--- base_model: avsolatorio/GIST-all-MiniLM-L6-v2 inference: true language: - en library_name: sentence-transformers license: mit model-index: - name: GIST-all-MiniLM-L6-v2 results: - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 72.8955223880597 - type: ap value: 35.447605103320775 - type: f1 value: 66.82951715365854 task: type: Classification - dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics: - type: accuracy value: 87.19474999999998 - type: ap value: 83.09577890808514 - type: f1 value: 87.13833121762009 task: type: Classification - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 42.556000000000004 - type: f1 value: 42.236256693772276 task: type: Classification - dataset: config: default name: MTEB ArguAna revision: None split: test type: arguana metrics: - type: map_at_1 value: 26.884999999999998 - type: map_at_10 value: 42.364000000000004 - type: map_at_100 value: 43.382 - type: map_at_1000 value: 43.391000000000005 - type: map_at_3 value: 37.162 - type: map_at_5 value: 40.139 - type: mrr_at_1 value: 26.884999999999998 - type: mrr_at_10 value: 42.193999999999996 - type: mrr_at_100 value: 43.211 - type: mrr_at_1000 value: 43.221 - type: mrr_at_3 value: 36.949 - type: mrr_at_5 value: 40.004 - type: ndcg_at_1 value: 26.884999999999998 - type: ndcg_at_10 value: 51.254999999999995 - type: ndcg_at_100 value: 55.481 - type: ndcg_at_1000 value: 55.68300000000001 - type: ndcg_at_3 value: 40.565 - type: ndcg_at_5 value: 45.882 - type: precision_at_1 value: 26.884999999999998 - type: precision_at_10 value: 7.9799999999999995 - type: precision_at_100 value: 0.98 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.808999999999997 - type: precision_at_5 value: 12.645999999999999 - type: recall_at_1 value: 26.884999999999998 - type: recall_at_10 value: 79.801 - type: recall_at_100 value: 98.009 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 50.427 - type: recall_at_5 value: 63.229 task: type: Retrieval - dataset: config: default name: MTEB ArxivClusteringP2P revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics: - type: v_measure value: 45.31044837358167 task: type: Clustering - dataset: config: default name: MTEB ArxivClusteringS2S revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics: - type: v_measure value: 35.44751738734691 task: type: Clustering - dataset: config: default name: MTEB AskUbuntuDupQuestions revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics: - type: map value: 62.96517580629869 - type: mrr value: 76.30051004704744 task: type: Reranking - dataset: config: default name: MTEB BIOSSES revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cos_sim_pearson value: 83.97262600499639 - type: cos_sim_spearman value: 81.25787561220484 - type: euclidean_pearson value: 64.96260261677082 - type: euclidean_spearman value: 64.17616109254686 - type: manhattan_pearson value: 65.05620628102835 - type: manhattan_spearman value: 64.71171546419122 task: type: STS - dataset: config: default name: MTEB Banking77Classification revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics: - type: accuracy value: 84.2435064935065 - type: f1 value: 84.2334859253828 task: type: Classification - dataset: config: default name: MTEB BiorxivClusteringP2P revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics: - type: v_measure value: 38.38358435972693 task: type: Clustering - dataset: config: default name: MTEB BiorxivClusteringS2S revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics: - type: v_measure value: 31.093619653843124 task: type: Clustering - dataset: config: default name: MTEB CQADupstackAndroidRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 35.016999999999996 - type: map_at_10 value: 47.019 - type: map_at_100 value: 48.634 - type: map_at_1000 value: 48.757 - type: map_at_3 value: 43.372 - type: map_at_5 value: 45.314 - type: mrr_at_1 value: 43.491 - type: mrr_at_10 value: 53.284 - type: mrr_at_100 value: 54.038 - type: mrr_at_1000 value: 54.071000000000005 - type: mrr_at_3 value: 51.001 - type: mrr_at_5 value: 52.282 - type: ndcg_at_1 value: 43.491 - type: ndcg_at_10 value: 53.498999999999995 - type: ndcg_at_100 value: 58.733999999999995 - type: ndcg_at_1000 value: 60.307 - type: ndcg_at_3 value: 48.841 - type: ndcg_at_5 value: 50.76199999999999 - type: precision_at_1 value: 43.491 - type: precision_at_10 value: 10.315000000000001 - type: precision_at_100 value: 1.6209999999999998 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 23.462 - type: precision_at_5 value: 16.652 - type: recall_at_1 value: 35.016999999999996 - type: recall_at_10 value: 64.92 - type: recall_at_100 value: 86.605 - type: recall_at_1000 value: 96.174 - type: recall_at_3 value: 50.99 - type: recall_at_5 value: 56.93 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackEnglishRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 29.866 - type: map_at_10 value: 40.438 - type: map_at_100 value: 41.77 - type: map_at_1000 value: 41.913 - type: map_at_3 value: 37.634 - type: map_at_5 value: 39.226 - type: mrr_at_1 value: 37.834 - type: mrr_at_10 value: 46.765 - type: mrr_at_100 value: 47.410000000000004 - type: mrr_at_1000 value: 47.461 - type: mrr_at_3 value: 44.735 - type: mrr_at_5 value: 46.028000000000006 - type: ndcg_at_1 value: 37.834 - type: ndcg_at_10 value: 46.303 - type: ndcg_at_100 value: 50.879 - type: ndcg_at_1000 value: 53.112 - type: ndcg_at_3 value: 42.601 - type: ndcg_at_5 value: 44.384 - type: precision_at_1 value: 37.834 - type: precision_at_10 value: 8.898 - type: precision_at_100 value: 1.4409999999999998 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 20.977 - type: precision_at_5 value: 14.841 - type: recall_at_1 value: 29.866 - type: recall_at_10 value: 56.06100000000001 - type: recall_at_100 value: 75.809 - type: recall_at_1000 value: 89.875 - type: recall_at_3 value: 44.707 - type: recall_at_5 value: 49.846000000000004 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGamingRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 38.985 - type: map_at_10 value: 51.165000000000006 - type: map_at_100 value: 52.17 - type: map_at_1000 value: 52.229000000000006 - type: map_at_3 value: 48.089999999999996 - type: map_at_5 value: 49.762 - type: mrr_at_1 value: 44.577 - type: mrr_at_10 value: 54.493 - type: mrr_at_100 value: 55.137 - type: mrr_at_1000 value: 55.167 - type: mrr_at_3 value: 52.079 - type: mrr_at_5 value: 53.518 - type: ndcg_at_1 value: 44.577 - type: ndcg_at_10 value: 56.825 - type: ndcg_at_100 value: 60.842 - type: ndcg_at_1000 value: 62.015 - type: ndcg_at_3 value: 51.699 - type: ndcg_at_5 value: 54.11 - type: precision_at_1 value: 44.577 - type: precision_at_10 value: 9.11 - type: precision_at_100 value: 1.206 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 23.156 - type: precision_at_5 value: 15.737000000000002 - type: recall_at_1 value: 38.985 - type: recall_at_10 value: 70.164 - type: recall_at_100 value: 87.708 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 56.285 - type: recall_at_5 value: 62.303 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGisRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 28.137 - type: map_at_10 value: 36.729 - type: map_at_100 value: 37.851 - type: map_at_1000 value: 37.932 - type: map_at_3 value: 34.074 - type: map_at_5 value: 35.398 - type: mrr_at_1 value: 30.621 - type: mrr_at_10 value: 39.007 - type: mrr_at_100 value: 39.961 - type: mrr_at_1000 value: 40.02 - type: mrr_at_3 value: 36.591 - type: mrr_at_5 value: 37.806 - type: ndcg_at_1 value: 30.621 - type: ndcg_at_10 value: 41.772 - type: ndcg_at_100 value: 47.181 - type: ndcg_at_1000 value: 49.053999999999995 - type: ndcg_at_3 value: 36.577 - type: ndcg_at_5 value: 38.777 - type: precision_at_1 value: 30.621 - type: precision_at_10 value: 6.372999999999999 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.367 - type: precision_at_5 value: 10.531 - type: recall_at_1 value: 28.137 - type: recall_at_10 value: 55.162 - type: recall_at_100 value: 79.931 - type: recall_at_1000 value: 93.67 - type: recall_at_3 value: 41.057 - type: recall_at_5 value: 46.327 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackMathematicaRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 16.798 - type: map_at_10 value: 25.267 - type: map_at_100 value: 26.579000000000004 - type: map_at_1000 value: 26.697 - type: map_at_3 value: 22.456 - type: map_at_5 value: 23.912 - type: mrr_at_1 value: 20.771 - type: mrr_at_10 value: 29.843999999999998 - type: mrr_at_100 value: 30.849 - type: mrr_at_1000 value: 30.916 - type: mrr_at_3 value: 27.156000000000002 - type: mrr_at_5 value: 28.518 - type: ndcg_at_1 value: 20.771 - type: ndcg_at_10 value: 30.792 - type: ndcg_at_100 value: 36.945 - type: ndcg_at_1000 value: 39.619 - type: ndcg_at_3 value: 25.52 - type: ndcg_at_5 value: 27.776 - type: precision_at_1 value: 20.771 - type: precision_at_10 value: 5.734 - type: precision_at_100 value: 1.031 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.148 - type: precision_at_5 value: 9.055 - type: recall_at_1 value: 16.798 - type: recall_at_10 value: 43.332 - type: recall_at_100 value: 70.016 - type: recall_at_1000 value: 88.90400000000001 - type: recall_at_3 value: 28.842000000000002 - type: recall_at_5 value: 34.37 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackPhysicsRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 31.180000000000003 - type: map_at_10 value: 41.78 - type: map_at_100 value: 43.102000000000004 - type: map_at_1000 value: 43.222 - type: map_at_3 value: 38.505 - type: map_at_5 value: 40.443 - type: mrr_at_1 value: 37.824999999999996 - type: mrr_at_10 value: 47.481 - type: mrr_at_100 value: 48.268 - type: mrr_at_1000 value: 48.313 - type: mrr_at_3 value: 44.946999999999996 - type: mrr_at_5 value: 46.492 - type: ndcg_at_1 value: 37.824999999999996 - type: ndcg_at_10 value: 47.827 - type: ndcg_at_100 value: 53.407000000000004 - type: ndcg_at_1000 value: 55.321 - type: ndcg_at_3 value: 42.815 - type: ndcg_at_5 value: 45.363 - type: precision_at_1 value: 37.824999999999996 - type: precision_at_10 value: 8.652999999999999 - type: precision_at_100 value: 1.354 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 20.372 - type: precision_at_5 value: 14.591000000000001 - type: recall_at_1 value: 31.180000000000003 - type: recall_at_10 value: 59.894000000000005 - type: recall_at_100 value: 83.722 - type: recall_at_1000 value: 95.705 - type: recall_at_3 value: 45.824 - type: recall_at_5 value: 52.349999999999994 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackProgrammersRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 24.66 - type: map_at_10 value: 34.141 - type: map_at_100 value: 35.478 - type: map_at_1000 value: 35.594 - type: map_at_3 value: 30.446 - type: map_at_5 value: 32.583 - type: mrr_at_1 value: 29.909000000000002 - type: mrr_at_10 value: 38.949 - type: mrr_at_100 value: 39.803 - type: mrr_at_1000 value: 39.867999999999995 - type: mrr_at_3 value: 35.921 - type: mrr_at_5 value: 37.753 - type: ndcg_at_1 value: 29.909000000000002 - type: ndcg_at_10 value: 40.012 - type: ndcg_at_100 value: 45.707 - type: ndcg_at_1000 value: 48.15 - type: ndcg_at_3 value: 34.015 - type: ndcg_at_5 value: 37.002 - type: precision_at_1 value: 29.909000000000002 - type: precision_at_10 value: 7.693999999999999 - type: precision_at_100 value: 1.2229999999999999 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 16.323999999999998 - type: precision_at_5 value: 12.306000000000001 - type: recall_at_1 value: 24.66 - type: recall_at_10 value: 52.478 - type: recall_at_100 value: 77.051 - type: recall_at_1000 value: 93.872 - type: recall_at_3 value: 36.382999999999996 - type: recall_at_5 value: 43.903999999999996 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 26.768416666666667 - type: map_at_10 value: 36.2485 - type: map_at_100 value: 37.520833333333336 - type: map_at_1000 value: 37.64033333333334 - type: map_at_3 value: 33.25791666666667 - type: map_at_5 value: 34.877250000000004 - type: mrr_at_1 value: 31.65408333333334 - type: mrr_at_10 value: 40.43866666666667 - type: mrr_at_100 value: 41.301249999999996 - type: mrr_at_1000 value: 41.357499999999995 - type: mrr_at_3 value: 37.938916666666664 - type: mrr_at_5 value: 39.35183333333334 - type: ndcg_at_1 value: 31.65408333333334 - type: ndcg_at_10 value: 41.76983333333334 - type: ndcg_at_100 value: 47.138 - type: ndcg_at_1000 value: 49.33816666666667 - type: ndcg_at_3 value: 36.76683333333333 - type: ndcg_at_5 value: 39.04441666666666 - type: precision_at_1 value: 31.65408333333334 - type: precision_at_10 value: 7.396249999999998 - type: precision_at_100 value: 1.1974166666666666 - type: precision_at_1000 value: 0.15791666666666668 - type: precision_at_3 value: 16.955583333333333 - type: precision_at_5 value: 12.09925 - type: recall_at_1 value: 26.768416666666667 - type: recall_at_10 value: 53.82366666666667 - type: recall_at_100 value: 77.39600000000002 - type: recall_at_1000 value: 92.46300000000001 - type: recall_at_3 value: 39.90166666666667 - type: recall_at_5 value: 45.754000000000005 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackStatsRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 24.369 - type: map_at_10 value: 32.025 - type: map_at_100 value: 33.08 - type: map_at_1000 value: 33.169 - type: map_at_3 value: 29.589 - type: map_at_5 value: 30.894 - type: mrr_at_1 value: 27.301 - type: mrr_at_10 value: 34.64 - type: mrr_at_100 value: 35.556 - type: mrr_at_1000 value: 35.616 - type: mrr_at_3 value: 32.515 - type: mrr_at_5 value: 33.666000000000004 - type: ndcg_at_1 value: 27.301 - type: ndcg_at_10 value: 36.386 - type: ndcg_at_100 value: 41.598 - type: ndcg_at_1000 value: 43.864999999999995 - type: ndcg_at_3 value: 32.07 - type: ndcg_at_5 value: 34.028999999999996 - type: precision_at_1 value: 27.301 - type: precision_at_10 value: 5.782 - type: precision_at_100 value: 0.923 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 13.804 - type: precision_at_5 value: 9.693 - type: recall_at_1 value: 24.369 - type: recall_at_10 value: 47.026 - type: recall_at_100 value: 70.76400000000001 - type: recall_at_1000 value: 87.705 - type: recall_at_3 value: 35.366 - type: recall_at_5 value: 40.077 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackTexRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 17.878 - type: map_at_10 value: 25.582 - type: map_at_100 value: 26.848 - type: map_at_1000 value: 26.985 - type: map_at_3 value: 22.997 - type: map_at_5 value: 24.487000000000002 - type: mrr_at_1 value: 22.023 - type: mrr_at_10 value: 29.615000000000002 - type: mrr_at_100 value: 30.656 - type: mrr_at_1000 value: 30.737 - type: mrr_at_3 value: 27.322999999999997 - type: mrr_at_5 value: 28.665000000000003 - type: ndcg_at_1 value: 22.023 - type: ndcg_at_10 value: 30.476999999999997 - type: ndcg_at_100 value: 36.258 - type: ndcg_at_1000 value: 39.287 - type: ndcg_at_3 value: 25.995 - type: ndcg_at_5 value: 28.174 - type: precision_at_1 value: 22.023 - type: precision_at_10 value: 5.657 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 12.491 - type: precision_at_5 value: 9.112 - type: recall_at_1 value: 17.878 - type: recall_at_10 value: 41.155 - type: recall_at_100 value: 66.62599999999999 - type: recall_at_1000 value: 88.08200000000001 - type: recall_at_3 value: 28.505000000000003 - type: recall_at_5 value: 34.284 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackUnixRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 26.369999999999997 - type: map_at_10 value: 36.115 - type: map_at_100 value: 37.346000000000004 - type: map_at_1000 value: 37.449 - type: map_at_3 value: 32.976 - type: map_at_5 value: 34.782000000000004 - type: mrr_at_1 value: 30.784 - type: mrr_at_10 value: 40.014 - type: mrr_at_100 value: 40.913 - type: mrr_at_1000 value: 40.967999999999996 - type: mrr_at_3 value: 37.205 - type: mrr_at_5 value: 38.995999999999995 - type: ndcg_at_1 value: 30.784 - type: ndcg_at_10 value: 41.797000000000004 - type: ndcg_at_100 value: 47.355000000000004 - type: ndcg_at_1000 value: 49.535000000000004 - type: ndcg_at_3 value: 36.29 - type: ndcg_at_5 value: 39.051 - type: precision_at_1 value: 30.784 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.122 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 16.636 - type: precision_at_5 value: 11.996 - type: recall_at_1 value: 26.369999999999997 - type: recall_at_10 value: 55.010000000000005 - type: recall_at_100 value: 79.105 - type: recall_at_1000 value: 94.053 - type: recall_at_3 value: 40.139 - type: recall_at_5 value: 47.089 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackWebmastersRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 26.421 - type: map_at_10 value: 35.253 - type: map_at_100 value: 36.97 - type: map_at_1000 value: 37.195 - type: map_at_3 value: 32.068000000000005 - type: map_at_5 value: 33.763 - type: mrr_at_1 value: 31.423000000000002 - type: mrr_at_10 value: 39.995999999999995 - type: mrr_at_100 value: 40.977999999999994 - type: mrr_at_1000 value: 41.024 - type: mrr_at_3 value: 36.989 - type: mrr_at_5 value: 38.629999999999995 - type: ndcg_at_1 value: 31.423000000000002 - type: ndcg_at_10 value: 41.382000000000005 - type: ndcg_at_100 value: 47.532000000000004 - type: ndcg_at_1000 value: 49.829 - type: ndcg_at_3 value: 35.809000000000005 - type: ndcg_at_5 value: 38.308 - type: precision_at_1 value: 31.423000000000002 - type: precision_at_10 value: 7.885000000000001 - type: precision_at_100 value: 1.609 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.469 - type: precision_at_5 value: 12.174 - type: recall_at_1 value: 26.421 - type: recall_at_10 value: 53.618 - type: recall_at_100 value: 80.456 - type: recall_at_1000 value: 94.505 - type: recall_at_3 value: 37.894 - type: recall_at_5 value: 44.352999999999994 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackWordpressRetrieval revision: None split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 21.54 - type: map_at_10 value: 29.468 - type: map_at_100 value: 30.422 - type: map_at_1000 value: 30.542 - type: map_at_3 value: 26.888 - type: map_at_5 value: 27.962999999999997 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 31.176 - type: mrr_at_100 value: 32.046 - type: mrr_at_1000 value: 32.129000000000005 - type: mrr_at_3 value: 28.804999999999996 - type: mrr_at_5 value: 29.868 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 34.166000000000004 - type: ndcg_at_100 value: 39.217999999999996 - type: ndcg_at_1000 value: 41.964 - type: ndcg_at_3 value: 28.970000000000002 - type: ndcg_at_5 value: 30.797 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 5.489999999999999 - type: precision_at_100 value: 0.874 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 12.261 - type: precision_at_5 value: 8.503 - type: recall_at_1 value: 21.54 - type: recall_at_10 value: 47.064 - type: recall_at_100 value: 70.959 - type: recall_at_1000 value: 91.032 - type: recall_at_3 value: 32.828 - type: recall_at_5 value: 37.214999999999996 task: type: Retrieval - dataset: config: default name: MTEB ClimateFEVER revision: None split: test type: climate-fever metrics: - type: map_at_1 value: 10.102 - type: map_at_10 value: 17.469 - type: map_at_100 value: 19.244 - type: map_at_1000 value: 19.435 - type: map_at_3 value: 14.257 - type: map_at_5 value: 16.028000000000002 - type: mrr_at_1 value: 22.866 - type: mrr_at_10 value: 33.535 - type: mrr_at_100 value: 34.583999999999996 - type: mrr_at_1000 value: 34.622 - type: mrr_at_3 value: 29.946 - type: mrr_at_5 value: 32.157000000000004 - type: ndcg_at_1 value: 22.866 - type: ndcg_at_10 value: 25.16 - type: ndcg_at_100 value: 32.347 - type: ndcg_at_1000 value: 35.821 - type: ndcg_at_3 value: 19.816 - type: ndcg_at_5 value: 22.026 - type: precision_at_1 value: 22.866 - type: precision_at_10 value: 8.072 - type: precision_at_100 value: 1.5709999999999997 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 14.701 - type: precision_at_5 value: 11.960999999999999 - type: recall_at_1 value: 10.102 - type: recall_at_10 value: 31.086000000000002 - type: recall_at_100 value: 55.896 - type: recall_at_1000 value: 75.375 - type: recall_at_3 value: 18.343999999999998 - type: recall_at_5 value: 24.102 task: type: Retrieval - dataset: config: default name: MTEB DBPedia revision: None split: test type: dbpedia-entity metrics: - type: map_at_1 value: 7.961 - type: map_at_10 value: 16.058 - type: map_at_100 value: 21.878 - type: map_at_1000 value: 23.156 - type: map_at_3 value: 12.206999999999999 - type: map_at_5 value: 13.747000000000002 - type: mrr_at_1 value: 60.5 - type: mrr_at_10 value: 68.488 - type: mrr_at_100 value: 69.02199999999999 - type: mrr_at_1000 value: 69.03200000000001 - type: mrr_at_3 value: 66.792 - type: mrr_at_5 value: 67.62899999999999 - type: ndcg_at_1 value: 49.125 - type: ndcg_at_10 value: 34.827999999999996 - type: ndcg_at_100 value: 38.723 - type: ndcg_at_1000 value: 45.988 - type: ndcg_at_3 value: 40.302 - type: ndcg_at_5 value: 36.781000000000006 - type: precision_at_1 value: 60.5 - type: precision_at_10 value: 26.825 - type: precision_at_100 value: 8.445 - type: precision_at_1000 value: 1.7000000000000002 - type: precision_at_3 value: 43.25 - type: precision_at_5 value: 34.5 - type: recall_at_1 value: 7.961 - type: recall_at_10 value: 20.843 - type: recall_at_100 value: 43.839 - type: recall_at_1000 value: 67.33 - type: recall_at_3 value: 13.516 - type: recall_at_5 value: 15.956000000000001 task: type: Retrieval - dataset: config: default name: MTEB EmotionClassification revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 split: test type: mteb/emotion metrics: - type: accuracy value: 52.06000000000001 - type: f1 value: 47.21494728335567 task: type: Classification - dataset: config: default name: MTEB FEVER revision: None split: test type: fever metrics: - type: map_at_1 value: 56.798 - type: map_at_10 value: 67.644 - type: map_at_100 value: 68.01700000000001 - type: map_at_1000 value: 68.038 - type: map_at_3 value: 65.539 - type: map_at_5 value: 66.912 - type: mrr_at_1 value: 61.221000000000004 - type: mrr_at_10 value: 71.97099999999999 - type: mrr_at_100 value: 72.262 - type: mrr_at_1000 value: 72.27 - type: mrr_at_3 value: 70.052 - type: mrr_at_5 value: 71.324 - type: ndcg_at_1 value: 61.221000000000004 - type: ndcg_at_10 value: 73.173 - type: ndcg_at_100 value: 74.779 - type: ndcg_at_1000 value: 75.229 - type: ndcg_at_3 value: 69.291 - type: ndcg_at_5 value: 71.552 - type: precision_at_1 value: 61.221000000000004 - type: precision_at_10 value: 9.449 - type: precision_at_100 value: 1.0370000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 27.467999999999996 - type: precision_at_5 value: 17.744 - type: recall_at_1 value: 56.798 - type: recall_at_10 value: 85.991 - type: recall_at_100 value: 92.973 - type: recall_at_1000 value: 96.089 - type: recall_at_3 value: 75.576 - type: recall_at_5 value: 81.12 task: type: Retrieval - dataset: config: default name: MTEB FiQA2018 revision: None split: test type: fiqa metrics: - type: map_at_1 value: 18.323 - type: map_at_10 value: 30.279 - type: map_at_100 value: 32.153999999999996 - type: map_at_1000 value: 32.339 - type: map_at_3 value: 26.336 - type: map_at_5 value: 28.311999999999998 - type: mrr_at_1 value: 35.339999999999996 - type: mrr_at_10 value: 44.931 - type: mrr_at_100 value: 45.818999999999996 - type: mrr_at_1000 value: 45.864 - type: mrr_at_3 value: 42.618 - type: mrr_at_5 value: 43.736999999999995 - type: ndcg_at_1 value: 35.339999999999996 - type: ndcg_at_10 value: 37.852999999999994 - type: ndcg_at_100 value: 44.888 - type: ndcg_at_1000 value: 48.069 - type: ndcg_at_3 value: 34.127 - type: ndcg_at_5 value: 35.026 - type: precision_at_1 value: 35.339999999999996 - type: precision_at_10 value: 10.617 - type: precision_at_100 value: 1.7930000000000001 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 22.582 - type: precision_at_5 value: 16.605 - type: recall_at_1 value: 18.323 - type: recall_at_10 value: 44.948 - type: recall_at_100 value: 71.11800000000001 - type: recall_at_1000 value: 90.104 - type: recall_at_3 value: 31.661 - type: recall_at_5 value: 36.498000000000005 task: type: Retrieval - dataset: config: default name: MTEB HotpotQA revision: None split: test type: hotpotqa metrics: - type: map_at_1 value: 30.668 - type: map_at_10 value: 43.669999999999995 - type: map_at_100 value: 44.646 - type: map_at_1000 value: 44.731 - type: map_at_3 value: 40.897 - type: map_at_5 value: 42.559999999999995 - type: mrr_at_1 value: 61.336999999999996 - type: mrr_at_10 value: 68.496 - type: mrr_at_100 value: 68.916 - type: mrr_at_1000 value: 68.938 - type: mrr_at_3 value: 66.90700000000001 - type: mrr_at_5 value: 67.91199999999999 - type: ndcg_at_1 value: 61.336999999999996 - type: ndcg_at_10 value: 52.588 - type: ndcg_at_100 value: 56.389 - type: ndcg_at_1000 value: 58.187999999999995 - type: ndcg_at_3 value: 48.109 - type: ndcg_at_5 value: 50.498 - type: precision_at_1 value: 61.336999999999996 - type: precision_at_10 value: 11.033 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 30.105999999999998 - type: precision_at_5 value: 19.954 - type: recall_at_1 value: 30.668 - type: recall_at_10 value: 55.165 - type: recall_at_100 value: 70.169 - type: recall_at_1000 value: 82.12 - type: recall_at_3 value: 45.159 - type: recall_at_5 value: 49.885000000000005 task: type: Retrieval - dataset: config: default name: MTEB ImdbClassification revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics: - type: accuracy value: 78.542 - type: ap value: 72.50692137216646 - type: f1 value: 78.40630687221642 task: type: Classification - dataset: config: default name: MTEB MSMARCO revision: None split: dev type: msmarco metrics: - type: map_at_1 value: 18.613 - type: map_at_10 value: 29.98 - type: map_at_100 value: 31.136999999999997 - type: map_at_1000 value: 31.196 - type: map_at_3 value: 26.339000000000002 - type: map_at_5 value: 28.351 - type: mrr_at_1 value: 19.054 - type: mrr_at_10 value: 30.476 - type: mrr_at_100 value: 31.588 - type: mrr_at_1000 value: 31.641000000000002 - type: mrr_at_3 value: 26.834000000000003 - type: mrr_at_5 value: 28.849000000000004 - type: ndcg_at_1 value: 19.083 - type: ndcg_at_10 value: 36.541000000000004 - type: ndcg_at_100 value: 42.35 - type: ndcg_at_1000 value: 43.9 - type: ndcg_at_3 value: 29.015 - type: ndcg_at_5 value: 32.622 - type: precision_at_1 value: 19.083 - type: precision_at_10 value: 5.914 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 12.483 - type: precision_at_5 value: 9.315 - type: recall_at_1 value: 18.613 - type: recall_at_10 value: 56.88999999999999 - type: recall_at_100 value: 84.207 - type: recall_at_1000 value: 96.20100000000001 - type: recall_at_3 value: 36.262 - type: recall_at_5 value: 44.925 task: type: Retrieval - dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 94.77656178750571 - type: f1 value: 94.37966073742972 task: type: Classification - dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 77.72457820337438 - type: f1 value: 59.11327646329634 task: type: Classification - dataset: config: en name: MTEB MassiveIntentClassification (en) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 73.17753866846 - type: f1 value: 71.22604635414544 task: type: Classification - dataset: config: en name: MTEB MassiveScenarioClassification (en) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 76.67787491593813 - type: f1 value: 76.87653151298177 task: type: Classification - dataset: config: default name: MTEB MedrxivClusteringP2P revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics: - type: v_measure value: 33.3485843514749 task: type: Clustering - dataset: config: default name: MTEB MedrxivClusteringS2S revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 split: test type: mteb/medrxiv-clustering-s2s metrics: - type: v_measure value: 29.792796913883617 task: type: Clustering - dataset: config: default name: MTEB MindSmallReranking revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 split: test type: mteb/mind_small metrics: - type: map value: 31.310305659169963 - type: mrr value: 32.38286775798406 task: type: Reranking - dataset: config: default name: MTEB NFCorpus revision: None split: test type: nfcorpus metrics: - type: map_at_1 value: 4.968 - type: map_at_10 value: 11.379 - type: map_at_100 value: 14.618999999999998 - type: map_at_1000 value: 16.055 - type: map_at_3 value: 8.34 - type: map_at_5 value: 9.690999999999999 - type: mrr_at_1 value: 43.034 - type: mrr_at_10 value: 51.019999999999996 - type: mrr_at_100 value: 51.63100000000001 - type: mrr_at_1000 value: 51.681 - type: mrr_at_3 value: 49.174 - type: mrr_at_5 value: 50.181 - type: ndcg_at_1 value: 41.176 - type: ndcg_at_10 value: 31.341 - type: ndcg_at_100 value: 29.451 - type: ndcg_at_1000 value: 38.007000000000005 - type: ndcg_at_3 value: 36.494 - type: ndcg_at_5 value: 34.499 - type: precision_at_1 value: 43.034 - type: precision_at_10 value: 23.375 - type: precision_at_100 value: 7.799 - type: precision_at_1000 value: 2.059 - type: precision_at_3 value: 34.675 - type: precision_at_5 value: 30.154999999999998 - type: recall_at_1 value: 4.968 - type: recall_at_10 value: 15.104999999999999 - type: recall_at_100 value: 30.741000000000003 - type: recall_at_1000 value: 61.182 - type: recall_at_3 value: 9.338000000000001 - type: recall_at_5 value: 11.484 task: type: Retrieval - dataset: config: default name: MTEB NQ revision: None split: test type: nq metrics: - type: map_at_1 value: 23.716 - type: map_at_10 value: 38.32 - type: map_at_100 value: 39.565 - type: map_at_1000 value: 39.602 - type: map_at_3 value: 33.848 - type: map_at_5 value: 36.471 - type: mrr_at_1 value: 26.912000000000003 - type: mrr_at_10 value: 40.607 - type: mrr_at_100 value: 41.589 - type: mrr_at_1000 value: 41.614000000000004 - type: mrr_at_3 value: 36.684 - type: mrr_at_5 value: 39.036 - type: ndcg_at_1 value: 26.883000000000003 - type: ndcg_at_10 value: 46.096 - type: ndcg_at_100 value: 51.513 - type: ndcg_at_1000 value: 52.366 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 41.971000000000004 - type: precision_at_1 value: 26.883000000000003 - type: precision_at_10 value: 8.004 - type: precision_at_100 value: 1.107 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 17.516000000000002 - type: precision_at_5 value: 13.019 - type: recall_at_1 value: 23.716 - type: recall_at_10 value: 67.656 - type: recall_at_100 value: 91.413 - type: recall_at_1000 value: 97.714 - type: recall_at_3 value: 45.449 - type: recall_at_5 value: 55.598000000000006 task: type: Retrieval - dataset: config: default name: MTEB QuoraRetrieval revision: None split: test type: quora metrics: - type: map_at_1 value: 70.486 - type: map_at_10 value: 84.292 - type: map_at_100 value: 84.954 - type: map_at_1000 value: 84.969 - type: map_at_3 value: 81.295 - type: map_at_5 value: 83.165 - type: mrr_at_1 value: 81.16 - type: mrr_at_10 value: 87.31 - type: mrr_at_100 value: 87.423 - type: mrr_at_1000 value: 87.423 - type: mrr_at_3 value: 86.348 - type: mrr_at_5 value: 86.991 - type: ndcg_at_1 value: 81.17 - type: ndcg_at_10 value: 88.067 - type: ndcg_at_100 value: 89.34 - type: ndcg_at_1000 value: 89.43900000000001 - type: ndcg_at_3 value: 85.162 - type: ndcg_at_5 value: 86.752 - type: precision_at_1 value: 81.17 - type: precision_at_10 value: 13.394 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.193 - type: precision_at_5 value: 24.482 - type: recall_at_1 value: 70.486 - type: recall_at_10 value: 95.184 - type: recall_at_100 value: 99.53999999999999 - type: recall_at_1000 value: 99.98700000000001 - type: recall_at_3 value: 86.89 - type: recall_at_5 value: 91.365 task: type: Retrieval - dataset: config: default name: MTEB RedditClustering revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics: - type: v_measure value: 44.118229475102154 task: type: Clustering - dataset: config: default name: MTEB RedditClusteringP2P revision: 282350215ef01743dc01b456c7f5241fa8937f16 split: test type: mteb/reddit-clustering-p2p metrics: - type: v_measure value: 48.68049097629063 task: type: Clustering - dataset: config: default name: MTEB SCIDOCS revision: None split: test type: scidocs metrics: - type: map_at_1 value: 4.888 - type: map_at_10 value: 12.770999999999999 - type: map_at_100 value: 15.238 - type: map_at_1000 value: 15.616 - type: map_at_3 value: 8.952 - type: map_at_5 value: 10.639999999999999 - type: mrr_at_1 value: 24.099999999999998 - type: mrr_at_10 value: 35.375 - type: mrr_at_100 value: 36.442 - type: mrr_at_1000 value: 36.488 - type: mrr_at_3 value: 31.717000000000002 - type: mrr_at_5 value: 33.722 - type: ndcg_at_1 value: 24.099999999999998 - type: ndcg_at_10 value: 21.438 - type: ndcg_at_100 value: 30.601 - type: ndcg_at_1000 value: 36.678 - type: ndcg_at_3 value: 19.861 - type: ndcg_at_5 value: 17.263 - type: precision_at_1 value: 24.099999999999998 - type: precision_at_10 value: 11.4 - type: precision_at_100 value: 2.465 - type: precision_at_1000 value: 0.392 - type: precision_at_3 value: 18.733 - type: precision_at_5 value: 15.22 - type: recall_at_1 value: 4.888 - type: recall_at_10 value: 23.118 - type: recall_at_100 value: 49.995 - type: recall_at_1000 value: 79.577 - type: recall_at_3 value: 11.398 - type: recall_at_5 value: 15.428 task: type: Retrieval - dataset: config: default name: MTEB SICK-R revision: a6ea5a8cab320b040a23452cc28066d9beae2cee split: test type: mteb/sickr-sts metrics: - type: cos_sim_pearson value: 85.33198632617024 - type: cos_sim_spearman value: 79.09232997136625 - type: euclidean_pearson value: 81.49986011523868 - type: euclidean_spearman value: 77.03530620283338 - type: manhattan_pearson value: 81.4741227286667 - type: manhattan_spearman value: 76.98641133116311 task: type: STS - dataset: config: default name: MTEB STS12 revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cos_sim_pearson value: 84.60103674582464 - type: cos_sim_spearman value: 75.03945035801914 - type: euclidean_pearson value: 80.82455267481467 - type: euclidean_spearman value: 70.3317366248871 - type: manhattan_pearson value: 80.8928091531445 - type: manhattan_spearman value: 70.43207370945672 task: type: STS - dataset: config: default name: MTEB STS13 revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cos_sim_pearson value: 82.52453177109315 - type: cos_sim_spearman value: 83.26431569305103 - type: euclidean_pearson value: 82.10494657997404 - type: euclidean_spearman value: 83.41028425949024 - type: manhattan_pearson value: 82.08669822983934 - type: manhattan_spearman value: 83.39959776442115 task: type: STS - dataset: config: default name: MTEB STS14 revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cos_sim_pearson value: 82.67472020277681 - type: cos_sim_spearman value: 78.61877889763109 - type: euclidean_pearson value: 80.07878012437722 - type: euclidean_spearman value: 77.44374494215397 - type: manhattan_pearson value: 79.95988483102258 - type: manhattan_spearman value: 77.36018101061366 task: type: STS - dataset: config: default name: MTEB STS15 revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cos_sim_pearson value: 85.55450610494437 - type: cos_sim_spearman value: 87.03494331841401 - type: euclidean_pearson value: 81.4319784394287 - type: euclidean_spearman value: 82.47893040599372 - type: manhattan_pearson value: 81.32627203699644 - type: manhattan_spearman value: 82.40660565070675 task: type: STS - dataset: config: default name: MTEB STS16 revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cos_sim_pearson value: 81.51576965454805 - type: cos_sim_spearman value: 83.0062959588245 - type: euclidean_pearson value: 79.98888882568556 - type: euclidean_spearman value: 81.08948911791873 - type: manhattan_pearson value: 79.77952719568583 - type: manhattan_spearman value: 80.79471040445408 task: type: STS - dataset: config: en-en name: MTEB STS17 (en-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 87.28313046682885 - type: cos_sim_spearman value: 87.35865211085007 - type: euclidean_pearson value: 84.11501613667811 - type: euclidean_spearman value: 82.82038954956121 - type: manhattan_pearson value: 83.891278147302 - type: manhattan_spearman value: 82.59947685165902 task: type: STS - dataset: config: en name: MTEB STS22 (en) revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 67.80653738006102 - type: cos_sim_spearman value: 68.11259151179601 - type: euclidean_pearson value: 43.16707985094242 - type: euclidean_spearman value: 58.96200382968696 - type: manhattan_pearson value: 43.84146858566507 - type: manhattan_spearman value: 59.05193977207514 task: type: STS - dataset: config: default name: MTEB STSBenchmark revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cos_sim_pearson value: 82.62068205073571 - type: cos_sim_spearman value: 84.40071593577095 - type: euclidean_pearson value: 80.90824726252514 - type: euclidean_spearman value: 80.54974812534094 - type: manhattan_pearson value: 80.6759008187939 - type: manhattan_spearman value: 80.31149103896973 task: type: STS - dataset: config: default name: MTEB SciDocsRR revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics: - type: map value: 87.13774787530915 - type: mrr value: 96.22233793802422 task: type: Reranking - dataset: config: default name: MTEB SciFact revision: None split: test type: scifact metrics: - type: map_at_1 value: 49.167 - type: map_at_10 value: 59.852000000000004 - type: map_at_100 value: 60.544 - type: map_at_1000 value: 60.577000000000005 - type: map_at_3 value: 57.242000000000004 - type: map_at_5 value: 58.704 - type: mrr_at_1 value: 51.0 - type: mrr_at_10 value: 60.575 - type: mrr_at_100 value: 61.144 - type: mrr_at_1000 value: 61.175000000000004 - type: mrr_at_3 value: 58.667 - type: mrr_at_5 value: 59.599999999999994 - type: ndcg_at_1 value: 51.0 - type: ndcg_at_10 value: 64.398 - type: ndcg_at_100 value: 67.581 - type: ndcg_at_1000 value: 68.551 - type: ndcg_at_3 value: 59.928000000000004 - type: ndcg_at_5 value: 61.986 - type: precision_at_1 value: 51.0 - type: precision_at_10 value: 8.7 - type: precision_at_100 value: 1.047 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 23.666999999999998 - type: precision_at_5 value: 15.6 - type: recall_at_1 value: 49.167 - type: recall_at_10 value: 77.333 - type: recall_at_100 value: 91.833 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 65.594 - type: recall_at_5 value: 70.52199999999999 task: type: Retrieval - dataset: config: default name: MTEB SprintDuplicateQuestions revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics: - type: cos_sim_accuracy value: 99.77227722772277 - type: cos_sim_ap value: 94.14261011689366 - type: cos_sim_f1 value: 88.37209302325581 - type: cos_sim_precision value: 89.36605316973414 - type: cos_sim_recall value: 87.4 - type: dot_accuracy value: 99.07128712871287 - type: dot_ap value: 27.325649239129486 - type: dot_f1 value: 33.295838020247466 - type: dot_precision value: 38.04627249357326 - type: dot_recall value: 29.599999999999998 - type: euclidean_accuracy value: 99.74158415841585 - type: euclidean_ap value: 92.32695359979576 - type: euclidean_f1 value: 86.90534575772439 - type: euclidean_precision value: 85.27430221366699 - type: euclidean_recall value: 88.6 - type: manhattan_accuracy value: 99.74257425742574 - type: manhattan_ap value: 92.40335687760499 - type: manhattan_f1 value: 86.96507624200687 - type: manhattan_precision value: 85.57599225556632 - type: manhattan_recall value: 88.4 - type: max_accuracy value: 99.77227722772277 - type: max_ap value: 94.14261011689366 - type: max_f1 value: 88.37209302325581 task: type: PairClassification - dataset: config: default name: MTEB StackExchangeClustering revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: v_measure value: 53.113809982945035 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - type: v_measure value: 33.90915908471812 task: type: Clustering - dataset: config: default name: MTEB StackOverflowDupQuestions revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics: - type: map value: 50.36481271702464 - type: mrr value: 51.05628236142942 task: type: Reranking - dataset: config: default name: MTEB SummEval revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cos_sim_pearson value: 30.311305530381826 - type: cos_sim_spearman value: 31.22029657606254 - type: dot_pearson value: 12.157032445910177 - type: dot_spearman value: 13.275185888551805 task: type: Summarization - dataset: config: default name: MTEB TRECCOVID revision: None split: test type: trec-covid metrics: - type: map_at_1 value: 0.167 - type: map_at_10 value: 1.113 - type: map_at_100 value: 5.926 - type: map_at_1000 value: 15.25 - type: map_at_3 value: 0.414 - type: map_at_5 value: 0.633 - type: mrr_at_1 value: 64.0 - type: mrr_at_10 value: 74.444 - type: mrr_at_100 value: 74.667 - type: mrr_at_1000 value: 74.679 - type: mrr_at_3 value: 72.0 - type: mrr_at_5 value: 74.0 - type: ndcg_at_1 value: 59.0 - type: ndcg_at_10 value: 51.468 - type: ndcg_at_100 value: 38.135000000000005 - type: ndcg_at_1000 value: 36.946 - type: ndcg_at_3 value: 55.827000000000005 - type: ndcg_at_5 value: 53.555 - type: precision_at_1 value: 64.0 - type: precision_at_10 value: 54.400000000000006 - type: precision_at_100 value: 39.08 - type: precision_at_1000 value: 16.618 - type: precision_at_3 value: 58.667 - type: precision_at_5 value: 56.8 - type: recall_at_1 value: 0.167 - type: recall_at_10 value: 1.38 - type: recall_at_100 value: 9.189 - type: recall_at_1000 value: 35.737 - type: recall_at_3 value: 0.455 - type: recall_at_5 value: 0.73 task: type: Retrieval - dataset: config: default name: MTEB Touche2020 revision: None split: test type: webis-touche2020 metrics: - type: map_at_1 value: 2.4299999999999997 - type: map_at_10 value: 8.539 - type: map_at_100 value: 14.155999999999999 - type: map_at_1000 value: 15.684999999999999 - type: map_at_3 value: 3.857 - type: map_at_5 value: 5.583 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 40.489999999999995 - type: mrr_at_100 value: 41.772999999999996 - type: mrr_at_1000 value: 41.772999999999996 - type: mrr_at_3 value: 35.034 - type: mrr_at_5 value: 38.81 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 20.787 - type: ndcg_at_100 value: 33.202 - type: ndcg_at_1000 value: 45.167 - type: ndcg_at_3 value: 18.233 - type: ndcg_at_5 value: 19.887 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.4079999999999995 - type: precision_at_1000 value: 1.5310000000000001 - type: precision_at_3 value: 19.728 - type: precision_at_5 value: 21.633 - type: recall_at_1 value: 2.4299999999999997 - type: recall_at_10 value: 14.901 - type: recall_at_100 value: 46.422000000000004 - type: recall_at_1000 value: 82.83500000000001 - type: recall_at_3 value: 4.655 - type: recall_at_5 value: 8.092 task: type: Retrieval - dataset: config: default name: MTEB ToxicConversationsClassification revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c split: test type: mteb/toxic_conversations_50k metrics: - type: accuracy value: 72.90140000000001 - type: ap value: 15.138716624430662 - type: f1 value: 56.08803013269606 task: type: Classification - dataset: config: default name: MTEB TweetSentimentExtractionClassification revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics: - type: accuracy value: 59.85285795132994 - type: f1 value: 60.17575819903709 task: type: Classification - dataset: config: default name: MTEB TwentyNewsgroupsClustering revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics: - type: v_measure value: 41.125150148437065 task: type: Clustering - dataset: config: default name: MTEB TwitterSemEval2015 revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics: - type: cos_sim_accuracy value: 84.96751505036657 - type: cos_sim_ap value: 70.45642872444971 - type: cos_sim_f1 value: 65.75274793133259 - type: cos_sim_precision value: 61.806361736707686 - type: cos_sim_recall value: 70.23746701846966 - type: dot_accuracy value: 77.84466829588126 - type: dot_ap value: 32.49904328313596 - type: dot_f1 value: 37.903122189387126 - type: dot_precision value: 25.050951086956523 - type: dot_recall value: 77.83641160949868 - type: euclidean_accuracy value: 84.5920009536866 - type: euclidean_ap value: 68.83700633574043 - type: euclidean_f1 value: 64.92803542871202 - type: euclidean_precision value: 60.820465545056464 - type: euclidean_recall value: 69.63060686015831 - type: manhattan_accuracy value: 84.52643500029802 - type: manhattan_ap value: 68.63286046599892 - type: manhattan_f1 value: 64.7476540705047 - type: manhattan_precision value: 62.3291015625 - type: manhattan_recall value: 67.36147757255937 - type: max_accuracy value: 84.96751505036657 - type: max_ap value: 70.45642872444971 - type: max_f1 value: 65.75274793133259 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cos_sim_accuracy value: 88.65603291031164 - type: cos_sim_ap value: 85.58148320880878 - type: cos_sim_f1 value: 77.63202920041064 - type: cos_sim_precision value: 76.68444377675957 - type: cos_sim_recall value: 78.60332614721281 - type: dot_accuracy value: 79.71048239996895 - type: dot_ap value: 59.31114839296281 - type: dot_f1 value: 57.13895527483783 - type: dot_precision value: 51.331125015335545 - type: dot_recall value: 64.4287034185402 - type: euclidean_accuracy value: 86.99305312997244 - type: euclidean_ap value: 81.87075965254876 - type: euclidean_f1 value: 73.53543008715421 - type: euclidean_precision value: 72.39964184450082 - type: euclidean_recall value: 74.70742223591007 - type: manhattan_accuracy value: 87.04156479217605 - type: manhattan_ap value: 81.7850497283247 - type: manhattan_f1 value: 73.52951955143475 - type: manhattan_precision value: 70.15875236030492 - type: manhattan_recall value: 77.2405297197413 - type: max_accuracy value: 88.65603291031164 - type: max_ap value: 85.58148320880878 - type: max_f1 value: 77.63202920041064 task: type: PairClassification model_creator: avsolatorio model_name: GIST-all-MiniLM-L6-v2 pipeline_tag: text-generation quantized_by: afrideva tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers - gguf - ggml - quantized --- # GIST-all-MiniLM-L6-v2-GGUF Quantized GGUF model files for [GIST-all-MiniLM-L6-v2](https://huggingface.co/avsolatorio/GIST-all-MiniLM-L6-v2) from [avsolatorio](https://huggingface.co/avsolatorio) ## Original Model Card:

GIST Embedding v0 - all-MiniLM-L6-v2

*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) # Data The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. # Usage The model can be easily loaded using the Sentence Transformers library. ```Python import torch.nn.functional as F from sentence_transformers import SentenceTransformer revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. model = SentenceTransformer("avsolatorio/GIST-all-MiniLM-L6-v2", revision=revision) texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = model.encode(texts, convert_to_tensor=True) # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) ``` # Training Parameters Below are the training parameters used to fine-tune the model: ``` Epochs = 40 Warmup ratio = 0.1 Learning rate = 5e-6 Batch size = 16 Checkpoint step = 102000 Contrastive loss temperature = 0.01 ``` # Evaluation The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. # Citation Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # Acknowledgements This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.