--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: sgpt-bloom-7b1-msmarco results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 68.05970149253731 - type: ap value: 31.640363460776193 - type: f1 value: 62.50025574145796 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 61.34903640256959 - type: ap value: 75.18797161500426 - type: f1 value: 59.04772570730417 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 67.78110944527737 - type: ap value: 19.218916023322706 - type: f1 value: 56.24477391445512 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 58.23340471092078 - type: ap value: 13.20222967424681 - type: f1 value: 47.511718095460296 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 68.97232499999998 - type: ap value: 63.53632885535693 - type: f1 value: 68.62038513152868 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 33.855999999999995 - type: f1 value: 33.43468222830134 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 29.697999999999997 - type: f1 value: 29.39935388885501 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.974000000000004 - type: f1 value: 35.25910820714383 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.922 - type: f1 value: 35.38637028933444 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 27.636 - type: f1 value: 27.178349955978266 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 32.632 - type: f1 value: 32.08014766494587 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 23.684 - type: map_at_10 value: 38.507999999999996 - type: map_at_100 value: 39.677 - type: map_at_1000 value: 39.690999999999995 - type: map_at_3 value: 33.369 - type: map_at_5 value: 36.15 - type: mrr_at_1 value: 24.04 - type: mrr_at_10 value: 38.664 - type: mrr_at_100 value: 39.833 - type: mrr_at_1000 value: 39.847 - type: mrr_at_3 value: 33.476 - type: mrr_at_5 value: 36.306 - type: ndcg_at_1 value: 23.684 - type: ndcg_at_10 value: 47.282000000000004 - type: ndcg_at_100 value: 52.215 - type: ndcg_at_1000 value: 52.551 - type: ndcg_at_3 value: 36.628 - type: ndcg_at_5 value: 41.653 - type: precision_at_1 value: 23.684 - type: precision_at_10 value: 7.553 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.363 - type: precision_at_5 value: 11.664 - type: recall_at_1 value: 23.684 - type: recall_at_10 value: 75.533 - type: recall_at_100 value: 97.013 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 46.088 - type: recall_at_5 value: 58.321 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 44.59375023881131 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 38.02921907752556 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 59.97321570342109 - type: mrr value: 73.18284746955106 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 89.09091435741429 - type: cos_sim_spearman value: 85.31459455332202 - type: euclidean_pearson value: 79.3587681410798 - type: euclidean_spearman value: 76.8174129874685 - type: manhattan_pearson value: 79.57051762121769 - type: manhattan_spearman value: 76.75837549768094 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 54.27974947807933 - type: f1 value: 54.00144411132214 - type: precision value: 53.87119374071357 - type: recall value: 54.27974947807933 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.3365617433414 - type: f1 value: 97.06141316310809 - type: precision value: 96.92567319685965 - type: recall value: 97.3365617433414 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 46.05472809144441 - type: f1 value: 45.30319274690595 - type: precision value: 45.00015469655234 - type: recall value: 46.05472809144441 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.10426540284361 - type: f1 value: 97.96384061786905 - type: precision value: 97.89362822538178 - type: recall value: 98.10426540284361 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 84.33441558441558 - type: f1 value: 84.31653077470322 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 36.025318694698086 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 32.484889034590346 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.203999999999997 - type: map_at_10 value: 41.314 - type: map_at_100 value: 42.66 - type: map_at_1000 value: 42.775999999999996 - type: map_at_3 value: 37.614999999999995 - type: map_at_5 value: 39.643 - type: mrr_at_1 value: 37.482 - type: mrr_at_10 value: 47.075 - type: mrr_at_100 value: 47.845 - type: mrr_at_1000 value: 47.887 - type: mrr_at_3 value: 44.635000000000005 - type: mrr_at_5 value: 45.966 - type: ndcg_at_1 value: 37.482 - type: ndcg_at_10 value: 47.676 - type: ndcg_at_100 value: 52.915 - type: ndcg_at_1000 value: 54.82900000000001 - type: ndcg_at_3 value: 42.562 - type: ndcg_at_5 value: 44.852 - type: precision_at_1 value: 37.482 - type: precision_at_10 value: 9.142 - type: precision_at_100 value: 1.436 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 20.458000000000002 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 30.203999999999997 - type: recall_at_10 value: 60.343 - type: recall_at_100 value: 82.58 - type: recall_at_1000 value: 94.813 - type: recall_at_3 value: 45.389 - type: recall_at_5 value: 51.800999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.889 - type: map_at_10 value: 40.949999999999996 - type: map_at_100 value: 42.131 - type: map_at_1000 value: 42.253 - type: map_at_3 value: 38.346999999999994 - type: map_at_5 value: 39.782000000000004 - type: mrr_at_1 value: 38.79 - type: mrr_at_10 value: 46.944 - type: mrr_at_100 value: 47.61 - type: mrr_at_1000 value: 47.650999999999996 - type: mrr_at_3 value: 45.053 - type: mrr_at_5 value: 46.101 - type: ndcg_at_1 value: 38.79 - type: ndcg_at_10 value: 46.286 - type: ndcg_at_100 value: 50.637 - type: ndcg_at_1000 value: 52.649 - type: ndcg_at_3 value: 42.851 - type: ndcg_at_5 value: 44.311 - type: precision_at_1 value: 38.79 - type: precision_at_10 value: 8.516 - type: precision_at_100 value: 1.3679999999999999 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 20.637 - type: precision_at_5 value: 14.318 - type: recall_at_1 value: 30.889 - type: recall_at_10 value: 55.327000000000005 - type: recall_at_100 value: 74.091 - type: recall_at_1000 value: 86.75500000000001 - type: recall_at_3 value: 44.557 - type: recall_at_5 value: 49.064 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 39.105000000000004 - type: map_at_10 value: 50.928 - type: map_at_100 value: 51.958000000000006 - type: map_at_1000 value: 52.017 - type: map_at_3 value: 47.638999999999996 - type: map_at_5 value: 49.624 - type: mrr_at_1 value: 44.639 - type: mrr_at_10 value: 54.261 - type: mrr_at_100 value: 54.913999999999994 - type: mrr_at_1000 value: 54.945 - type: mrr_at_3 value: 51.681999999999995 - type: mrr_at_5 value: 53.290000000000006 - type: ndcg_at_1 value: 44.639 - type: ndcg_at_10 value: 56.678 - type: ndcg_at_100 value: 60.649 - type: ndcg_at_1000 value: 61.855000000000004 - type: ndcg_at_3 value: 51.092999999999996 - type: ndcg_at_5 value: 54.096999999999994 - type: precision_at_1 value: 44.639 - type: precision_at_10 value: 9.028 - type: precision_at_100 value: 1.194 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.508 - type: precision_at_5 value: 15.661 - type: recall_at_1 value: 39.105000000000004 - type: recall_at_10 value: 70.367 - type: recall_at_100 value: 87.359 - type: recall_at_1000 value: 95.88 - type: recall_at_3 value: 55.581 - type: recall_at_5 value: 62.821000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 23.777 - type: map_at_10 value: 32.297 - type: map_at_100 value: 33.516 - type: map_at_1000 value: 33.592 - type: map_at_3 value: 30.001 - type: map_at_5 value: 31.209999999999997 - type: mrr_at_1 value: 25.989 - type: mrr_at_10 value: 34.472 - type: mrr_at_100 value: 35.518 - type: mrr_at_1000 value: 35.577 - type: mrr_at_3 value: 32.185 - type: mrr_at_5 value: 33.399 - type: ndcg_at_1 value: 25.989 - type: ndcg_at_10 value: 37.037 - type: ndcg_at_100 value: 42.699 - type: ndcg_at_1000 value: 44.725 - type: ndcg_at_3 value: 32.485 - type: ndcg_at_5 value: 34.549 - type: precision_at_1 value: 25.989 - type: precision_at_10 value: 5.718 - type: precision_at_100 value: 0.89 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 14.049 - type: precision_at_5 value: 9.672 - type: recall_at_1 value: 23.777 - type: recall_at_10 value: 49.472 - type: recall_at_100 value: 74.857 - type: recall_at_1000 value: 90.289 - type: recall_at_3 value: 37.086000000000006 - type: recall_at_5 value: 42.065999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 13.377 - type: map_at_10 value: 21.444 - type: map_at_100 value: 22.663 - type: map_at_1000 value: 22.8 - type: map_at_3 value: 18.857 - type: map_at_5 value: 20.426 - type: mrr_at_1 value: 16.542 - type: mrr_at_10 value: 25.326999999999998 - type: mrr_at_100 value: 26.323 - type: mrr_at_1000 value: 26.406000000000002 - type: mrr_at_3 value: 22.823 - type: mrr_at_5 value: 24.340999999999998 - type: ndcg_at_1 value: 16.542 - type: ndcg_at_10 value: 26.479000000000003 - type: ndcg_at_100 value: 32.29 - type: ndcg_at_1000 value: 35.504999999999995 - type: ndcg_at_3 value: 21.619 - type: ndcg_at_5 value: 24.19 - type: precision_at_1 value: 16.542 - type: precision_at_10 value: 5.075 - type: precision_at_100 value: 0.9339999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 10.697 - type: precision_at_5 value: 8.134 - type: recall_at_1 value: 13.377 - type: recall_at_10 value: 38.027 - type: recall_at_100 value: 63.439 - type: recall_at_1000 value: 86.354 - type: recall_at_3 value: 25.0 - type: recall_at_5 value: 31.306 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 28.368 - type: map_at_10 value: 39.305 - type: map_at_100 value: 40.637 - type: map_at_1000 value: 40.753 - type: map_at_3 value: 36.077999999999996 - type: map_at_5 value: 37.829 - type: mrr_at_1 value: 34.937000000000005 - type: mrr_at_10 value: 45.03 - type: mrr_at_100 value: 45.78 - type: mrr_at_1000 value: 45.827 - type: mrr_at_3 value: 42.348 - type: mrr_at_5 value: 43.807 - type: ndcg_at_1 value: 34.937000000000005 - type: ndcg_at_10 value: 45.605000000000004 - type: ndcg_at_100 value: 50.941 - type: ndcg_at_1000 value: 52.983000000000004 - type: ndcg_at_3 value: 40.366 - type: ndcg_at_5 value: 42.759 - type: precision_at_1 value: 34.937000000000005 - type: precision_at_10 value: 8.402 - type: precision_at_100 value: 1.2959999999999998 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 19.217000000000002 - type: precision_at_5 value: 13.725000000000001 - type: recall_at_1 value: 28.368 - type: recall_at_10 value: 58.5 - type: recall_at_100 value: 80.67999999999999 - type: recall_at_1000 value: 93.925 - type: recall_at_3 value: 43.956 - type: recall_at_5 value: 50.065000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 24.851 - type: map_at_10 value: 34.758 - type: map_at_100 value: 36.081 - type: map_at_1000 value: 36.205999999999996 - type: map_at_3 value: 31.678 - type: map_at_5 value: 33.398 - type: mrr_at_1 value: 31.279 - type: mrr_at_10 value: 40.138 - type: mrr_at_100 value: 41.005 - type: mrr_at_1000 value: 41.065000000000005 - type: mrr_at_3 value: 37.519000000000005 - type: mrr_at_5 value: 38.986 - type: ndcg_at_1 value: 31.279 - type: ndcg_at_10 value: 40.534 - type: ndcg_at_100 value: 46.093 - type: ndcg_at_1000 value: 48.59 - type: ndcg_at_3 value: 35.473 - type: ndcg_at_5 value: 37.801 - type: precision_at_1 value: 31.279 - type: precision_at_10 value: 7.477 - type: precision_at_100 value: 1.2 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 17.047 - type: precision_at_5 value: 12.306000000000001 - type: recall_at_1 value: 24.851 - type: recall_at_10 value: 52.528 - type: recall_at_100 value: 76.198 - type: recall_at_1000 value: 93.12 - type: recall_at_3 value: 38.257999999999996 - type: recall_at_5 value: 44.440000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 25.289833333333334 - type: map_at_10 value: 34.379333333333335 - type: map_at_100 value: 35.56916666666666 - type: map_at_1000 value: 35.68633333333333 - type: map_at_3 value: 31.63916666666666 - type: map_at_5 value: 33.18383333333334 - type: mrr_at_1 value: 30.081749999999996 - type: mrr_at_10 value: 38.53658333333333 - type: mrr_at_100 value: 39.37825 - type: mrr_at_1000 value: 39.43866666666666 - type: mrr_at_3 value: 36.19025 - type: mrr_at_5 value: 37.519749999999995 - type: ndcg_at_1 value: 30.081749999999996 - type: ndcg_at_10 value: 39.62041666666667 - type: ndcg_at_100 value: 44.74825 - type: ndcg_at_1000 value: 47.11366666666667 - type: ndcg_at_3 value: 35.000499999999995 - type: ndcg_at_5 value: 37.19283333333333 - type: precision_at_1 value: 30.081749999999996 - type: precision_at_10 value: 6.940249999999999 - type: precision_at_100 value: 1.1164166666666668 - type: precision_at_1000 value: 0.15025000000000002 - type: precision_at_3 value: 16.110416666666666 - type: precision_at_5 value: 11.474416666666668 - type: recall_at_1 value: 25.289833333333334 - type: recall_at_10 value: 51.01591666666667 - type: recall_at_100 value: 73.55275000000002 - type: recall_at_1000 value: 90.02666666666667 - type: recall_at_3 value: 38.15208333333334 - type: recall_at_5 value: 43.78458333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 23.479 - type: map_at_10 value: 31.2 - type: map_at_100 value: 32.11 - type: map_at_1000 value: 32.214 - type: map_at_3 value: 29.093999999999998 - type: map_at_5 value: 30.415 - type: mrr_at_1 value: 26.840000000000003 - type: mrr_at_10 value: 34.153 - type: mrr_at_100 value: 34.971000000000004 - type: mrr_at_1000 value: 35.047 - type: mrr_at_3 value: 32.285000000000004 - type: mrr_at_5 value: 33.443 - type: ndcg_at_1 value: 26.840000000000003 - type: ndcg_at_10 value: 35.441 - type: ndcg_at_100 value: 40.150000000000006 - type: ndcg_at_1000 value: 42.74 - type: ndcg_at_3 value: 31.723000000000003 - type: ndcg_at_5 value: 33.71 - type: precision_at_1 value: 26.840000000000003 - type: precision_at_10 value: 5.552 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.804 - type: precision_at_5 value: 9.600999999999999 - type: recall_at_1 value: 23.479 - type: recall_at_10 value: 45.442 - type: recall_at_100 value: 67.465 - type: recall_at_1000 value: 86.53 - type: recall_at_3 value: 35.315999999999995 - type: recall_at_5 value: 40.253 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.887 - type: map_at_10 value: 23.805 - type: map_at_100 value: 24.804000000000002 - type: map_at_1000 value: 24.932000000000002 - type: map_at_3 value: 21.632 - type: map_at_5 value: 22.845 - type: mrr_at_1 value: 20.75 - type: mrr_at_10 value: 27.686 - type: mrr_at_100 value: 28.522 - type: mrr_at_1000 value: 28.605000000000004 - type: mrr_at_3 value: 25.618999999999996 - type: mrr_at_5 value: 26.723999999999997 - type: ndcg_at_1 value: 20.75 - type: ndcg_at_10 value: 28.233000000000004 - type: ndcg_at_100 value: 33.065 - type: ndcg_at_1000 value: 36.138999999999996 - type: ndcg_at_3 value: 24.361 - type: ndcg_at_5 value: 26.111 - type: precision_at_1 value: 20.75 - type: precision_at_10 value: 5.124 - 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type: dot_accuracy value: 85.08169363915086 - type: dot_ap value: 74.96808060965559 - type: dot_f1 value: 71.39685033990366 - type: dot_precision value: 64.16948111759288 - type: dot_recall value: 80.45888512473051 - type: euclidean_accuracy value: 85.84235650250321 - type: euclidean_ap value: 78.42045145247211 - type: euclidean_f1 value: 70.32669630775179 - type: euclidean_precision value: 70.6298050788227 - type: euclidean_recall value: 70.02617801047121 - type: manhattan_accuracy value: 85.86176116738464 - type: manhattan_ap value: 78.54012451558276 - type: manhattan_f1 value: 70.56508080693389 - type: manhattan_precision value: 69.39626293456413 - type: manhattan_recall value: 71.77394518016631 - type: max_accuracy value: 88.75305623471883 - type: max_ap value: 85.46387153880272 - type: max_f1 value: 77.91527673159008 --- ## Usage For usage instructions, refer to: https://github.com/Muennighoff/sgpt#asymmetric-semantic-search The model was trained with the command ```bash CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch examples/training/ms_marco/train_bi-encoder_mnrl.py --model_name bigscience/bloom-7b1 --train_batch_size 32 --eval_batch_size 16 --freezenonbias --specb --lr 4e-4 --wandb --wandbwatchlog gradients --pooling weightedmean --gradcache --chunksize 8 ``` ## Evaluation Results `{"ndcgs": {"sgpt-bloom-7b1-msmarco": {"scifact": {"NDCG@10": 0.71824}, "nfcorpus": {"NDCG@10": 0.35748}, "arguana": {"NDCG@10": 0.47281}, "scidocs": {"NDCG@10": 0.18435}, "fiqa": {"NDCG@10": 0.35736}, "cqadupstack": {"NDCG@10": 0.3708525}, "quora": {"NDCG@10": 0.74655}, "trec-covid": {"NDCG@10": 0.82731}, "webis-touche2020": {"NDCG@10": 0.2365}}}` See the evaluation folder or [MTEB](https://huggingface.co/spaces/mteb/leaderboard) for more results. ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 15600 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` The model uses BitFit, weighted-mean pooling & GradCache, for details see: https://arxiv.org/abs/2202.08904 **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { "lr": 0.0004 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: BloomModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```