617000000000004 - type: map_at_3 value: 23.247999999999998 - type: map_at_5 value: 24.81 - type: mrr_at_1 value: 21.144 - type: mrr_at_10 value: 30.516 - type: mrr_at_100 value: 31.607000000000003 - type: mrr_at_1000 value: 31.673000000000002 - type: mrr_at_3 value: 27.716 - type: mrr_at_5 value: 29.357 - type: ndcg_at_1 value: 21.144 - type: ndcg_at_10 value: 31.86 - type: ndcg_at_100 value: 38.12 - type: ndcg_at_1000 value: 40.699000000000005 - type: ndcg_at_3 value: 26.411 - type: ndcg_at_5 value: 28.896 - type: precision_at_1 value: 21.144 - type: precision_at_10 value: 5.995 - type: precision_at_100 value: 1.058 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 12.894 - type: precision_at_5 value: 9.428 - type: recall_at_1 value: 17.264 - type: recall_at_10 value: 45.074 - type: recall_at_100 value: 71.817 - type: recall_at_1000 value: 89.846 - type: recall_at_3 value: 30.031000000000002 - type: recall_at_5 value: 36.233 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test metrics: - type: map_at_1 value: 28.668 - type: map_at_10 value: 40.382 - type: map_at_100 value: 41.836 - type: map_at_1000 value: 41.954 - type: map_at_3 value: 37.136 - type: map_at_5 value: 38.755 - type: mrr_at_1 value: 35.13 - type: mrr_at_10 value: 45.928999999999995 - type: mrr_at_100 value: 46.814 - type: mrr_at_1000 value: 46.854 - type: mrr_at_3 value: 43.423 - type: mrr_at_5 value: 44.79 - type: ndcg_at_1 value: 35.13 - type: ndcg_at_10 value: 46.81 - type: ndcg_at_100 value: 52.552 - type: ndcg_at_1000 value: 54.493 - type: ndcg_at_3 value: 41.732 - type: ndcg_at_5 value: 43.847 - type: precision_at_1 value: 35.13 - type: precision_at_10 value: 8.738999999999999 - type: precision_at_100 value: 1.373 - type: precision_at_1000 value: 0.174 - type: precision_at_3 value: 20.372 - type: precision_at_5 value: 14.302000000000001 - type: recall_at_1 value: 28.668 - type: recall_at_10 value: 60.038000000000004 - type: recall_at_100 value: 83.736 - type: recall_at_1000 value: 96.184 - type: recall_at_3 value: 45.647999999999996 - type: recall_at_5 value: 51.212 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test metrics: - type: map_at_1 value: 25.287 - type: map_at_10 value: 35.351 - type: map_at_100 value: 36.867 - type: map_at_1000 value: 36.973 - type: map_at_3 value: 32.176 - type: map_at_5 value: 33.894999999999996 - type: mrr_at_1 value: 31.735000000000003 - type: mrr_at_10 value: 40.832 - type: mrr_at_100 value: 41.812 - type: mrr_at_1000 value: 41.864000000000004 - type: mrr_at_3 value: 38.489000000000004 - type: mrr_at_5 value: 39.654 - type: ndcg_at_1 value: 31.735000000000003 - type: ndcg_at_10 value: 41.327999999999996 - type: ndcg_at_100 value: 47.565000000000005 - type: ndcg_at_1000 value: 49.708000000000006 - type: ndcg_at_3 value: 36.391 - type: ndcg_at_5 value: 38.489000000000004 - type: precision_at_1 value: 31.735000000000003 - type: precision_at_10 value: 7.7170000000000005 - type: precision_at_100 value: 1.2670000000000001 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.808 - type: precision_at_5 value: 12.534 - type: recall_at_1 value: 25.287 - type: recall_at_10 value: 53.735 - type: recall_at_100 value: 80.149 - type: recall_at_1000 value: 94.756 - type: recall_at_3 value: 39.475 - type: recall_at_5 value: 45.532000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test metrics: - type: map_at_1 value: 26.613 - type: map_at_10 value: 36.747416666666666 - type: map_at_100 value: 38.091416666666674 - type: map_at_1000 value: 38.2075 - type: map_at_3 value: 33.630833333333335 - type: map_at_5 value: 35.28225 - type: mrr_at_1 value: 31.654 - type: mrr_at_10 value: 40.94166666666666 - type: mrr_at_100 value: 41.85883333333334 - type: mrr_at_1000 value: 41.910666666666664 - type: mrr_at_3 value: 38.44458333333334 - type: mrr_at_5 value: 39.84525000000001 - type: ndcg_at_1 value: 31.654 - type: ndcg_at_10 value: 42.533 - type: ndcg_at_100 value: 48.09741666666667 - type: ndcg_at_1000 value: 50.170166666666674 - type: ndcg_at_3 value: 37.37858333333333 - type: ndcg_at_5 value: 39.666666666666664 - type: precision_at_1 value: 31.654 - type: precision_at_10 value: 7.649500000000001 - type: precision_at_100 value: 1.2425 - type: precision_at_1000 value: 0.16175 - type: precision_at_3 value: 17.49625 - type: precision_at_5 value: 12.410333333333332 - type: recall_at_1 value: 26.613 - type: recall_at_10 value: 55.33375 - type: recall_at_100 value: 79.52791666666667 - type: recall_at_1000 value: 93.73391666666667 - type: recall_at_3 value: 40.861333333333334 - type: recall_at_5 value: 46.84675 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test metrics: - type: map_at_1 value: 26.079 - type: map_at_10 value: 33.481 - type: map_at_100 value: 34.494 - type: map_at_1000 value: 34.589999999999996 - type: map_at_3 value: 31.165 - type: map_at_5 value: 32.482 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.303000000000004 - type: mrr_at_100 value: 37.183 - type: mrr_at_1000 value: 37.254 - type: mrr_at_3 value: 34.33 - type: mrr_at_5 value: 35.519 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 37.817 - type: ndcg_at_100 value: 42.91 - type: ndcg_at_1000 value: 45.342 - type: ndcg_at_3 value: 33.695 - type: ndcg_at_5 value: 35.747 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 5.951 - type: precision_at_100 value: 0.9400000000000001 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 14.519000000000002 - type: precision_at_5 value: 10.123 - type: recall_at_1 value: 26.079 - type: recall_at_10 value: 48.27 - type: recall_at_100 value: 71.64 - type: recall_at_1000 value: 89.775 - type: recall_at_3 value: 36.858000000000004 - type: recall_at_5 value: 42.013 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test metrics: - type: map_at_1 value: 18.17 - type: map_at_10 value: 26.483 - type: map_at_100 value: 27.732 - type: map_at_1000 value: 27.864 - type: map_at_3 value: 23.76 - type: map_at_5 value: 25.290000000000003 - type: mrr_at_1 value: 22.436 - type: mrr_at_10 value: 30.448999999999998 - type: mrr_at_100 value: 31.476 - type: mrr_at_1000 value: 31.548 - type: mrr_at_3 value: 28.051 - type: mrr_at_5 value: 29.421999999999997 - type: ndcg_at_1 value: 22.436 - type: ndcg_at_10 value: 31.662000000000003 - type: ndcg_at_100 value: 37.611 - type: ndcg_at_1000 value: 40.439 - type: ndcg_at_3 value: 26.939999999999998 - type: ndcg_at_5 value: 29.177999999999997 - type: precision_at_1 value: 22.436 - type: precision_at_10 value: 5.908 - type: precision_at_100 value: 1.056 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 12.962000000000002 - type: precision_at_5 value: 9.476999999999999 - type: recall_at_1 value: 18.17 - type: recall_at_10 value: 43.219 - type: recall_at_100 value: 70.106 - type: recall_at_1000 value: 90.04100000000001 - type: recall_at_3 value: 30.023 - type: recall_at_5 value: 35.845 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test metrics: - type: map_at_1 value: 28.016999999999996 - type: map_at_10 value: 38.123000000000005 - type: map_at_100 value: 39.367000000000004 - type: map_at_1000 value: 39.467999999999996 - type: map_at_3 value: 34.836 - type: map_at_5 value: 36.661 - type: mrr_at_1 value: 33.116 - type: mrr_at_10 value: 42.211 - type: mrr_at_100 value: 43.118 - type: mrr_at_1000 value: 43.169000000000004 - type: mrr_at_3 value: 39.521 - type: mrr_at_5 value: 41.154 - type: ndcg_at_1 value: 33.116 - type: ndcg_at_10 value: 43.86 - type: ndcg_at_100 value: 49.486000000000004 - type: ndcg_at_1000 value: 51.487 - type: ndcg_at_3 value: 38.303 - type: ndcg_at_5 value: 40.927 - type: precision_at_1 value: 33.116 - type: precision_at_10 value: 7.649 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 17.724 - type: precision_at_5 value: 12.668 - type: recall_at_1 value: 28.016999999999996 - type: recall_at_10 value: 57.032000000000004 - type: recall_at_100 value: 81.828 - type: recall_at_1000 value: 95.273 - type: recall_at_3 value: 41.733 - type: recall_at_5 value: 48.496 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test metrics: - type: map_at_1 value: 24.295 - type: map_at_10 value: 34.94 - type: map_at_100 value: 36.659000000000006 - type: map_at_1000 value: 36.902 - type: map_at_3 value: 31.562 - type: map_at_5 value: 33.28 - type: mrr_at_1 value: 29.644 - type: mrr_at_10 value: 39.467999999999996 - type: mrr_at_100 value: 40.561 - type: mrr_at_1000 value: 40.61 - type: mrr_at_3 value: 36.759 - type: mrr_at_5 value: 38.251000000000005 - type: ndcg_at_1 value: 29.644 - type: ndcg_at_10 value: 41.376000000000005 - type: ndcg_at_100 value: 47.701 - type: ndcg_at_1000 value: 49.925999999999995 - type: ndcg_at_3 value: 36.009 - type: ndcg_at_5 value: 38.23 - type: precision_at_1 value: 29.644 - type: precision_at_10 value: 8.182 - type: precision_at_100 value: 1.672 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 17.325 - type: precision_at_5 value: 12.450999999999999 - type: recall_at_1 value: 24.295 - type: recall_at_10 value: 54.478 - type: recall_at_100 value: 81.85 - type: recall_at_1000 value: 95.395 - type: recall_at_3 value: 39.121 - type: recall_at_5 value: 45.465 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test metrics: - type: map_at_1 value: 19.476 - type: map_at_10 value: 28.274 - type: map_at_100 value: 29.509999999999998 - type: map_at_1000 value: 29.614 - type: map_at_3 value: 25.413000000000004 - type: map_at_5 value: 26.758 - type: mrr_at_1 value: 20.887 - type: mrr_at_10 value: 29.975 - type: mrr_at_100 value: 31.063000000000002 - type: mrr_at_1000 value: 31.14 - type: mrr_at_3 value: 27.326 - type: mrr_at_5 value: 28.666000000000004 - type: ndcg_at_1 value: 20.887 - type: ndcg_at_10 value: 33.456 - type: ndcg_at_100 value: 39.421 - type: ndcg_at_1000 value: 41.873 - type: ndcg_at_3 value: 27.755000000000003 - 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type: recall_at_3 value: 4.476999999999999 - type: recall_at_5 value: 7.904999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test metrics: - type: accuracy value: 67.4694 - type: ap value: 12.602604676283388 - type: f1 value: 51.82471949507483 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test metrics: - type: accuracy value: 54.25297113752122 - type: f1 value: 54.50148311546008 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test metrics: - type: v_measure value: 47.467044776612376 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test metrics: - type: cos_sim_accuracy value: 84.78869881385229 - type: cos_sim_ap value: 70.01722500181003 - type: cos_sim_f1 value: 65.943384461903 - type: cos_sim_precision value: 62.52069047056041 - type: cos_sim_recall value: 69.76253298153034 - type: dot_accuracy value: 84.78869881385229 - type: dot_ap value: 70.01721947474665 - type: dot_f1 value: 65.943384461903 - type: dot_precision value: 62.52069047056041 - type: dot_recall value: 69.76253298153034 - type: euclidean_accuracy value: 84.78869881385229 - type: euclidean_ap value: 70.01721811552584 - type: euclidean_f1 value: 65.943384461903 - type: euclidean_precision value: 62.52069047056041 - type: euclidean_recall value: 69.76253298153034 - type: manhattan_accuracy value: 84.68140907194373 - type: manhattan_ap value: 69.90669388421887 - type: manhattan_f1 value: 66.00842865743527 - type: manhattan_precision value: 60.70874861572536 - type: manhattan_recall value: 72.32189973614776 - type: max_accuracy value: 84.78869881385229 - type: max_ap value: 70.01722500181003 - type: max_f1 value: 66.00842865743527 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test metrics: - type: cos_sim_accuracy value: 88.4367601971514 - type: cos_sim_ap value: 84.77318195783158 - type: cos_sim_f1 value: 77.13502703503444 - type: cos_sim_precision value: 74.31140288283146 - type: cos_sim_recall value: 80.18170619032954 - type: dot_accuracy value: 88.4367601971514 - type: dot_ap value: 84.77317449778201 - type: dot_f1 value: 77.13502703503444 - type: dot_precision value: 74.31140288283146 - type: dot_recall value: 80.18170619032954 - type: euclidean_accuracy value: 88.4367601971514 - type: euclidean_ap value: 84.77314948093711 - type: euclidean_f1 value: 77.13502703503444 - type: euclidean_precision value: 74.31140288283146 - type: euclidean_recall value: 80.18170619032954 - type: manhattan_accuracy value: 88.43287926417511 - type: manhattan_ap value: 84.71097141640011 - type: manhattan_f1 value: 77.08356453223837 - type: manhattan_precision value: 74.18298326806692 - type: manhattan_recall value: 80.2202032645519 - type: max_accuracy value: 88.4367601971514 - type: max_ap value: 84.77318195783158 - type: max_f1 value: 77.13502703503444 --- # all-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L12-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L12-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L12-v2) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |