--- tags: - ctranslate2 - int8 - float16 - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-base results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.17910447761193 - type: ap value: 36.827146398068926 - type: f1 value: 68.11292888046363 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.77345000000001 - type: ap value: 88.33530426691347 - type: f1 value: 91.76549906404642 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.964 - type: f1 value: 48.22995586184998 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.147999999999996 - type: map_at_10 value: 48.253 - type: map_at_100 value: 49.038 - type: map_at_1000 value: 49.042 - type: map_at_3 value: 43.433 - type: map_at_5 value: 46.182 - type: mrr_at_1 value: 32.717 - type: mrr_at_10 value: 48.467 - type: mrr_at_100 value: 49.252 - type: mrr_at_1000 value: 49.254999999999995 - type: mrr_at_3 value: 43.599 - type: mrr_at_5 value: 46.408 - type: ndcg_at_1 value: 32.147999999999996 - type: ndcg_at_10 value: 57.12199999999999 - type: ndcg_at_100 value: 60.316 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 47.178 - type: ndcg_at_5 value: 52.146 - type: precision_at_1 value: 32.147999999999996 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.346 - type: precision_at_5 value: 14.026 - type: recall_at_1 value: 32.147999999999996 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 99.004 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 58.037000000000006 - type: recall_at_5 value: 70.128 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.59706013699614 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.01463593002057 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.80250355752458 - type: mrr value: 74.79455216989844 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.87448576082345 - type: cos_sim_spearman value: 87.64235843637468 - type: euclidean_pearson value: 88.4901825511062 - type: euclidean_spearman value: 87.74537283182033 - type: manhattan_pearson value: 88.39040638362911 - type: manhattan_spearman value: 87.62669542888003 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.06818181818183 - type: f1 value: 85.02524460098233 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.20471092679967 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.58967592147641 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.411 - type: map_at_10 value: 45.162 - type: map_at_100 value: 46.717 - type: map_at_1000 value: 46.836 - type: map_at_3 value: 41.428 - type: map_at_5 value: 43.54 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 51.534 - type: mrr_at_100 value: 52.185 - type: mrr_at_1000 value: 52.22 - type: mrr_at_3 value: 49.046 - type: mrr_at_5 value: 50.548 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 52.235 - type: ndcg_at_100 value: 57.4 - type: ndcg_at_1000 value: 58.982 - type: ndcg_at_3 value: 47.332 - type: ndcg_at_5 value: 49.62 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 10.258000000000001 - type: precision_at_100 value: 1.6219999999999999 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 23.462 - type: precision_at_5 value: 16.71 - type: recall_at_1 value: 32.411 - type: recall_at_10 value: 65.408 - type: recall_at_100 value: 87.248 - type: recall_at_1000 value: 96.951 - type: recall_at_3 value: 50.349999999999994 - type: recall_at_5 value: 57.431 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.911 - type: map_at_10 value: 42.608000000000004 - type: map_at_100 value: 43.948 - type: map_at_1000 value: 44.089 - type: map_at_3 value: 39.652 - type: map_at_5 value: 41.236 - type: mrr_at_1 value: 40.064 - type: mrr_at_10 value: 48.916 - type: mrr_at_100 value: 49.539 - type: mrr_at_1000 value: 49.583 - type: mrr_at_3 value: 46.741 - type: mrr_at_5 value: 48.037 - type: ndcg_at_1 value: 40.064 - type: ndcg_at_10 value: 48.442 - type: ndcg_at_100 value: 52.798 - type: ndcg_at_1000 value: 54.871 - type: ndcg_at_3 value: 44.528 - type: ndcg_at_5 value: 46.211 - type: precision_at_1 value: 40.064 - type: precision_at_10 value: 9.178 - type: precision_at_100 value: 1.452 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 21.614 - type: precision_at_5 value: 15.185 - type: recall_at_1 value: 31.911 - type: recall_at_10 value: 58.155 - type: recall_at_100 value: 76.46300000000001 - type: recall_at_1000 value: 89.622 - type: recall_at_3 value: 46.195 - type: recall_at_5 value: 51.288999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.597 - type: map_at_10 value: 54.290000000000006 - type: map_at_100 value: 55.340999999999994 - type: map_at_1000 value: 55.388999999999996 - type: map_at_3 value: 50.931000000000004 - type: map_at_5 value: 52.839999999999996 - type: mrr_at_1 value: 46.646 - type: mrr_at_10 value: 57.524 - type: mrr_at_100 value: 58.225 - type: mrr_at_1000 value: 58.245999999999995 - type: mrr_at_3 value: 55.235 - type: mrr_at_5 value: 56.589 - type: ndcg_at_1 value: 46.646 - type: ndcg_at_10 value: 60.324999999999996 - type: ndcg_at_100 value: 64.30900000000001 - type: ndcg_at_1000 value: 65.19 - type: ndcg_at_3 value: 54.983000000000004 - type: ndcg_at_5 value: 57.621 - type: precision_at_1 value: 46.646 - type: precision_at_10 value: 9.774 - type: precision_at_100 value: 1.265 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 24.911 - type: precision_at_5 value: 16.977999999999998 - type: recall_at_1 value: 40.597 - type: recall_at_10 value: 74.773 - type: recall_at_100 value: 91.61200000000001 - type: recall_at_1000 value: 97.726 - type: recall_at_3 value: 60.458 - type: recall_at_5 value: 66.956 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.122 - type: map_at_10 value: 36.711 - type: map_at_100 value: 37.775 - type: map_at_1000 value: 37.842999999999996 - type: map_at_3 value: 33.693 - type: map_at_5 value: 35.607 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.873999999999995 - type: mrr_at_100 value: 39.739000000000004 - type: mrr_at_1000 value: 39.794000000000004 - type: mrr_at_3 value: 36.102000000000004 - type: mrr_at_5 value: 37.876 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 42.048 - type: ndcg_at_100 value: 47.144999999999996 - type: ndcg_at_1000 value: 48.901 - type: ndcg_at_3 value: 36.402 - type: ndcg_at_5 value: 39.562999999999995 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.4750000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 15.479999999999999 - type: precision_at_5 value: 11.028 - type: recall_at_1 value: 27.122 - type: recall_at_10 value: 56.279999999999994 - type: recall_at_100 value: 79.597 - type: recall_at_1000 value: 92.804 - type: recall_at_3 value: 41.437000000000005 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.757 - type: map_at_10 value: 26.739 - type: map_at_100 value: 28.015 - type: map_at_1000 value: 28.127999999999997 - type: map_at_3 value: 23.986 - type: map_at_5 value: 25.514 - type: mrr_at_1 value: 22.015 - type: mrr_at_10 value: 31.325999999999997 - type: mrr_at_100 value: 32.368 - type: mrr_at_1000 value: 32.426 - type: mrr_at_3 value: 28.897000000000002 - type: mrr_at_5 value: 30.147000000000002 - type: ndcg_at_1 value: 22.015 - type: ndcg_at_10 value: 32.225 - type: ndcg_at_100 value: 38.405 - type: ndcg_at_1000 value: 40.932 - type: ndcg_at_3 value: 27.403 - type: ndcg_at_5 value: 29.587000000000003 - type: precision_at_1 value: 22.015 - type: precision_at_10 value: 5.9830000000000005 - type: precision_at_100 value: 1.051 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.602 - type: recall_at_1 value: 17.757 - type: recall_at_10 value: 44.467 - type: recall_at_100 value: 71.53699999999999 - type: recall_at_1000 value: 89.281 - type: recall_at_3 value: 31.095 - type: recall_at_5 value: 36.818 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.354 - type: map_at_10 value: 42.134 - type: map_at_100 value: 43.429 - type: map_at_1000 value: 43.532 - type: map_at_3 value: 38.491 - type: map_at_5 value: 40.736 - type: mrr_at_1 value: 37.247 - type: mrr_at_10 value: 47.775 - type: mrr_at_100 value: 48.522999999999996 - type: mrr_at_1000 value: 48.567 - type: mrr_at_3 value: 45.059 - type: mrr_at_5 value: 46.811 - type: ndcg_at_1 value: 37.247 - type: ndcg_at_10 value: 48.609 - type: ndcg_at_100 value: 53.782 - type: ndcg_at_1000 value: 55.666000000000004 - type: ndcg_at_3 value: 42.866 - type: ndcg_at_5 value: 46.001 - type: precision_at_1 value: 37.247 - type: precision_at_10 value: 8.892999999999999 - type: precision_at_100 value: 1.341 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 20.5 - type: precision_at_5 value: 14.976 - type: recall_at_1 value: 30.354 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 83.65599999999999 - type: recall_at_1000 value: 95.82000000000001 - type: recall_at_3 value: 46.464 - type: recall_at_5 value: 54.225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.949 - type: map_at_10 value: 37.230000000000004 - type: map_at_100 value: 38.644 - type: map_at_1000 value: 38.751999999999995 - type: map_at_3 value: 33.816 - type: map_at_5 value: 35.817 - type: mrr_at_1 value: 33.446999999999996 - type: mrr_at_10 value: 42.970000000000006 - type: mrr_at_100 value: 43.873 - type: mrr_at_1000 value: 43.922 - type: mrr_at_3 value: 40.467999999999996 - type: mrr_at_5 value: 41.861 - type: ndcg_at_1 value: 33.446999999999996 - type: ndcg_at_10 value: 43.403000000000006 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 51.361999999999995 - type: ndcg_at_3 value: 38.155 - type: ndcg_at_5 value: 40.643 - type: precision_at_1 value: 33.446999999999996 - type: precision_at_10 value: 8.128 - type: precision_at_100 value: 1.274 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.493000000000002 - type: precision_at_5 value: 13.333 - type: recall_at_1 value: 26.949 - type: recall_at_10 value: 56.006 - type: recall_at_100 value: 80.99199999999999 - type: recall_at_1000 value: 95.074 - type: recall_at_3 value: 40.809 - type: recall_at_5 value: 47.57 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.243583333333333 - type: map_at_10 value: 37.193250000000006 - type: map_at_100 value: 38.44833333333334 - type: map_at_1000 value: 38.56083333333333 - type: map_at_3 value: 34.06633333333333 - type: map_at_5 value: 35.87858333333334 - type: mrr_at_1 value: 32.291583333333335 - type: mrr_at_10 value: 41.482749999999996 - type: mrr_at_100 value: 42.33583333333333 - type: mrr_at_1000 value: 42.38683333333333 - type: mrr_at_3 value: 38.952999999999996 - type: mrr_at_5 value: 40.45333333333333 - type: ndcg_at_1 value: 32.291583333333335 - type: ndcg_at_10 value: 42.90533333333334 - type: ndcg_at_100 value: 48.138666666666666 - type: ndcg_at_1000 value: 50.229083333333335 - type: ndcg_at_3 value: 37.76133333333334 - type: ndcg_at_5 value: 40.31033333333334 - type: precision_at_1 value: 32.291583333333335 - type: precision_at_10 value: 7.585583333333333 - type: precision_at_100 value: 1.2045000000000001 - type: precision_at_1000 value: 0.15733333333333335 - type: precision_at_3 value: 17.485416666666666 - type: precision_at_5 value: 12.5145 - type: recall_at_1 value: 27.243583333333333 - type: recall_at_10 value: 55.45108333333334 - type: recall_at_100 value: 78.25858333333335 - type: recall_at_1000 value: 92.61716666666665 - type: recall_at_3 value: 41.130583333333334 - type: recall_at_5 value: 47.73133333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.325 - type: map_at_10 value: 32.795 - type: map_at_100 value: 33.96 - type: map_at_1000 value: 34.054 - type: map_at_3 value: 30.64 - type: map_at_5 value: 31.771 - type: mrr_at_1 value: 29.908 - type: mrr_at_10 value: 35.83 - type: mrr_at_100 value: 36.868 - type: mrr_at_1000 value: 36.928 - type: mrr_at_3 value: 33.896 - type: mrr_at_5 value: 34.893 - type: ndcg_at_1 value: 29.908 - type: ndcg_at_10 value: 36.746 - type: ndcg_at_100 value: 42.225 - type: ndcg_at_1000 value: 44.523 - type: ndcg_at_3 value: 32.82 - type: ndcg_at_5 value: 34.583000000000006 - type: precision_at_1 value: 29.908 - type: precision_at_10 value: 5.6129999999999995 - type: precision_at_100 value: 0.9079999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 13.753000000000002 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 26.325 - type: recall_at_10 value: 45.975 - type: recall_at_100 value: 70.393 - type: recall_at_1000 value: 87.217 - type: recall_at_3 value: 35.195 - type: recall_at_5 value: 39.69 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.828 - type: map_at_10 value: 25.759 - type: map_at_100 value: 26.961000000000002 - type: map_at_1000 value: 27.094 - type: map_at_3 value: 23.166999999999998 - type: map_at_5 value: 24.610000000000003 - type: mrr_at_1 value: 21.61 - type: mrr_at_10 value: 29.605999999999998 - type: mrr_at_100 value: 30.586000000000002 - type: mrr_at_1000 value: 30.664 - type: mrr_at_3 value: 27.214 - type: mrr_at_5 value: 28.571 - type: ndcg_at_1 value: 21.61 - type: ndcg_at_10 value: 30.740000000000002 - type: ndcg_at_100 value: 36.332 - type: ndcg_at_1000 value: 39.296 - type: ndcg_at_3 value: 26.11 - type: ndcg_at_5 value: 28.297 - type: precision_at_1 value: 21.61 - type: precision_at_10 value: 5.643 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 12.4 - type: precision_at_5 value: 9.119 - type: recall_at_1 value: 17.828 - type: recall_at_10 value: 41.876000000000005 - type: recall_at_100 value: 66.648 - type: recall_at_1000 value: 87.763 - type: recall_at_3 value: 28.957 - type: recall_at_5 value: 34.494 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.921000000000003 - type: map_at_10 value: 37.156 - type: map_at_100 value: 38.399 - type: map_at_1000 value: 38.498 - type: map_at_3 value: 34.134 - type: map_at_5 value: 35.936 - type: mrr_at_1 value: 32.649 - type: mrr_at_10 value: 41.19 - type: mrr_at_100 value: 42.102000000000004 - type: mrr_at_1000 value: 42.157 - type: mrr_at_3 value: 38.464 - type: mrr_at_5 value: 40.148 - type: ndcg_at_1 value: 32.649 - type: ndcg_at_10 value: 42.679 - type: ndcg_at_100 value: 48.27 - type: ndcg_at_1000 value: 50.312 - type: ndcg_at_3 value: 37.269000000000005 - type: ndcg_at_5 value: 40.055 - type: precision_at_1 value: 32.649 - type: precision_at_10 value: 7.155 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 16.791 - type: precision_at_5 value: 12.015 - type: recall_at_1 value: 27.921000000000003 - type: recall_at_10 value: 55.357 - type: recall_at_100 value: 79.476 - type: recall_at_1000 value: 93.314 - type: recall_at_3 value: 40.891 - type: recall_at_5 value: 47.851 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.524 - type: map_at_10 value: 35.135 - type: map_at_100 value: 36.665 - type: map_at_1000 value: 36.886 - type: map_at_3 value: 31.367 - type: map_at_5 value: 33.724 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.616 - type: mrr_at_100 value: 40.54 - type: mrr_at_1000 value: 40.585 - type: mrr_at_3 value: 36.462 - type: mrr_at_5 value: 38.507999999999996 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 41.61 - type: ndcg_at_100 value: 47.249 - type: ndcg_at_1000 value: 49.662 - type: ndcg_at_3 value: 35.421 - type: ndcg_at_5 value: 38.811 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 8.123 - type: precision_at_100 value: 1.5810000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 12.568999999999999 - type: recall_at_1 value: 25.524 - type: recall_at_10 value: 54.994 - type: recall_at_100 value: 80.03099999999999 - type: recall_at_1000 value: 95.25099999999999 - type: recall_at_3 value: 37.563 - type: recall_at_5 value: 46.428999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.224 - type: map_at_10 value: 30.599999999999998 - type: map_at_100 value: 31.526 - type: map_at_1000 value: 31.629 - type: map_at_3 value: 27.491 - type: map_at_5 value: 29.212 - type: mrr_at_1 value: 24.214 - type: mrr_at_10 value: 32.632 - type: mrr_at_100 value: 33.482 - type: mrr_at_1000 value: 33.550000000000004 - type: mrr_at_3 value: 29.852 - type: mrr_at_5 value: 31.451 - type: ndcg_at_1 value: 24.214 - type: ndcg_at_10 value: 35.802 - type: ndcg_at_100 value: 40.502 - type: ndcg_at_1000 value: 43.052 - type: ndcg_at_3 value: 29.847 - type: ndcg_at_5 value: 32.732 - type: precision_at_1 value: 24.214 - type: precision_at_10 value: 5.804 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.692999999999998 - type: precision_at_5 value: 9.242 - type: recall_at_1 value: 22.224 - type: recall_at_10 value: 49.849 - type: recall_at_100 value: 71.45 - type: recall_at_1000 value: 90.583 - type: recall_at_3 value: 34.153 - type: recall_at_5 value: 41.004000000000005 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.386999999999999 - type: map_at_10 value: 20.182 - type: map_at_100 value: 21.86 - type: map_at_1000 value: 22.054000000000002 - type: map_at_3 value: 17.165 - type: map_at_5 value: 18.643 - type: mrr_at_1 value: 26.906000000000002 - type: mrr_at_10 value: 37.907999999999994 - type: mrr_at_100 value: 38.868 - type: mrr_at_1000 value: 38.913 - type: mrr_at_3 value: 34.853 - type: mrr_at_5 value: 36.567 - type: ndcg_at_1 value: 26.906000000000002 - type: ndcg_at_10 value: 28.103 - type: ndcg_at_100 value: 35.073 - type: ndcg_at_1000 value: 38.653 - type: ndcg_at_3 value: 23.345 - type: ndcg_at_5 value: 24.828 - type: precision_at_1 value: 26.906000000000002 - type: precision_at_10 value: 8.547 - type: precision_at_100 value: 1.617 - type: precision_at_1000 value: 0.22799999999999998 - type: precision_at_3 value: 17.025000000000002 - type: precision_at_5 value: 12.834000000000001 - type: recall_at_1 value: 12.386999999999999 - type: recall_at_10 value: 33.306999999999995 - type: recall_at_100 value: 57.516 - type: recall_at_1000 value: 77.74799999999999 - type: recall_at_3 value: 21.433 - type: recall_at_5 value: 25.915 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.322 - type: map_at_10 value: 20.469 - type: map_at_100 value: 28.638 - type: map_at_1000 value: 30.433 - type: map_at_3 value: 14.802000000000001 - type: map_at_5 value: 17.297 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.29599999999999 - type: mrr_at_100 value: 76.62400000000001 - type: mrr_at_1000 value: 76.633 - type: mrr_at_3 value: 75.083 - type: mrr_at_5 value: 75.771 - type: ndcg_at_1 value: 54.87499999999999 - type: ndcg_at_10 value: 41.185 - type: ndcg_at_100 value: 46.400000000000006 - type: ndcg_at_1000 value: 54.223 - type: ndcg_at_3 value: 45.489000000000004 - type: ndcg_at_5 value: 43.161 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.300000000000004 - type: precision_at_100 value: 10.607999999999999 - type: precision_at_1000 value: 2.237 - type: precision_at_3 value: 49.083 - type: precision_at_5 value: 41.6 - type: recall_at_1 value: 9.322 - type: recall_at_10 value: 25.696 - type: recall_at_100 value: 52.898 - type: recall_at_1000 value: 77.281 - type: recall_at_3 value: 15.943 - type: recall_at_5 value: 19.836000000000002 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.650000000000006 - type: f1 value: 43.528467245539396 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.56 - type: map_at_10 value: 76.767 - type: map_at_100 value: 77.054 - type: map_at_1000 value: 77.068 - type: map_at_3 value: 75.29299999999999 - type: map_at_5 value: 76.24 - type: mrr_at_1 value: 71.842 - type: mrr_at_10 value: 81.459 - type: mrr_at_100 value: 81.58800000000001 - type: mrr_at_1000 value: 81.59100000000001 - type: mrr_at_3 value: 80.188 - type: mrr_at_5 value: 81.038 - type: ndcg_at_1 value: 71.842 - type: ndcg_at_10 value: 81.51899999999999 - type: ndcg_at_100 value: 82.544 - type: ndcg_at_1000 value: 82.829 - type: ndcg_at_3 value: 78.92 - type: ndcg_at_5 value: 80.406 - type: precision_at_1 value: 71.842 - type: precision_at_10 value: 10.066 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.703000000000003 - type: precision_at_5 value: 19.301 - type: recall_at_1 value: 66.56 - type: recall_at_10 value: 91.55 - type: recall_at_100 value: 95.67099999999999 - type: recall_at_1000 value: 97.539 - type: recall_at_3 value: 84.46900000000001 - type: recall_at_5 value: 88.201 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.087 - type: map_at_10 value: 32.830999999999996 - type: map_at_100 value: 34.814 - type: map_at_1000 value: 34.999 - type: map_at_3 value: 28.198 - type: map_at_5 value: 30.779 - type: mrr_at_1 value: 38.889 - type: mrr_at_10 value: 48.415 - type: mrr_at_100 value: 49.187 - type: mrr_at_1000 value: 49.226 - type: mrr_at_3 value: 45.705 - type: mrr_at_5 value: 47.225 - type: ndcg_at_1 value: 38.889 - type: ndcg_at_10 value: 40.758 - type: ndcg_at_100 value: 47.671 - type: ndcg_at_1000 value: 50.744 - type: ndcg_at_3 value: 36.296 - type: ndcg_at_5 value: 37.852999999999994 - type: precision_at_1 value: 38.889 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.8499999999999999 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 18.21 - type: recall_at_1 value: 20.087 - type: recall_at_10 value: 48.042 - type: recall_at_100 value: 73.493 - type: recall_at_1000 value: 91.851 - type: recall_at_3 value: 32.694 - type: recall_at_5 value: 39.099000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.096000000000004 - type: map_at_10 value: 56.99999999999999 - type: map_at_100 value: 57.914 - type: map_at_1000 value: 57.984 - type: map_at_3 value: 53.900999999999996 - type: map_at_5 value: 55.827000000000005 - type: mrr_at_1 value: 76.19200000000001 - type: mrr_at_10 value: 81.955 - type: mrr_at_100 value: 82.164 - type: mrr_at_1000 value: 82.173 - type: mrr_at_3 value: 80.963 - type: mrr_at_5 value: 81.574 - type: ndcg_at_1 value: 76.19200000000001 - type: ndcg_at_10 value: 65.75 - type: ndcg_at_100 value: 68.949 - type: ndcg_at_1000 value: 70.342 - type: ndcg_at_3 value: 61.29 - type: ndcg_at_5 value: 63.747 - type: precision_at_1 value: 76.19200000000001 - type: precision_at_10 value: 13.571 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.663 - type: precision_at_5 value: 25.136999999999997 - type: recall_at_1 value: 38.096000000000004 - type: recall_at_10 value: 67.853 - type: recall_at_100 value: 80.365 - type: recall_at_1000 value: 89.629 - type: recall_at_3 value: 57.995 - type: recall_at_5 value: 62.843 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.95200000000001 - type: ap value: 80.73847277002109 - type: f1 value: 85.92406135678594 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.916999999999998 - type: map_at_10 value: 33.23 - type: map_at_100 value: 34.427 - type: map_at_1000 value: 34.477000000000004 - type: map_at_3 value: 29.292 - type: map_at_5 value: 31.6 - type: mrr_at_1 value: 21.547 - type: mrr_at_10 value: 33.839999999999996 - type: mrr_at_100 value: 34.979 - type: mrr_at_1000 value: 35.022999999999996 - type: mrr_at_3 value: 29.988 - type: mrr_at_5 value: 32.259 - type: ndcg_at_1 value: 21.519 - type: ndcg_at_10 value: 40.209 - type: ndcg_at_100 value: 45.954 - type: ndcg_at_1000 value: 47.187 - type: ndcg_at_3 value: 32.227 - type: ndcg_at_5 value: 36.347 - type: precision_at_1 value: 21.519 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.877999999999998 - type: precision_at_5 value: 10.404 - type: recall_at_1 value: 20.916999999999998 - type: recall_at_10 value: 61.7 - type: recall_at_100 value: 88.202 - type: recall_at_1000 value: 97.588 - type: recall_at_3 value: 40.044999999999995 - type: recall_at_5 value: 49.964999999999996 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.02781577747379 - type: f1 value: 92.83653922768306 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.04286365709075 - type: f1 value: 53.43867658525793 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.47276395427035 - type: f1 value: 69.77017399597342 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.3819771351715 - type: f1 value: 76.8484533435409 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.16515993299593 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.77145323314774 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53637706586391 - type: mrr value: 33.7312926288863 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.063999999999999 - type: map_at_10 value: 15.046999999999999 - type: map_at_100 value: 19.116 - type: map_at_1000 value: 20.702 - type: map_at_3 value: 10.932 - type: map_at_5 value: 12.751999999999999 - type: mrr_at_1 value: 50.464 - type: mrr_at_10 value: 58.189 - type: mrr_at_100 value: 58.733999999999995 - type: mrr_at_1000 value: 58.769000000000005 - type: mrr_at_3 value: 56.24400000000001 - type: mrr_at_5 value: 57.68299999999999 - type: ndcg_at_1 value: 48.142 - type: ndcg_at_10 value: 37.897 - type: ndcg_at_100 value: 35.264 - type: ndcg_at_1000 value: 44.033 - type: ndcg_at_3 value: 42.967 - type: ndcg_at_5 value: 40.815 - type: precision_at_1 value: 50.15500000000001 - type: precision_at_10 value: 28.235 - type: precision_at_100 value: 8.994 - type: precision_at_1000 value: 2.218 - type: precision_at_3 value: 40.041 - type: precision_at_5 value: 35.046 - type: recall_at_1 value: 7.063999999999999 - type: recall_at_10 value: 18.598 - type: recall_at_100 value: 35.577999999999996 - type: recall_at_1000 value: 67.43 - type: recall_at_3 value: 11.562999999999999 - type: recall_at_5 value: 14.771 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 29.046 - type: map_at_10 value: 44.808 - type: map_at_100 value: 45.898 - type: map_at_1000 value: 45.927 - type: map_at_3 value: 40.19 - type: map_at_5 value: 42.897 - type: mrr_at_1 value: 32.706 - type: mrr_at_10 value: 47.275 - type: mrr_at_100 value: 48.075 - type: mrr_at_1000 value: 48.095 - type: mrr_at_3 value: 43.463 - type: mrr_at_5 value: 45.741 - type: ndcg_at_1 value: 32.706 - type: ndcg_at_10 value: 52.835 - type: ndcg_at_100 value: 57.345 - type: ndcg_at_1000 value: 57.985 - type: ndcg_at_3 value: 44.171 - type: ndcg_at_5 value: 48.661 - type: precision_at_1 value: 32.706 - type: precision_at_10 value: 8.895999999999999 - type: precision_at_100 value: 1.143 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.238999999999997 - type: precision_at_5 value: 14.728 - type: recall_at_1 value: 29.046 - type: recall_at_10 value: 74.831 - type: recall_at_100 value: 94.192 - type: recall_at_1000 value: 98.897 - type: recall_at_3 value: 52.37500000000001 - type: recall_at_5 value: 62.732 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.38799999999999 - type: map_at_10 value: 84.315 - type: map_at_100 value: 84.955 - type: map_at_1000 value: 84.971 - type: map_at_3 value: 81.33399999999999 - type: map_at_5 value: 83.21300000000001 - type: mrr_at_1 value: 81.03 - type: mrr_at_10 value: 87.395 - type: mrr_at_100 value: 87.488 - type: mrr_at_1000 value: 87.48899999999999 - type: mrr_at_3 value: 86.41499999999999 - type: mrr_at_5 value: 87.074 - type: ndcg_at_1 value: 81.04 - type: ndcg_at_10 value: 88.151 - type: ndcg_at_100 value: 89.38199999999999 - type: ndcg_at_1000 value: 89.479 - type: ndcg_at_3 value: 85.24000000000001 - type: ndcg_at_5 value: 86.856 - type: precision_at_1 value: 81.04 - type: precision_at_10 value: 13.372 - type: precision_at_100 value: 1.526 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.217 - type: precision_at_5 value: 24.502 - type: recall_at_1 value: 70.38799999999999 - type: recall_at_10 value: 95.452 - type: recall_at_100 value: 99.59700000000001 - type: recall_at_1000 value: 99.988 - type: recall_at_3 value: 87.11 - type: recall_at_5 value: 91.662 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.334991029213235 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.586500854616666 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.153 - type: map_at_10 value: 14.277000000000001 - type: map_at_100 value: 16.922 - type: map_at_1000 value: 17.302999999999997 - type: map_at_3 value: 9.961 - type: map_at_5 value: 12.257 - type: mrr_at_1 value: 25.4 - type: mrr_at_10 value: 37.458000000000006 - type: mrr_at_100 value: 38.681 - type: mrr_at_1000 value: 38.722 - type: mrr_at_3 value: 34.1 - type: mrr_at_5 value: 36.17 - type: ndcg_at_1 value: 25.4 - type: ndcg_at_10 value: 23.132 - type: ndcg_at_100 value: 32.908 - type: ndcg_at_1000 value: 38.754 - type: ndcg_at_3 value: 21.82 - type: ndcg_at_5 value: 19.353 - type: precision_at_1 value: 25.4 - type: precision_at_10 value: 12.1 - type: precision_at_100 value: 2.628 - type: precision_at_1000 value: 0.402 - type: precision_at_3 value: 20.732999999999997 - type: precision_at_5 value: 17.34 - type: recall_at_1 value: 5.153 - type: recall_at_10 value: 24.54 - type: recall_at_100 value: 53.293 - type: recall_at_1000 value: 81.57 - type: recall_at_3 value: 12.613 - type: recall_at_5 value: 17.577 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.86284404925333 - type: cos_sim_spearman value: 78.85870555294795 - type: euclidean_pearson value: 82.20105295276093 - type: euclidean_spearman value: 78.92125617009592 - type: manhattan_pearson value: 82.15840025289069 - type: manhattan_spearman value: 78.85955732900803 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.98747423389027 - type: cos_sim_spearman value: 75.71298531799367 - type: euclidean_pearson value: 81.59709559192291 - type: euclidean_spearman value: 75.40622749225653 - type: manhattan_pearson value: 81.55553547608804 - type: manhattan_spearman value: 75.39380235424899 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.76861330695503 - type: cos_sim_spearman value: 85.72991921531624 - type: euclidean_pearson value: 84.84504307397536 - type: euclidean_spearman value: 86.02679162824732 - type: manhattan_pearson value: 84.79969439220142 - type: manhattan_spearman value: 85.99238837291625 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.31929747511796 - type: cos_sim_spearman value: 81.50806522502528 - type: euclidean_pearson value: 82.93936686512777 - type: euclidean_spearman value: 81.54403447993224 - type: manhattan_pearson value: 82.89696981900828 - type: manhattan_spearman value: 81.52817825470865 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.14413295332908 - type: cos_sim_spearman value: 88.81032027008195 - type: euclidean_pearson value: 88.19205563407645 - type: euclidean_spearman value: 88.89738339479216 - type: manhattan_pearson value: 88.11075942004189 - type: manhattan_spearman value: 88.8297061675564 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.15980075557017 - type: cos_sim_spearman value: 83.81896308594801 - type: euclidean_pearson value: 83.11195254311338 - type: euclidean_spearman value: 84.10479481755407 - type: manhattan_pearson value: 83.13915225100556 - type: manhattan_spearman value: 84.09895591027859 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.93669480147919 - type: cos_sim_spearman value: 87.89861394614361 - type: euclidean_pearson value: 88.37316413202339 - type: euclidean_spearman value: 88.18033817842569 - type: manhattan_pearson value: 88.39427578879469 - type: manhattan_spearman value: 88.09185009236847 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.62215083348255 - type: cos_sim_spearman value: 67.33243665716736 - type: euclidean_pearson value: 67.60871701996284 - type: euclidean_spearman value: 66.75929225238659 - type: manhattan_pearson value: 67.63907838970992 - type: manhattan_spearman value: 66.79313656754846 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.65549191934764 - type: cos_sim_spearman value: 85.73266847750143 - type: euclidean_pearson value: 85.75609932254318 - type: euclidean_spearman value: 85.9452287759371 - type: manhattan_pearson value: 85.69717413063573 - type: manhattan_spearman value: 85.86546318377046 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.08164129085783 - type: mrr value: 96.2877273416489 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 62.09400000000001 - type: map_at_10 value: 71.712 - type: map_at_100 value: 72.128 - type: map_at_1000 value: 72.14399999999999 - type: map_at_3 value: 68.93 - type: map_at_5 value: 70.694 - type: mrr_at_1 value: 65.0 - type: mrr_at_10 value: 72.572 - type: mrr_at_100 value: 72.842 - type: mrr_at_1000 value: 72.856 - type: mrr_at_3 value: 70.44399999999999 - type: mrr_at_5 value: 71.744 - type: ndcg_at_1 value: 65.0 - type: ndcg_at_10 value: 76.178 - type: ndcg_at_100 value: 77.887 - type: ndcg_at_1000 value: 78.227 - type: ndcg_at_3 value: 71.367 - type: ndcg_at_5 value: 73.938 - type: precision_at_1 value: 65.0 - type: precision_at_10 value: 10.033 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 27.667 - type: precision_at_5 value: 18.4 - type: recall_at_1 value: 62.09400000000001 - type: recall_at_10 value: 89.022 - type: recall_at_100 value: 96.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 75.922 - type: recall_at_5 value: 82.428 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82178217821782 - type: cos_sim_ap value: 95.71282508220798 - type: cos_sim_f1 value: 90.73120494335737 - type: cos_sim_precision value: 93.52441613588111 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.73960396039604 - type: dot_ap value: 92.98534606529098 - type: dot_f1 value: 86.83024536805209 - type: dot_precision value: 86.96088264794383 - type: dot_recall value: 86.7 - type: euclidean_accuracy value: 99.82475247524752 - type: euclidean_ap value: 95.72927039014849 - type: euclidean_f1 value: 90.89974293059126 - type: euclidean_precision value: 93.54497354497354 - type: euclidean_recall value: 88.4 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.72142177390405 - type: manhattan_f1 value: 91.00152516522625 - type: manhattan_precision value: 92.55429162357808 - type: manhattan_recall value: 89.5 - type: max_accuracy value: 99.82574257425742 - type: max_ap value: 95.72927039014849 - type: max_f1 value: 91.00152516522625 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.63957663468679 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.003307257923964 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.005825525863905 - type: mrr value: 53.854683919022165 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.503611569974098 - type: cos_sim_spearman value: 31.17155564248449 - type: dot_pearson value: 26.740428413981306 - type: dot_spearman value: 26.55727635469746 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.23600000000000002 - type: map_at_10 value: 1.7670000000000001 - type: map_at_100 value: 10.208 - type: map_at_1000 value: 25.997999999999998 - type: map_at_3 value: 0.605 - type: map_at_5 value: 0.9560000000000001 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 90.167 - type: mrr_at_100 value: 90.167 - type: mrr_at_1000 value: 90.167 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 90.167 - type: ndcg_at_1 value: 77.0 - type: ndcg_at_10 value: 68.783 - type: ndcg_at_100 value: 54.196 - type: ndcg_at_1000 value: 52.077 - type: ndcg_at_3 value: 71.642 - type: ndcg_at_5 value: 70.45700000000001 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 55.48 - type: precision_at_1000 value: 23.102 - type: precision_at_3 value: 76.0 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.23600000000000002 - type: recall_at_10 value: 1.9869999999999999 - type: recall_at_100 value: 13.749 - type: recall_at_1000 value: 50.157 - type: recall_at_3 value: 0.633 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.437 - type: map_at_10 value: 8.791 - type: map_at_100 value: 15.001999999999999 - type: map_at_1000 value: 16.549 - type: map_at_3 value: 3.8080000000000003 - type: map_at_5 value: 5.632000000000001 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 36.96 - type: mrr_at_100 value: 37.912 - type: mrr_at_1000 value: 37.912 - type: mrr_at_3 value: 29.592000000000002 - type: mrr_at_5 value: 34.489999999999995 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 22.554 - type: ndcg_at_100 value: 35.197 - type: ndcg_at_1000 value: 46.58 - type: ndcg_at_3 value: 20.285 - type: ndcg_at_5 value: 21.924 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 21.837 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 1.437 - type: recall_at_10 value: 16.314999999999998 - type: recall_at_100 value: 47.635 - type: recall_at_1000 value: 82.963 - type: recall_at_3 value: 4.955 - type: recall_at_5 value: 8.805 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.6128 - type: ap value: 14.279639861175664 - type: f1 value: 54.922292491204274 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.01188455008489 - type: f1 value: 57.377953019225515 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.306769136544254 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.64701674912082 - type: cos_sim_ap value: 72.46600945328552 - type: cos_sim_f1 value: 67.96572367648784 - type: cos_sim_precision value: 61.21801649397336 - 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type: max_f1 value: 78.07565728654365 language: - en license: mit --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [thenlper/gte-base](https://huggingface.co./thenlper/gte-base) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-gte-base" model_name_orig="thenlper/gte-base" from hf_hub_ctranslate2 import EncoderCT2fromHfHub model = EncoderCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16" ) outputs = model.generate( text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"], max_length=64, ) # perform downstream tasks on outputs outputs["pooler_output"] outputs["last_hidden_state"] outputs["attention_mask"] # alternative, use SentenceTransformer Mix-In # for end-to-end Sentence embeddings generation # (not pulling from this CT2fast-HF repo) from hf_hub_ctranslate2 import CT2SentenceTransformer model = CT2SentenceTransformer( model_name_orig, compute_type="int8_float16", device="cuda" ) embeddings = model.encode( ["I like soccer", "I like tennis", "The eiffel tower is in Paris"], batch_size=32, convert_to_numpy=True, normalize_embeddings=True, ) print(embeddings.shape, embeddings) scores = (embeddings @ embeddings.T) * 100 # Hint: you can also host this code via REST API and # via github.com/michaelfeil/infinity ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-10-13 using ``` LLama-2 -> removed token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # gte-base General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co./thenlper/gte-large), [GTE-base](https://huggingface.co./thenlper/gte-base), and [GTE-small](https://huggingface.co./thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co./spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co./thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co./thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co./intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co./intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co./thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co./intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co./sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co./bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co./nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co./sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base") model = AutoModel.from_pretrained("thenlper/gte-base") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python 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('thenlper/gte-base') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @misc{li2023general, title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang}, year={2023}, eprint={2308.03281}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```