--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.51805 - type: ap value: 89.49842783330848 - type: f1 value: 92.51112169431808 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.074 - type: f1 value: 48.44785682572955 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.077 - type: map_at_10 value: 48.153 - type: map_at_100 value: 48.963 - type: map_at_1000 value: 48.966 - type: map_at_3 value: 43.184 - type: map_at_5 value: 46.072 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.54 - type: mrr_at_100 value: 49.335 - type: mrr_at_1000 value: 49.338 - type: mrr_at_3 value: 43.563 - type: mrr_at_5 value: 46.383 - type: ndcg_at_1 value: 32.077 - type: ndcg_at_10 value: 57.158 - type: ndcg_at_100 value: 60.324999999999996 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 46.934 - type: ndcg_at_5 value: 52.158 - type: precision_at_1 value: 32.077 - type: precision_at_10 value: 8.591999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.275000000000002 - type: precision_at_5 value: 14.111 - type: recall_at_1 value: 32.077 - type: recall_at_10 value: 85.917 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 57.824 - type: recall_at_5 value: 70.555 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.619246083417295 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.3574067664688 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.06359661829253 - type: mrr value: 76.15596007562766 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.25407547368691 - type: cos_sim_spearman value: 88.65081514968477 - type: euclidean_pearson value: 88.14857116664494 - type: euclidean_spearman value: 88.50683596540692 - type: manhattan_pearson value: 87.9654797992225 - type: manhattan_spearman value: 88.21164851646908 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.05844155844157 - type: f1 value: 86.01555597681825 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10510519739522 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.84689960264385 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.800000000000004 - type: map_at_10 value: 44.857 - type: map_at_100 value: 46.512 - type: map_at_1000 value: 46.635 - type: map_at_3 value: 41.062 - type: map_at_5 value: 43.126 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 50.879 - type: mrr_at_100 value: 51.605000000000004 - type: mrr_at_1000 value: 51.641000000000005 - type: mrr_at_3 value: 48.14 - type: mrr_at_5 value: 49.835 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 51.819 - type: ndcg_at_100 value: 57.318999999999996 - type: ndcg_at_1000 value: 58.955999999999996 - type: ndcg_at_3 value: 46.409 - type: ndcg_at_5 value: 48.825 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 10.072000000000001 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 22.556 - type: precision_at_5 value: 16.309 - type: recall_at_1 value: 32.800000000000004 - type: recall_at_10 value: 65.078 - type: recall_at_100 value: 87.491 - type: recall_at_1000 value: 97.514 - type: recall_at_3 value: 49.561 - type: recall_at_5 value: 56.135999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.614 - type: map_at_10 value: 43.578 - type: map_at_100 value: 44.897 - type: map_at_1000 value: 45.023 - type: map_at_3 value: 40.282000000000004 - type: map_at_5 value: 42.117 - type: mrr_at_1 value: 40.510000000000005 - type: mrr_at_10 value: 49.428 - type: mrr_at_100 value: 50.068999999999996 - type: mrr_at_1000 value: 50.111000000000004 - type: mrr_at_3 value: 47.176 - type: mrr_at_5 value: 48.583999999999996 - type: ndcg_at_1 value: 40.510000000000005 - type: ndcg_at_10 value: 49.478 - type: ndcg_at_100 value: 53.852 - type: ndcg_at_1000 value: 55.782 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 47.19 - type: precision_at_1 value: 40.510000000000005 - type: precision_at_10 value: 9.363000000000001 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 21.741 - type: precision_at_5 value: 15.465000000000002 - type: recall_at_1 value: 32.614 - type: recall_at_10 value: 59.782000000000004 - type: recall_at_100 value: 78.012 - type: recall_at_1000 value: 90.319 - type: recall_at_3 value: 46.825 - type: recall_at_5 value: 52.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.266000000000005 - type: map_at_10 value: 53.756 - type: map_at_100 value: 54.809 - type: map_at_1000 value: 54.855 - type: map_at_3 value: 50.073 - type: map_at_5 value: 52.293 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 57.116 - type: mrr_at_100 value: 57.767 - type: mrr_at_1000 value: 57.791000000000004 - type: mrr_at_3 value: 54.461999999999996 - type: mrr_at_5 value: 56.092 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 60.092 - type: ndcg_at_100 value: 64.034 - type: ndcg_at_1000 value: 64.937 - type: ndcg_at_3 value: 54.071000000000005 - type: ndcg_at_5 value: 57.254000000000005 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.799 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.89 - type: recall_at_1 value: 40.266000000000005 - type: recall_at_10 value: 75.41499999999999 - type: recall_at_100 value: 92.01700000000001 - type: recall_at_1000 value: 98.379 - type: recall_at_3 value: 59.476 - type: recall_at_5 value: 67.297 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.589 - type: map_at_10 value: 37.755 - type: map_at_100 value: 38.881 - type: map_at_1000 value: 38.954 - type: map_at_3 value: 34.759 - type: map_at_5 value: 36.544 - type: mrr_at_1 value: 30.734 - type: mrr_at_10 value: 39.742 - type: mrr_at_100 value: 40.774 - type: mrr_at_1000 value: 40.824 - type: mrr_at_3 value: 37.137 - type: mrr_at_5 value: 38.719 - type: ndcg_at_1 value: 30.734 - type: ndcg_at_10 value: 42.978 - type: ndcg_at_100 value: 48.309000000000005 - type: ndcg_at_1000 value: 50.068 - type: ndcg_at_3 value: 37.361 - type: ndcg_at_5 value: 40.268 - type: precision_at_1 value: 30.734 - type: precision_at_10 value: 6.565 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.096 - type: recall_at_1 value: 28.589 - type: recall_at_10 value: 57.126999999999995 - type: recall_at_100 value: 81.051 - type: recall_at_1000 value: 94.027 - type: recall_at_3 value: 42.045 - 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: 18.5 - type: map_at_10 value: 27.950999999999997 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.298000000000002 - type: map_at_3 value: 25.141000000000002 - type: map_at_5 value: 26.848 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 32.572 - type: mrr_at_100 value: 33.472 - type: mrr_at_1000 value: 33.533 - type: mrr_at_3 value: 29.747 - type: mrr_at_5 value: 31.482 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 33.73 - type: ndcg_at_100 value: 39.568 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.505999999999997 - type: ndcg_at_5 value: 31.255 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 6.281000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 13.847000000000001 - type: precision_at_5 value: 10.224 - type: recall_at_1 value: 18.5 - type: recall_at_10 value: 46.744 - type: recall_at_100 value: 72.072 - type: recall_at_1000 value: 91.03999999999999 - type: recall_at_3 value: 32.551 - type: recall_at_5 value: 39.533 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.602 - type: map_at_10 value: 42.18 - type: map_at_100 value: 43.6 - type: map_at_1000 value: 43.704 - type: map_at_3 value: 38.413000000000004 - type: map_at_5 value: 40.626 - type: mrr_at_1 value: 37.344 - type: mrr_at_10 value: 47.638000000000005 - type: mrr_at_100 value: 48.485 - type: mrr_at_1000 value: 48.52 - type: mrr_at_3 value: 44.867000000000004 - type: mrr_at_5 value: 46.566 - type: ndcg_at_1 value: 37.344 - type: ndcg_at_10 value: 48.632 - type: ndcg_at_100 value: 54.215 - type: ndcg_at_1000 value: 55.981 - type: ndcg_at_3 value: 42.681999999999995 - type: ndcg_at_5 value: 45.732 - type: precision_at_1 value: 37.344 - type: precision_at_10 value: 8.932 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 30.602 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 85.12100000000001 - type: recall_at_1000 value: 96.439 - type: recall_at_3 value: 45.848 - type: recall_at_5 value: 53.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.952 - type: map_at_10 value: 35.177 - type: map_at_100 value: 36.59 - type: map_at_1000 value: 36.703 - type: map_at_3 value: 31.261 - type: map_at_5 value: 33.222 - type: mrr_at_1 value: 29.337999999999997 - type: mrr_at_10 value: 40.152 - type: mrr_at_100 value: 40.963 - type: mrr_at_1000 value: 41.016999999999996 - type: mrr_at_3 value: 36.91 - type: mrr_at_5 value: 38.685 - type: ndcg_at_1 value: 29.337999999999997 - type: ndcg_at_10 value: 41.994 - type: ndcg_at_100 value: 47.587 - type: ndcg_at_1000 value: 49.791000000000004 - type: ndcg_at_3 value: 35.27 - type: ndcg_at_5 value: 38.042 - type: precision_at_1 value: 29.337999999999997 - type: precision_at_10 value: 8.276 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.161 - type: precision_at_5 value: 12.671 - type: recall_at_1 value: 23.952 - type: recall_at_10 value: 57.267 - type: recall_at_100 value: 80.886 - type: recall_at_1000 value: 95.611 - type: recall_at_3 value: 38.622 - type: recall_at_5 value: 45.811 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.092083333333335 - type: map_at_10 value: 37.2925 - type: map_at_100 value: 38.57041666666666 - type: map_at_1000 value: 38.68141666666667 - type: map_at_3 value: 34.080000000000005 - type: map_at_5 value: 35.89958333333333 - type: mrr_at_1 value: 31.94758333333333 - type: mrr_at_10 value: 41.51049999999999 - type: mrr_at_100 value: 42.36099999999999 - type: mrr_at_1000 value: 42.4125 - type: mrr_at_3 value: 38.849583333333335 - type: mrr_at_5 value: 40.448249999999994 - type: ndcg_at_1 value: 31.94758333333333 - type: ndcg_at_10 value: 43.17633333333333 - type: ndcg_at_100 value: 48.45241666666668 - type: ndcg_at_1000 value: 50.513999999999996 - type: ndcg_at_3 value: 37.75216666666667 - type: ndcg_at_5 value: 40.393833333333326 - type: precision_at_1 value: 31.94758333333333 - type: precision_at_10 value: 7.688916666666666 - type: precision_at_100 value: 1.2250833333333333 - type: precision_at_1000 value: 0.1595 - type: precision_at_3 value: 17.465999999999998 - type: precision_at_5 value: 12.548083333333333 - type: recall_at_1 value: 27.092083333333335 - type: recall_at_10 value: 56.286583333333326 - type: recall_at_100 value: 79.09033333333333 - type: recall_at_1000 value: 93.27483333333335 - type: recall_at_3 value: 41.35325 - type: recall_at_5 value: 48.072750000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.825 - type: map_at_10 value: 33.723 - type: map_at_100 value: 34.74 - type: map_at_1000 value: 34.824 - type: map_at_3 value: 31.369000000000003 - type: map_at_5 value: 32.533 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.84 - type: mrr_at_100 value: 37.681 - type: mrr_at_1000 value: 37.742 - type: mrr_at_3 value: 34.79 - type: mrr_at_5 value: 35.872 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 38.385999999999996 - type: ndcg_at_100 value: 43.327 - type: ndcg_at_1000 value: 45.53 - type: ndcg_at_3 value: 33.985 - type: ndcg_at_5 value: 35.817 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 6.12 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 14.621999999999998 - type: precision_at_5 value: 10.030999999999999 - type: recall_at_1 value: 25.825 - type: recall_at_10 value: 49.647000000000006 - type: recall_at_100 value: 72.32300000000001 - type: recall_at_1000 value: 88.62400000000001 - type: recall_at_3 value: 37.366 - type: recall_at_5 value: 41.957 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.139 - type: map_at_10 value: 26.107000000000003 - type: map_at_100 value: 27.406999999999996 - type: map_at_1000 value: 27.535999999999998 - type: map_at_3 value: 23.445 - type: map_at_5 value: 24.916 - type: mrr_at_1 value: 21.817 - type: mrr_at_10 value: 29.99 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.128 - type: mrr_at_3 value: 27.627000000000002 - type: mrr_at_5 value: 29.005 - type: ndcg_at_1 value: 21.817 - type: ndcg_at_10 value: 31.135 - type: ndcg_at_100 value: 37.108000000000004 - type: ndcg_at_1000 value: 39.965 - type: ndcg_at_3 value: 26.439 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 21.817 - type: precision_at_10 value: 5.757000000000001 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 12.537 - type: precision_at_5 value: 9.229 - type: recall_at_1 value: 18.139 - type: recall_at_10 value: 42.272999999999996 - type: recall_at_100 value: 68.657 - type: recall_at_1000 value: 88.93799999999999 - type: recall_at_3 value: 29.266 - type: recall_at_5 value: 34.892 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.755000000000003 - type: map_at_10 value: 37.384 - type: map_at_100 value: 38.56 - type: map_at_1000 value: 38.655 - type: map_at_3 value: 34.214 - type: map_at_5 value: 35.96 - type: mrr_at_1 value: 32.369 - type: mrr_at_10 value: 41.625 - type: mrr_at_100 value: 42.449 - type: mrr_at_1000 value: 42.502 - type: mrr_at_3 value: 38.899 - type: mrr_at_5 value: 40.489999999999995 - type: ndcg_at_1 value: 32.369 - type: ndcg_at_10 value: 43.287 - type: ndcg_at_100 value: 48.504999999999995 - type: ndcg_at_1000 value: 50.552 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 40.204 - type: precision_at_1 value: 32.369 - type: precision_at_10 value: 7.425 - type: precision_at_100 value: 1.134 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 27.755000000000003 - type: recall_at_10 value: 57.071000000000005 - type: recall_at_100 value: 79.456 - type: recall_at_1000 value: 93.54299999999999 - type: recall_at_3 value: 41.298 - type: recall_at_5 value: 48.037 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.855 - type: map_at_10 value: 34.53 - type: map_at_100 value: 36.167 - type: map_at_1000 value: 36.394999999999996 - type: map_at_3 value: 31.037 - type: map_at_5 value: 33.119 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.763999999999996 - type: mrr_at_100 value: 40.77 - type: mrr_at_1000 value: 40.826 - type: mrr_at_3 value: 36.495 - type: mrr_at_5 value: 38.561 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 40.942 - type: ndcg_at_100 value: 47.07 - type: ndcg_at_1000 value: 49.363 - type: ndcg_at_3 value: 35.038000000000004 - type: ndcg_at_5 value: 38.161 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 7.983999999999999 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 24.855 - type: recall_at_10 value: 53.291999999999994 - type: recall_at_100 value: 80.283 - type: recall_at_1000 value: 94.309 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 45.282 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.208 - type: map_at_10 value: 30.512 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.595000000000002 - type: map_at_3 value: 27.904 - type: map_at_5 value: 29.491 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 32.379999999999995 - type: mrr_at_100 value: 33.245000000000005 - type: mrr_at_1000 value: 33.315 - type: mrr_at_3 value: 29.945 - type: mrr_at_5 value: 31.488 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 35.643 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.042 - type: ndcg_at_3 value: 30.625000000000004 - type: ndcg_at_5 value: 33.323 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 13.431999999999999 - type: precision_at_5 value: 9.575 - type: recall_at_1 value: 21.208 - type: recall_at_10 value: 49.47 - type: recall_at_100 value: 71.71499999999999 - type: recall_at_1000 value: 90.55499999999999 - type: recall_at_3 value: 36.124 - type: recall_at_5 value: 42.606 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.363 - type: map_at_10 value: 20.312 - type: map_at_100 value: 22.225 - type: map_at_1000 value: 22.411 - type: map_at_3 value: 16.68 - type: map_at_5 value: 18.608 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 37.933 - type: mrr_at_100 value: 38.875 - type: mrr_at_1000 value: 38.911 - type: mrr_at_3 value: 34.387 - type: mrr_at_5 value: 36.51 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 28.82 - type: ndcg_at_100 value: 36.341 - type: ndcg_at_1000 value: 39.615 - type: ndcg_at_3 value: 23.01 - type: ndcg_at_5 value: 25.269000000000002 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 9.153 - type: precision_at_100 value: 1.7319999999999998 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 17.22 - type: precision_at_5 value: 13.629 - type: recall_at_1 value: 11.363 - type: recall_at_10 value: 35.382999999999996 - type: recall_at_100 value: 61.367000000000004 - type: recall_at_1000 value: 79.699 - type: recall_at_3 value: 21.495 - type: recall_at_5 value: 27.42 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.65 - type: map_at_10 value: 20.742 - type: map_at_100 value: 29.614 - type: map_at_1000 value: 31.373 - type: map_at_3 value: 14.667 - type: map_at_5 value: 17.186 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 76.762 - type: mrr_at_100 value: 77.171 - type: mrr_at_1000 value: 77.179 - type: mrr_at_3 value: 75.125 - type: mrr_at_5 value: 76.287 - type: ndcg_at_1 value: 57.62500000000001 - type: ndcg_at_10 value: 42.370999999999995 - type: ndcg_at_100 value: 47.897 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 46.317 - type: ndcg_at_5 value: 43.906 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.95 - type: precision_at_100 value: 10.885 - type: precision_at_1000 value: 2.2239999999999998 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.3 - type: recall_at_1 value: 9.65 - type: recall_at_10 value: 26.117 - type: recall_at_100 value: 55.084 - type: recall_at_1000 value: 78.62400000000001 - type: recall_at_3 value: 15.823 - type: recall_at_5 value: 19.652 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.885 - type: f1 value: 42.99567641346983 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 70.97 - type: map_at_10 value: 80.34599999999999 - type: map_at_100 value: 80.571 - type: map_at_1000 value: 80.584 - type: map_at_3 value: 79.279 - type: map_at_5 value: 79.94 - type: mrr_at_1 value: 76.613 - type: mrr_at_10 value: 85.15700000000001 - type: mrr_at_100 value: 85.249 - type: mrr_at_1000 value: 85.252 - type: mrr_at_3 value: 84.33800000000001 - type: mrr_at_5 value: 84.89 - type: ndcg_at_1 value: 76.613 - type: ndcg_at_10 value: 84.53399999999999 - type: ndcg_at_100 value: 85.359 - type: ndcg_at_1000 value: 85.607 - type: ndcg_at_3 value: 82.76599999999999 - type: ndcg_at_5 value: 83.736 - type: precision_at_1 value: 76.613 - type: precision_at_10 value: 10.206 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 31.913000000000004 - type: precision_at_5 value: 19.769000000000002 - type: recall_at_1 value: 70.97 - type: recall_at_10 value: 92.674 - type: recall_at_100 value: 95.985 - type: recall_at_1000 value: 97.57000000000001 - type: recall_at_3 value: 87.742 - type: recall_at_5 value: 90.28 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.494 - type: map_at_10 value: 36.491 - type: map_at_100 value: 38.550000000000004 - type: map_at_1000 value: 38.726 - type: map_at_3 value: 31.807000000000002 - type: map_at_5 value: 34.299 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.146 - type: mrr_at_100 value: 54.013999999999996 - type: mrr_at_1000 value: 54.044000000000004 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.124 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.499 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 54.367 - type: ndcg_at_3 value: 40.900999999999996 - type: ndcg_at_5 value: 41.737 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.346 - type: precision_at_100 value: 1.974 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 27.366 - type: precision_at_5 value: 19.846 - type: recall_at_1 value: 22.494 - type: recall_at_10 value: 51.156 - type: recall_at_100 value: 77.11200000000001 - type: recall_at_1000 value: 93.44 - type: recall_at_3 value: 36.574 - type: recall_at_5 value: 42.361 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.568999999999996 - type: map_at_10 value: 58.485 - type: map_at_100 value: 59.358999999999995 - type: map_at_1000 value: 59.429 - type: map_at_3 value: 55.217000000000006 - type: map_at_5 value: 57.236 - type: mrr_at_1 value: 77.137 - type: mrr_at_10 value: 82.829 - type: mrr_at_100 value: 83.04599999999999 - type: mrr_at_1000 value: 83.05399999999999 - type: mrr_at_3 value: 81.904 - type: mrr_at_5 value: 82.50800000000001 - type: ndcg_at_1 value: 77.137 - type: ndcg_at_10 value: 67.156 - type: ndcg_at_100 value: 70.298 - type: ndcg_at_1000 value: 71.65700000000001 - type: ndcg_at_3 value: 62.535 - type: ndcg_at_5 value: 65.095 - type: precision_at_1 value: 77.137 - type: precision_at_10 value: 13.911999999999999 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.572 - type: precision_at_5 value: 25.766 - type: recall_at_1 value: 38.568999999999996 - type: recall_at_10 value: 69.56099999999999 - type: recall_at_100 value: 81.931 - type: recall_at_1000 value: 90.91799999999999 - type: recall_at_3 value: 59.358999999999995 - type: recall_at_5 value: 64.416 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.45600000000002 - type: ap value: 84.09725115338568 - type: f1 value: 88.41874909080512 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.404999999999998 - type: map_at_10 value: 33.921 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.164 - type: map_at_3 value: 30.043999999999997 - type: map_at_5 value: 32.327 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 34.505 - type: mrr_at_100 value: 35.638999999999996 - type: mrr_at_1000 value: 35.68 - type: mrr_at_3 value: 30.703999999999997 - type: mrr_at_5 value: 32.96 - type: ndcg_at_1 value: 21.963 - type: ndcg_at_10 value: 40.859 - type: ndcg_at_100 value: 46.614 - type: ndcg_at_1000 value: 47.789 - type: ndcg_at_3 value: 33.007999999999996 - type: ndcg_at_5 value: 37.084 - type: precision_at_1 value: 21.963 - type: precision_at_10 value: 6.493 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.155000000000001 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.404999999999998 - type: recall_at_10 value: 62.175000000000004 - type: recall_at_100 value: 88.786 - type: recall_at_1000 value: 97.738 - type: recall_at_3 value: 40.925 - type: recall_at_5 value: 50.722 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.50661194710442 - type: f1 value: 93.30311193153668 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24669402644778 - type: f1 value: 54.23122108002977 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.61936785474109 - type: f1 value: 70.52644941025565 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.76529926025555 - type: f1 value: 77.26872729322514 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.39450293021839 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.757796879839294 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.62512146657428 - type: mrr value: 33.84624322066173 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.462 - type: map_at_10 value: 14.947 - type: map_at_100 value: 19.344 - type: map_at_1000 value: 20.933 - type: map_at_3 value: 10.761999999999999 - type: map_at_5 value: 12.744 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.365 - type: mrr_at_100 value: 57.931 - type: mrr_at_1000 value: 57.96 - type: mrr_at_3 value: 54.85 - type: mrr_at_5 value: 56.569 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.173 - type: ndcg_at_100 value: 35.983 - type: ndcg_at_1000 value: 44.507000000000005 - type: ndcg_at_3 value: 42.495 - type: ndcg_at_5 value: 41.019 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 28.731 - type: precision_at_100 value: 9.232 - type: precision_at_1000 value: 2.202 - type: precision_at_3 value: 39.628 - type: precision_at_5 value: 35.851 - type: recall_at_1 value: 6.462 - type: recall_at_10 value: 18.968 - type: recall_at_100 value: 37.131 - type: recall_at_1000 value: 67.956 - type: recall_at_3 value: 11.905000000000001 - type: recall_at_5 value: 15.097 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 30.335 - type: map_at_10 value: 46.611999999999995 - type: map_at_100 value: 47.632000000000005 - type: map_at_1000 value: 47.661 - type: map_at_3 value: 41.876999999999995 - type: map_at_5 value: 44.799 - type: mrr_at_1 value: 34.125 - type: mrr_at_10 value: 49.01 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.768 - type: mrr_at_3 value: 45.153 - type: mrr_at_5 value: 47.589999999999996 - type: ndcg_at_1 value: 34.125 - type: ndcg_at_10 value: 54.777 - type: ndcg_at_100 value: 58.914 - type: ndcg_at_1000 value: 59.521 - type: ndcg_at_3 value: 46.015 - type: ndcg_at_5 value: 50.861000000000004 - type: precision_at_1 value: 34.125 - type: precision_at_10 value: 9.166 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 21.147 - type: precision_at_5 value: 15.469 - type: recall_at_1 value: 30.335 - type: recall_at_10 value: 77.194 - type: recall_at_100 value: 94.812 - type: recall_at_1000 value: 99.247 - type: recall_at_3 value: 54.681000000000004 - type: recall_at_5 value: 65.86800000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.62 - type: map_at_10 value: 84.536 - type: map_at_100 value: 85.167 - type: map_at_1000 value: 85.184 - type: map_at_3 value: 81.607 - type: map_at_5 value: 83.423 - type: mrr_at_1 value: 81.36 - type: mrr_at_10 value: 87.506 - type: mrr_at_100 value: 87.601 - type: mrr_at_1000 value: 87.601 - type: mrr_at_3 value: 86.503 - type: mrr_at_5 value: 87.179 - type: ndcg_at_1 value: 81.36 - type: ndcg_at_10 value: 88.319 - type: ndcg_at_100 value: 89.517 - type: ndcg_at_1000 value: 89.60900000000001 - type: ndcg_at_3 value: 85.423 - type: ndcg_at_5 value: 86.976 - type: precision_at_1 value: 81.36 - type: precision_at_10 value: 13.415 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.342999999999996 - type: precision_at_5 value: 24.534 - type: recall_at_1 value: 70.62 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 99.624 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 87.22 - type: recall_at_5 value: 91.654 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.826438478212744 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.24027467551447 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.997999999999999 - type: map_at_10 value: 14.267 - type: map_at_100 value: 16.843 - type: map_at_1000 value: 17.229 - type: map_at_3 value: 9.834 - type: map_at_5 value: 11.92 - type: mrr_at_1 value: 24.7 - type: mrr_at_10 value: 37.685 - type: mrr_at_100 value: 38.704 - type: mrr_at_1000 value: 38.747 - type: mrr_at_3 value: 34.150000000000006 - type: mrr_at_5 value: 36.075 - type: ndcg_at_1 value: 24.7 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 32.617000000000004 - type: ndcg_at_1000 value: 38.628 - type: ndcg_at_3 value: 21.747 - type: ndcg_at_5 value: 19.076 - type: precision_at_1 value: 24.7 - type: precision_at_10 value: 12.47 - type: precision_at_100 value: 2.564 - type: precision_at_1000 value: 0.4 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.06 - type: recall_at_1 value: 4.997999999999999 - type: recall_at_10 value: 25.3 - type: recall_at_100 value: 52.048 - type: recall_at_1000 value: 81.093 - type: recall_at_3 value: 12.642999999999999 - type: recall_at_5 value: 17.312 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.44942006292234 - type: cos_sim_spearman value: 79.80930790660699 - type: euclidean_pearson value: 82.93400777494863 - type: euclidean_spearman value: 80.04664991110705 - type: manhattan_pearson value: 82.93551681854949 - type: manhattan_spearman value: 80.03156736837379 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.63574059135726 - type: cos_sim_spearman value: 76.80552915288186 - type: euclidean_pearson value: 82.46368529820518 - type: euclidean_spearman value: 76.60338474719275 - type: manhattan_pearson value: 82.4558617035968 - type: manhattan_spearman value: 76.57936082895705 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.24116811084211 - type: cos_sim_spearman value: 88.10998662068769 - type: euclidean_pearson value: 87.04961732352689 - type: euclidean_spearman value: 88.12543945864087 - type: manhattan_pearson value: 86.9905224528854 - type: manhattan_spearman value: 88.07827944705546 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.74847296555048 - type: cos_sim_spearman value: 82.66200957916445 - type: euclidean_pearson value: 84.48132256004965 - type: euclidean_spearman value: 82.67915286000596 - type: manhattan_pearson value: 84.44950477268334 - type: manhattan_spearman value: 82.63327639173352 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.23056258027053 - type: cos_sim_spearman value: 88.92791680286955 - type: euclidean_pearson value: 88.13819235461933 - type: euclidean_spearman value: 88.87294661361716 - type: manhattan_pearson value: 88.14212133687899 - type: manhattan_spearman value: 88.88551854529777 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.64179522732887 - type: cos_sim_spearman value: 84.25028809903114 - type: euclidean_pearson value: 83.40175015236979 - type: euclidean_spearman value: 84.23369296429406 - type: manhattan_pearson value: 83.43768174261321 - type: manhattan_spearman value: 84.27855229214734 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.20378955494732 - type: cos_sim_spearman value: 88.46863559173111 - type: euclidean_pearson value: 88.8249295811663 - type: euclidean_spearman value: 88.6312737724905 - type: manhattan_pearson value: 88.87744466378827 - type: manhattan_spearman value: 88.82908423767314 - 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: 69.91342028796086 - type: cos_sim_spearman value: 69.71495021867864 - type: euclidean_pearson value: 70.65334330405646 - type: euclidean_spearman value: 69.4321253472211 - type: manhattan_pearson value: 70.59743494727465 - type: manhattan_spearman value: 69.11695509297482 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.42451709766952 - type: cos_sim_spearman value: 86.07166710670508 - type: euclidean_pearson value: 86.12711421258899 - type: euclidean_spearman value: 86.05232086925126 - type: manhattan_pearson value: 86.15591089932126 - type: manhattan_spearman value: 86.0890128623439 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.1976344717285 - type: mrr value: 96.3703145075694 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.511 - type: map_at_10 value: 69.724 - type: map_at_100 value: 70.208 - type: map_at_1000 value: 70.22800000000001 - type: map_at_3 value: 66.986 - type: map_at_5 value: 68.529 - type: mrr_at_1 value: 62.333000000000006 - type: mrr_at_10 value: 70.55 - type: mrr_at_100 value: 70.985 - type: mrr_at_1000 value: 71.004 - type: mrr_at_3 value: 68.611 - type: mrr_at_5 value: 69.728 - type: ndcg_at_1 value: 62.333000000000006 - type: ndcg_at_10 value: 74.265 - type: ndcg_at_100 value: 76.361 - type: ndcg_at_1000 value: 76.82900000000001 - type: ndcg_at_3 value: 69.772 - type: ndcg_at_5 value: 71.94800000000001 - type: precision_at_1 value: 62.333000000000006 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.444000000000003 - type: precision_at_5 value: 18 - type: recall_at_1 value: 59.511 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100 - 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en license: mit --- # gte-large 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-large") model = AutoModel.from_pretrained("thenlper/gte-large") # 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-large') 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} } ```