| --- |
| tags: |
| - mteb |
| - Sentence Transformers |
| - sentence-similarity |
| - sentence-transformers |
| model-index: |
| - name: e5-base |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 79.71641791044777 |
| - type: ap |
| value: 44.15426065428253 |
| - type: f1 |
| value: 73.89474407693241 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 87.9649 |
| - type: ap |
| value: 84.10171551915973 |
| - type: f1 |
| value: 87.94148377827356 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 42.645999999999994 |
| - type: f1 |
| value: 42.230574673549 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.814 |
| - type: map_at_10 |
| value: 42.681999999999995 |
| - type: map_at_100 |
| value: 43.714 |
| - type: map_at_1000 |
| value: 43.724000000000004 |
| - type: map_at_3 |
| value: 38.11 |
| - type: map_at_5 |
| value: 40.666999999999994 |
| - type: mrr_at_1 |
| value: 27.168999999999997 |
| - type: mrr_at_10 |
| value: 42.84 |
| - type: mrr_at_100 |
| value: 43.864 |
| - type: mrr_at_1000 |
| value: 43.875 |
| - type: mrr_at_3 |
| value: 38.193 |
| - type: mrr_at_5 |
| value: 40.793 |
| - type: ndcg_at_1 |
| value: 26.814 |
| - type: ndcg_at_10 |
| value: 51.410999999999994 |
| - type: ndcg_at_100 |
| value: 55.713 |
| - type: ndcg_at_1000 |
| value: 55.957 |
| - type: ndcg_at_3 |
| value: 41.955 |
| - type: ndcg_at_5 |
| value: 46.558 |
| - type: precision_at_1 |
| value: 26.814 |
| - type: precision_at_10 |
| value: 7.922999999999999 |
| - type: precision_at_100 |
| value: 0.9780000000000001 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 17.71 |
| - type: precision_at_5 |
| value: 12.859000000000002 |
| - type: recall_at_1 |
| value: 26.814 |
| - type: recall_at_10 |
| value: 79.232 |
| - type: recall_at_100 |
| value: 97.795 |
| - type: recall_at_1000 |
| value: 99.644 |
| - type: recall_at_3 |
| value: 53.129000000000005 |
| - type: recall_at_5 |
| value: 64.29599999999999 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 44.56933066536439 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 40.47647746165173 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 59.65675531567043 |
| - type: mrr |
| value: 72.95255683067317 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.83147014162338 |
| - type: cos_sim_spearman |
| value: 85.1031439521441 |
| - type: euclidean_pearson |
| value: 83.53609085510973 |
| - type: euclidean_spearman |
| value: 84.59650590202833 |
| - type: manhattan_pearson |
| value: 83.14611947586386 |
| - type: manhattan_spearman |
| value: 84.13384475757064 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 83.32792207792208 |
| - type: f1 |
| value: 83.32037485050513 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 36.18605446588703 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 32.72379130181917 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 30.659 |
| - type: map_at_10 |
| value: 40.333999999999996 |
| - type: map_at_100 |
| value: 41.763 |
| - type: map_at_1000 |
| value: 41.894 |
| - type: map_at_3 |
| value: 37.561 |
| - type: map_at_5 |
| value: 39.084 |
| - type: mrr_at_1 |
| value: 37.482 |
| - type: mrr_at_10 |
| value: 45.736 |
| - type: mrr_at_100 |
| value: 46.591 |
| - type: mrr_at_1000 |
| value: 46.644999999999996 |
| - type: mrr_at_3 |
| value: 43.491 |
| - type: mrr_at_5 |
| value: 44.75 |
| - type: ndcg_at_1 |
| value: 37.482 |
| - type: ndcg_at_10 |
| value: 45.606 |
| - type: ndcg_at_100 |
| value: 51.172 |
| - type: ndcg_at_1000 |
| value: 53.407000000000004 |
| - type: ndcg_at_3 |
| value: 41.808 |
| - type: ndcg_at_5 |
| value: 43.449 |
| - type: precision_at_1 |
| value: 37.482 |
| - type: precision_at_10 |
| value: 8.254999999999999 |
| - type: precision_at_100 |
| value: 1.3719999999999999 |
| - type: precision_at_1000 |
| value: 0.186 |
| - type: precision_at_3 |
| value: 19.695 |
| - type: precision_at_5 |
| value: 13.847999999999999 |
| - type: recall_at_1 |
| value: 30.659 |
| - type: recall_at_10 |
| value: 55.409 |
| - type: recall_at_100 |
| value: 78.687 |
| - type: recall_at_1000 |
| value: 93.068 |
| - type: recall_at_3 |
| value: 43.891999999999996 |
| - type: recall_at_5 |
| value: 48.678 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 30.977 |
| - type: map_at_10 |
| value: 40.296 |
| - type: map_at_100 |
| value: 41.453 |
| - type: map_at_1000 |
| value: 41.581 |
| - type: map_at_3 |
| value: 37.619 |
| - type: map_at_5 |
| value: 39.181 |
| - type: mrr_at_1 |
| value: 39.108 |
| - type: mrr_at_10 |
| value: 46.894000000000005 |
| - type: mrr_at_100 |
| value: 47.55 |
| - type: mrr_at_1000 |
| value: 47.598 |
| - type: mrr_at_3 |
| value: 44.766 |
| - type: mrr_at_5 |
| value: 46.062999999999995 |
| - type: ndcg_at_1 |
| value: 39.108 |
| - type: ndcg_at_10 |
| value: 45.717 |
| - type: ndcg_at_100 |
| value: 49.941 |
| - type: ndcg_at_1000 |
| value: 52.138 |
| - type: ndcg_at_3 |
| value: 42.05 |
| - type: ndcg_at_5 |
| value: 43.893 |
| - type: precision_at_1 |
| value: 39.108 |
| - type: precision_at_10 |
| value: 8.306 |
| - type: precision_at_100 |
| value: 1.3419999999999999 |
| - type: precision_at_1000 |
| value: 0.184 |
| - type: precision_at_3 |
| value: 19.979 |
| - type: precision_at_5 |
| value: 14.038 |
| - type: recall_at_1 |
| value: 30.977 |
| - type: recall_at_10 |
| value: 54.688 |
| - type: recall_at_100 |
| value: 72.556 |
| - type: recall_at_1000 |
| value: 86.53800000000001 |
| - type: recall_at_3 |
| value: 43.388 |
| - type: recall_at_5 |
| value: 48.717 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 39.812 |
| - type: map_at_10 |
| value: 50.1 |
| - type: map_at_100 |
| value: 51.193999999999996 |
| - type: map_at_1000 |
| value: 51.258 |
| - type: map_at_3 |
| value: 47.510999999999996 |
| - type: map_at_5 |
| value: 48.891 |
| - type: mrr_at_1 |
| value: 45.266 |
| - type: mrr_at_10 |
| value: 53.459999999999994 |
| - type: mrr_at_100 |
| value: 54.19199999999999 |
| - type: mrr_at_1000 |
| value: 54.228 |
| - type: mrr_at_3 |
| value: 51.296 |
| - type: mrr_at_5 |
| value: 52.495999999999995 |
| - type: ndcg_at_1 |
| value: 45.266 |
| - type: ndcg_at_10 |
| value: 55.034000000000006 |
| - type: ndcg_at_100 |
| value: 59.458 |
| - type: ndcg_at_1000 |
| value: 60.862 |
| - type: ndcg_at_3 |
| value: 50.52799999999999 |
| - type: ndcg_at_5 |
| value: 52.564 |
| - type: precision_at_1 |
| value: 45.266 |
| - type: precision_at_10 |
| value: 8.483 |
| - type: precision_at_100 |
| value: 1.162 |
| - type: precision_at_1000 |
| value: 0.133 |
| - type: precision_at_3 |
| value: 21.944 |
| - type: precision_at_5 |
| value: 14.721 |
| - type: recall_at_1 |
| value: 39.812 |
| - type: recall_at_10 |
| value: 66.36 |
| - type: recall_at_100 |
| value: 85.392 |
| - type: recall_at_1000 |
| value: 95.523 |
| - type: recall_at_3 |
| value: 54.127 |
| - type: recall_at_5 |
| value: 59.245000000000005 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.186 |
| - type: map_at_10 |
| value: 33.18 |
| - type: map_at_100 |
| value: 34.052 |
| - type: map_at_1000 |
| value: 34.149 |
| - type: map_at_3 |
| value: 31.029 |
| - type: map_at_5 |
| value: 32.321 |
| - type: mrr_at_1 |
| value: 28.136 |
| - type: mrr_at_10 |
| value: 35.195 |
| - type: mrr_at_100 |
| value: 35.996 |
| - type: mrr_at_1000 |
| value: 36.076 |
| - type: mrr_at_3 |
| value: 33.051 |
| - type: mrr_at_5 |
| value: 34.407 |
| - type: ndcg_at_1 |
| value: 28.136 |
| - type: ndcg_at_10 |
| value: 37.275999999999996 |
| - type: ndcg_at_100 |
| value: 41.935 |
| - type: ndcg_at_1000 |
| value: 44.389 |
| - type: ndcg_at_3 |
| value: 33.059 |
| - type: ndcg_at_5 |
| value: 35.313 |
| - type: precision_at_1 |
| value: 28.136 |
| - type: precision_at_10 |
| value: 5.457999999999999 |
| - type: precision_at_100 |
| value: 0.826 |
| - type: precision_at_1000 |
| value: 0.107 |
| - type: precision_at_3 |
| value: 13.522 |
| - type: precision_at_5 |
| value: 9.424000000000001 |
| - type: recall_at_1 |
| value: 26.186 |
| - type: recall_at_10 |
| value: 47.961999999999996 |
| - type: recall_at_100 |
| value: 70.072 |
| - type: recall_at_1000 |
| value: 88.505 |
| - type: recall_at_3 |
| value: 36.752 |
| - type: recall_at_5 |
| value: 42.168 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 16.586000000000002 |
| - type: map_at_10 |
| value: 23.637 |
| - type: map_at_100 |
| value: 24.82 |
| - type: map_at_1000 |
| value: 24.95 |
| - type: map_at_3 |
| value: 21.428 |
| - type: map_at_5 |
| value: 22.555 |
| - type: mrr_at_1 |
| value: 20.771 |
| - type: mrr_at_10 |
| value: 27.839999999999996 |
| - type: mrr_at_100 |
| value: 28.887 |
| - type: mrr_at_1000 |
| value: 28.967 |
| - type: mrr_at_3 |
| value: 25.56 |
| - type: mrr_at_5 |
| value: 26.723000000000003 |
| - type: ndcg_at_1 |
| value: 20.771 |
| - type: ndcg_at_10 |
| value: 28.255000000000003 |
| - type: ndcg_at_100 |
| value: 33.886 |
| - type: ndcg_at_1000 |
| value: 36.963 |
| - type: ndcg_at_3 |
| value: 24.056 |
| - type: ndcg_at_5 |
| value: 25.818 |
| - type: precision_at_1 |
| value: 20.771 |
| - type: precision_at_10 |
| value: 5.1 |
| - type: precision_at_100 |
| value: 0.9119999999999999 |
| - type: precision_at_1000 |
| value: 0.132 |
| - type: precision_at_3 |
| value: 11.526 |
| - type: precision_at_5 |
| value: 8.158999999999999 |
| - type: recall_at_1 |
| value: 16.586000000000002 |
| - type: recall_at_10 |
| value: 38.456 |
| - type: recall_at_100 |
| value: 62.666 |
| - type: recall_at_1000 |
| value: 84.47 |
| - type: recall_at_3 |
| value: 26.765 |
| - type: recall_at_5 |
| value: 31.297000000000004 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 28.831 |
| - type: map_at_10 |
| value: 37.545 |
| - type: map_at_100 |
| value: 38.934999999999995 |
| - type: map_at_1000 |
| value: 39.044000000000004 |
| - type: map_at_3 |
| value: 34.601 |
| - type: map_at_5 |
| value: 36.302 |
| - type: mrr_at_1 |
| value: 34.264 |
| - type: mrr_at_10 |
| value: 42.569 |
| - type: mrr_at_100 |
| value: 43.514 |
| - type: mrr_at_1000 |
| value: 43.561 |
| - type: mrr_at_3 |
| value: 40.167 |
| - type: mrr_at_5 |
| value: 41.678 |
| - type: ndcg_at_1 |
| value: 34.264 |
| - type: ndcg_at_10 |
| value: 42.914 |
| - type: ndcg_at_100 |
| value: 48.931999999999995 |
| - type: ndcg_at_1000 |
| value: 51.004000000000005 |
| - type: ndcg_at_3 |
| value: 38.096999999999994 |
| - type: ndcg_at_5 |
| value: 40.509 |
| - type: precision_at_1 |
| value: 34.264 |
| - type: precision_at_10 |
| value: 7.642 |
| - type: precision_at_100 |
| value: 1.258 |
| - type: precision_at_1000 |
| value: 0.161 |
| - type: precision_at_3 |
| value: 17.453 |
| - type: precision_at_5 |
| value: 12.608 |
| - type: recall_at_1 |
| value: 28.831 |
| - type: recall_at_10 |
| value: 53.56999999999999 |
| - type: recall_at_100 |
| value: 79.26100000000001 |
| - type: recall_at_1000 |
| value: 92.862 |
| - type: recall_at_3 |
| value: 40.681 |
| - type: recall_at_5 |
| value: 46.597 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 27.461000000000002 |
| - type: map_at_10 |
| value: 35.885 |
| - type: map_at_100 |
| value: 37.039 |
| - type: map_at_1000 |
| value: 37.16 |
| - type: map_at_3 |
| value: 33.451 |
| - type: map_at_5 |
| value: 34.807 |
| - type: mrr_at_1 |
| value: 34.018 |
| - type: mrr_at_10 |
| value: 41.32 |
| - type: mrr_at_100 |
| value: 42.157 |
| - type: mrr_at_1000 |
| value: 42.223 |
| - type: mrr_at_3 |
| value: 39.288000000000004 |
| - type: mrr_at_5 |
| value: 40.481 |
| - type: ndcg_at_1 |
| value: 34.018 |
| - type: ndcg_at_10 |
| value: 40.821000000000005 |
| - type: ndcg_at_100 |
| value: 46.053 |
| - type: ndcg_at_1000 |
| value: 48.673 |
| - type: ndcg_at_3 |
| value: 36.839 |
| - type: ndcg_at_5 |
| value: 38.683 |
| - type: precision_at_1 |
| value: 34.018 |
| - type: precision_at_10 |
| value: 7.009 |
| - type: precision_at_100 |
| value: 1.123 |
| - type: precision_at_1000 |
| value: 0.153 |
| - type: precision_at_3 |
| value: 16.933 |
| - type: precision_at_5 |
| value: 11.826 |
| - type: recall_at_1 |
| value: 27.461000000000002 |
| - type: recall_at_10 |
| value: 50.285000000000004 |
| - type: recall_at_100 |
| value: 73.25500000000001 |
| - type: recall_at_1000 |
| value: 91.17699999999999 |
| - type: recall_at_3 |
| value: 39.104 |
| - type: recall_at_5 |
| value: 43.968 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.980083333333337 |
| - type: map_at_10 |
| value: 34.47208333333333 |
| - type: map_at_100 |
| value: 35.609249999999996 |
| - type: map_at_1000 |
| value: 35.72833333333333 |
| - type: map_at_3 |
| value: 32.189416666666666 |
| - type: map_at_5 |
| value: 33.44683333333334 |
| - type: mrr_at_1 |
| value: 31.731666666666662 |
| - type: mrr_at_10 |
| value: 38.518 |
| - type: mrr_at_100 |
| value: 39.38166666666667 |
| - type: mrr_at_1000 |
| value: 39.446999999999996 |
| - type: mrr_at_3 |
| value: 36.49966666666668 |
| - type: mrr_at_5 |
| value: 37.639916666666664 |
| - type: ndcg_at_1 |
| value: 31.731666666666662 |
| - type: ndcg_at_10 |
| value: 38.92033333333333 |
| - type: ndcg_at_100 |
| value: 44.01675 |
| - type: ndcg_at_1000 |
| value: 46.51075 |
| - type: ndcg_at_3 |
| value: 35.09766666666667 |
| - type: ndcg_at_5 |
| value: 36.842999999999996 |
| - type: precision_at_1 |
| value: 31.731666666666662 |
| - type: precision_at_10 |
| value: 6.472583333333332 |
| - type: precision_at_100 |
| value: 1.0665 |
| - type: precision_at_1000 |
| value: 0.14725000000000002 |
| - type: precision_at_3 |
| value: 15.659083333333331 |
| - type: precision_at_5 |
| value: 10.878833333333333 |
| - type: recall_at_1 |
| value: 26.980083333333337 |
| - type: recall_at_10 |
| value: 48.13925 |
| - type: recall_at_100 |
| value: 70.70149999999998 |
| - type: recall_at_1000 |
| value: 88.10775000000001 |
| - type: recall_at_3 |
| value: 37.30091666666667 |
| - type: recall_at_5 |
| value: 41.90358333333333 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.607999999999997 |
| - type: map_at_10 |
| value: 30.523 |
| - type: map_at_100 |
| value: 31.409 |
| - type: map_at_1000 |
| value: 31.507 |
| - type: map_at_3 |
| value: 28.915000000000003 |
| - type: map_at_5 |
| value: 29.756 |
| - type: mrr_at_1 |
| value: 28.681 |
| - type: mrr_at_10 |
| value: 33.409 |
| - type: mrr_at_100 |
| value: 34.241 |
| - type: mrr_at_1000 |
| value: 34.313 |
| - type: mrr_at_3 |
| value: 32.029999999999994 |
| - type: mrr_at_5 |
| value: 32.712 |
| - type: ndcg_at_1 |
| value: 28.681 |
| - type: ndcg_at_10 |
| value: 33.733000000000004 |
| - type: ndcg_at_100 |
| value: 38.32 |
| - type: ndcg_at_1000 |
| value: 40.937 |
| - type: ndcg_at_3 |
| value: 30.898999999999997 |
| - type: ndcg_at_5 |
| value: 32.088 |
| - type: precision_at_1 |
| value: 28.681 |
| - type: precision_at_10 |
| value: 4.968999999999999 |
| - type: precision_at_100 |
| value: 0.79 |
| - type: precision_at_1000 |
| value: 0.11 |
| - type: precision_at_3 |
| value: 12.73 |
| - type: precision_at_5 |
| value: 8.558 |
| - type: recall_at_1 |
| value: 25.607999999999997 |
| - type: recall_at_10 |
| value: 40.722 |
| - type: recall_at_100 |
| value: 61.956999999999994 |
| - type: recall_at_1000 |
| value: 81.43 |
| - type: recall_at_3 |
| value: 32.785 |
| - type: recall_at_5 |
| value: 35.855 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 20.399 |
| - type: map_at_10 |
| value: 25.968000000000004 |
| - type: map_at_100 |
| value: 26.985999999999997 |
| - type: map_at_1000 |
| value: 27.105 |
| - type: map_at_3 |
| value: 24.215 |
| - type: map_at_5 |
| value: 25.157 |
| - type: mrr_at_1 |
| value: 24.708 |
| - type: mrr_at_10 |
| value: 29.971999999999998 |
| - type: mrr_at_100 |
| value: 30.858 |
| - type: mrr_at_1000 |
| value: 30.934 |
| - type: mrr_at_3 |
| value: 28.304000000000002 |
| - type: mrr_at_5 |
| value: 29.183999999999997 |
| - type: ndcg_at_1 |
| value: 24.708 |
| - type: ndcg_at_10 |
| value: 29.676000000000002 |
| - type: ndcg_at_100 |
| value: 34.656 |
| - type: ndcg_at_1000 |
| value: 37.588 |
| - type: ndcg_at_3 |
| value: 26.613 |
| - type: ndcg_at_5 |
| value: 27.919 |
| - type: precision_at_1 |
| value: 24.708 |
| - type: precision_at_10 |
| value: 5.01 |
| - type: precision_at_100 |
| value: 0.876 |
| - type: precision_at_1000 |
| value: 0.13 |
| - type: precision_at_3 |
| value: 11.975 |
| - type: precision_at_5 |
| value: 8.279 |
| - type: recall_at_1 |
| value: 20.399 |
| - type: recall_at_10 |
| value: 36.935 |
| - type: recall_at_100 |
| value: 59.532 |
| - type: recall_at_1000 |
| value: 80.58 |
| - type: recall_at_3 |
| value: 27.979 |
| - type: recall_at_5 |
| value: 31.636999999999997 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 27.606 |
| - type: map_at_10 |
| value: 34.213 |
| - type: map_at_100 |
| value: 35.339999999999996 |
| - type: map_at_1000 |
| value: 35.458 |
| - type: map_at_3 |
| value: 31.987 |
| - type: map_at_5 |
| value: 33.322 |
| - type: mrr_at_1 |
| value: 31.53 |
| - type: mrr_at_10 |
| value: 37.911 |
| - type: mrr_at_100 |
| value: 38.879000000000005 |
| - type: mrr_at_1000 |
| value: 38.956 |
| - type: mrr_at_3 |
| value: 35.868 |
| - type: mrr_at_5 |
| value: 37.047999999999995 |
| - type: ndcg_at_1 |
| value: 31.53 |
| - type: ndcg_at_10 |
| value: 38.312000000000005 |
| - type: ndcg_at_100 |
| value: 43.812 |
| - type: ndcg_at_1000 |
| value: 46.414 |
| - type: ndcg_at_3 |
| value: 34.319 |
| - type: ndcg_at_5 |
| value: 36.312 |
| - type: precision_at_1 |
| value: 31.53 |
| - type: precision_at_10 |
| value: 5.970000000000001 |
| - type: precision_at_100 |
| value: 0.9939999999999999 |
| - type: precision_at_1000 |
| value: 0.133 |
| - type: precision_at_3 |
| value: 14.738999999999999 |
| - type: precision_at_5 |
| value: 10.242999999999999 |
| - type: recall_at_1 |
| value: 27.606 |
| - type: recall_at_10 |
| value: 47.136 |
| - type: recall_at_100 |
| value: 71.253 |
| - type: recall_at_1000 |
| value: 89.39399999999999 |
| - type: recall_at_3 |
| value: 36.342 |
| - type: recall_at_5 |
| value: 41.388999999999996 |
| - 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: 31.963 |
| - type: map_at_100 |
| value: 33.371 |
| - type: map_at_1000 |
| value: 33.584 |
| - type: map_at_3 |
| value: 29.543999999999997 |
| - type: map_at_5 |
| value: 30.793 |
| - type: mrr_at_1 |
| value: 29.644 |
| - type: mrr_at_10 |
| value: 35.601 |
| - type: mrr_at_100 |
| value: 36.551 |
| - type: mrr_at_1000 |
| value: 36.623 |
| - type: mrr_at_3 |
| value: 33.399 |
| - type: mrr_at_5 |
| value: 34.575 |
| - type: ndcg_at_1 |
| value: 29.644 |
| - type: ndcg_at_10 |
| value: 36.521 |
| - type: ndcg_at_100 |
| value: 42.087 |
| - type: ndcg_at_1000 |
| value: 45.119 |
| - type: ndcg_at_3 |
| value: 32.797 |
| - type: ndcg_at_5 |
| value: 34.208 |
| - type: precision_at_1 |
| value: 29.644 |
| - type: precision_at_10 |
| value: 6.7 |
| - type: precision_at_100 |
| value: 1.374 |
| - type: precision_at_1000 |
| value: 0.22899999999999998 |
| - type: precision_at_3 |
| value: 15.152 |
| - type: precision_at_5 |
| value: 10.671999999999999 |
| - type: recall_at_1 |
| value: 24.855 |
| - type: recall_at_10 |
| value: 45.449 |
| - type: recall_at_100 |
| value: 70.921 |
| - type: recall_at_1000 |
| value: 90.629 |
| - type: recall_at_3 |
| value: 33.526 |
| - type: recall_at_5 |
| value: 37.848 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.781 |
| - type: map_at_10 |
| value: 30.020999999999997 |
| - type: map_at_100 |
| value: 30.948999999999998 |
| - type: map_at_1000 |
| value: 31.05 |
| - type: map_at_3 |
| value: 28.412 |
| - type: map_at_5 |
| value: 29.193 |
| - type: mrr_at_1 |
| value: 27.172 |
| - type: mrr_at_10 |
| value: 32.309 |
| - type: mrr_at_100 |
| value: 33.164 |
| - type: mrr_at_1000 |
| value: 33.239999999999995 |
| - type: mrr_at_3 |
| value: 30.775999999999996 |
| - type: mrr_at_5 |
| value: 31.562 |
| - type: ndcg_at_1 |
| value: 27.172 |
| - type: ndcg_at_10 |
| value: 33.178999999999995 |
| - type: ndcg_at_100 |
| value: 37.949 |
| - type: ndcg_at_1000 |
| value: 40.635 |
| - type: ndcg_at_3 |
| value: 30.107 |
| - type: ndcg_at_5 |
| value: 31.36 |
| - type: precision_at_1 |
| value: 27.172 |
| - type: precision_at_10 |
| value: 4.769 |
| - type: precision_at_100 |
| value: 0.769 |
| - type: precision_at_1000 |
| value: 0.109 |
| - type: precision_at_3 |
| value: 12.261 |
| - type: precision_at_5 |
| value: 8.17 |
| - type: recall_at_1 |
| value: 24.781 |
| - type: recall_at_10 |
| value: 40.699000000000005 |
| - type: recall_at_100 |
| value: 62.866 |
| - type: recall_at_1000 |
| value: 83.11699999999999 |
| - type: recall_at_3 |
| value: 32.269999999999996 |
| - type: recall_at_5 |
| value: 35.443999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 5.2139999999999995 |
| - type: map_at_10 |
| value: 9.986 |
| - type: map_at_100 |
| value: 11.343 |
| - type: map_at_1000 |
| value: 11.55 |
| - type: map_at_3 |
| value: 7.961 |
| - type: map_at_5 |
| value: 8.967 |
| - type: mrr_at_1 |
| value: 12.052 |
| - type: mrr_at_10 |
| value: 20.165 |
| - type: mrr_at_100 |
| value: 21.317 |
| - type: mrr_at_1000 |
| value: 21.399 |
| - type: mrr_at_3 |
| value: 17.079 |
| - type: mrr_at_5 |
| value: 18.695 |
| - type: ndcg_at_1 |
| value: 12.052 |
| - type: ndcg_at_10 |
| value: 15.375 |
| - type: ndcg_at_100 |
| value: 21.858 |
| - type: ndcg_at_1000 |
| value: 26.145000000000003 |
| - type: ndcg_at_3 |
| value: 11.334 |
| - type: ndcg_at_5 |
| value: 12.798000000000002 |
| - type: precision_at_1 |
| value: 12.052 |
| - type: precision_at_10 |
| value: 5.16 |
| - type: precision_at_100 |
| value: 1.206 |
| - type: precision_at_1000 |
| value: 0.198 |
| - type: precision_at_3 |
| value: 8.73 |
| - type: precision_at_5 |
| value: 7.114 |
| - type: recall_at_1 |
| value: 5.2139999999999995 |
| - type: recall_at_10 |
| value: 20.669999999999998 |
| - type: recall_at_100 |
| value: 43.901 |
| - type: recall_at_1000 |
| value: 68.447 |
| - type: recall_at_3 |
| value: 11.049000000000001 |
| - type: recall_at_5 |
| value: 14.652999999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 8.511000000000001 |
| - type: map_at_10 |
| value: 19.503 |
| - type: map_at_100 |
| value: 27.46 |
| - type: map_at_1000 |
| value: 29.187 |
| - type: map_at_3 |
| value: 14.030999999999999 |
| - type: map_at_5 |
| value: 16.329 |
| - type: mrr_at_1 |
| value: 63.74999999999999 |
| - type: mrr_at_10 |
| value: 73.419 |
| - type: mrr_at_100 |
| value: 73.691 |
| - type: mrr_at_1000 |
| value: 73.697 |
| - type: mrr_at_3 |
| value: 71.792 |
| - type: mrr_at_5 |
| value: 72.979 |
| - type: ndcg_at_1 |
| value: 53.125 |
| - type: ndcg_at_10 |
| value: 41.02 |
| - type: ndcg_at_100 |
| value: 45.407 |
| - type: ndcg_at_1000 |
| value: 52.68000000000001 |
| - type: ndcg_at_3 |
| value: 46.088 |
| - type: ndcg_at_5 |
| value: 43.236000000000004 |
| - type: precision_at_1 |
| value: 63.74999999999999 |
| - type: precision_at_10 |
| value: 32.35 |
| - type: precision_at_100 |
| value: 10.363 |
| - type: precision_at_1000 |
| value: 2.18 |
| - type: precision_at_3 |
| value: 49.667 |
| - type: precision_at_5 |
| value: 41.5 |
| - type: recall_at_1 |
| value: 8.511000000000001 |
| - type: recall_at_10 |
| value: 24.851 |
| - type: recall_at_100 |
| value: 50.745 |
| - type: recall_at_1000 |
| value: 73.265 |
| - type: recall_at_3 |
| value: 15.716 |
| - type: recall_at_5 |
| value: 19.256 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 49.43500000000001 |
| - type: f1 |
| value: 44.56288273966374 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 40.858 |
| - type: map_at_10 |
| value: 52.276 |
| - type: map_at_100 |
| value: 52.928 |
| - type: map_at_1000 |
| value: 52.966 |
| - type: map_at_3 |
| value: 49.729 |
| - type: map_at_5 |
| value: 51.27 |
| - type: mrr_at_1 |
| value: 43.624 |
| - type: mrr_at_10 |
| value: 55.22899999999999 |
| - type: mrr_at_100 |
| value: 55.823 |
| - type: mrr_at_1000 |
| value: 55.85 |
| - type: mrr_at_3 |
| value: 52.739999999999995 |
| - type: mrr_at_5 |
| value: 54.251000000000005 |
| - type: ndcg_at_1 |
| value: 43.624 |
| - type: ndcg_at_10 |
| value: 58.23500000000001 |
| - type: ndcg_at_100 |
| value: 61.315 |
| - type: ndcg_at_1000 |
| value: 62.20099999999999 |
| - type: ndcg_at_3 |
| value: 53.22 |
| - type: ndcg_at_5 |
| value: 55.88999999999999 |
| - type: precision_at_1 |
| value: 43.624 |
| - type: precision_at_10 |
| value: 8.068999999999999 |
| - type: precision_at_100 |
| value: 0.975 |
| - type: precision_at_1000 |
| value: 0.107 |
| - type: precision_at_3 |
| value: 21.752 |
| - type: precision_at_5 |
| value: 14.515 |
| - type: recall_at_1 |
| value: 40.858 |
| - type: recall_at_10 |
| value: 73.744 |
| - type: recall_at_100 |
| value: 87.667 |
| - type: recall_at_1000 |
| value: 94.15599999999999 |
| - type: recall_at_3 |
| value: 60.287 |
| - type: recall_at_5 |
| value: 66.703 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 17.864 |
| - type: map_at_10 |
| value: 28.592000000000002 |
| - type: map_at_100 |
| value: 30.165 |
| - type: map_at_1000 |
| value: 30.364 |
| - type: map_at_3 |
| value: 24.586 |
| - type: map_at_5 |
| value: 26.717000000000002 |
| - type: mrr_at_1 |
| value: 35.031 |
| - type: mrr_at_10 |
| value: 43.876 |
| - type: mrr_at_100 |
| value: 44.683 |
| - type: mrr_at_1000 |
| value: 44.736 |
| - type: mrr_at_3 |
| value: 40.998000000000005 |
| - type: mrr_at_5 |
| value: 42.595 |
| - type: ndcg_at_1 |
| value: 35.031 |
| - type: ndcg_at_10 |
| value: 36.368 |
| - type: ndcg_at_100 |
| value: 42.472 |
| - type: ndcg_at_1000 |
| value: 45.973000000000006 |
| - type: ndcg_at_3 |
| value: 31.915 |
| - type: ndcg_at_5 |
| value: 33.394 |
| - type: precision_at_1 |
| value: 35.031 |
| - type: precision_at_10 |
| value: 10.139 |
| - type: precision_at_100 |
| value: 1.6420000000000001 |
| - type: precision_at_1000 |
| value: 0.22699999999999998 |
| - type: precision_at_3 |
| value: 21.142 |
| - type: precision_at_5 |
| value: 15.772 |
| - type: recall_at_1 |
| value: 17.864 |
| - type: recall_at_10 |
| value: 43.991 |
| - type: recall_at_100 |
| value: 66.796 |
| - type: recall_at_1000 |
| value: 87.64 |
| - type: recall_at_3 |
| value: 28.915999999999997 |
| - type: recall_at_5 |
| value: 35.185 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 36.556 |
| - type: map_at_10 |
| value: 53.056000000000004 |
| - type: map_at_100 |
| value: 53.909 |
| - type: map_at_1000 |
| value: 53.98 |
| - type: map_at_3 |
| value: 49.982 |
| - type: map_at_5 |
| value: 51.9 |
| - type: mrr_at_1 |
| value: 73.113 |
| - type: mrr_at_10 |
| value: 79.381 |
| - type: mrr_at_100 |
| value: 79.60300000000001 |
| - type: mrr_at_1000 |
| value: 79.617 |
| - type: mrr_at_3 |
| value: 78.298 |
| - type: mrr_at_5 |
| value: 78.995 |
| - type: ndcg_at_1 |
| value: 73.113 |
| - type: ndcg_at_10 |
| value: 62.21 |
| - type: ndcg_at_100 |
| value: 65.242 |
| - type: ndcg_at_1000 |
| value: 66.667 |
| - type: ndcg_at_3 |
| value: 57.717 |
| - type: ndcg_at_5 |
| value: 60.224 |
| - type: precision_at_1 |
| value: 73.113 |
| - type: precision_at_10 |
| value: 12.842999999999998 |
| - type: precision_at_100 |
| value: 1.522 |
| - type: precision_at_1000 |
| value: 0.17099999999999999 |
| - type: precision_at_3 |
| value: 36.178 |
| - type: precision_at_5 |
| value: 23.695 |
| - type: recall_at_1 |
| value: 36.556 |
| - type: recall_at_10 |
| value: 64.213 |
| - type: recall_at_100 |
| value: 76.077 |
| - type: recall_at_1000 |
| value: 85.53699999999999 |
| - type: recall_at_3 |
| value: 54.266999999999996 |
| - type: recall_at_5 |
| value: 59.236999999999995 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 75.958 |
| - type: ap |
| value: 69.82869527654348 |
| - type: f1 |
| value: 75.89120903005633 |
| - task: |
| type: Retrieval |
| dataset: |
| type: msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.608 |
| - type: map_at_10 |
| value: 36.144 |
| - type: map_at_100 |
| value: 37.244 |
| - type: map_at_1000 |
| value: 37.291999999999994 |
| - type: map_at_3 |
| value: 32.287 |
| - type: map_at_5 |
| value: 34.473 |
| - type: mrr_at_1 |
| value: 24.226 |
| - type: mrr_at_10 |
| value: 36.711 |
| - type: mrr_at_100 |
| value: 37.758 |
| - type: mrr_at_1000 |
| value: 37.8 |
| - type: mrr_at_3 |
| value: 32.92 |
| - type: mrr_at_5 |
| value: 35.104 |
| - type: ndcg_at_1 |
| value: 24.269 |
| - type: ndcg_at_10 |
| value: 43.138 |
| - type: ndcg_at_100 |
| value: 48.421 |
| - type: ndcg_at_1000 |
| value: 49.592000000000006 |
| - type: ndcg_at_3 |
| value: 35.269 |
| - type: ndcg_at_5 |
| value: 39.175 |
| - type: precision_at_1 |
| value: 24.269 |
| - type: precision_at_10 |
| value: 6.755999999999999 |
| - type: precision_at_100 |
| value: 0.941 |
| - type: precision_at_1000 |
| value: 0.104 |
| - type: precision_at_3 |
| value: 14.938 |
| - type: precision_at_5 |
| value: 10.934000000000001 |
| - type: recall_at_1 |
| value: 23.608 |
| - type: recall_at_10 |
| value: 64.679 |
| - type: recall_at_100 |
| value: 89.027 |
| - type: recall_at_1000 |
| value: 97.91 |
| - type: recall_at_3 |
| value: 43.25 |
| - type: recall_at_5 |
| value: 52.617000000000004 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 93.21477428180576 |
| - type: f1 |
| value: 92.92502305092152 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 74.76744186046511 |
| - type: f1 |
| value: 59.19855520057899 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 72.24613315400134 |
| - type: f1 |
| value: 70.19950395651232 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 76.75857431069268 |
| - type: f1 |
| value: 76.5433450230191 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 31.525463791623604 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 28.28695907385136 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.068174046665224 |
| - type: mrr |
| value: 30.827586642840803 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 6.322 |
| - type: map_at_10 |
| value: 13.919999999999998 |
| - type: map_at_100 |
| value: 17.416 |
| - type: map_at_1000 |
| value: 18.836 |
| - type: map_at_3 |
| value: 10.111 |
| - type: map_at_5 |
| value: 11.991999999999999 |
| - type: mrr_at_1 |
| value: 48.297000000000004 |
| - type: mrr_at_10 |
| value: 57.114 |
| - type: mrr_at_100 |
| value: 57.713 |
| - type: mrr_at_1000 |
| value: 57.751 |
| - type: mrr_at_3 |
| value: 55.108000000000004 |
| - type: mrr_at_5 |
| value: 56.533 |
| - type: ndcg_at_1 |
| value: 46.44 |
| - type: ndcg_at_10 |
| value: 36.589 |
| - type: ndcg_at_100 |
| value: 33.202 |
| - type: ndcg_at_1000 |
| value: 41.668 |
| - type: ndcg_at_3 |
| value: 41.302 |
| - type: ndcg_at_5 |
| value: 39.829 |
| - type: precision_at_1 |
| value: 47.988 |
| - type: precision_at_10 |
| value: 27.059 |
| - type: precision_at_100 |
| value: 8.235000000000001 |
| - type: precision_at_1000 |
| value: 2.091 |
| - type: precision_at_3 |
| value: 38.184000000000005 |
| - type: precision_at_5 |
| value: 34.365 |
| - type: recall_at_1 |
| value: 6.322 |
| - type: recall_at_10 |
| value: 18.288 |
| - type: recall_at_100 |
| value: 32.580999999999996 |
| - type: recall_at_1000 |
| value: 63.605999999999995 |
| - type: recall_at_3 |
| value: 11.266 |
| - type: recall_at_5 |
| value: 14.69 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 36.586999999999996 |
| - type: map_at_10 |
| value: 52.464 |
| - type: map_at_100 |
| value: 53.384 |
| - type: map_at_1000 |
| value: 53.405 |
| - type: map_at_3 |
| value: 48.408 |
| - type: map_at_5 |
| value: 50.788999999999994 |
| - type: mrr_at_1 |
| value: 40.904 |
| - type: mrr_at_10 |
| value: 54.974000000000004 |
| - type: mrr_at_100 |
| value: 55.60699999999999 |
| - type: mrr_at_1000 |
| value: 55.623 |
| - type: mrr_at_3 |
| value: 51.73799999999999 |
| - type: mrr_at_5 |
| value: 53.638 |
| - type: ndcg_at_1 |
| value: 40.904 |
| - type: ndcg_at_10 |
| value: 59.965999999999994 |
| - type: ndcg_at_100 |
| value: 63.613 |
| - type: ndcg_at_1000 |
| value: 64.064 |
| - type: ndcg_at_3 |
| value: 52.486 |
| - type: ndcg_at_5 |
| value: 56.377 |
| - type: precision_at_1 |
| value: 40.904 |
| - type: precision_at_10 |
| value: 9.551 |
| - type: precision_at_100 |
| value: 1.162 |
| - type: precision_at_1000 |
| value: 0.12 |
| - type: precision_at_3 |
| value: 23.552 |
| - type: precision_at_5 |
| value: 16.436999999999998 |
| - type: recall_at_1 |
| value: 36.586999999999996 |
| - type: recall_at_10 |
| value: 80.094 |
| - type: recall_at_100 |
| value: 95.515 |
| - type: recall_at_1000 |
| value: 98.803 |
| - type: recall_at_3 |
| value: 60.907 |
| - type: recall_at_5 |
| value: 69.817 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 70.422 |
| - type: map_at_10 |
| value: 84.113 |
| - type: map_at_100 |
| value: 84.744 |
| - type: map_at_1000 |
| value: 84.762 |
| - type: map_at_3 |
| value: 81.171 |
| - type: map_at_5 |
| value: 83.039 |
| - type: mrr_at_1 |
| value: 81.12 |
| - type: mrr_at_10 |
| value: 87.277 |
| - type: mrr_at_100 |
| value: 87.384 |
| - type: mrr_at_1000 |
| value: 87.385 |
| - type: mrr_at_3 |
| value: 86.315 |
| - type: mrr_at_5 |
| value: 86.981 |
| - type: ndcg_at_1 |
| value: 81.12 |
| - type: ndcg_at_10 |
| value: 87.92 |
| - type: ndcg_at_100 |
| value: 89.178 |
| - type: ndcg_at_1000 |
| value: 89.29899999999999 |
| - type: ndcg_at_3 |
| value: 85.076 |
| - type: ndcg_at_5 |
| value: 86.67099999999999 |
| - type: precision_at_1 |
| value: 81.12 |
| - type: precision_at_10 |
| value: 13.325999999999999 |
| - type: precision_at_100 |
| value: 1.524 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 37.16 |
| - type: precision_at_5 |
| value: 24.456 |
| - type: recall_at_1 |
| value: 70.422 |
| - type: recall_at_10 |
| value: 95.00800000000001 |
| - type: recall_at_100 |
| value: 99.38 |
| - type: recall_at_1000 |
| value: 99.94800000000001 |
| - type: recall_at_3 |
| value: 86.809 |
| - type: recall_at_5 |
| value: 91.334 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 48.18491891699636 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 62.190639679711914 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.478 |
| - type: map_at_10 |
| value: 11.268 |
| - type: map_at_100 |
| value: 13.129 |
| - type: map_at_1000 |
| value: 13.41 |
| - type: map_at_3 |
| value: 8.103 |
| - type: map_at_5 |
| value: 9.609 |
| - type: mrr_at_1 |
| value: 22 |
| - type: mrr_at_10 |
| value: 32.248 |
| - type: mrr_at_100 |
| value: 33.355000000000004 |
| - type: mrr_at_1000 |
| value: 33.42 |
| - type: mrr_at_3 |
| value: 29.15 |
| - type: mrr_at_5 |
| value: 30.785 |
| - type: ndcg_at_1 |
| value: 22 |
| - type: ndcg_at_10 |
| value: 18.990000000000002 |
| - type: ndcg_at_100 |
| value: 26.302999999999997 |
| - type: ndcg_at_1000 |
| value: 31.537 |
| - type: ndcg_at_3 |
| value: 18.034 |
| - type: ndcg_at_5 |
| value: 15.655 |
| - type: precision_at_1 |
| value: 22 |
| - type: precision_at_10 |
| value: 9.91 |
| - type: precision_at_100 |
| value: 2.0420000000000003 |
| - type: precision_at_1000 |
| value: 0.33 |
| - type: precision_at_3 |
| value: 16.933 |
| - type: precision_at_5 |
| value: 13.719999999999999 |
| - type: recall_at_1 |
| value: 4.478 |
| - type: recall_at_10 |
| value: 20.087 |
| - type: recall_at_100 |
| value: 41.457 |
| - type: recall_at_1000 |
| value: 67.10199999999999 |
| - type: recall_at_3 |
| value: 10.313 |
| - type: recall_at_5 |
| value: 13.927999999999999 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.27341574565806 |
| - type: cos_sim_spearman |
| value: 79.66419880841734 |
| - type: euclidean_pearson |
| value: 81.32473321838208 |
| - type: euclidean_spearman |
| value: 79.29828832085133 |
| - type: manhattan_pearson |
| value: 81.25554065883132 |
| - type: manhattan_spearman |
| value: 79.23275543279853 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.40468875905418 |
| - type: cos_sim_spearman |
| value: 74.2189990321174 |
| - type: euclidean_pearson |
| value: 80.74376966290956 |
| - type: euclidean_spearman |
| value: 74.97663839079335 |
| - type: manhattan_pearson |
| value: 80.69779331646207 |
| - type: manhattan_spearman |
| value: 75.00225252917613 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.5745290053095 |
| - type: cos_sim_spearman |
| value: 83.31401180333397 |
| - type: euclidean_pearson |
| value: 82.96500607325534 |
| - type: euclidean_spearman |
| value: 83.8534967935793 |
| - type: manhattan_pearson |
| value: 82.83112050632508 |
| - type: manhattan_spearman |
| value: 83.70877296557838 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 80.67833656607704 |
| - type: cos_sim_spearman |
| value: 78.52252410630707 |
| - type: euclidean_pearson |
| value: 80.071189514343 |
| - type: euclidean_spearman |
| value: 78.95143545742796 |
| - type: manhattan_pearson |
| value: 80.0128926165121 |
| - type: manhattan_spearman |
| value: 78.91236678732628 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.48437639980746 |
| - type: cos_sim_spearman |
| value: 88.34876527774259 |
| - type: euclidean_pearson |
| value: 87.64898081823888 |
| - type: euclidean_spearman |
| value: 88.58937180804213 |
| - type: manhattan_pearson |
| value: 87.5942417815288 |
| - type: manhattan_spearman |
| value: 88.53013922267687 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.69189187164781 |
| - type: cos_sim_spearman |
| value: 84.15327883572112 |
| - type: euclidean_pearson |
| value: 83.64202266685898 |
| - type: euclidean_spearman |
| value: 84.6219602318862 |
| - type: manhattan_pearson |
| value: 83.53256698709998 |
| - type: manhattan_spearman |
| value: 84.49260712904946 |
| - 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: 87.09508017611589 |
| - type: cos_sim_spearman |
| value: 87.23010990417097 |
| - type: euclidean_pearson |
| value: 87.62545569077133 |
| - type: euclidean_spearman |
| value: 86.71152051711714 |
| - type: manhattan_pearson |
| value: 87.5057154278377 |
| - type: manhattan_spearman |
| value: 86.60611898281267 |
| - 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: 61.72129893941176 |
| - type: cos_sim_spearman |
| value: 62.87871412069194 |
| - type: euclidean_pearson |
| value: 63.21077648290454 |
| - type: euclidean_spearman |
| value: 63.03263080805978 |
| - type: manhattan_pearson |
| value: 63.20740860135976 |
| - type: manhattan_spearman |
| value: 62.89930471802817 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.039118236799 |
| - type: cos_sim_spearman |
| value: 86.18102563389962 |
| - type: euclidean_pearson |
| value: 85.62977041471879 |
| - type: euclidean_spearman |
| value: 86.02478990544347 |
| - type: manhattan_pearson |
| value: 85.60786740521806 |
| - type: manhattan_spearman |
| value: 85.99546210442547 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 82.89875069737266 |
| - type: mrr |
| value: 95.42621322033087 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 58.660999999999994 |
| - type: map_at_10 |
| value: 68.738 |
| - type: map_at_100 |
| value: 69.33200000000001 |
| - type: map_at_1000 |
| value: 69.352 |
| - type: map_at_3 |
| value: 66.502 |
| - type: map_at_5 |
| value: 67.686 |
| - type: mrr_at_1 |
| value: 61.667 |
| - type: mrr_at_10 |
| value: 70.003 |
| - type: mrr_at_100 |
| value: 70.441 |
| - type: mrr_at_1000 |
| value: 70.46 |
| - type: mrr_at_3 |
| value: 68.278 |
| - type: mrr_at_5 |
| value: 69.194 |
| - type: ndcg_at_1 |
| value: 61.667 |
| - type: ndcg_at_10 |
| value: 73.083 |
| - type: ndcg_at_100 |
| value: 75.56 |
| - type: ndcg_at_1000 |
| value: 76.01400000000001 |
| - type: ndcg_at_3 |
| value: 69.28699999999999 |
| - type: ndcg_at_5 |
| value: 70.85000000000001 |
| - type: precision_at_1 |
| value: 61.667 |
| - type: precision_at_10 |
| value: 9.6 |
| - type: precision_at_100 |
| value: 1.087 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 27.111 |
| - type: precision_at_5 |
| value: 17.467 |
| - type: recall_at_1 |
| value: 58.660999999999994 |
| - type: recall_at_10 |
| value: 85.02199999999999 |
| - type: recall_at_100 |
| value: 95.933 |
| - type: recall_at_1000 |
| value: 99.333 |
| - type: recall_at_3 |
| value: 74.506 |
| - type: recall_at_5 |
| value: 78.583 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.8029702970297 |
| - type: cos_sim_ap |
| value: 94.87673936635738 |
| - type: cos_sim_f1 |
| value: 90.00502260170768 |
| - type: cos_sim_precision |
| value: 90.41372351160445 |
| - type: cos_sim_recall |
| value: 89.60000000000001 |
| - type: dot_accuracy |
| value: 99.57524752475247 |
| - type: dot_ap |
| value: 84.81717934496321 |
| - type: dot_f1 |
| value: 78.23026646556059 |
| - type: dot_precision |
| value: 78.66531850353893 |
| - type: dot_recall |
| value: 77.8 |
| - type: euclidean_accuracy |
| value: 99.8029702970297 |
| - type: euclidean_ap |
| value: 94.74658253135284 |
| - type: euclidean_f1 |
| value: 90.08470353761834 |
| - type: euclidean_precision |
| value: 89.77159880834161 |
| - type: euclidean_recall |
| value: 90.4 |
| - type: manhattan_accuracy |
| value: 99.8 |
| - type: manhattan_ap |
| value: 94.69224030742787 |
| - type: manhattan_f1 |
| value: 89.9502487562189 |
| - type: manhattan_precision |
| value: 89.50495049504951 |
| - type: manhattan_recall |
| value: 90.4 |
| - type: max_accuracy |
| value: 99.8029702970297 |
| - type: max_ap |
| value: 94.87673936635738 |
| - type: max_f1 |
| value: 90.08470353761834 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 63.906039623153035 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 32.56053830923281 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 50.15326538775145 |
| - type: mrr |
| value: 50.99279295051355 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 31.44030762047337 |
| - type: cos_sim_spearman |
| value: 31.00910300264562 |
| - type: dot_pearson |
| value: 26.88257194766013 |
| - type: dot_spearman |
| value: 27.646202679013577 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.247 |
| - type: map_at_10 |
| value: 1.9429999999999998 |
| - type: map_at_100 |
| value: 10.82 |
| - type: map_at_1000 |
| value: 25.972 |
| - type: map_at_3 |
| value: 0.653 |
| - type: map_at_5 |
| value: 1.057 |
| - type: mrr_at_1 |
| value: 94 |
| - type: mrr_at_10 |
| value: 96.333 |
| - type: mrr_at_100 |
| value: 96.333 |
| - type: mrr_at_1000 |
| value: 96.333 |
| - type: mrr_at_3 |
| value: 96.333 |
| - type: mrr_at_5 |
| value: 96.333 |
| - type: ndcg_at_1 |
| value: 89 |
| - type: ndcg_at_10 |
| value: 79.63799999999999 |
| - type: ndcg_at_100 |
| value: 57.961 |
| - type: ndcg_at_1000 |
| value: 50.733 |
| - type: ndcg_at_3 |
| value: 84.224 |
| - type: ndcg_at_5 |
| value: 82.528 |
| - type: precision_at_1 |
| value: 94 |
| - type: precision_at_10 |
| value: 84.2 |
| - type: precision_at_100 |
| value: 59.36 |
| - type: precision_at_1000 |
| value: 22.738 |
| - type: precision_at_3 |
| value: 88 |
| - type: precision_at_5 |
| value: 86.8 |
| - type: recall_at_1 |
| value: 0.247 |
| - type: recall_at_10 |
| value: 2.131 |
| - type: recall_at_100 |
| value: 14.035 |
| - type: recall_at_1000 |
| value: 47.457 |
| - type: recall_at_3 |
| value: 0.6779999999999999 |
| - type: recall_at_5 |
| value: 1.124 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 2.603 |
| - type: map_at_10 |
| value: 11.667 |
| - type: map_at_100 |
| value: 16.474 |
| - type: map_at_1000 |
| value: 18.074 |
| - type: map_at_3 |
| value: 6.03 |
| - type: map_at_5 |
| value: 8.067 |
| - type: mrr_at_1 |
| value: 34.694 |
| - type: mrr_at_10 |
| value: 51.063 |
| - type: mrr_at_100 |
| value: 51.908 |
| - type: mrr_at_1000 |
| value: 51.908 |
| - type: mrr_at_3 |
| value: 47.959 |
| - type: mrr_at_5 |
| value: 49.694 |
| - type: ndcg_at_1 |
| value: 32.653 |
| - type: ndcg_at_10 |
| value: 28.305000000000003 |
| - type: ndcg_at_100 |
| value: 35.311 |
| - type: ndcg_at_1000 |
| value: 47.644999999999996 |
| - type: ndcg_at_3 |
| value: 32.187 |
| - type: ndcg_at_5 |
| value: 29.134999999999998 |
| - type: precision_at_1 |
| value: 34.694 |
| - type: precision_at_10 |
| value: 26.122 |
| - type: precision_at_100 |
| value: 6.755 |
| - type: precision_at_1000 |
| value: 1.467 |
| - type: precision_at_3 |
| value: 34.694 |
| - type: precision_at_5 |
| value: 30.203999999999997 |
| - type: recall_at_1 |
| value: 2.603 |
| - type: recall_at_10 |
| value: 18.716 |
| - type: recall_at_100 |
| value: 42.512 |
| - type: recall_at_1000 |
| value: 79.32000000000001 |
| - type: recall_at_3 |
| value: 7.59 |
| - type: recall_at_5 |
| value: 10.949 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 74.117 |
| - type: ap |
| value: 15.89357321699319 |
| - type: f1 |
| value: 57.14385866369257 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 61.38370118845502 |
| - type: f1 |
| value: 61.67038693866553 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 42.57754941537969 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 86.1775049174465 |
| - type: cos_sim_ap |
| value: 74.3994879581554 |
| - type: cos_sim_f1 |
| value: 69.32903671308551 |
| - type: cos_sim_precision |
| value: 61.48193508879363 |
| - type: cos_sim_recall |
| value: 79.47229551451187 |
| - type: dot_accuracy |
| value: 81.65345413363534 |
| - type: dot_ap |
| value: 59.690898346685096 |
| - type: dot_f1 |
| value: 57.27622826467499 |
| - type: dot_precision |
| value: 51.34965473948525 |
| - type: dot_recall |
| value: 64.74934036939314 |
| - type: euclidean_accuracy |
| value: 86.04637301066937 |
| - type: euclidean_ap |
| value: 74.33009001775268 |
| - type: euclidean_f1 |
| value: 69.2458374142997 |
| - type: euclidean_precision |
| value: 64.59570580173595 |
| - type: euclidean_recall |
| value: 74.6174142480211 |
| - type: manhattan_accuracy |
| value: 86.11193896405793 |
| - type: manhattan_ap |
| value: 74.2964140130421 |
| - type: manhattan_f1 |
| value: 69.11601528788066 |
| - type: manhattan_precision |
| value: 64.86924323073363 |
| - type: manhattan_recall |
| value: 73.95778364116094 |
| - type: max_accuracy |
| value: 86.1775049174465 |
| - type: max_ap |
| value: 74.3994879581554 |
| - type: max_f1 |
| value: 69.32903671308551 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 89.01501921061823 |
| - type: cos_sim_ap |
| value: 85.97819287477351 |
| - type: cos_sim_f1 |
| value: 78.33882858518875 |
| - type: cos_sim_precision |
| value: 75.49446626204926 |
| - type: cos_sim_recall |
| value: 81.40591315060055 |
| - type: dot_accuracy |
| value: 86.47494857763806 |
| - type: dot_ap |
| value: 78.77420360340282 |
| - type: dot_f1 |
| value: 73.06433247936238 |
| - type: dot_precision |
| value: 67.92140777983595 |
| - type: dot_recall |
| value: 79.04989220819218 |
| - type: euclidean_accuracy |
| value: 88.7297706368611 |
| - type: euclidean_ap |
| value: 85.61550568529317 |
| - type: euclidean_f1 |
| value: 77.84805525263539 |
| - type: euclidean_precision |
| value: 73.73639994491117 |
| - type: euclidean_recall |
| value: 82.44533415460425 |
| - type: manhattan_accuracy |
| value: 88.75111576823068 |
| - type: manhattan_ap |
| value: 85.58701671476263 |
| - type: manhattan_f1 |
| value: 77.70169909067856 |
| - type: manhattan_precision |
| value: 73.37666780704755 |
| - type: manhattan_recall |
| value: 82.5685247921158 |
| - type: max_accuracy |
| value: 89.01501921061823 |
| - type: max_ap |
| value: 85.97819287477351 |
| - type: max_f1 |
| value: 78.33882858518875 |
| language: |
| - en |
| license: mit |
| --- |
| |
| ## E5-base |
|
|
| **News (May 2023): please switch to [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2), which has better performance and same method of usage.** |
|
|
| [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). |
| Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 |
|
|
| This model has 12 layers and the embedding size is 768. |
|
|
| ## Usage |
|
|
| Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
|
|
| ```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] |
| |
| |
| # Each input text should start with "query: " or "passage: ". |
| # For tasks other than retrieval, you can simply use the "query: " prefix. |
| input_texts = ['query: how much protein should a female eat', |
| 'query: summit define', |
| "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] |
| |
| tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base') |
| model = AutoModel.from_pretrained('intfloat/e5-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']) |
| |
| # normalize embeddings |
| embeddings = F.normalize(embeddings, p=2, dim=1) |
| scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
| print(scores.tolist()) |
| ``` |
|
|
| ## Training Details |
|
|
| Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). |
|
|
| ## Benchmark Evaluation |
|
|
| Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
| on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
|
|
| ## Support for Sentence Transformers |
|
|
| Below is an example for usage with sentence_transformers. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| model = SentenceTransformer('intfloat/e5-base') |
| input_texts = [ |
| 'query: how much protein should a female eat', |
| 'query: summit define', |
| "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
| ] |
| embeddings = model.encode(input_texts, normalize_embeddings=True) |
| ``` |
| |
| Package requirements |
| |
| `pip install sentence_transformers~=2.2.2` |
|
|
| Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
|
|
| ## FAQ |
|
|
| **1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
|
|
| Yes, this is how the model is trained, otherwise you will see a performance degradation. |
|
|
| Here are some rules of thumb: |
| - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
|
|
| - Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. |
|
|
| - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
|
|
| **2. Why are my reproduced results slightly different from reported in the model card?** |
|
|
| Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
|
|
| **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
|
|
| This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
|
|
| For text embedding tasks like text retrieval or semantic similarity, |
| what matters is the relative order of the scores instead of the absolute values, |
| so this should not be an issue. |
|
|
| ## Citation |
|
|
| If you find our paper or models helpful, please consider cite as follows: |
|
|
| ``` |
| @article{wang2022text, |
| title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, |
| author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, |
| journal={arXiv preprint arXiv:2212.03533}, |
| year={2022} |
| } |
| ``` |
|
|
| ## Limitations |
|
|
| This model only works for English texts. Long texts will be truncated to at most 512 tokens. |
|
|
