--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers model-index: - name: GIST-small-Embedding-v0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.26865671641791 - type: ap value: 38.25623793370476 - type: f1 value: 69.26434651320257 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.232225 - type: ap value: 89.97936072879344 - type: f1 value: 93.22122653806187 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.715999999999994 - type: f1 value: 49.169789920136076 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 34.922 - type: map_at_10 value: 50.524 - type: map_at_100 value: 51.247 - type: map_at_1000 value: 51.249 - type: map_at_3 value: 45.887 - type: map_at_5 value: 48.592999999999996 - type: mrr_at_1 value: 34.922 - type: mrr_at_10 value: 50.382000000000005 - type: mrr_at_100 value: 51.104000000000006 - type: mrr_at_1000 value: 51.105999999999995 - type: mrr_at_3 value: 45.733000000000004 - type: mrr_at_5 value: 48.428 - type: ndcg_at_1 value: 34.922 - type: ndcg_at_10 value: 59.12 - type: ndcg_at_100 value: 62.083999999999996 - type: ndcg_at_1000 value: 62.137 - type: ndcg_at_3 value: 49.616 - type: ndcg_at_5 value: 54.501 - type: precision_at_1 value: 34.922 - type: precision_at_10 value: 8.649 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.152 - type: precision_at_5 value: 14.466999999999999 - type: recall_at_1 value: 34.922 - type: recall_at_10 value: 86.48599999999999 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 60.455000000000005 - type: recall_at_5 value: 72.333 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.623282347623714 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.86487843524932 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.3290291318171 - type: mrr value: 75.2379853141626 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.52002953574285 - type: cos_sim_spearman value: 86.98752423842483 - type: euclidean_pearson value: 86.89442688314197 - type: euclidean_spearman value: 86.88631711307471 - type: manhattan_pearson value: 87.03723618507175 - type: manhattan_spearman value: 86.76041062975224 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.64935064935065 - type: f1 value: 86.61903824934998 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.21904455377494 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.43342755570654 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.843 - type: map_at_10 value: 43.379 - type: map_at_100 value: 44.946999999999996 - type: map_at_1000 value: 45.078 - type: map_at_3 value: 39.598 - type: map_at_5 value: 41.746 - type: mrr_at_1 value: 39.199 - type: mrr_at_10 value: 49.672 - type: mrr_at_100 value: 50.321000000000005 - type: mrr_at_1000 value: 50.365 - type: mrr_at_3 value: 46.805 - type: mrr_at_5 value: 48.579 - type: ndcg_at_1 value: 39.199 - type: ndcg_at_10 value: 50.163999999999994 - type: ndcg_at_100 value: 55.418 - type: ndcg_at_1000 value: 57.353 - type: ndcg_at_3 value: 44.716 - type: ndcg_at_5 value: 47.268 - type: precision_at_1 value: 39.199 - type: precision_at_10 value: 9.757 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 21.602 - type: precision_at_5 value: 15.479000000000001 - type: recall_at_1 value: 31.843 - type: recall_at_10 value: 62.743 - type: recall_at_100 value: 84.78099999999999 - type: recall_at_1000 value: 96.86099999999999 - type: recall_at_3 value: 46.927 - type: recall_at_5 value: 54.355 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.321 - type: map_at_10 value: 39.062999999999995 - type: map_at_100 value: 40.403 - type: map_at_1000 value: 40.534 - type: map_at_3 value: 36.367 - type: map_at_5 value: 37.756 - type: mrr_at_1 value: 35.987 - type: mrr_at_10 value: 44.708999999999996 - type: mrr_at_100 value: 45.394 - type: mrr_at_1000 value: 45.436 - type: mrr_at_3 value: 42.463 - type: mrr_at_5 value: 43.663000000000004 - type: ndcg_at_1 value: 35.987 - type: ndcg_at_10 value: 44.585 - type: ndcg_at_100 value: 49.297999999999995 - type: ndcg_at_1000 value: 51.315 - type: ndcg_at_3 value: 40.569 - type: ndcg_at_5 value: 42.197 - type: precision_at_1 value: 35.987 - type: precision_at_10 value: 8.369 - type: precision_at_100 value: 1.366 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 19.427 - type: precision_at_5 value: 13.58 - type: recall_at_1 value: 29.321 - type: recall_at_10 value: 54.333 - type: recall_at_100 value: 74.178 - type: recall_at_1000 value: 86.732 - type: recall_at_3 value: 42.46 - type: recall_at_5 value: 47.089999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.811 - type: map_at_10 value: 51.114000000000004 - type: map_at_100 value: 52.22 - type: map_at_1000 value: 52.275000000000006 - type: map_at_3 value: 47.644999999999996 - type: map_at_5 value: 49.675000000000004 - type: mrr_at_1 value: 44.389 - type: mrr_at_10 value: 54.459 - type: mrr_at_100 value: 55.208999999999996 - type: mrr_at_1000 value: 55.239000000000004 - type: mrr_at_3 value: 51.954 - type: mrr_at_5 value: 53.571999999999996 - type: ndcg_at_1 value: 44.389 - type: ndcg_at_10 value: 56.979 - type: ndcg_at_100 value: 61.266 - type: ndcg_at_1000 value: 62.315 - type: ndcg_at_3 value: 51.342 - type: ndcg_at_5 value: 54.33 - type: precision_at_1 value: 44.389 - type: precision_at_10 value: 9.26 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 22.926 - type: precision_at_5 value: 15.987000000000002 - type: recall_at_1 value: 38.811 - type: recall_at_10 value: 70.841 - type: recall_at_100 value: 89.218 - type: recall_at_1000 value: 96.482 - type: recall_at_3 value: 56.123999999999995 - type: recall_at_5 value: 63.322 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.378 - type: map_at_10 value: 34.311 - type: map_at_100 value: 35.399 - type: map_at_1000 value: 35.482 - type: map_at_3 value: 31.917 - type: map_at_5 value: 33.275 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 36.575 - type: mrr_at_100 value: 37.492 - type: mrr_at_1000 value: 37.556 - type: mrr_at_3 value: 34.35 - type: mrr_at_5 value: 35.525 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 39.247 - type: ndcg_at_100 value: 44.424 - type: ndcg_at_1000 value: 46.478 - type: ndcg_at_3 value: 34.684 - type: ndcg_at_5 value: 36.886 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.989 - type: precision_at_100 value: 0.899 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.84 - type: precision_at_5 value: 10.215 - type: recall_at_1 value: 25.378 - type: recall_at_10 value: 52.195 - type: recall_at_100 value: 75.764 - type: recall_at_1000 value: 91.012 - type: recall_at_3 value: 39.885999999999996 - type: recall_at_5 value: 45.279 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.326 - type: map_at_10 value: 25.247000000000003 - type: map_at_100 value: 26.473000000000003 - type: map_at_1000 value: 26.579000000000004 - type: map_at_3 value: 22.466 - type: map_at_5 value: 24.113 - type: mrr_at_1 value: 21.393 - type: mrr_at_10 value: 30.187 - type: mrr_at_100 value: 31.089 - type: mrr_at_1000 value: 31.15 - type: mrr_at_3 value: 27.279999999999998 - type: mrr_at_5 value: 29.127 - type: ndcg_at_1 value: 21.393 - type: ndcg_at_10 value: 30.668 - type: ndcg_at_100 value: 36.543 - type: ndcg_at_1000 value: 39.181 - type: ndcg_at_3 value: 25.552000000000003 - type: ndcg_at_5 value: 28.176000000000002 - type: precision_at_1 value: 21.393 - type: precision_at_10 value: 5.784000000000001 - type: precision_at_100 value: 1.001 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 12.231 - type: precision_at_5 value: 9.179 - type: recall_at_1 value: 17.326 - type: recall_at_10 value: 42.415000000000006 - type: recall_at_100 value: 68.605 - type: recall_at_1000 value: 87.694 - type: recall_at_3 value: 28.343 - type: recall_at_5 value: 35.086 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.069 - type: map_at_10 value: 40.027 - type: map_at_100 value: 41.308 - type: map_at_1000 value: 41.412 - type: map_at_3 value: 36.864000000000004 - type: map_at_5 value: 38.641999999999996 - type: mrr_at_1 value: 35.707 - type: mrr_at_10 value: 45.527 - type: mrr_at_100 value: 46.348 - type: mrr_at_1000 value: 46.392 - type: mrr_at_3 value: 43.086 - type: mrr_at_5 value: 44.645 - type: ndcg_at_1 value: 35.707 - type: ndcg_at_10 value: 46.117000000000004 - type: ndcg_at_100 value: 51.468 - type: ndcg_at_1000 value: 53.412000000000006 - type: ndcg_at_3 value: 41.224 - type: ndcg_at_5 value: 43.637 - type: precision_at_1 value: 35.707 - type: precision_at_10 value: 8.459999999999999 - type: precision_at_100 value: 1.2970000000000002 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 19.731 - type: precision_at_5 value: 14.013 - type: recall_at_1 value: 29.069 - type: recall_at_10 value: 58.343999999999994 - type: recall_at_100 value: 81.296 - type: recall_at_1000 value: 93.974 - type: recall_at_3 value: 44.7 - type: recall_at_5 value: 50.88700000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.905 - type: map_at_10 value: 33.983000000000004 - type: map_at_100 value: 35.372 - type: map_at_1000 value: 35.487 - type: map_at_3 value: 30.902 - type: map_at_5 value: 32.505 - type: mrr_at_1 value: 29.794999999999998 - type: mrr_at_10 value: 39.28 - type: mrr_at_100 value: 40.215 - type: mrr_at_1000 value: 40.276 - type: mrr_at_3 value: 36.701 - type: mrr_at_5 value: 38.105 - type: ndcg_at_1 value: 29.794999999999998 - type: ndcg_at_10 value: 40.041 - type: ndcg_at_100 value: 45.884 - type: ndcg_at_1000 value: 48.271 - type: ndcg_at_3 value: 34.931 - type: ndcg_at_5 value: 37.044 - type: precision_at_1 value: 29.794999999999998 - type: precision_at_10 value: 7.546 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.933 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 23.905 - type: recall_at_10 value: 52.945 - type: recall_at_100 value: 77.551 - type: recall_at_1000 value: 93.793 - type: recall_at_3 value: 38.364 - type: recall_at_5 value: 44.044 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.24441666666667 - type: map_at_10 value: 34.4595 - type: map_at_100 value: 35.699999999999996 - type: map_at_1000 value: 35.8155 - type: map_at_3 value: 31.608333333333338 - type: map_at_5 value: 33.189416666666666 - type: mrr_at_1 value: 29.825250000000004 - type: mrr_at_10 value: 38.60875 - type: mrr_at_100 value: 39.46575 - type: mrr_at_1000 value: 39.52458333333333 - type: mrr_at_3 value: 36.145166666666675 - type: mrr_at_5 value: 37.57625 - type: ndcg_at_1 value: 29.825250000000004 - type: ndcg_at_10 value: 39.88741666666667 - type: ndcg_at_100 value: 45.17966666666667 - type: ndcg_at_1000 value: 47.440583333333336 - type: ndcg_at_3 value: 35.04591666666666 - type: ndcg_at_5 value: 37.32025 - type: precision_at_1 value: 29.825250000000004 - type: precision_at_10 value: 7.07225 - type: precision_at_100 value: 1.1462499999999998 - type: precision_at_1000 value: 0.15325 - type: precision_at_3 value: 16.18375 - type: precision_at_5 value: 11.526833333333334 - type: recall_at_1 value: 25.24441666666667 - type: recall_at_10 value: 51.744916666666676 - type: recall_at_100 value: 75.04574999999998 - type: recall_at_1000 value: 90.65558333333334 - type: recall_at_3 value: 38.28349999999999 - type: recall_at_5 value: 44.16591666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.237000000000002 - type: map_at_10 value: 30.667 - type: map_at_100 value: 31.592 - type: map_at_1000 value: 31.688 - type: map_at_3 value: 28.810999999999996 - type: map_at_5 value: 29.788999999999998 - type: mrr_at_1 value: 26.840000000000003 - type: mrr_at_10 value: 33.305 - type: mrr_at_100 value: 34.089000000000006 - type: mrr_at_1000 value: 34.159 - type: mrr_at_3 value: 31.518 - type: mrr_at_5 value: 32.469 - type: ndcg_at_1 value: 26.840000000000003 - type: ndcg_at_10 value: 34.541 - type: ndcg_at_100 value: 39.206 - type: ndcg_at_1000 value: 41.592 - type: ndcg_at_3 value: 31.005 - type: ndcg_at_5 value: 32.554 - type: precision_at_1 value: 26.840000000000003 - type: precision_at_10 value: 5.3069999999999995 - type: precision_at_100 value: 0.8340000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 13.292000000000002 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 24.237000000000002 - type: recall_at_10 value: 43.862 - type: recall_at_100 value: 65.352 - type: recall_at_1000 value: 82.704 - type: recall_at_3 value: 34.009 - type: recall_at_5 value: 37.878 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.482 - type: map_at_10 value: 23.249 - type: map_at_100 value: 24.388 - type: map_at_1000 value: 24.519 - type: map_at_3 value: 20.971 - type: map_at_5 value: 22.192 - type: mrr_at_1 value: 19.993 - type: mrr_at_10 value: 26.985 - type: mrr_at_100 value: 27.975 - type: mrr_at_1000 value: 28.052 - type: mrr_at_3 value: 24.954 - type: mrr_at_5 value: 26.070999999999998 - type: ndcg_at_1 value: 19.993 - type: ndcg_at_10 value: 27.656 - type: ndcg_at_100 value: 33.256 - type: ndcg_at_1000 value: 36.275 - type: ndcg_at_3 value: 23.644000000000002 - type: ndcg_at_5 value: 25.466 - type: precision_at_1 value: 19.993 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.149000000000001 - type: precision_at_5 value: 8.149000000000001 - type: recall_at_1 value: 16.482 - type: recall_at_10 value: 37.141999999999996 - type: recall_at_100 value: 62.696 - type: recall_at_1000 value: 84.333 - type: recall_at_3 value: 26.031 - type: recall_at_5 value: 30.660999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.887999999999998 - type: map_at_10 value: 34.101 - type: map_at_100 value: 35.27 - type: map_at_1000 value: 35.370000000000005 - type: map_at_3 value: 31.283 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 29.011 - type: mrr_at_10 value: 38.004 - type: mrr_at_100 value: 38.879000000000005 - type: mrr_at_1000 value: 38.938 - type: mrr_at_3 value: 35.571999999999996 - type: mrr_at_5 value: 36.789 - type: ndcg_at_1 value: 29.011 - type: ndcg_at_10 value: 39.586 - type: ndcg_at_100 value: 44.939 - type: ndcg_at_1000 value: 47.236 - type: ndcg_at_3 value: 34.4 - type: ndcg_at_5 value: 36.519 - type: precision_at_1 value: 29.011 - type: precision_at_10 value: 6.763 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.609 - type: precision_at_5 value: 10.896 - type: recall_at_1 value: 24.887999999999998 - type: recall_at_10 value: 52.42 - type: recall_at_100 value: 75.803 - type: recall_at_1000 value: 91.725 - type: recall_at_3 value: 38.080999999999996 - type: recall_at_5 value: 43.47 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.953 - type: map_at_10 value: 32.649 - type: map_at_100 value: 34.181 - type: map_at_1000 value: 34.398 - type: map_at_3 value: 29.567 - type: map_at_5 value: 31.263 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.419999999999995 - type: mrr_at_100 value: 38.396 - type: mrr_at_1000 value: 38.458 - type: mrr_at_3 value: 34.782999999999994 - type: mrr_at_5 value: 36.254999999999995 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 38.595 - type: ndcg_at_100 value: 44.6 - type: ndcg_at_1000 value: 47.158 - type: ndcg_at_3 value: 33.56 - type: ndcg_at_5 value: 35.870000000000005 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.53 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.542 - type: recall_at_1 value: 23.953 - type: recall_at_10 value: 50.08200000000001 - type: recall_at_100 value: 77.364 - type: recall_at_1000 value: 93.57799999999999 - type: recall_at_3 value: 35.432 - type: recall_at_5 value: 41.875 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.72 - type: map_at_10 value: 25.724000000000004 - type: map_at_100 value: 26.846999999999998 - type: map_at_1000 value: 26.964 - type: map_at_3 value: 22.909 - type: map_at_5 value: 24.596999999999998 - type: mrr_at_1 value: 18.854000000000003 - type: mrr_at_10 value: 27.182000000000002 - type: mrr_at_100 value: 28.182000000000002 - type: mrr_at_1000 value: 28.274 - type: mrr_at_3 value: 24.276 - type: mrr_at_5 value: 26.115 - type: ndcg_at_1 value: 18.854000000000003 - type: ndcg_at_10 value: 30.470000000000002 - type: ndcg_at_100 value: 35.854 - type: ndcg_at_1000 value: 38.701 - type: ndcg_at_3 value: 24.924 - type: ndcg_at_5 value: 27.895999999999997 - type: precision_at_1 value: 18.854000000000003 - type: precision_at_10 value: 5.009 - type: precision_at_100 value: 0.835 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 10.721 - type: precision_at_5 value: 8.133 - type: recall_at_1 value: 17.72 - type: recall_at_10 value: 43.617 - type: recall_at_100 value: 67.941 - type: recall_at_1000 value: 88.979 - type: recall_at_3 value: 29.044999999999998 - type: recall_at_5 value: 36.044 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.427 - type: map_at_10 value: 22.935 - type: map_at_100 value: 24.808 - type: map_at_1000 value: 24.994 - type: map_at_3 value: 19.533 - type: map_at_5 value: 21.261 - type: mrr_at_1 value: 30.945 - type: mrr_at_10 value: 43.242000000000004 - type: mrr_at_100 value: 44.013999999999996 - type: mrr_at_1000 value: 44.048 - type: mrr_at_3 value: 40.109 - type: mrr_at_5 value: 42.059999999999995 - type: ndcg_at_1 value: 30.945 - type: ndcg_at_10 value: 31.828 - type: ndcg_at_100 value: 38.801 - type: ndcg_at_1000 value: 42.126999999999995 - type: ndcg_at_3 value: 26.922 - type: ndcg_at_5 value: 28.483999999999998 - type: precision_at_1 value: 30.945 - type: precision_at_10 value: 9.844 - type: precision_at_100 value: 1.7309999999999999 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 20.477999999999998 - type: precision_at_5 value: 15.27 - type: recall_at_1 value: 13.427 - type: recall_at_10 value: 37.141000000000005 - type: recall_at_100 value: 61.007 - type: recall_at_1000 value: 79.742 - type: recall_at_3 value: 24.431 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.122 - type: map_at_10 value: 18.799 - type: map_at_100 value: 25.724999999999998 - type: map_at_1000 value: 27.205000000000002 - type: map_at_3 value: 14.194999999999999 - type: map_at_5 value: 16.225 - type: mrr_at_1 value: 68.0 - type: mrr_at_10 value: 76.035 - type: mrr_at_100 value: 76.292 - type: mrr_at_1000 value: 76.297 - type: mrr_at_3 value: 74.458 - type: mrr_at_5 value: 75.558 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 39.761 - type: ndcg_at_100 value: 43.736999999999995 - type: ndcg_at_1000 value: 51.146 - type: ndcg_at_3 value: 45.921 - type: ndcg_at_5 value: 42.756 - type: precision_at_1 value: 68.0 - type: precision_at_10 value: 30.275000000000002 - type: precision_at_100 value: 9.343 - type: precision_at_1000 value: 1.8270000000000002 - type: precision_at_3 value: 49.167 - type: precision_at_5 value: 40.699999999999996 - type: recall_at_1 value: 9.122 - type: recall_at_10 value: 23.669999999999998 - type: recall_at_100 value: 48.719 - type: recall_at_1000 value: 72.033 - type: recall_at_3 value: 15.498999999999999 - type: recall_at_5 value: 18.657 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 55.885000000000005 - type: f1 value: 50.70726446938571 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 75.709 - type: map_at_10 value: 83.345 - type: map_at_100 value: 83.557 - type: map_at_1000 value: 83.572 - type: map_at_3 value: 82.425 - type: map_at_5 value: 83.013 - type: mrr_at_1 value: 81.593 - type: mrr_at_10 value: 88.331 - type: mrr_at_100 value: 88.408 - type: mrr_at_1000 value: 88.41 - type: mrr_at_3 value: 87.714 - type: mrr_at_5 value: 88.122 - type: ndcg_at_1 value: 81.593 - type: ndcg_at_10 value: 86.925 - type: ndcg_at_100 value: 87.67 - type: ndcg_at_1000 value: 87.924 - type: ndcg_at_3 value: 85.5 - type: ndcg_at_5 value: 86.283 - type: precision_at_1 value: 81.593 - type: precision_at_10 value: 10.264 - type: precision_at_100 value: 1.084 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 32.388 - type: precision_at_5 value: 19.991 - type: recall_at_1 value: 75.709 - type: recall_at_10 value: 93.107 - type: recall_at_100 value: 96.024 - type: recall_at_1000 value: 97.603 - type: recall_at_3 value: 89.08500000000001 - type: recall_at_5 value: 91.15299999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.121 - type: map_at_10 value: 31.78 - type: map_at_100 value: 33.497 - type: map_at_1000 value: 33.696 - type: map_at_3 value: 27.893 - type: map_at_5 value: 30.087000000000003 - type: mrr_at_1 value: 38.272 - type: mrr_at_10 value: 47.176 - type: mrr_at_100 value: 48.002 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 45.086999999999996 - type: mrr_at_5 value: 46.337 - type: ndcg_at_1 value: 38.272 - type: ndcg_at_10 value: 39.145 - type: ndcg_at_100 value: 45.696999999999996 - type: ndcg_at_1000 value: 49.0 - type: ndcg_at_3 value: 36.148 - type: ndcg_at_5 value: 37.023 - type: precision_at_1 value: 38.272 - type: precision_at_10 value: 11.065 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.587999999999997 - type: precision_at_5 value: 18.056 - type: recall_at_1 value: 19.121 - type: recall_at_10 value: 44.857 - type: recall_at_100 value: 69.774 - type: recall_at_1000 value: 89.645 - type: recall_at_3 value: 32.588 - type: recall_at_5 value: 37.939 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.428 - type: map_at_10 value: 56.891999999999996 - type: map_at_100 value: 57.82899999999999 - type: map_at_1000 value: 57.896 - type: map_at_3 value: 53.762 - type: map_at_5 value: 55.718 - type: mrr_at_1 value: 72.856 - type: mrr_at_10 value: 79.245 - type: mrr_at_100 value: 79.515 - type: mrr_at_1000 value: 79.525 - type: mrr_at_3 value: 78.143 - type: mrr_at_5 value: 78.822 - type: ndcg_at_1 value: 72.856 - type: ndcg_at_10 value: 65.204 - type: ndcg_at_100 value: 68.552 - type: ndcg_at_1000 value: 69.902 - type: ndcg_at_3 value: 60.632 - type: ndcg_at_5 value: 63.161 - type: precision_at_1 value: 72.856 - type: precision_at_10 value: 13.65 - type: precision_at_100 value: 1.6260000000000001 - type: precision_at_1000 value: 0.181 - type: precision_at_3 value: 38.753 - type: precision_at_5 value: 25.251 - type: recall_at_1 value: 36.428 - type: recall_at_10 value: 68.25099999999999 - type: recall_at_100 value: 81.317 - type: recall_at_1000 value: 90.27 - type: recall_at_3 value: 58.13 - type: recall_at_5 value: 63.126000000000005 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 89.4868 - type: ap value: 84.88319192880247 - type: f1 value: 89.46144458052846 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.282999999999998 - type: map_at_10 value: 33.045 - type: map_at_100 value: 34.238 - type: map_at_1000 value: 34.29 - type: map_at_3 value: 29.305999999999997 - type: map_at_5 value: 31.391000000000002 - type: mrr_at_1 value: 21.92 - type: mrr_at_10 value: 33.649 - type: mrr_at_100 value: 34.791 - type: mrr_at_1000 value: 34.837 - type: mrr_at_3 value: 30.0 - type: mrr_at_5 value: 32.039 - type: ndcg_at_1 value: 21.92 - type: ndcg_at_10 value: 39.729 - type: ndcg_at_100 value: 45.484 - type: ndcg_at_1000 value: 46.817 - type: ndcg_at_3 value: 32.084 - type: ndcg_at_5 value: 35.789 - type: precision_at_1 value: 21.92 - type: precision_at_10 value: 6.297 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 13.639000000000001 - type: precision_at_5 value: 10.054 - type: recall_at_1 value: 21.282999999999998 - type: recall_at_10 value: 60.343999999999994 - type: recall_at_100 value: 86.981 - type: recall_at_1000 value: 97.205 - type: recall_at_3 value: 39.452999999999996 - type: recall_at_5 value: 48.333 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.47879616963064 - type: f1 value: 95.21800589958251 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.09256725946192 - type: f1 value: 60.554043889452515 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 75.53463349024882 - type: f1 value: 73.14418495756476 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.22663080026899 - type: f1 value: 79.331456217501 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.50316010430136 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.15612040042282 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.36227552557184 - type: mrr value: 33.57901344209811 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.6610000000000005 - type: map_at_10 value: 12.992 - type: map_at_100 value: 16.756999999999998 - type: map_at_1000 value: 18.25 - type: map_at_3 value: 9.471 - type: map_at_5 value: 11.116 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 53.388999999999996 - type: mrr_at_100 value: 53.982 - type: mrr_at_1000 value: 54.033 - type: mrr_at_3 value: 51.858000000000004 - type: mrr_at_5 value: 53.019000000000005 - type: ndcg_at_1 value: 41.641 - type: ndcg_at_10 value: 34.691 - type: ndcg_at_100 value: 32.305 - type: ndcg_at_1000 value: 41.132999999999996 - type: ndcg_at_3 value: 40.614 - type: ndcg_at_5 value: 38.456 - type: precision_at_1 value: 43.344 - type: precision_at_10 value: 25.881999999999998 - type: precision_at_100 value: 8.483 - type: precision_at_1000 value: 2.131 - type: precision_at_3 value: 38.803 - type: precision_at_5 value: 33.87 - type: recall_at_1 value: 5.6610000000000005 - type: recall_at_10 value: 16.826 - type: recall_at_100 value: 32.939 - type: recall_at_1000 value: 65.161 - type: recall_at_3 value: 10.756 - type: recall_at_5 value: 13.331000000000001 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 26.692 - type: map_at_10 value: 41.065000000000005 - type: map_at_100 value: 42.235 - type: map_at_1000 value: 42.27 - type: map_at_3 value: 36.635 - type: map_at_5 value: 39.219 - type: mrr_at_1 value: 30.214000000000002 - type: mrr_at_10 value: 43.443 - type: mrr_at_100 value: 44.326 - type: mrr_at_1000 value: 44.352000000000004 - type: mrr_at_3 value: 39.623999999999995 - type: mrr_at_5 value: 41.898 - type: ndcg_at_1 value: 30.214000000000002 - type: ndcg_at_10 value: 48.692 - type: ndcg_at_100 value: 53.671 - type: ndcg_at_1000 value: 54.522000000000006 - type: ndcg_at_3 value: 40.245 - type: ndcg_at_5 value: 44.580999999999996 - type: precision_at_1 value: 30.214000000000002 - type: precision_at_10 value: 8.3 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 18.521 - type: precision_at_5 value: 13.627 - type: recall_at_1 value: 26.692 - type: recall_at_10 value: 69.699 - type: recall_at_100 value: 91.425 - type: recall_at_1000 value: 97.78099999999999 - type: recall_at_3 value: 47.711 - type: recall_at_5 value: 57.643 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.962 - type: map_at_10 value: 84.772 - type: map_at_100 value: 85.402 - type: map_at_1000 value: 85.418 - type: map_at_3 value: 81.89 - type: map_at_5 value: 83.685 - type: mrr_at_1 value: 81.67 - type: mrr_at_10 value: 87.681 - type: mrr_at_100 value: 87.792 - type: mrr_at_1000 value: 87.79299999999999 - type: mrr_at_3 value: 86.803 - type: mrr_at_5 value: 87.392 - type: ndcg_at_1 value: 81.69 - type: ndcg_at_10 value: 88.429 - type: ndcg_at_100 value: 89.66 - type: ndcg_at_1000 value: 89.762 - type: ndcg_at_3 value: 85.75 - type: ndcg_at_5 value: 87.20700000000001 - type: precision_at_1 value: 81.69 - type: precision_at_10 value: 13.395000000000001 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.507000000000005 - type: precision_at_5 value: 24.614 - type: recall_at_1 value: 70.962 - type: recall_at_10 value: 95.339 - type: recall_at_100 value: 99.543 - type: recall_at_1000 value: 99.984 - type: recall_at_3 value: 87.54899999999999 - type: recall_at_5 value: 91.726 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.506631779239555 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.63731341848479 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.852 - type: map_at_10 value: 13.175 - type: map_at_100 value: 15.623999999999999 - type: map_at_1000 value: 16.002 - type: map_at_3 value: 9.103 - type: map_at_5 value: 11.068999999999999 - type: mrr_at_1 value: 23.9 - type: mrr_at_10 value: 35.847 - type: mrr_at_100 value: 36.968 - type: mrr_at_1000 value: 37.018 - type: mrr_at_3 value: 32.300000000000004 - type: mrr_at_5 value: 34.14 - type: ndcg_at_1 value: 23.9 - type: ndcg_at_10 value: 21.889 - type: ndcg_at_100 value: 30.903000000000002 - type: ndcg_at_1000 value: 36.992000000000004 - type: ndcg_at_3 value: 20.274 - type: ndcg_at_5 value: 17.773 - type: precision_at_1 value: 23.9 - type: precision_at_10 value: 11.61 - type: precision_at_100 value: 2.4539999999999997 - type: precision_at_1000 value: 0.391 - type: precision_at_3 value: 19.133 - type: precision_at_5 value: 15.740000000000002 - type: recall_at_1 value: 4.852 - type: recall_at_10 value: 23.507 - type: recall_at_100 value: 49.775000000000006 - type: recall_at_1000 value: 79.308 - type: recall_at_3 value: 11.637 - type: recall_at_5 value: 15.947 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 86.03345827446948 - type: cos_sim_spearman value: 80.53174518259549 - type: euclidean_pearson value: 83.44538971660883 - type: euclidean_spearman value: 80.57344324098692 - type: manhattan_pearson value: 83.36528808195459 - type: manhattan_spearman value: 80.48931287157902 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.21363088257881 - type: cos_sim_spearman value: 75.56589127055523 - type: euclidean_pearson value: 82.32868324521908 - type: euclidean_spearman value: 75.31928550664554 - type: manhattan_pearson value: 82.31332875713211 - type: manhattan_spearman value: 75.35376322099196 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.09085593258487 - type: cos_sim_spearman value: 86.26355088415221 - type: euclidean_pearson value: 85.49646115361156 - type: euclidean_spearman value: 86.20652472228703 - type: manhattan_pearson value: 85.44084081123815 - type: manhattan_spearman value: 86.1162623448951 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.68250248349368 - type: cos_sim_spearman value: 82.29883673695083 - type: euclidean_pearson value: 84.17633035446019 - type: euclidean_spearman value: 82.19990511264791 - type: manhattan_pearson value: 84.17408410692279 - type: manhattan_spearman value: 82.249873895981 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.31878760045024 - type: cos_sim_spearman value: 88.7364409031183 - type: euclidean_pearson value: 88.230537618603 - type: euclidean_spearman value: 88.76484309646318 - type: manhattan_pearson value: 88.17689071136469 - type: manhattan_spearman value: 88.72809249037928 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.41078559110638 - type: cos_sim_spearman value: 85.27439135411049 - type: euclidean_pearson value: 84.5333571592088 - type: euclidean_spearman value: 85.25645460575957 - type: manhattan_pearson value: 84.38428921610226 - type: manhattan_spearman value: 85.07796040798796 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.82374132382576 - type: cos_sim_spearman value: 89.02101343562433 - type: euclidean_pearson value: 89.50729765458932 - type: euclidean_spearman value: 89.04184772869253 - type: manhattan_pearson value: 89.51737904059856 - type: manhattan_spearman value: 89.12925950440676 - 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: 67.56051823873482 - type: cos_sim_spearman value: 68.50988748185463 - type: euclidean_pearson value: 69.16524346147456 - type: euclidean_spearman value: 68.61859952449579 - type: manhattan_pearson value: 69.10618915706995 - type: manhattan_spearman value: 68.36401769459522 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.4159693872625 - type: cos_sim_spearman value: 87.07819121764247 - type: euclidean_pearson value: 87.03013260863153 - type: euclidean_spearman value: 87.06547293631309 - type: manhattan_pearson value: 86.8129744446062 - type: manhattan_spearman value: 86.88494096335627 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.47758088996575 - type: mrr value: 96.17891458577733 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.538999999999994 - type: map_at_10 value: 66.562 - type: map_at_100 value: 67.254 - type: map_at_1000 value: 67.284 - type: map_at_3 value: 63.722 - type: map_at_5 value: 65.422 - type: mrr_at_1 value: 60.0 - type: mrr_at_10 value: 67.354 - type: mrr_at_100 value: 67.908 - type: mrr_at_1000 value: 67.93299999999999 - type: mrr_at_3 value: 65.056 - type: mrr_at_5 value: 66.43900000000001 - type: ndcg_at_1 value: 60.0 - type: ndcg_at_10 value: 70.858 - type: ndcg_at_100 value: 73.67099999999999 - type: ndcg_at_1000 value: 74.26700000000001 - type: ndcg_at_3 value: 65.911 - type: ndcg_at_5 value: 68.42200000000001 - type: precision_at_1 value: 60.0 - type: precision_at_10 value: 9.4 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 17.0 - type: recall_at_1 value: 57.538999999999994 - type: recall_at_10 value: 83.233 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 69.883 - type: recall_at_5 value: 76.19399999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82574257425742 - type: cos_sim_ap value: 95.78722833053911 - type: cos_sim_f1 value: 90.94650205761316 - type: cos_sim_precision value: 93.64406779661016 - type: cos_sim_recall value: 88.4 - type: dot_accuracy value: 99.83366336633664 - type: dot_ap value: 95.89733601612964 - type: dot_f1 value: 91.41981613891727 - type: dot_precision value: 93.42379958246346 - type: dot_recall value: 89.5 - type: euclidean_accuracy value: 99.82574257425742 - type: euclidean_ap value: 95.75227035138846 - type: euclidean_f1 value: 90.96509240246407 - type: euclidean_precision value: 93.45991561181435 - type: euclidean_recall value: 88.6 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.76278266220176 - type: manhattan_f1 value: 91.08409321175279 - type: manhattan_precision value: 92.29979466119097 - type: manhattan_recall value: 89.9 - type: max_accuracy value: 99.83366336633664 - type: max_ap value: 95.89733601612964 - type: max_f1 value: 91.41981613891727 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 61.905425988638605 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.159589881679736 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.0605499476397 - type: mrr value: 53.91594516594517 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.202718009067 - type: cos_sim_spearman value: 31.136199912366987 - type: dot_pearson value: 30.66329011927951 - type: dot_spearman value: 30.107664909625107 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.209 - type: map_at_10 value: 1.712 - type: map_at_100 value: 9.464 - type: map_at_1000 value: 23.437 - type: map_at_3 value: 0.609 - type: map_at_5 value: 0.9440000000000001 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 86.833 - type: mrr_at_100 value: 86.833 - type: mrr_at_1000 value: 86.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 86.833 - type: ndcg_at_1 value: 74.0 - type: ndcg_at_10 value: 69.14 - type: ndcg_at_100 value: 53.047999999999995 - type: ndcg_at_1000 value: 48.577 - type: ndcg_at_3 value: 75.592 - type: ndcg_at_5 value: 72.509 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 54.44 - type: precision_at_1000 value: 21.326 - type: precision_at_3 value: 80.667 - type: precision_at_5 value: 77.2 - type: recall_at_1 value: 0.209 - type: recall_at_10 value: 1.932 - type: recall_at_100 value: 13.211999999999998 - type: recall_at_1000 value: 45.774 - type: recall_at_3 value: 0.644 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.609 - type: map_at_10 value: 8.334999999999999 - type: map_at_100 value: 14.604000000000001 - type: map_at_1000 value: 16.177 - type: map_at_3 value: 4.87 - type: map_at_5 value: 6.3149999999999995 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 45.047 - type: mrr_at_100 value: 45.808 - type: mrr_at_1000 value: 45.808 - type: mrr_at_3 value: 41.497 - type: mrr_at_5 value: 43.231 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 21.193 - type: ndcg_at_100 value: 34.97 - type: ndcg_at_1000 value: 46.69 - type: ndcg_at_3 value: 24.823 - type: ndcg_at_5 value: 22.872999999999998 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 7.4079999999999995 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 2.609 - type: recall_at_10 value: 13.63 - type: recall_at_100 value: 47.014 - type: recall_at_1000 value: 83.176 - type: recall_at_3 value: 5.925 - type: recall_at_5 value: 8.574 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.80239999999999 - type: ap value: 15.497911013214791 - type: f1 value: 56.258411577947285 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.00452744765139 - type: f1 value: 61.42228624410908 - 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type: dot_ap value: 84.62999949222983 - type: dot_f1 value: 77.3074661963551 - type: dot_precision value: 73.93871239808828 - type: dot_recall value: 80.99784416384355 - type: euclidean_accuracy value: 88.70066363953894 - type: euclidean_ap value: 85.34184508966621 - type: euclidean_f1 value: 77.76871756856931 - type: euclidean_precision value: 74.97855917667239 - type: euclidean_recall value: 80.77456113335386 - type: manhattan_accuracy value: 88.68319944114566 - type: manhattan_ap value: 85.3026464242333 - type: manhattan_f1 value: 77.66561049296294 - type: manhattan_precision value: 74.4665818849795 - type: manhattan_recall value: 81.15183246073299 - type: max_accuracy value: 88.70066363953894 - type: max_ap value: 85.34184508966621 - type: max_f1 value: 77.76871756856931 ---

GIST small Embedding v0

*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) # Data The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. # Usage The model can be easily loaded using the Sentence Transformers library. ```Python import torch.nn.functional as F from sentence_transformers import SentenceTransformer revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision) texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = model.encode(texts, convert_to_tensor=True) # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) ``` # Training Parameters Below are the training parameters used to fine-tune the model: ``` Epochs = 40 Warmup ratio = 0.1 Learning rate = 5e-6 Batch size = 16 Checkpoint step = 102000 Contrastive loss temperature = 0.01 ``` # Evaluation The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. # Citation Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # Acknowledgements This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.