--- model-index: - name: gte_tiny results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.76119402985076 - type: ap value: 34.63659287952359 - type: f1 value: 65.88939512571113 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 86.61324999999998 - type: ap value: 81.7476302802319 - type: f1 value: 86.5863470912001 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 42.61000000000001 - type: f1 value: 42.2217180000715 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 28.377999999999997 - type: map_at_10 value: 44.565 - type: map_at_100 value: 45.48 - type: map_at_1000 value: 45.487 - type: map_at_3 value: 39.841 - type: map_at_5 value: 42.284 - type: mrr_at_1 value: 29.445 - type: mrr_at_10 value: 44.956 - type: mrr_at_100 value: 45.877 - type: mrr_at_1000 value: 45.884 - type: mrr_at_3 value: 40.209 - type: mrr_at_5 value: 42.719 - type: ndcg_at_1 value: 28.377999999999997 - type: ndcg_at_10 value: 53.638 - type: ndcg_at_100 value: 57.354000000000006 - type: ndcg_at_1000 value: 57.513000000000005 - type: ndcg_at_3 value: 43.701 - type: ndcg_at_5 value: 48.114000000000004 - type: precision_at_1 value: 28.377999999999997 - type: precision_at_10 value: 8.272 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.303 - type: precision_at_5 value: 13.129 - type: recall_at_1 value: 28.377999999999997 - type: recall_at_10 value: 82.717 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 54.908 - type: recall_at_5 value: 65.647 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.637318326729876 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 36.01134479855804 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.82917555338909 - type: mrr value: 74.7888361254012 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.1657730995964 - type: cos_sim_spearman value: 86.62787748941281 - type: euclidean_pearson value: 85.48127914481798 - type: euclidean_spearman value: 86.48148861167424 - type: manhattan_pearson value: 85.07496934780823 - type: manhattan_spearman value: 86.39473964708843 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 81.73051948051948 - type: f1 value: 81.66368364988331 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.18623707448217 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.12697757150375 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.160000000000004 - type: map_at_10 value: 40.474 - type: map_at_100 value: 41.905 - type: map_at_1000 value: 42.041000000000004 - type: map_at_3 value: 37.147000000000006 - type: map_at_5 value: 38.873999999999995 - type: mrr_at_1 value: 36.91 - type: mrr_at_10 value: 46.495999999999995 - type: mrr_at_100 value: 47.288000000000004 - type: mrr_at_1000 value: 47.339999999999996 - type: mrr_at_3 value: 43.777 - type: mrr_at_5 value: 45.257999999999996 - type: ndcg_at_1 value: 36.91 - type: ndcg_at_10 value: 46.722 - type: ndcg_at_100 value: 51.969 - type: ndcg_at_1000 value: 54.232 - type: ndcg_at_3 value: 41.783 - type: ndcg_at_5 value: 43.797000000000004 - type: precision_at_1 value: 36.91 - type: precision_at_10 value: 9.013 - type: precision_at_100 value: 1.455 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 20.124 - type: precision_at_5 value: 14.363000000000001 - type: recall_at_1 value: 29.160000000000004 - type: recall_at_10 value: 58.521 - type: recall_at_100 value: 80.323 - type: recall_at_1000 value: 95.13000000000001 - type: recall_at_3 value: 44.205 - type: recall_at_5 value: 49.97 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.750000000000004 - type: map_at_10 value: 36.39 - type: map_at_100 value: 37.5 - type: map_at_1000 value: 37.625 - type: map_at_3 value: 33.853 - type: map_at_5 value: 35.397 - type: mrr_at_1 value: 34.14 - type: mrr_at_10 value: 41.841 - type: mrr_at_100 value: 42.469 - type: mrr_at_1000 value: 42.521 - type: mrr_at_3 value: 39.724 - type: mrr_at_5 value: 40.955999999999996 - type: ndcg_at_1 value: 34.14 - type: ndcg_at_10 value: 41.409 - type: ndcg_at_100 value: 45.668 - type: ndcg_at_1000 value: 47.916 - type: ndcg_at_3 value: 37.836 - type: ndcg_at_5 value: 39.650999999999996 - type: precision_at_1 value: 34.14 - type: precision_at_10 value: 7.739 - type: precision_at_100 value: 1.2630000000000001 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 18.217 - type: precision_at_5 value: 12.854 - type: recall_at_1 value: 27.750000000000004 - type: recall_at_10 value: 49.882 - type: recall_at_100 value: 68.556 - type: recall_at_1000 value: 83.186 - type: recall_at_3 value: 39.047 - type: recall_at_5 value: 44.458 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 36.879 - type: map_at_10 value: 48.878 - type: map_at_100 value: 49.918 - type: map_at_1000 value: 49.978 - type: map_at_3 value: 45.867999999999995 - type: map_at_5 value: 47.637 - type: mrr_at_1 value: 42.696 - type: mrr_at_10 value: 52.342 - type: mrr_at_100 value: 53.044000000000004 - type: mrr_at_1000 value: 53.077 - type: mrr_at_3 value: 50.01 - type: mrr_at_5 value: 51.437 - type: ndcg_at_1 value: 42.696 - type: ndcg_at_10 value: 54.469 - type: ndcg_at_100 value: 58.664 - type: ndcg_at_1000 value: 59.951 - type: ndcg_at_3 value: 49.419999999999995 - type: ndcg_at_5 value: 52.007000000000005 - type: precision_at_1 value: 42.696 - type: precision_at_10 value: 8.734 - type: precision_at_100 value: 1.1769999999999998 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 22.027 - type: precision_at_5 value: 15.135000000000002 - type: recall_at_1 value: 36.879 - type: recall_at_10 value: 67.669 - type: recall_at_100 value: 85.822 - type: recall_at_1000 value: 95.092 - type: recall_at_3 value: 54.157999999999994 - type: recall_at_5 value: 60.436 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.942 - type: map_at_10 value: 31.741999999999997 - type: map_at_100 value: 32.721000000000004 - type: map_at_1000 value: 32.809 - type: map_at_3 value: 29.17 - type: map_at_5 value: 30.714000000000002 - type: mrr_at_1 value: 24.746000000000002 - type: mrr_at_10 value: 33.517 - type: mrr_at_100 value: 34.451 - type: mrr_at_1000 value: 34.522000000000006 - type: mrr_at_3 value: 31.148999999999997 - type: mrr_at_5 value: 32.606 - type: ndcg_at_1 value: 24.746000000000002 - type: ndcg_at_10 value: 36.553000000000004 - type: ndcg_at_100 value: 41.53 - type: ndcg_at_1000 value: 43.811 - type: ndcg_at_3 value: 31.674000000000003 - type: ndcg_at_5 value: 34.241 - type: precision_at_1 value: 24.746000000000002 - type: precision_at_10 value: 5.684 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.597000000000001 - type: precision_at_5 value: 9.672 - type: recall_at_1 value: 22.942 - type: recall_at_10 value: 49.58 - type: recall_at_100 value: 72.614 - type: recall_at_1000 value: 89.89200000000001 - type: recall_at_3 value: 36.552 - type: recall_at_5 value: 42.702 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.345 - type: map_at_10 value: 22.428 - type: map_at_100 value: 23.756 - type: map_at_1000 value: 23.872 - type: map_at_3 value: 20.212 - type: map_at_5 value: 21.291 - type: mrr_at_1 value: 19.279 - type: mrr_at_10 value: 27.1 - type: mrr_at_100 value: 28.211000000000002 - type: mrr_at_1000 value: 28.279 - type: mrr_at_3 value: 24.813 - type: mrr_at_5 value: 25.889 - type: ndcg_at_1 value: 19.279 - type: ndcg_at_10 value: 27.36 - type: ndcg_at_100 value: 33.499 - type: ndcg_at_1000 value: 36.452 - type: ndcg_at_3 value: 23.233999999999998 - type: ndcg_at_5 value: 24.806 - type: precision_at_1 value: 19.279 - type: precision_at_10 value: 5.149 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 11.360000000000001 - type: precision_at_5 value: 8.035 - type: recall_at_1 value: 15.345 - type: recall_at_10 value: 37.974999999999994 - type: recall_at_100 value: 64.472 - type: recall_at_1000 value: 85.97200000000001 - type: recall_at_3 value: 26.203 - type: recall_at_5 value: 30.485 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.362000000000002 - type: map_at_10 value: 36.406 - type: map_at_100 value: 37.726 - type: map_at_1000 value: 37.84 - type: map_at_3 value: 33.425 - type: map_at_5 value: 35.043 - type: mrr_at_1 value: 32.146 - type: mrr_at_10 value: 41.674 - type: mrr_at_100 value: 42.478 - type: mrr_at_1000 value: 42.524 - type: mrr_at_3 value: 38.948 - type: mrr_at_5 value: 40.415 - type: ndcg_at_1 value: 32.146 - type: ndcg_at_10 value: 42.374 - type: ndcg_at_100 value: 47.919 - type: ndcg_at_1000 value: 50.013 - type: ndcg_at_3 value: 37.29 - type: ndcg_at_5 value: 39.531 - type: precision_at_1 value: 32.146 - type: precision_at_10 value: 7.767 - type: precision_at_100 value: 1.236 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 17.965999999999998 - type: precision_at_5 value: 12.742999999999999 - type: recall_at_1 value: 26.362000000000002 - type: recall_at_10 value: 54.98800000000001 - type: recall_at_100 value: 78.50200000000001 - type: recall_at_1000 value: 92.146 - type: recall_at_3 value: 40.486 - type: recall_at_5 value: 46.236 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.417 - type: map_at_10 value: 33.161 - type: map_at_100 value: 34.357 - type: map_at_1000 value: 34.473 - type: map_at_3 value: 30.245 - type: map_at_5 value: 31.541999999999998 - type: mrr_at_1 value: 29.909000000000002 - type: mrr_at_10 value: 38.211 - type: mrr_at_100 value: 39.056999999999995 - type: mrr_at_1000 value: 39.114 - type: mrr_at_3 value: 35.769 - type: mrr_at_5 value: 36.922 - type: ndcg_at_1 value: 29.909000000000002 - type: ndcg_at_10 value: 38.694 - type: ndcg_at_100 value: 44.057 - type: ndcg_at_1000 value: 46.6 - type: ndcg_at_3 value: 33.822 - type: ndcg_at_5 value: 35.454 - type: precision_at_1 value: 29.909000000000002 - type: precision_at_10 value: 7.180000000000001 - type: precision_at_100 value: 1.153 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 16.134 - type: precision_at_5 value: 11.256 - type: recall_at_1 value: 24.417 - type: recall_at_10 value: 50.260000000000005 - type: recall_at_100 value: 73.55699999999999 - type: recall_at_1000 value: 91.216 - type: recall_at_3 value: 35.971 - type: recall_at_5 value: 40.793 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.266916666666663 - type: map_at_10 value: 32.75025 - type: map_at_100 value: 33.91341666666667 - type: map_at_1000 value: 34.031749999999995 - type: map_at_3 value: 30.166416666666674 - type: map_at_5 value: 31.577000000000005 - type: mrr_at_1 value: 28.828166666666664 - type: mrr_at_10 value: 36.80991666666667 - type: mrr_at_100 value: 37.67075 - type: mrr_at_1000 value: 37.733 - type: mrr_at_3 value: 34.513416666666664 - type: mrr_at_5 value: 35.788 - type: ndcg_at_1 value: 28.828166666666664 - type: ndcg_at_10 value: 37.796 - type: ndcg_at_100 value: 42.94783333333333 - type: ndcg_at_1000 value: 45.38908333333333 - type: ndcg_at_3 value: 33.374750000000006 - type: ndcg_at_5 value: 35.379666666666665 - type: precision_at_1 value: 28.828166666666664 - type: precision_at_10 value: 6.615749999999999 - type: precision_at_100 value: 1.0848333333333333 - type: precision_at_1000 value: 0.1484166666666667 - type: precision_at_3 value: 15.347833333333332 - type: precision_at_5 value: 10.848916666666666 - type: recall_at_1 value: 24.266916666666663 - type: recall_at_10 value: 48.73458333333333 - type: recall_at_100 value: 71.56341666666667 - type: recall_at_1000 value: 88.63091666666668 - type: recall_at_3 value: 36.31208333333333 - type: recall_at_5 value: 41.55633333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.497 - type: map_at_10 value: 30.249 - type: map_at_100 value: 30.947000000000003 - type: map_at_1000 value: 31.049 - type: map_at_3 value: 28.188000000000002 - type: map_at_5 value: 29.332 - type: mrr_at_1 value: 26.687 - type: mrr_at_10 value: 33.182 - type: mrr_at_100 value: 33.794999999999995 - type: mrr_at_1000 value: 33.873 - type: mrr_at_3 value: 31.263 - type: mrr_at_5 value: 32.428000000000004 - type: ndcg_at_1 value: 26.687 - type: ndcg_at_10 value: 34.252 - type: ndcg_at_100 value: 38.083 - type: ndcg_at_1000 value: 40.682 - type: ndcg_at_3 value: 30.464999999999996 - type: ndcg_at_5 value: 32.282 - type: precision_at_1 value: 26.687 - type: precision_at_10 value: 5.2909999999999995 - type: precision_at_100 value: 0.788 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.037 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.497 - type: recall_at_10 value: 43.813 - type: recall_at_100 value: 61.88399999999999 - type: recall_at_1000 value: 80.926 - type: recall_at_3 value: 33.332 - type: recall_at_5 value: 37.862 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.073 - type: map_at_10 value: 22.705000000000002 - type: map_at_100 value: 23.703 - type: map_at_1000 value: 23.833 - type: map_at_3 value: 20.593 - type: map_at_5 value: 21.7 - type: mrr_at_1 value: 19.683 - type: mrr_at_10 value: 26.39 - type: mrr_at_100 value: 27.264 - type: mrr_at_1000 value: 27.349 - type: mrr_at_3 value: 24.409 - type: mrr_at_5 value: 25.474000000000004 - type: ndcg_at_1 value: 19.683 - type: ndcg_at_10 value: 27.014 - type: ndcg_at_100 value: 31.948 - type: ndcg_at_1000 value: 35.125 - type: ndcg_at_3 value: 23.225 - type: ndcg_at_5 value: 24.866 - type: precision_at_1 value: 19.683 - type: precision_at_10 value: 4.948 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.943 - type: precision_at_5 value: 7.86 - type: recall_at_1 value: 16.073 - type: recall_at_10 value: 36.283 - type: recall_at_100 value: 58.745999999999995 - type: recall_at_1000 value: 81.711 - type: recall_at_3 value: 25.637 - type: recall_at_5 value: 29.919 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.776 - type: map_at_10 value: 33.317 - type: map_at_100 value: 34.437 - type: map_at_1000 value: 34.54 - type: map_at_3 value: 30.706 - type: map_at_5 value: 32.202999999999996 - type: mrr_at_1 value: 30.224 - type: mrr_at_10 value: 37.34 - type: mrr_at_100 value: 38.268 - type: mrr_at_1000 value: 38.335 - type: mrr_at_3 value: 35.075 - type: mrr_at_5 value: 36.348 - type: ndcg_at_1 value: 30.224 - type: ndcg_at_10 value: 38.083 - type: ndcg_at_100 value: 43.413000000000004 - type: ndcg_at_1000 value: 45.856 - type: ndcg_at_3 value: 33.437 - type: ndcg_at_5 value: 35.661 - type: precision_at_1 value: 30.224 - type: precision_at_10 value: 6.1850000000000005 - type: precision_at_100 value: 1.0030000000000001 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 14.646 - type: precision_at_5 value: 10.428999999999998 - type: recall_at_1 value: 25.776 - type: recall_at_10 value: 48.787000000000006 - type: recall_at_100 value: 72.04899999999999 - type: recall_at_1000 value: 89.339 - type: recall_at_3 value: 36.192 - type: recall_at_5 value: 41.665 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.156 - type: map_at_10 value: 30.886000000000003 - type: map_at_100 value: 32.551 - type: map_at_1000 value: 32.769 - type: map_at_3 value: 28.584 - type: map_at_5 value: 29.959999999999997 - type: mrr_at_1 value: 28.260999999999996 - type: mrr_at_10 value: 35.555 - type: mrr_at_100 value: 36.687 - type: mrr_at_1000 value: 36.742999999999995 - type: mrr_at_3 value: 33.531 - type: mrr_at_5 value: 34.717 - type: ndcg_at_1 value: 28.260999999999996 - type: ndcg_at_10 value: 36.036 - type: ndcg_at_100 value: 42.675000000000004 - type: ndcg_at_1000 value: 45.303 - type: ndcg_at_3 value: 32.449 - type: ndcg_at_5 value: 34.293 - type: precision_at_1 value: 28.260999999999996 - type: precision_at_10 value: 6.837999999999999 - type: precision_at_100 value: 1.4569999999999999 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.217 - type: precision_at_5 value: 11.028 - type: recall_at_1 value: 23.156 - type: recall_at_10 value: 45.251999999999995 - type: recall_at_100 value: 75.339 - type: recall_at_1000 value: 91.56 - type: recall_at_3 value: 34.701 - type: recall_at_5 value: 39.922999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.846 - type: map_at_10 value: 26.367 - type: map_at_100 value: 27.439999999999998 - type: map_at_1000 value: 27.552 - type: map_at_3 value: 24.006 - type: map_at_5 value: 25.230999999999998 - type: mrr_at_1 value: 21.257 - type: mrr_at_10 value: 28.071 - type: mrr_at_100 value: 29.037000000000003 - type: mrr_at_1000 value: 29.119 - type: mrr_at_3 value: 25.692999999999998 - type: mrr_at_5 value: 27.006000000000004 - type: ndcg_at_1 value: 21.257 - type: ndcg_at_10 value: 30.586000000000002 - type: ndcg_at_100 value: 35.949 - type: ndcg_at_1000 value: 38.728 - type: ndcg_at_3 value: 25.862000000000002 - type: ndcg_at_5 value: 27.967 - type: precision_at_1 value: 21.257 - type: precision_at_10 value: 4.861 - type: precision_at_100 value: 0.8130000000000001 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 10.906 - type: precision_at_5 value: 7.763000000000001 - type: recall_at_1 value: 19.846 - type: recall_at_10 value: 41.805 - type: recall_at_100 value: 66.89699999999999 - type: recall_at_1000 value: 87.401 - type: recall_at_3 value: 29.261 - type: recall_at_5 value: 34.227000000000004 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.333 - type: map_at_10 value: 17.14 - type: map_at_100 value: 18.878 - type: map_at_1000 value: 19.067 - type: map_at_3 value: 14.123 - type: map_at_5 value: 15.699 - type: mrr_at_1 value: 23.192 - type: mrr_at_10 value: 33.553 - type: mrr_at_100 value: 34.553 - type: mrr_at_1000 value: 34.603 - type: mrr_at_3 value: 29.848000000000003 - type: mrr_at_5 value: 32.18 - type: ndcg_at_1 value: 23.192 - type: ndcg_at_10 value: 24.707 - type: ndcg_at_100 value: 31.701 - type: ndcg_at_1000 value: 35.260999999999996 - type: ndcg_at_3 value: 19.492 - type: ndcg_at_5 value: 21.543 - type: precision_at_1 value: 23.192 - type: precision_at_10 value: 7.824000000000001 - type: precision_at_100 value: 1.52 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 14.180000000000001 - type: precision_at_5 value: 11.530999999999999 - type: recall_at_1 value: 10.333 - type: recall_at_10 value: 30.142999999999997 - type: recall_at_100 value: 54.298 - type: recall_at_1000 value: 74.337 - type: recall_at_3 value: 17.602999999999998 - type: recall_at_5 value: 22.938 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.03 - type: map_at_10 value: 17.345 - type: map_at_100 value: 23.462 - type: map_at_1000 value: 24.77 - type: map_at_3 value: 12.714 - type: map_at_5 value: 14.722 - type: mrr_at_1 value: 61.0 - type: mrr_at_10 value: 69.245 - type: mrr_at_100 value: 69.715 - type: mrr_at_1000 value: 69.719 - type: mrr_at_3 value: 67.583 - type: mrr_at_5 value: 68.521 - type: ndcg_at_1 value: 47.625 - type: ndcg_at_10 value: 35.973 - type: ndcg_at_100 value: 39.875 - type: ndcg_at_1000 value: 46.922000000000004 - type: ndcg_at_3 value: 40.574 - type: ndcg_at_5 value: 38.18 - type: precision_at_1 value: 61.0 - type: precision_at_10 value: 29.049999999999997 - type: precision_at_100 value: 8.828 - type: precision_at_1000 value: 1.8290000000000002 - type: precision_at_3 value: 45.333 - type: precision_at_5 value: 37.9 - type: recall_at_1 value: 8.03 - type: recall_at_10 value: 22.334 - type: recall_at_100 value: 45.919 - type: recall_at_1000 value: 68.822 - type: recall_at_3 value: 14.038999999999998 - type: recall_at_5 value: 17.118 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.714999999999996 - type: f1 value: 39.83929362259356 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 52.242999999999995 - type: map_at_10 value: 64.087 - type: map_at_100 value: 64.549 - type: map_at_1000 value: 64.567 - type: map_at_3 value: 61.667 - type: map_at_5 value: 63.266 - type: mrr_at_1 value: 56.271 - type: mrr_at_10 value: 68.146 - type: mrr_at_100 value: 68.524 - type: mrr_at_1000 value: 68.53200000000001 - type: mrr_at_3 value: 65.869 - type: mrr_at_5 value: 67.37100000000001 - type: ndcg_at_1 value: 56.271 - type: ndcg_at_10 value: 70.109 - type: ndcg_at_100 value: 72.09 - type: ndcg_at_1000 value: 72.479 - type: ndcg_at_3 value: 65.559 - type: ndcg_at_5 value: 68.242 - type: precision_at_1 value: 56.271 - type: precision_at_10 value: 9.286999999999999 - type: precision_at_100 value: 1.039 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 26.308 - type: precision_at_5 value: 17.291 - type: recall_at_1 value: 52.242999999999995 - type: recall_at_10 value: 84.71 - type: recall_at_100 value: 93.309 - type: recall_at_1000 value: 96.013 - type: recall_at_3 value: 72.554 - type: recall_at_5 value: 79.069 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 14.346 - type: map_at_10 value: 24.552 - type: map_at_100 value: 26.161 - type: map_at_1000 value: 26.345000000000002 - type: map_at_3 value: 21.208 - type: map_at_5 value: 22.959 - type: mrr_at_1 value: 29.166999999999998 - type: mrr_at_10 value: 38.182 - type: mrr_at_100 value: 39.22 - type: mrr_at_1000 value: 39.263 - type: mrr_at_3 value: 35.983 - type: mrr_at_5 value: 37.14 - type: ndcg_at_1 value: 29.166999999999998 - type: ndcg_at_10 value: 31.421 - type: ndcg_at_100 value: 38.129999999999995 - type: ndcg_at_1000 value: 41.569 - type: ndcg_at_3 value: 28.172000000000004 - type: ndcg_at_5 value: 29.029 - type: precision_at_1 value: 29.166999999999998 - type: precision_at_10 value: 8.997 - type: precision_at_100 value: 1.5709999999999997 - type: precision_at_1000 value: 0.22 - type: precision_at_3 value: 19.187 - type: precision_at_5 value: 13.980999999999998 - type: recall_at_1 value: 14.346 - type: recall_at_10 value: 37.963 - type: recall_at_100 value: 63.43299999999999 - type: recall_at_1000 value: 84.057 - type: recall_at_3 value: 26.119999999999997 - type: recall_at_5 value: 30.988 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 33.059 - type: map_at_10 value: 46.421 - type: map_at_100 value: 47.323 - type: map_at_1000 value: 47.403 - type: map_at_3 value: 43.553999999999995 - type: map_at_5 value: 45.283 - type: mrr_at_1 value: 66.117 - type: mrr_at_10 value: 73.10900000000001 - type: mrr_at_100 value: 73.444 - type: mrr_at_1000 value: 73.46000000000001 - type: mrr_at_3 value: 71.70400000000001 - type: mrr_at_5 value: 72.58099999999999 - type: ndcg_at_1 value: 66.117 - type: ndcg_at_10 value: 55.696999999999996 - type: ndcg_at_100 value: 59.167 - type: ndcg_at_1000 value: 60.809000000000005 - type: ndcg_at_3 value: 51.243 - type: ndcg_at_5 value: 53.627 - type: precision_at_1 value: 66.117 - type: precision_at_10 value: 11.538 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 31.861 - type: precision_at_5 value: 20.997 - type: recall_at_1 value: 33.059 - type: recall_at_10 value: 57.691 - type: recall_at_100 value: 71.458 - type: recall_at_1000 value: 82.35 - type: recall_at_3 value: 47.792 - type: recall_at_5 value: 52.492000000000004 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 80.544 - type: ap value: 74.69592367984956 - type: f1 value: 80.51138138449883 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 17.095 - type: map_at_10 value: 28.038999999999998 - type: map_at_100 value: 29.246 - type: map_at_1000 value: 29.311 - type: map_at_3 value: 24.253 - type: map_at_5 value: 26.442 - type: mrr_at_1 value: 17.535999999999998 - type: mrr_at_10 value: 28.53 - type: mrr_at_100 value: 29.697000000000003 - type: mrr_at_1000 value: 29.755 - type: mrr_at_3 value: 24.779999999999998 - type: mrr_at_5 value: 26.942 - type: ndcg_at_1 value: 17.549999999999997 - type: ndcg_at_10 value: 34.514 - type: ndcg_at_100 value: 40.497 - type: ndcg_at_1000 value: 42.17 - type: ndcg_at_3 value: 26.764 - type: ndcg_at_5 value: 30.678 - type: precision_at_1 value: 17.549999999999997 - type: precision_at_10 value: 5.692 - type: precision_at_100 value: 0.8699999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 11.562 - type: precision_at_5 value: 8.917 - type: recall_at_1 value: 17.095 - type: recall_at_10 value: 54.642 - type: recall_at_100 value: 82.652 - type: recall_at_1000 value: 95.555 - type: recall_at_3 value: 33.504 - type: recall_at_5 value: 42.925000000000004 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.75558595531236 - type: f1 value: 91.25979279648296 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 69.90424076607387 - type: f1 value: 52.067408707562244 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.13449899125757 - type: f1 value: 67.62456762910598 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.862138533961 - type: f1 value: 74.66457222091381 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.10761942610792 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.673172170578408 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.058704977250315 - type: mrr value: 33.24327760839221 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.163 - type: map_at_10 value: 11.652999999999999 - type: map_at_100 value: 14.849 - type: map_at_1000 value: 16.253999999999998 - type: map_at_3 value: 8.616999999999999 - type: map_at_5 value: 10.100000000000001 - type: mrr_at_1 value: 44.272 - type: mrr_at_10 value: 52.25 - type: mrr_at_100 value: 52.761 - type: mrr_at_1000 value: 52.811 - type: mrr_at_3 value: 50.31 - type: mrr_at_5 value: 51.347 - type: ndcg_at_1 value: 42.105 - type: ndcg_at_10 value: 32.044 - type: ndcg_at_100 value: 29.763 - type: ndcg_at_1000 value: 38.585 - type: ndcg_at_3 value: 36.868 - type: ndcg_at_5 value: 35.154999999999994 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 23.622 - type: precision_at_100 value: 7.7490000000000006 - type: precision_at_1000 value: 2.054 - type: precision_at_3 value: 34.262 - type: precision_at_5 value: 30.154999999999998 - type: recall_at_1 value: 5.163 - type: recall_at_10 value: 15.478 - type: recall_at_100 value: 30.424 - type: recall_at_1000 value: 62.67 - type: recall_at_3 value: 9.615 - type: recall_at_5 value: 12.369 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 21.618000000000002 - type: map_at_10 value: 35.465 - type: map_at_100 value: 36.712 - type: map_at_1000 value: 36.757 - type: map_at_3 value: 31.189 - type: map_at_5 value: 33.537 - type: mrr_at_1 value: 24.305 - type: mrr_at_10 value: 37.653 - type: mrr_at_100 value: 38.662 - type: mrr_at_1000 value: 38.694 - type: mrr_at_3 value: 33.889 - type: mrr_at_5 value: 35.979 - type: ndcg_at_1 value: 24.305 - type: ndcg_at_10 value: 43.028 - type: ndcg_at_100 value: 48.653999999999996 - type: ndcg_at_1000 value: 49.733 - type: ndcg_at_3 value: 34.768 - type: ndcg_at_5 value: 38.753 - type: precision_at_1 value: 24.305 - type: precision_at_10 value: 7.59 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 16.271 - type: precision_at_5 value: 12.068 - type: recall_at_1 value: 21.618000000000002 - type: recall_at_10 value: 63.977 - type: recall_at_100 value: 89.03999999999999 - type: recall_at_1000 value: 97.10600000000001 - type: recall_at_3 value: 42.422 - type: recall_at_5 value: 51.629000000000005 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 69.405 - type: map_at_10 value: 83.05 - type: map_at_100 value: 83.684 - type: map_at_1000 value: 83.70400000000001 - type: map_at_3 value: 80.08800000000001 - type: map_at_5 value: 81.937 - type: mrr_at_1 value: 79.85 - type: mrr_at_10 value: 86.369 - type: mrr_at_100 value: 86.48599999999999 - type: mrr_at_1000 value: 86.48700000000001 - type: mrr_at_3 value: 85.315 - type: mrr_at_5 value: 86.044 - type: ndcg_at_1 value: 79.86999999999999 - type: ndcg_at_10 value: 87.04499999999999 - type: ndcg_at_100 value: 88.373 - type: ndcg_at_1000 value: 88.531 - type: ndcg_at_3 value: 84.04 - type: ndcg_at_5 value: 85.684 - type: precision_at_1 value: 79.86999999999999 - type: precision_at_10 value: 13.183 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.67 - type: precision_at_5 value: 24.12 - type: recall_at_1 value: 69.405 - type: recall_at_10 value: 94.634 - type: recall_at_100 value: 99.214 - type: recall_at_1000 value: 99.958 - type: recall_at_3 value: 85.992 - type: recall_at_5 value: 90.656 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 50.191676323145465 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 56.4874020363744 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.228 - type: map_at_10 value: 11.245 - type: map_at_100 value: 13.353000000000002 - type: map_at_1000 value: 13.665 - type: map_at_3 value: 7.779999999999999 - type: map_at_5 value: 9.405 - type: mrr_at_1 value: 20.9 - type: mrr_at_10 value: 31.657999999999998 - type: mrr_at_100 value: 32.769999999999996 - type: mrr_at_1000 value: 32.833 - type: mrr_at_3 value: 28.333000000000002 - type: mrr_at_5 value: 30.043 - type: ndcg_at_1 value: 20.9 - type: ndcg_at_10 value: 19.073 - type: ndcg_at_100 value: 27.055 - type: ndcg_at_1000 value: 32.641 - type: ndcg_at_3 value: 17.483999999999998 - type: ndcg_at_5 value: 15.42 - type: precision_at_1 value: 20.9 - type: precision_at_10 value: 10.17 - type: precision_at_100 value: 2.162 - type: precision_at_1000 value: 0.35100000000000003 - type: precision_at_3 value: 16.467000000000002 - type: precision_at_5 value: 13.68 - type: recall_at_1 value: 4.228 - type: recall_at_10 value: 20.573 - type: recall_at_100 value: 43.887 - type: recall_at_1000 value: 71.22 - type: recall_at_3 value: 10.023 - type: recall_at_5 value: 13.873 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.77965135067481 - type: cos_sim_spearman value: 75.85121335808076 - type: euclidean_pearson value: 80.09115175262697 - type: euclidean_spearman value: 75.72249155647123 - type: manhattan_pearson value: 79.89723577351782 - type: manhattan_spearman value: 75.49855259442387 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 80.46084116030949 - type: cos_sim_spearman value: 72.57579204392951 - type: euclidean_pearson value: 76.39020830763684 - type: euclidean_spearman value: 72.3718627025895 - type: manhattan_pearson value: 76.6148833027359 - type: manhattan_spearman value: 72.57570008442319 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 80.43678068337017 - type: cos_sim_spearman value: 82.38941154076062 - type: euclidean_pearson value: 81.59260573633661 - type: euclidean_spearman value: 82.31144262574114 - type: manhattan_pearson value: 81.43266909137056 - type: manhattan_spearman value: 82.14704293004861 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.73713431763163 - type: cos_sim_spearman value: 77.97860512809388 - type: euclidean_pearson value: 80.35755041527027 - type: euclidean_spearman value: 78.021703511412 - type: manhattan_pearson value: 80.24440317109162 - type: manhattan_spearman value: 77.93165415697575 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.15111852351204 - type: cos_sim_spearman value: 86.54032447238258 - type: euclidean_pearson value: 86.14157021537433 - type: euclidean_spearman value: 86.67537291929713 - type: manhattan_pearson value: 86.081041854808 - type: manhattan_spearman value: 86.61561701560558 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.34532445104026 - type: cos_sim_spearman value: 83.31325001474116 - type: euclidean_pearson value: 82.81892375201032 - type: euclidean_spearman value: 83.4521695148055 - type: manhattan_pearson value: 82.72503790526163 - type: manhattan_spearman value: 83.37833652941349 - 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: 87.25463453839801 - type: cos_sim_spearman value: 88.27655263515948 - type: euclidean_pearson value: 88.0248334411439 - type: euclidean_spearman value: 88.18141448876868 - type: manhattan_pearson value: 87.8080451127279 - type: manhattan_spearman value: 88.01028114423058 - 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: 63.57551045355218 - type: cos_sim_spearman value: 66.67614095126629 - type: euclidean_pearson value: 66.0787243112528 - type: euclidean_spearman value: 66.83660560636939 - type: manhattan_pearson value: 66.74684019662031 - type: manhattan_spearman value: 67.11761598074368 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 83.70881496766829 - type: cos_sim_spearman value: 84.37803542941634 - type: euclidean_pearson value: 84.84501245857096 - type: euclidean_spearman value: 84.47088079741476 - type: manhattan_pearson value: 84.77244090794765 - type: manhattan_spearman value: 84.43307343706205 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 81.53946254759089 - type: mrr value: 94.68259953554072 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 51.817 - type: map_at_10 value: 62.339999999999996 - type: map_at_100 value: 62.88 - type: map_at_1000 value: 62.909000000000006 - type: map_at_3 value: 59.004 - type: map_at_5 value: 60.906000000000006 - type: mrr_at_1 value: 54.333 - type: mrr_at_10 value: 63.649 - type: mrr_at_100 value: 64.01 - type: mrr_at_1000 value: 64.039 - type: mrr_at_3 value: 61.056 - type: mrr_at_5 value: 62.639 - type: ndcg_at_1 value: 54.333 - type: ndcg_at_10 value: 67.509 - type: ndcg_at_100 value: 69.69999999999999 - type: ndcg_at_1000 value: 70.613 - type: ndcg_at_3 value: 61.729 - type: ndcg_at_5 value: 64.696 - type: precision_at_1 value: 54.333 - type: precision_at_10 value: 9.2 - type: precision_at_100 value: 1.043 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 24.0 - type: precision_at_5 value: 16.2 - type: recall_at_1 value: 51.817 - type: recall_at_10 value: 82.056 - type: recall_at_100 value: 91.667 - type: recall_at_1000 value: 99.0 - type: recall_at_3 value: 66.717 - type: recall_at_5 value: 74.17200000000001 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82475247524752 - type: cos_sim_ap value: 95.4781199603258 - type: cos_sim_f1 value: 91.16186693147964 - type: cos_sim_precision value: 90.53254437869822 - type: cos_sim_recall value: 91.8 - type: dot_accuracy value: 99.75049504950495 - type: dot_ap value: 93.05183539809457 - type: dot_f1 value: 87.31117824773412 - type: dot_precision value: 87.93103448275862 - type: dot_recall value: 86.7 - type: euclidean_accuracy value: 99.82475247524752 - type: euclidean_ap value: 95.38547978154382 - type: euclidean_f1 value: 91.16325511732403 - type: euclidean_precision value: 91.02691924227318 - type: euclidean_recall value: 91.3 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.47237521890308 - type: manhattan_f1 value: 91.27849355797821 - type: manhattan_precision value: 90.47151277013754 - type: manhattan_recall value: 92.10000000000001 - type: max_accuracy value: 99.82574257425742 - type: max_ap value: 95.4781199603258 - type: max_f1 value: 91.27849355797821 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 57.542169376331245 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.74399302634387 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.65076347632749 - type: mrr value: 50.418099057804945 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.73997756592847 - type: cos_sim_spearman value: 29.465208011593308 - type: dot_pearson value: 24.83735342474541 - type: dot_spearman value: 26.005180528584855 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.208 - type: map_at_10 value: 1.434 - type: map_at_100 value: 7.829 - type: map_at_1000 value: 19.807 - type: map_at_3 value: 0.549 - type: map_at_5 value: 0.8330000000000001 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 85.35199999999999 - type: mrr_at_100 value: 85.673 - type: mrr_at_1000 value: 85.673 - type: mrr_at_3 value: 84.667 - type: mrr_at_5 value: 85.06700000000001 - type: ndcg_at_1 value: 72.0 - type: ndcg_at_10 value: 59.214999999999996 - type: ndcg_at_100 value: 44.681 - type: ndcg_at_1000 value: 43.035000000000004 - type: ndcg_at_3 value: 66.53099999999999 - type: ndcg_at_5 value: 63.23 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 62.4 - type: precision_at_100 value: 45.76 - type: precision_at_1000 value: 19.05 - type: precision_at_3 value: 71.333 - type: precision_at_5 value: 67.2 - type: recall_at_1 value: 0.208 - type: recall_at_10 value: 1.6580000000000001 - type: recall_at_100 value: 11.324 - type: recall_at_1000 value: 41.537 - type: recall_at_3 value: 0.579 - type: recall_at_5 value: 0.8959999999999999 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.442 - type: map_at_10 value: 8.863 - type: map_at_100 value: 14.606 - type: map_at_1000 value: 16.258 - type: map_at_3 value: 4.396 - type: map_at_5 value: 6.199000000000001 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 43.492 - type: mrr_at_100 value: 44.557 - type: mrr_at_1000 value: 44.557 - type: mrr_at_3 value: 40.816 - type: mrr_at_5 value: 42.143 - type: ndcg_at_1 value: 25.509999999999998 - type: ndcg_at_10 value: 22.076 - type: ndcg_at_100 value: 34.098 - type: ndcg_at_1000 value: 46.265 - type: ndcg_at_3 value: 24.19 - type: ndcg_at_5 value: 23.474 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.286 - type: precision_at_1000 value: 1.5310000000000001 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.442 - type: recall_at_10 value: 15.012 - type: recall_at_100 value: 45.865 - type: recall_at_1000 value: 82.958 - type: recall_at_3 value: 5.731 - type: recall_at_5 value: 9.301 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.974 - type: ap value: 14.534996211286682 - type: f1 value: 54.785946183399005 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 58.56819468024901 - type: f1 value: 58.92391487111204 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 43.273202335218194 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.37742146986946 - type: cos_sim_ap value: 68.1684129575579 - type: cos_sim_f1 value: 64.93475108748189 - type: cos_sim_precision value: 59.89745876058849 - type: cos_sim_recall value: 70.89709762532982 - type: dot_accuracy value: 80.49710913750968 - type: dot_ap value: 54.699790073944186 - type: dot_f1 value: 54.45130013221684 - type: dot_precision value: 46.74612183125236 - type: dot_recall value: 65.19788918205805 - type: euclidean_accuracy value: 84.5085533766466 - type: euclidean_ap value: 68.38835695236224 - type: euclidean_f1 value: 65.3391121002694 - type: euclidean_precision value: 58.75289656625237 - type: euclidean_recall value: 73.58839050131925 - type: manhattan_accuracy value: 84.40126363473803 - type: manhattan_ap value: 68.09539181555348 - type: manhattan_f1 value: 64.99028182701653 - type: manhattan_precision value: 60.22062134173795 - type: manhattan_recall value: 70.58047493403694 - type: max_accuracy value: 84.5085533766466 - type: max_ap value: 68.38835695236224 - type: max_f1 value: 65.3391121002694 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.34167733923235 - type: cos_sim_ap value: 84.84136381147736 - type: cos_sim_f1 value: 77.01434980904001 - type: cos_sim_precision value: 74.27937915742794 - type: cos_sim_recall value: 79.95842315983985 - type: dot_accuracy value: 85.06422944075756 - type: dot_ap value: 76.49446747522325 - type: dot_f1 value: 71.11606520830432 - type: dot_precision value: 64.93638676844785 - type: dot_recall value: 78.59562673236834 - type: euclidean_accuracy value: 88.45810532852097 - type: euclidean_ap value: 84.91526721863501 - type: euclidean_f1 value: 77.04399001750662 - type: euclidean_precision value: 74.62298867162133 - type: euclidean_recall value: 79.62734832152756 - type: manhattan_accuracy value: 88.46004579500912 - type: manhattan_ap value: 84.81590026238194 - type: manhattan_f1 value: 76.97804626491822 - type: manhattan_precision value: 73.79237288135593 - type: manhattan_recall value: 80.45118570988605 - type: max_accuracy value: 88.46004579500912 - type: max_ap value: 84.91526721863501 - type: max_f1 value: 77.04399001750662 pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb --- # {gte-tiny} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is distilled from `thenlper/gte-small`, with comparable (slightly worse) performance at around half the size. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors