--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu tags: - mteb model-index: - name: udever-bloom-7b1 results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 31.3788313486292 - type: cos_sim_spearman value: 31.87117445808444 - type: euclidean_pearson value: 30.66886666881808 - type: euclidean_spearman value: 31.28368681542041 - type: manhattan_pearson value: 30.679984531432936 - type: manhattan_spearman value: 31.22208726593753 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 38.403248424956764 - type: cos_sim_spearman value: 38.798254852046504 - type: euclidean_pearson value: 41.154981142995084 - type: euclidean_spearman value: 38.73503172297125 - type: manhattan_pearson value: 41.20226384035751 - type: manhattan_spearman value: 38.77085234568287 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.11940298507463 - type: ap value: 35.692863077186466 - type: f1 value: 67.02733552778966 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.885175 - type: ap value: 84.75400736514149 - type: f1 value: 88.85806225869703 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.202 - type: f1 value: 42.63847450850621 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 25.676 - type: map_at_10 value: 42.539 - type: map_at_100 value: 43.383 - type: map_at_1000 value: 43.39 - type: map_at_3 value: 36.996 - type: map_at_5 value: 40.175 - type: mrr_at_1 value: 26.387 - type: mrr_at_10 value: 42.792 - type: mrr_at_100 value: 43.637 - type: mrr_at_1000 value: 43.644 - type: mrr_at_3 value: 37.21 - type: mrr_at_5 value: 40.407 - type: ndcg_at_1 value: 25.676 - type: ndcg_at_10 value: 52.207 - type: ndcg_at_100 value: 55.757999999999996 - type: ndcg_at_1000 value: 55.913999999999994 - type: ndcg_at_3 value: 40.853 - type: ndcg_at_5 value: 46.588 - type: precision_at_1 value: 25.676 - type: precision_at_10 value: 8.314 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 17.354 - type: precision_at_5 value: 13.200999999999999 - type: recall_at_1 value: 25.676 - type: recall_at_10 value: 83.14399999999999 - type: recall_at_100 value: 98.506 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 52.063 - type: recall_at_5 value: 66.003 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.66024127046263 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.418361433667336 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.60189642383972 - type: mrr value: 75.26678538451391 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.85884182572595 - type: cos_sim_spearman value: 85.5242378844044 - type: euclidean_pearson value: 85.37705073557146 - type: euclidean_spearman value: 84.65132642825964 - type: manhattan_pearson value: 85.42179213807349 - type: manhattan_spearman value: 84.6959057572829 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 47.81802155652125 - type: cos_sim_spearman value: 47.66691834501235 - type: euclidean_pearson value: 47.781824357030935 - type: euclidean_spearman value: 48.03322284408188 - type: manhattan_pearson value: 47.871159981038346 - type: manhattan_spearman value: 48.18240784527666 - 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: 88.29853862212944 - type: f1 value: 87.70994966904566 - type: precision value: 87.43152897902377 - type: recall value: 88.29853862212944 - 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: 98.6022452124147 - type: f1 value: 98.40597255851495 - type: precision value: 98.30875339349916 - type: recall value: 98.6022452124147 - 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: 79.64669206789054 - type: f1 value: 78.74831345770036 - type: precision value: 78.33899087865143 - type: recall value: 79.64669206789054 - 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: 98.78883622959452 - type: f1 value: 98.7712831314727 - type: precision value: 98.76250658241179 - type: recall value: 98.78883622959452 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.36363636363637 - type: f1 value: 85.33381612267455 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.54276849354455 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.18953191097238 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 36.00041315364012 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 36.35255790689628 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 70.54141681949504 - type: mrr value: 74.81400793650795 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 71.3534829537025 - type: mrr value: 75.85095238095238 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.5 - type: map_at_10 value: 43.37 - type: map_at_100 value: 44.926 - type: map_at_1000 value: 45.047 - type: map_at_3 value: 40.083999999999996 - type: map_at_5 value: 41.71 - type: mrr_at_1 value: 40.343 - type: mrr_at_10 value: 49.706 - type: mrr_at_100 value: 50.470000000000006 - type: mrr_at_1000 value: 50.515 - type: mrr_at_3 value: 47.306 - type: mrr_at_5 value: 48.379 - type: ndcg_at_1 value: 40.343 - type: ndcg_at_10 value: 49.461 - type: ndcg_at_100 value: 55.084999999999994 - type: ndcg_at_1000 value: 56.994 - type: ndcg_at_3 value: 44.896 - type: ndcg_at_5 value: 46.437 - type: precision_at_1 value: 40.343 - type: precision_at_10 value: 9.27 - type: precision_at_100 value: 1.5190000000000001 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 21.412 - type: precision_at_5 value: 15.021 - type: recall_at_1 value: 32.5 - type: recall_at_10 value: 60.857000000000006 - type: recall_at_100 value: 83.761 - type: recall_at_1000 value: 96.003 - type: recall_at_3 value: 46.675 - type: recall_at_5 value: 51.50900000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.931 - type: map_at_10 value: 35.769 - type: map_at_100 value: 36.8 - type: map_at_1000 value: 36.925999999999995 - type: map_at_3 value: 33.068999999999996 - type: map_at_5 value: 34.615 - type: mrr_at_1 value: 34.013 - type: mrr_at_10 value: 41.293 - type: mrr_at_100 value: 41.945 - type: mrr_at_1000 value: 42.002 - type: mrr_at_3 value: 39.204 - type: mrr_at_5 value: 40.436 - type: ndcg_at_1 value: 34.013 - type: ndcg_at_10 value: 40.935 - type: ndcg_at_100 value: 44.879999999999995 - type: ndcg_at_1000 value: 47.342 - type: ndcg_at_3 value: 37.071 - type: ndcg_at_5 value: 38.903 - type: precision_at_1 value: 34.013 - type: precision_at_10 value: 7.617999999999999 - type: precision_at_100 value: 1.185 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 17.855999999999998 - type: precision_at_5 value: 12.65 - type: recall_at_1 value: 26.931 - type: recall_at_10 value: 50.256 - type: recall_at_100 value: 67.026 - type: recall_at_1000 value: 83.138 - type: recall_at_3 value: 38.477 - type: recall_at_5 value: 43.784 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.474000000000004 - type: map_at_10 value: 50.486 - type: map_at_100 value: 51.620999999999995 - type: map_at_1000 value: 51.675000000000004 - type: map_at_3 value: 47.64 - type: map_at_5 value: 49.187999999999995 - type: mrr_at_1 value: 43.824000000000005 - type: mrr_at_10 value: 53.910000000000004 - type: mrr_at_100 value: 54.601 - type: mrr_at_1000 value: 54.632000000000005 - type: mrr_at_3 value: 51.578 - type: mrr_at_5 value: 52.922999999999995 - type: ndcg_at_1 value: 43.824000000000005 - type: ndcg_at_10 value: 56.208000000000006 - type: ndcg_at_100 value: 60.624 - type: ndcg_at_1000 value: 61.78 - type: ndcg_at_3 value: 51.27 - type: ndcg_at_5 value: 53.578 - type: precision_at_1 value: 43.824000000000005 - type: precision_at_10 value: 8.978 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 22.884 - type: precision_at_5 value: 15.498000000000001 - type: recall_at_1 value: 38.474000000000004 - type: recall_at_10 value: 69.636 - type: recall_at_100 value: 88.563 - type: recall_at_1000 value: 96.86200000000001 - type: recall_at_3 value: 56.347 - type: recall_at_5 value: 61.980000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.13 - type: map_at_10 value: 31.892 - type: map_at_100 value: 32.938 - type: map_at_1000 value: 33.025999999999996 - type: map_at_3 value: 29.072 - type: map_at_5 value: 30.775000000000002 - type: mrr_at_1 value: 25.197999999999997 - type: mrr_at_10 value: 34.224 - type: mrr_at_100 value: 35.149 - type: mrr_at_1000 value: 35.215999999999994 - type: mrr_at_3 value: 31.563000000000002 - type: mrr_at_5 value: 33.196 - type: ndcg_at_1 value: 25.197999999999997 - type: ndcg_at_10 value: 37.117 - type: ndcg_at_100 value: 42.244 - type: ndcg_at_1000 value: 44.432 - type: ndcg_at_3 value: 31.604 - type: ndcg_at_5 value: 34.543 - type: precision_at_1 value: 25.197999999999997 - type: precision_at_10 value: 5.876 - type: precision_at_100 value: 0.886 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.672 - type: precision_at_5 value: 9.831 - type: recall_at_1 value: 23.13 - type: recall_at_10 value: 50.980000000000004 - type: recall_at_100 value: 74.565 - type: recall_at_1000 value: 90.938 - type: recall_at_3 value: 36.038 - type: recall_at_5 value: 43.326 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.317 - type: map_at_10 value: 24.517 - type: map_at_100 value: 25.771 - type: map_at_1000 value: 25.915 - type: map_at_3 value: 22.332 - type: map_at_5 value: 23.526 - type: mrr_at_1 value: 21.766 - type: mrr_at_10 value: 29.096 - type: mrr_at_100 value: 30.165 - type: mrr_at_1000 value: 30.253000000000004 - type: mrr_at_3 value: 27.114 - type: mrr_at_5 value: 28.284 - type: ndcg_at_1 value: 21.766 - type: ndcg_at_10 value: 29.060999999999996 - type: ndcg_at_100 value: 35.107 - type: ndcg_at_1000 value: 38.339 - type: ndcg_at_3 value: 25.121 - type: ndcg_at_5 value: 26.953 - type: precision_at_1 value: 21.766 - type: precision_at_10 value: 5.274 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.816 - type: precision_at_5 value: 8.433 - type: recall_at_1 value: 17.317 - type: recall_at_10 value: 38.379999999999995 - type: recall_at_100 value: 64.792 - type: recall_at_1000 value: 87.564 - type: recall_at_3 value: 27.737000000000002 - type: recall_at_5 value: 32.340999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.876 - type: map_at_10 value: 40.02 - type: map_at_100 value: 41.367 - type: map_at_1000 value: 41.482 - type: map_at_3 value: 36.651 - type: map_at_5 value: 38.411 - type: mrr_at_1 value: 35.804 - type: mrr_at_10 value: 45.946999999999996 - type: mrr_at_100 value: 46.696 - type: mrr_at_1000 value: 46.741 - type: mrr_at_3 value: 43.118 - type: mrr_at_5 value: 44.74 - type: ndcg_at_1 value: 35.804 - type: ndcg_at_10 value: 46.491 - type: ndcg_at_100 value: 51.803 - type: ndcg_at_1000 value: 53.845 - type: ndcg_at_3 value: 40.97 - type: ndcg_at_5 value: 43.431 - type: precision_at_1 value: 35.804 - type: precision_at_10 value: 8.595 - type: precision_at_100 value: 1.312 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 19.634 - type: precision_at_5 value: 13.879 - type: recall_at_1 value: 28.876 - type: recall_at_10 value: 59.952000000000005 - type: recall_at_100 value: 81.978 - type: recall_at_1000 value: 95.03399999999999 - type: recall_at_3 value: 44.284 - type: recall_at_5 value: 50.885999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.238 - type: map_at_10 value: 34.276 - type: map_at_100 value: 35.65 - type: map_at_1000 value: 35.769 - type: map_at_3 value: 31.227 - type: map_at_5 value: 33.046 - type: mrr_at_1 value: 30.137000000000004 - type: mrr_at_10 value: 39.473 - type: mrr_at_100 value: 40.400999999999996 - type: mrr_at_1000 value: 40.455000000000005 - type: mrr_at_3 value: 36.891 - type: mrr_at_5 value: 38.391999999999996 - type: ndcg_at_1 value: 30.137000000000004 - type: ndcg_at_10 value: 40.08 - type: ndcg_at_100 value: 46.01 - type: ndcg_at_1000 value: 48.36 - type: ndcg_at_3 value: 35.163 - type: ndcg_at_5 value: 37.583 - type: precision_at_1 value: 30.137000000000004 - type: precision_at_10 value: 7.466 - type: precision_at_100 value: 1.228 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.122999999999998 - type: precision_at_5 value: 12.283 - type: recall_at_1 value: 24.238 - type: recall_at_10 value: 52.078 - type: recall_at_100 value: 77.643 - type: recall_at_1000 value: 93.49199999999999 - type: recall_at_3 value: 38.161 - type: recall_at_5 value: 44.781 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.915250000000004 - type: map_at_10 value: 33.98191666666666 - type: map_at_100 value: 35.19166666666667 - type: map_at_1000 value: 35.30983333333333 - type: map_at_3 value: 31.27391666666666 - type: map_at_5 value: 32.74366666666666 - type: mrr_at_1 value: 29.800749999999994 - type: mrr_at_10 value: 38.235749999999996 - type: mrr_at_100 value: 39.10616666666667 - type: mrr_at_1000 value: 39.166583333333335 - type: mrr_at_3 value: 35.91033333333334 - type: mrr_at_5 value: 37.17766666666667 - type: ndcg_at_1 value: 29.800749999999994 - type: ndcg_at_10 value: 39.287833333333325 - type: ndcg_at_100 value: 44.533833333333334 - type: ndcg_at_1000 value: 46.89608333333333 - type: ndcg_at_3 value: 34.676 - type: ndcg_at_5 value: 36.75208333333333 - type: precision_at_1 value: 29.800749999999994 - type: precision_at_10 value: 6.9134166666666665 - type: precision_at_100 value: 1.1206666666666665 - type: precision_at_1000 value: 0.15116666666666667 - type: precision_at_3 value: 16.069083333333335 - type: precision_at_5 value: 11.337916666666668 - type: recall_at_1 value: 24.915250000000004 - type: recall_at_10 value: 50.86333333333334 - type: recall_at_100 value: 73.85574999999999 - type: recall_at_1000 value: 90.24041666666666 - type: recall_at_3 value: 37.80116666666666 - type: recall_at_5 value: 43.263 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.853 - type: map_at_10 value: 30.349999999999998 - type: map_at_100 value: 31.341 - type: map_at_1000 value: 31.44 - type: map_at_3 value: 28.294999999999998 - type: map_at_5 value: 29.412 - type: mrr_at_1 value: 25.919999999999998 - type: mrr_at_10 value: 33.194 - type: mrr_at_100 value: 34.071 - type: mrr_at_1000 value: 34.136 - type: mrr_at_3 value: 31.391000000000002 - type: mrr_at_5 value: 32.311 - type: ndcg_at_1 value: 25.919999999999998 - type: ndcg_at_10 value: 34.691 - type: ndcg_at_100 value: 39.83 - type: ndcg_at_1000 value: 42.193000000000005 - type: ndcg_at_3 value: 30.91 - type: ndcg_at_5 value: 32.634 - type: precision_at_1 value: 25.919999999999998 - type: precision_at_10 value: 5.521 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 13.547999999999998 - type: precision_at_5 value: 9.293999999999999 - type: recall_at_1 value: 22.853 - type: recall_at_10 value: 45.145 - type: recall_at_100 value: 69.158 - type: recall_at_1000 value: 86.354 - type: recall_at_3 value: 34.466 - type: recall_at_5 value: 39.044000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.151 - type: map_at_10 value: 23.674 - type: map_at_100 value: 24.738 - type: map_at_1000 value: 24.864 - type: map_at_3 value: 21.514 - type: map_at_5 value: 22.695 - type: mrr_at_1 value: 20.991 - type: mrr_at_10 value: 27.612 - type: mrr_at_100 value: 28.526 - type: mrr_at_1000 value: 28.603 - type: mrr_at_3 value: 25.618999999999996 - type: mrr_at_5 value: 26.674 - type: ndcg_at_1 value: 20.991 - type: ndcg_at_10 value: 27.983000000000004 - type: ndcg_at_100 value: 33.190999999999995 - type: ndcg_at_1000 value: 36.172 - type: ndcg_at_3 value: 24.195 - type: ndcg_at_5 value: 25.863999999999997 - type: precision_at_1 value: 20.991 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.8959999999999999 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 11.402 - type: precision_at_5 value: 8.197000000000001 - type: recall_at_1 value: 17.151 - type: recall_at_10 value: 37.025000000000006 - type: recall_at_100 value: 60.787 - type: recall_at_1000 value: 82.202 - type: recall_at_3 value: 26.19 - type: recall_at_5 value: 30.657 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.463 - type: map_at_10 value: 34.372 - type: map_at_100 value: 35.475 - type: map_at_1000 value: 35.582 - type: map_at_3 value: 31.791000000000004 - type: map_at_5 value: 33.292 - type: mrr_at_1 value: 30.784 - type: mrr_at_10 value: 38.948 - type: mrr_at_100 value: 39.792 - type: mrr_at_1000 value: 39.857 - type: mrr_at_3 value: 36.614000000000004 - type: mrr_at_5 value: 37.976 - type: ndcg_at_1 value: 30.784 - type: ndcg_at_10 value: 39.631 - type: ndcg_at_100 value: 44.747 - type: ndcg_at_1000 value: 47.172 - type: ndcg_at_3 value: 34.976 - type: ndcg_at_5 value: 37.241 - type: precision_at_1 value: 30.784 - type: precision_at_10 value: 6.622999999999999 - type: precision_at_100 value: 1.04 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 16.014 - type: precision_at_5 value: 11.286999999999999 - type: recall_at_1 value: 25.463 - type: recall_at_10 value: 51.23799999999999 - type: recall_at_100 value: 73.4 - type: recall_at_1000 value: 90.634 - type: recall_at_3 value: 38.421 - type: recall_at_5 value: 44.202999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.714 - type: map_at_10 value: 32.712 - type: map_at_100 value: 34.337 - type: map_at_1000 value: 34.556 - type: map_at_3 value: 29.747 - type: map_at_5 value: 31.208000000000002 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.589 - type: mrr_at_100 value: 38.638 - type: mrr_at_1000 value: 38.692 - type: mrr_at_3 value: 35.079 - type: mrr_at_5 value: 36.265 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 38.681 - type: ndcg_at_100 value: 44.775999999999996 - type: ndcg_at_1000 value: 47.354 - type: ndcg_at_3 value: 33.888 - type: ndcg_at_5 value: 35.854 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.489999999999999 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 11.66 - type: recall_at_1 value: 23.714 - type: recall_at_10 value: 50.324000000000005 - type: recall_at_100 value: 77.16 - type: recall_at_1000 value: 93.186 - type: recall_at_3 value: 36.356 - type: recall_at_5 value: 41.457 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.336 - type: map_at_10 value: 26.345000000000002 - type: map_at_100 value: 27.336 - type: map_at_1000 value: 27.436 - type: map_at_3 value: 23.865 - type: map_at_5 value: 25.046000000000003 - type: mrr_at_1 value: 19.778000000000002 - type: mrr_at_10 value: 27.837 - type: mrr_at_100 value: 28.82 - type: mrr_at_1000 value: 28.897000000000002 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.556 - type: ndcg_at_1 value: 19.778000000000002 - type: ndcg_at_10 value: 31.115 - type: ndcg_at_100 value: 36.109 - type: ndcg_at_1000 value: 38.769999999999996 - type: ndcg_at_3 value: 26.048 - type: ndcg_at_5 value: 28.004 - type: precision_at_1 value: 19.778000000000002 - type: precision_at_10 value: 5.157 - type: precision_at_100 value: 0.808 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.022 - type: recall_at_1 value: 18.336 - type: recall_at_10 value: 44.489000000000004 - type: recall_at_100 value: 67.43599999999999 - type: recall_at_1000 value: 87.478 - type: recall_at_3 value: 30.462 - type: recall_at_5 value: 35.188 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.747 - type: map_at_10 value: 18.625 - type: map_at_100 value: 20.465 - type: map_at_1000 value: 20.639 - type: map_at_3 value: 15.57 - type: map_at_5 value: 17.089 - type: mrr_at_1 value: 24.169 - type: mrr_at_10 value: 35.96 - type: mrr_at_100 value: 36.888 - type: mrr_at_1000 value: 36.931999999999995 - type: mrr_at_3 value: 32.443 - type: mrr_at_5 value: 34.433 - type: ndcg_at_1 value: 24.169 - type: ndcg_at_10 value: 26.791999999999998 - type: ndcg_at_100 value: 34.054 - type: ndcg_at_1000 value: 37.285000000000004 - type: ndcg_at_3 value: 21.636 - type: ndcg_at_5 value: 23.394000000000002 - type: precision_at_1 value: 24.169 - type: precision_at_10 value: 8.476 - type: precision_at_100 value: 1.6209999999999998 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 16.156000000000002 - type: precision_at_5 value: 12.520999999999999 - type: recall_at_1 value: 10.747 - type: recall_at_10 value: 32.969 - type: recall_at_100 value: 57.99999999999999 - type: recall_at_1000 value: 76.12299999999999 - type: recall_at_3 value: 20.315 - type: recall_at_5 value: 25.239 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 14.751 - type: map_at_10 value: 22.03 - type: map_at_100 value: 23.471 - type: map_at_1000 value: 23.644000000000002 - type: map_at_3 value: 19.559 - type: map_at_5 value: 20.863 - type: mrr_at_1 value: 23.581 - type: mrr_at_10 value: 29.863 - type: mrr_at_100 value: 30.839 - type: mrr_at_1000 value: 30.925000000000004 - type: mrr_at_3 value: 27.894000000000002 - type: mrr_at_5 value: 28.965999999999998 - type: ndcg_at_1 value: 23.581 - type: ndcg_at_10 value: 26.996 - type: ndcg_at_100 value: 33.537 - type: ndcg_at_1000 value: 37.307 - type: ndcg_at_3 value: 23.559 - type: ndcg_at_5 value: 24.839 - type: precision_at_1 value: 23.581 - type: precision_at_10 value: 6.209 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 13.62 - type: precision_at_5 value: 9.882 - type: recall_at_1 value: 14.751 - type: recall_at_10 value: 34.075 - type: recall_at_100 value: 61.877 - type: recall_at_1000 value: 88.212 - type: recall_at_3 value: 23.519000000000002 - type: recall_at_5 value: 27.685 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 76.36800962116656 - type: cos_sim_ap value: 85.14376065556142 - type: cos_sim_f1 value: 77.81474723623485 - type: cos_sim_precision value: 71.92460317460318 - type: cos_sim_recall value: 84.75566986205284 - type: dot_accuracy value: 71.94227300060132 - type: dot_ap value: 79.03676891584456 - type: dot_f1 value: 74.95833333333334 - type: dot_precision value: 67.59346233327072 - type: dot_recall value: 84.12438625204582 - type: euclidean_accuracy value: 76.043295249549 - type: euclidean_ap value: 85.28765360616536 - type: euclidean_f1 value: 78.01733248784612 - type: euclidean_precision value: 71.1861137897782 - type: euclidean_recall value: 86.29880757540333 - type: manhattan_accuracy value: 76.17558628983764 - type: manhattan_ap value: 85.52739323094916 - type: manhattan_f1 value: 78.30788804071246 - type: manhattan_precision value: 71.63918525703201 - type: manhattan_recall value: 86.34556932429273 - type: max_accuracy value: 76.36800962116656 - type: max_ap value: 85.52739323094916 - type: max_f1 value: 78.30788804071246 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 56.164 - type: map_at_10 value: 64.575 - type: map_at_100 value: 65.098 - type: map_at_1000 value: 65.118 - type: map_at_3 value: 62.329 - type: map_at_5 value: 63.535 - type: mrr_at_1 value: 56.269999999999996 - type: mrr_at_10 value: 64.63600000000001 - type: mrr_at_100 value: 65.14 - type: mrr_at_1000 value: 65.16 - type: mrr_at_3 value: 62.522 - type: mrr_at_5 value: 63.57000000000001 - type: ndcg_at_1 value: 56.269999999999996 - type: ndcg_at_10 value: 68.855 - type: ndcg_at_100 value: 71.47099999999999 - type: ndcg_at_1000 value: 72.02499999999999 - type: ndcg_at_3 value: 64.324 - type: ndcg_at_5 value: 66.417 - type: precision_at_1 value: 56.269999999999996 - type: precision_at_10 value: 8.303 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.427999999999997 - type: precision_at_5 value: 15.09 - type: recall_at_1 value: 56.164 - type: recall_at_10 value: 82.271 - type: recall_at_100 value: 94.626 - type: recall_at_1000 value: 99.05199999999999 - type: recall_at_3 value: 69.94200000000001 - type: recall_at_5 value: 74.947 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.686 - type: map_at_10 value: 17.766000000000002 - type: map_at_100 value: 23.507 - type: map_at_1000 value: 24.757 - type: map_at_3 value: 13.238 - type: map_at_5 value: 15.161 - type: mrr_at_1 value: 65.25 - type: mrr_at_10 value: 72.88 - type: mrr_at_100 value: 73.246 - type: mrr_at_1000 value: 73.261 - type: mrr_at_3 value: 71.542 - type: mrr_at_5 value: 72.392 - type: ndcg_at_1 value: 53.75 - type: ndcg_at_10 value: 37.623 - type: ndcg_at_100 value: 40.302 - type: ndcg_at_1000 value: 47.471999999999994 - type: ndcg_at_3 value: 43.324 - type: ndcg_at_5 value: 39.887 - type: precision_at_1 value: 65.25 - type: precision_at_10 value: 28.749999999999996 - type: precision_at_100 value: 8.34 - type: precision_at_1000 value: 1.703 - type: precision_at_3 value: 46.583000000000006 - type: precision_at_5 value: 38.0 - type: recall_at_1 value: 8.686 - type: recall_at_10 value: 22.966 - type: recall_at_100 value: 44.3 - type: recall_at_1000 value: 67.77499999999999 - type: recall_at_3 value: 14.527999999999999 - type: recall_at_5 value: 17.617 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 22.439 - type: map_at_10 value: 68.484 - type: map_at_100 value: 71.67999999999999 - type: map_at_1000 value: 71.761 - type: map_at_3 value: 46.373999999999995 - type: map_at_5 value: 58.697 - type: mrr_at_1 value: 80.65 - type: mrr_at_10 value: 86.53 - type: mrr_at_100 value: 86.624 - type: mrr_at_1000 value: 86.631 - type: mrr_at_3 value: 85.95 - type: mrr_at_5 value: 86.297 - type: ndcg_at_1 value: 80.65 - type: ndcg_at_10 value: 78.075 - type: ndcg_at_100 value: 82.014 - type: ndcg_at_1000 value: 82.903 - type: ndcg_at_3 value: 75.785 - type: ndcg_at_5 value: 74.789 - type: precision_at_1 value: 80.65 - type: precision_at_10 value: 38.425 - type: precision_at_100 value: 4.62 - type: precision_at_1000 value: 0.483 - type: precision_at_3 value: 68.25 - type: precision_at_5 value: 57.92 - type: recall_at_1 value: 22.439 - type: recall_at_10 value: 80.396 - type: recall_at_100 value: 92.793 - type: recall_at_1000 value: 97.541 - type: recall_at_3 value: 49.611 - type: recall_at_5 value: 65.065 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 43.9 - type: map_at_10 value: 53.394 - type: map_at_100 value: 54.078 - type: map_at_1000 value: 54.105000000000004 - type: map_at_3 value: 50.583 - type: map_at_5 value: 52.443 - type: mrr_at_1 value: 43.9 - type: mrr_at_10 value: 53.394 - type: mrr_at_100 value: 54.078 - type: mrr_at_1000 value: 54.105000000000004 - type: mrr_at_3 value: 50.583 - type: mrr_at_5 value: 52.443 - type: ndcg_at_1 value: 43.9 - type: ndcg_at_10 value: 58.341 - type: ndcg_at_100 value: 61.753 - type: ndcg_at_1000 value: 62.525 - type: ndcg_at_3 value: 52.699 - type: ndcg_at_5 value: 56.042 - type: precision_at_1 value: 43.9 - type: precision_at_10 value: 7.3999999999999995 - type: precision_at_100 value: 0.901 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 19.6 - type: precision_at_5 value: 13.38 - type: recall_at_1 value: 43.9 - type: recall_at_10 value: 74.0 - type: recall_at_100 value: 90.10000000000001 - type: recall_at_1000 value: 96.3 - type: recall_at_3 value: 58.8 - type: recall_at_5 value: 66.9 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.765 - type: f1 value: 44.2791193129597 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 56.89999999999999 - type: map_at_10 value: 68.352 - type: map_at_100 value: 68.768 - type: map_at_1000 value: 68.782 - type: map_at_3 value: 66.27300000000001 - type: map_at_5 value: 67.67699999999999 - type: mrr_at_1 value: 61.476 - type: mrr_at_10 value: 72.662 - type: mrr_at_100 value: 72.993 - type: mrr_at_1000 value: 72.99799999999999 - type: mrr_at_3 value: 70.75200000000001 - type: mrr_at_5 value: 72.056 - type: ndcg_at_1 value: 61.476 - type: ndcg_at_10 value: 73.98400000000001 - type: ndcg_at_100 value: 75.744 - type: ndcg_at_1000 value: 76.036 - type: ndcg_at_3 value: 70.162 - type: ndcg_at_5 value: 72.482 - type: precision_at_1 value: 61.476 - type: precision_at_10 value: 9.565 - type: precision_at_100 value: 1.054 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 27.943 - type: precision_at_5 value: 18.056 - type: recall_at_1 value: 56.89999999999999 - type: recall_at_10 value: 87.122 - type: recall_at_100 value: 94.742 - type: recall_at_1000 value: 96.70100000000001 - type: recall_at_3 value: 76.911 - type: recall_at_5 value: 82.607 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 17.610999999999997 - type: map_at_10 value: 29.12 - type: map_at_100 value: 30.958000000000002 - type: map_at_1000 value: 31.151 - type: map_at_3 value: 25.369000000000003 - type: map_at_5 value: 27.445000000000004 - type: mrr_at_1 value: 35.185 - type: mrr_at_10 value: 44.533 - type: mrr_at_100 value: 45.385 - type: mrr_at_1000 value: 45.432 - type: mrr_at_3 value: 42.258 - type: mrr_at_5 value: 43.608999999999995 - type: ndcg_at_1 value: 35.185 - type: ndcg_at_10 value: 36.696 - type: ndcg_at_100 value: 43.491 - type: ndcg_at_1000 value: 46.800000000000004 - type: ndcg_at_3 value: 33.273 - type: ndcg_at_5 value: 34.336 - type: precision_at_1 value: 35.185 - type: precision_at_10 value: 10.309 - type: precision_at_100 value: 1.719 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 22.479 - type: precision_at_5 value: 16.481 - type: recall_at_1 value: 17.610999999999997 - type: recall_at_10 value: 43.29 - type: recall_at_100 value: 68.638 - type: recall_at_1000 value: 88.444 - type: recall_at_3 value: 30.303 - type: recall_at_5 value: 35.856 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 34.18 - type: map_at_10 value: 47.753 - type: map_at_100 value: 48.522 - type: map_at_1000 value: 48.596000000000004 - type: map_at_3 value: 45.222 - type: map_at_5 value: 46.793 - type: mrr_at_1 value: 68.35900000000001 - type: mrr_at_10 value: 74.503 - type: mrr_at_100 value: 74.811 - type: mrr_at_1000 value: 74.82799999999999 - type: mrr_at_3 value: 73.347 - type: mrr_at_5 value: 74.06700000000001 - type: ndcg_at_1 value: 68.35900000000001 - type: ndcg_at_10 value: 56.665 - type: ndcg_at_100 value: 59.629 - type: ndcg_at_1000 value: 61.222 - type: ndcg_at_3 value: 52.81400000000001 - type: ndcg_at_5 value: 54.94 - type: precision_at_1 value: 68.35900000000001 - type: precision_at_10 value: 11.535 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 32.784 - type: precision_at_5 value: 21.348 - type: recall_at_1 value: 34.18 - type: recall_at_10 value: 57.677 - type: recall_at_100 value: 69.379 - type: recall_at_1000 value: 80.061 - type: recall_at_3 value: 49.175999999999995 - type: recall_at_5 value: 53.369 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 46.23316660253944 - type: f1 value: 39.09397722262806 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 78.46119999999999 - type: ap value: 72.53477126781094 - type: f1 value: 78.28701752379332 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 84.16510318949344 - type: ap value: 50.10324581565756 - type: f1 value: 78.34748161287605 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 68.71925879533819 - type: cos_sim_spearman value: 75.33926640820977 - type: euclidean_pearson value: 74.59557932790653 - type: euclidean_spearman value: 75.76006440878783 - type: manhattan_pearson value: 74.7461963483351 - type: manhattan_spearman value: 75.87111519308131 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 66.249 - type: map_at_10 value: 75.236 - type: map_at_100 value: 75.581 - type: map_at_1000 value: 75.593 - type: map_at_3 value: 73.463 - type: map_at_5 value: 74.602 - type: mrr_at_1 value: 68.42399999999999 - type: mrr_at_10 value: 75.81099999999999 - type: mrr_at_100 value: 76.115 - type: mrr_at_1000 value: 76.126 - type: mrr_at_3 value: 74.26899999999999 - type: mrr_at_5 value: 75.24300000000001 - type: ndcg_at_1 value: 68.42399999999999 - type: ndcg_at_10 value: 78.81700000000001 - type: ndcg_at_100 value: 80.379 - type: ndcg_at_1000 value: 80.667 - type: ndcg_at_3 value: 75.476 - type: ndcg_at_5 value: 77.38199999999999 - type: precision_at_1 value: 68.42399999999999 - type: precision_at_10 value: 9.491 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.352 - type: precision_at_5 value: 18.043 - type: recall_at_1 value: 66.249 - type: recall_at_10 value: 89.238 - type: recall_at_100 value: 96.319 - type: recall_at_1000 value: 98.524 - type: recall_at_3 value: 80.438 - type: recall_at_5 value: 84.95 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 23.083000000000002 - type: map_at_10 value: 35.251 - type: map_at_100 value: 36.461 - type: map_at_1000 value: 36.507 - type: map_at_3 value: 31.474999999999998 - type: map_at_5 value: 33.658 - type: mrr_at_1 value: 23.724999999999998 - type: mrr_at_10 value: 35.88 - type: mrr_at_100 value: 37.021 - type: mrr_at_1000 value: 37.062 - type: mrr_at_3 value: 32.159 - type: mrr_at_5 value: 34.325 - type: ndcg_at_1 value: 23.724999999999998 - type: ndcg_at_10 value: 42.018 - type: ndcg_at_100 value: 47.764 - type: ndcg_at_1000 value: 48.916 - type: ndcg_at_3 value: 34.369 - type: ndcg_at_5 value: 38.266 - type: precision_at_1 value: 23.724999999999998 - type: precision_at_10 value: 6.553000000000001 - type: precision_at_100 value: 0.942 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.532 - type: precision_at_5 value: 10.696 - type: recall_at_1 value: 23.083000000000002 - type: recall_at_10 value: 62.739 - type: recall_at_100 value: 89.212 - type: recall_at_1000 value: 97.991 - type: recall_at_3 value: 42.064 - type: recall_at_5 value: 51.417 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.43365253077975 - type: f1 value: 93.07455671032345 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 71.72822617419061 - type: f1 value: 55.6093871673643 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.03765971755212 - type: f1 value: 70.88235592002572 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.86281102891728 - type: f1 value: 77.15496923811003 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 41.8 - type: map_at_10 value: 46.993 - type: map_at_100 value: 47.534 - type: map_at_1000 value: 47.587 - type: map_at_3 value: 45.717 - type: map_at_5 value: 46.357 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 47.093 - type: mrr_at_100 value: 47.634 - type: mrr_at_1000 value: 47.687000000000005 - type: mrr_at_3 value: 45.817 - type: mrr_at_5 value: 46.457 - type: ndcg_at_1 value: 41.8 - type: ndcg_at_10 value: 49.631 - type: ndcg_at_100 value: 52.53 - type: ndcg_at_1000 value: 54.238 - type: ndcg_at_3 value: 46.949000000000005 - type: ndcg_at_5 value: 48.102000000000004 - type: precision_at_1 value: 41.8 - type: precision_at_10 value: 5.800000000000001 - type: precision_at_100 value: 0.722 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 16.833000000000002 - type: precision_at_5 value: 10.66 - type: recall_at_1 value: 41.8 - type: recall_at_10 value: 57.99999999999999 - type: recall_at_100 value: 72.2 - type: recall_at_1000 value: 86.3 - type: recall_at_3 value: 50.5 - type: recall_at_5 value: 53.300000000000004 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.949060810392886 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.87339864059011 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.217934626189926 - type: mrr value: 32.27509143911496 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 26.691638884089574 - type: mrr value: 25.15674603174603 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 68.35666666666667 - type: f1 value: 68.30294399725629 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.759 - type: map_at_10 value: 13.425999999999998 - type: map_at_100 value: 16.988 - type: map_at_1000 value: 18.512 - type: map_at_3 value: 9.737 - type: map_at_5 value: 11.558 - type: mrr_at_1 value: 48.297000000000004 - type: mrr_at_10 value: 56.788000000000004 - type: mrr_at_100 value: 57.306000000000004 - type: mrr_at_1000 value: 57.349000000000004 - type: mrr_at_3 value: 54.386 - type: mrr_at_5 value: 56.135000000000005 - type: ndcg_at_1 value: 46.285 - type: ndcg_at_10 value: 36.016 - type: ndcg_at_100 value: 32.984 - type: ndcg_at_1000 value: 42.093 - type: ndcg_at_3 value: 41.743 - type: ndcg_at_5 value: 39.734 - type: precision_at_1 value: 48.297000000000004 - type: precision_at_10 value: 26.779999999999998 - type: precision_at_100 value: 8.505 - type: precision_at_1000 value: 2.1420000000000003 - type: precision_at_3 value: 39.422000000000004 - type: precision_at_5 value: 34.675 - type: recall_at_1 value: 5.759 - type: recall_at_10 value: 17.251 - type: recall_at_100 value: 33.323 - type: recall_at_1000 value: 66.759 - type: recall_at_3 value: 10.703 - type: recall_at_5 value: 13.808000000000002 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.696999999999996 - type: map_at_10 value: 46.099000000000004 - type: map_at_100 value: 47.143 - type: map_at_1000 value: 47.178 - type: map_at_3 value: 41.948 - type: map_at_5 value: 44.504 - type: mrr_at_1 value: 35.717999999999996 - type: mrr_at_10 value: 48.653 - type: mrr_at_100 value: 49.456 - type: mrr_at_1000 value: 49.479 - type: mrr_at_3 value: 45.283 - type: mrr_at_5 value: 47.422 - type: ndcg_at_1 value: 35.689 - type: ndcg_at_10 value: 53.312000000000005 - type: ndcg_at_100 value: 57.69 - type: ndcg_at_1000 value: 58.489000000000004 - type: ndcg_at_3 value: 45.678999999999995 - type: ndcg_at_5 value: 49.897000000000006 - type: precision_at_1 value: 35.689 - type: precision_at_10 value: 8.685 - type: precision_at_100 value: 1.111 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 20.558 - type: precision_at_5 value: 14.802999999999999 - type: recall_at_1 value: 31.696999999999996 - type: recall_at_10 value: 72.615 - type: recall_at_100 value: 91.563 - type: recall_at_1000 value: 97.52300000000001 - type: recall_at_3 value: 53.203 - type: recall_at_5 value: 62.836000000000006 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 67.94802382241473 - type: cos_sim_ap value: 72.1545049768353 - type: cos_sim_f1 value: 71.24658780709737 - type: cos_sim_precision value: 62.589928057553955 - type: cos_sim_recall value: 82.68215417106653 - type: dot_accuracy value: 63.56253383865729 - type: dot_ap value: 66.5298825401086 - type: dot_f1 value: 69.31953840031835 - type: dot_precision value: 55.61941251596424 - type: dot_recall value: 91.97465681098205 - type: euclidean_accuracy value: 69.46399566865186 - type: euclidean_ap value: 73.63177936887436 - type: euclidean_f1 value: 72.91028446389497 - type: euclidean_precision value: 62.25710014947683 - type: euclidean_recall value: 87.96198521647307 - type: manhattan_accuracy value: 69.89713048186248 - type: manhattan_ap value: 74.11555425121965 - type: manhattan_f1 value: 72.8923476005188 - type: manhattan_precision value: 61.71303074670571 - type: manhattan_recall value: 89.01795142555439 - type: max_accuracy value: 69.89713048186248 - type: max_ap value: 74.11555425121965 - type: max_f1 value: 72.91028446389497 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 90.93 - type: ap value: 88.66185083484555 - type: f1 value: 90.91685771516175 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 14.385178129184318 - type: cos_sim_spearman value: 17.246549728263478 - type: euclidean_pearson value: 18.921969136664913 - type: euclidean_spearman value: 17.245713577354014 - type: manhattan_pearson value: 18.98503959815216 - type: manhattan_spearman value: 17.37740013639568 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 32.04198138050403 - type: cos_sim_spearman value: 34.4844617563846 - type: euclidean_pearson value: 34.2634608256121 - type: euclidean_spearman value: 36.322207068208066 - type: manhattan_pearson value: 34.414939622012284 - type: manhattan_spearman value: 36.49437789416394 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.858 - type: map_at_10 value: 84.516 - type: map_at_100 value: 85.138 - type: map_at_1000 value: 85.153 - type: map_at_3 value: 81.487 - type: map_at_5 value: 83.41199999999999 - type: mrr_at_1 value: 81.55 - type: mrr_at_10 value: 87.51400000000001 - type: mrr_at_100 value: 87.607 - type: mrr_at_1000 value: 87.60900000000001 - type: mrr_at_3 value: 86.49 - type: mrr_at_5 value: 87.21 - type: ndcg_at_1 value: 81.57 - type: ndcg_at_10 value: 88.276 - type: ndcg_at_100 value: 89.462 - type: ndcg_at_1000 value: 89.571 - type: ndcg_at_3 value: 85.294 - type: ndcg_at_5 value: 86.979 - type: precision_at_1 value: 81.57 - type: precision_at_10 value: 13.389999999999999 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.2 - type: precision_at_5 value: 24.544 - type: recall_at_1 value: 70.858 - type: recall_at_10 value: 95.428 - type: recall_at_100 value: 99.46000000000001 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 86.896 - type: recall_at_5 value: 91.617 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 47.90089115942085 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 55.948584594903515 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.513 - type: map_at_10 value: 11.189 - type: map_at_100 value: 13.034 - type: map_at_1000 value: 13.312 - type: map_at_3 value: 8.124 - type: map_at_5 value: 9.719999999999999 - type: mrr_at_1 value: 22.1 - type: mrr_at_10 value: 32.879999999999995 - type: mrr_at_100 value: 33.916000000000004 - type: mrr_at_1000 value: 33.982 - type: mrr_at_3 value: 29.633 - type: mrr_at_5 value: 31.663000000000004 - type: ndcg_at_1 value: 22.1 - type: ndcg_at_10 value: 18.944 - type: ndcg_at_100 value: 26.240000000000002 - type: ndcg_at_1000 value: 31.282 - type: ndcg_at_3 value: 18.17 - type: ndcg_at_5 value: 15.976 - type: precision_at_1 value: 22.1 - type: precision_at_10 value: 9.700000000000001 - type: precision_at_100 value: 2.025 - type: precision_at_1000 value: 0.32299999999999995 - type: precision_at_3 value: 16.933 - type: precision_at_5 value: 14.02 - type: recall_at_1 value: 4.513 - type: recall_at_10 value: 19.723 - type: recall_at_100 value: 41.117 - type: recall_at_1000 value: 65.718 - type: recall_at_3 value: 10.333 - type: recall_at_5 value: 14.252 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.93526522406187 - type: cos_sim_spearman value: 81.4067321748142 - type: euclidean_pearson value: 82.23783344725466 - type: euclidean_spearman value: 80.88990344685583 - type: manhattan_pearson value: 82.3367264631989 - type: manhattan_spearman value: 80.9278067738814 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.23458296088118 - type: cos_sim_spearman value: 77.47310329678291 - type: euclidean_pearson value: 83.73584591194671 - type: euclidean_spearman value: 80.15616176452284 - type: manhattan_pearson value: 84.03063128849925 - type: manhattan_spearman value: 80.36472448270416 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.11807249122802 - type: cos_sim_spearman value: 86.37854318479079 - type: euclidean_pearson value: 86.65850909046301 - type: euclidean_spearman value: 87.85344963531178 - type: manhattan_pearson value: 86.77920459868837 - type: manhattan_spearman value: 87.97331161741792 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.4649953305265 - type: cos_sim_spearman value: 81.17166984686445 - type: euclidean_pearson value: 82.36880883967271 - type: euclidean_spearman value: 81.28206358558401 - type: manhattan_pearson value: 82.56994704487155 - type: manhattan_spearman value: 81.52094918949243 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.5328930220188 - type: cos_sim_spearman value: 88.23398394823562 - type: euclidean_pearson value: 88.0817998861656 - type: euclidean_spearman value: 88.68995789914679 - type: manhattan_pearson value: 88.11885742601258 - type: manhattan_spearman value: 88.7318106493293 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.81883368511858 - type: cos_sim_spearman value: 86.28679308000675 - type: euclidean_pearson value: 84.33705182713047 - type: euclidean_spearman value: 84.83018555455023 - type: manhattan_pearson value: 84.3271850394614 - type: manhattan_spearman value: 84.77974015415639 - 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: 90.71845282522295 - type: cos_sim_spearman value: 90.6215253553308 - type: euclidean_pearson value: 89.486847313806 - type: euclidean_spearman value: 89.11692037511729 - type: manhattan_pearson value: 89.53911733450684 - type: manhattan_spearman value: 89.2507288145461 - 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: 65.81961557635002 - type: cos_sim_spearman value: 65.01437718770094 - type: euclidean_pearson value: 66.53720271639384 - type: euclidean_spearman value: 65.66538718470727 - type: manhattan_pearson value: 66.85160833477023 - type: manhattan_spearman value: 65.86253623736344 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 81.74904608584143 - type: cos_sim_spearman value: 82.02672847550606 - type: euclidean_pearson value: 81.47843718306068 - type: euclidean_spearman value: 81.7259314292303 - type: manhattan_pearson value: 81.70320276859634 - type: manhattan_spearman value: 81.94903024173293 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.37129233774877 - type: cos_sim_spearman value: 88.02311088852667 - type: euclidean_pearson value: 85.864664021262 - type: euclidean_spearman value: 86.24775921494894 - type: manhattan_pearson value: 85.85401868812795 - type: manhattan_spearman value: 86.22999105137849 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 80.2684105571225 - type: mrr value: 94.3528194753685 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 55.161 - type: map_at_10 value: 64.794 - type: map_at_100 value: 65.66499999999999 - type: map_at_1000 value: 65.684 - type: map_at_3 value: 62.326 - type: map_at_5 value: 63.863 - type: mrr_at_1 value: 58.333 - type: mrr_at_10 value: 66.396 - type: mrr_at_100 value: 67.07300000000001 - type: mrr_at_1000 value: 67.092 - type: mrr_at_3 value: 64.61099999999999 - type: mrr_at_5 value: 65.744 - type: ndcg_at_1 value: 58.333 - type: ndcg_at_10 value: 69.294 - type: ndcg_at_100 value: 72.612 - type: ndcg_at_1000 value: 73.083 - type: ndcg_at_3 value: 65.226 - type: ndcg_at_5 value: 67.44 - type: precision_at_1 value: 58.333 - type: precision_at_10 value: 9.2 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 25.667 - type: precision_at_5 value: 16.866999999999997 - type: recall_at_1 value: 55.161 - type: recall_at_10 value: 81.289 - type: recall_at_100 value: 95.333 - type: recall_at_1000 value: 99.0 - type: recall_at_3 value: 70.45 - type: recall_at_5 value: 76.128 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81980198019802 - type: cos_sim_ap value: 95.61939598272275 - type: cos_sim_f1 value: 91.00684261974584 - type: cos_sim_precision value: 89.0057361376673 - type: cos_sim_recall value: 93.10000000000001 - type: dot_accuracy value: 99.78910891089109 - type: dot_ap value: 94.52852299178002 - type: dot_f1 value: 89.2586989409985 - type: dot_precision value: 90.03051881993896 - type: dot_recall value: 88.5 - type: euclidean_accuracy value: 99.81782178217821 - type: euclidean_ap value: 95.41313424258671 - 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type: recall_at_3 value: 6.145 - type: recall_at_5 value: 9.728 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.5842 - type: ap value: 14.770823761227014 - type: f1 value: 55.22772349179383 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 62.13921901528015 - type: f1 value: 62.450042974251694 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 40.81463922932671 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.86755677415509 - type: cos_sim_ap value: 73.8131664470889 - type: cos_sim_f1 value: 68.03196803196803 - type: cos_sim_precision value: 64.58036984352773 - type: cos_sim_recall value: 71.87335092348285 - type: dot_accuracy value: 84.58604041246946 - type: dot_ap value: 69.43165607336826 - type: dot_f1 value: 65.84285381207741 - type: dot_precision value: 58.980785296574766 - type: dot_recall value: 74.51187335092348 - type: euclidean_accuracy value: 85.60529296060082 - type: euclidean_ap value: 72.48939155702391 - type: euclidean_f1 value: 66.84775898259045 - type: euclidean_precision value: 62.822000464144814 - type: euclidean_recall value: 71.42480211081794 - type: manhattan_accuracy value: 85.5456875484294 - type: manhattan_ap value: 72.37178636434892 - type: manhattan_f1 value: 66.6751398068124 - type: manhattan_precision value: 64.32074546346249 - type: manhattan_recall value: 69.2084432717678 - type: max_accuracy value: 85.86755677415509 - type: max_ap value: 73.8131664470889 - type: max_f1 value: 68.03196803196803 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.39341017580627 - type: cos_sim_ap value: 86.7769866448429 - type: cos_sim_f1 value: 79.26586570354536 - type: cos_sim_precision value: 76.02149017390076 - type: cos_sim_recall value: 82.79950723744996 - type: dot_accuracy value: 89.15861373074087 - type: dot_ap value: 85.15235322715995 - type: dot_f1 value: 78.97118887294403 - type: dot_precision value: 75.6290083867785 - type: dot_recall value: 82.62242069602709 - type: euclidean_accuracy value: 89.0266620095471 - type: euclidean_ap value: 86.18904940615533 - type: euclidean_f1 value: 78.37750135208222 - type: euclidean_precision value: 73.70312605953754 - type: euclidean_recall value: 83.68493994456422 - type: manhattan_accuracy value: 88.98397174680794 - type: manhattan_ap value: 86.18302538523727 - type: manhattan_f1 value: 78.42197035745423 - type: manhattan_precision value: 74.23658872077029 - type: manhattan_recall value: 83.10748383122882 - type: max_accuracy value: 89.39341017580627 - type: max_ap value: 86.7769866448429 - type: max_f1 value: 79.26586570354536 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 46.9 - type: map_at_10 value: 57.399 - type: map_at_100 value: 57.976000000000006 - type: map_at_1000 value: 58.00300000000001 - type: map_at_3 value: 54.967 - type: map_at_5 value: 56.562 - type: mrr_at_1 value: 46.800000000000004 - type: mrr_at_10 value: 57.349000000000004 - type: mrr_at_100 value: 57.926 - type: mrr_at_1000 value: 57.952999999999996 - type: mrr_at_3 value: 54.917 - type: mrr_at_5 value: 56.51199999999999 - type: ndcg_at_1 value: 46.9 - type: ndcg_at_10 value: 62.437 - type: ndcg_at_100 value: 65.273 - type: ndcg_at_1000 value: 65.999 - type: ndcg_at_3 value: 57.524 - type: ndcg_at_5 value: 60.402 - type: precision_at_1 value: 46.9 - type: precision_at_10 value: 7.82 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 21.633 - type: precision_at_5 value: 14.38 - type: recall_at_1 value: 46.9 - type: recall_at_10 value: 78.2 - type: recall_at_100 value: 91.5 - type: recall_at_1000 value: 97.2 - type: recall_at_3 value: 64.9 - type: recall_at_5 value: 71.89999999999999 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 84.68 - type: ap value: 66.4749730574293 - type: f1 value: 82.93606561551698 --- # Model Card for udever-bloom `udever-bloom-7b1` is finetuned from [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) via [BitFit](https://aclanthology.org/2022.acl-short.1/) on MS MARCO Passage Ranking, SNLI and MultiNLI data. It is a universal embedding model across tasks, natural and programming languages. (From the technical view, `udever` is merely with some minor improvements to `sgpt-bloom`) ## Model Details ### Model Description - **Developed by:** Alibaba Group - **Model type:** Transformer-based Language Model (decoder-only) - **Language(s) (NLP):** Multiple; see [bloom training data](https://huggingface.co/bigscience/bloom-7b1#training-data) - **Finetuned from model :** [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) ### Model Sources - **Repository:** [github.com/izhx/uni-rep](https://github.com/izhx/uni-rep) - **Paper :** [Language Models are Universal Embedders](https://arxiv.org/pdf/2310.08232.pdf) - **Training Date :** 2023-06 ### Checkpoints - [udever-bloom-560m](https://huggingface.co/izhx/udever-bloom-560m) - [udever-bloom-1b1](https://huggingface.co/izhx/udever-bloom-1b1) - [udever-bloom-3b](https://huggingface.co/izhx/udever-bloom-3b) - [udever-bloom-7b1](https://huggingface.co/izhx/udever-bloom-7b1) On ModelScope / 魔搭社区: [udever-bloom-560m](https://modelscope.cn/models/damo/udever-bloom-560m), [udever-bloom-1b1](https://modelscope.cn/models/damo/udever-bloom-1b1), [udever-bloom-3b](https://modelscope.cn/models/damo/udever-bloom-3b), [udever-bloom-7b1](https://modelscope.cn/models/damo/udever-bloom-7b1) ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch from transformers import AutoTokenizer, BloomModel tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-7b1') model = BloomModel.from_pretrained('izhx/udever-bloom-7b1') boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]' eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod]) if tokenizer.padding_side != 'left': print('!!!', tokenizer.padding_side) tokenizer.padding_side = 'left' def encode(texts: list, is_query: bool = True, max_length=300): bos = boq if is_query else bod eos_id = eoq_id if is_query else eod_id texts = [bos + t for t in texts] encoding = tokenizer( texts, truncation=True, max_length=max_length - 1, padding=True ) for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']): ids.append(eos_id) mask.append(1) inputs = tokenizer.pad(encoding, return_tensors='pt') with torch.inference_mode(): outputs = model(**inputs) embeds = outputs.last_hidden_state[:, -1] return embeds encode(['I am Bert', 'You are Elmo']) ``` ## Training Details ### Training Data - MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86) - SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz) ### Training Procedure #### Preprocessing MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86). Negatives for SNLI and MultiNLI are randomly sampled. #### Training Hyperparameters - **Training regime:** tf32, BitFit - **Batch size:** 1024 - **Epochs:** 3 - **Optimizer:** AdamW - **Learning rate:** 1e-4 - **Scheduler:** constant with warmup. - **Warmup:** 0.25 epoch ## Evaluation ### Table 1: Massive Text Embedding Benchmark [MTEB](https://huggingface.co/spaces/mteb/leaderboard) | MTEB | Avg. | Class. | Clust. | PairClass. | Rerank. | Retr. | STS | Summ. | |-----------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------| | #Datasets ➡️ | 56 | 12 | 11 | 3 | 4 | 15 | 10 | 1 | || | bge-large-en-v1.5 | **64.23** | **75.97** | 46.08| **87.12** | **60.03** | **54.29** | 83.11| 31.61 | | bge-base-en-v1.5 | 63.55| 75.53| 45.77| 86.55| 58.86| 53.25| 82.4| 31.07 | | gte-large | 63.13| 73.33| **46.84** | 85| 59.13| 52.22| **83.35** | 31.66 | | gte-base | 62.39| 73.01| 46.2| 84.57| 58.61| 51.14| 82.3| 31.17 | | e5-large-v2 | 62.25| 75.24| 44.49| 86.03| 56.61| 50.56| 82.05| 30.19 | | instructor-xl | 61.79| 73.12| 44.74| 86.62| 57.29| 49.26| 83.06| 32.32 | | instructor-large | 61.59| 73.86| 45.29| 85.89| 57.54| 47.57| 83.15| 31.84 | | e5-base-v2 | 61.5 | 73.84| 43.8| 85.73| 55.91| 50.29| 81.05| 30.28 | | e5-large | 61.42| 73.14| 43.33| 85.94| 56.53| 49.99| 82.06| 30.97 | | text-embedding-ada-002 (OpenAI API) | 60.99| 70.93| 45.9 | 84.89| 56.32| 49.25| 80.97| 30.8 | | e5-base | 60.44| 72.63| 42.11| 85.09| 55.7 | 48.75| 80.96| 31.01 | | SGPT-5.8B-msmarco | 58.93| 68.13| 40.34| 82 | 56.56| 50.25| 78.1 | 31.46 | | sgpt-bloom-7b1-msmarco | 57.59| 66.19| 38.93| 81.9 | 55.65| 48.22| 77.74| **33.6** | || | Udever-bloom-560m | 55.80| 68.04| 36.89| 81.05| 52.60| 41.19| 79.93| 32.06 | | Udever-bloom-1b1 | 58.28| 70.18| 39.11| 83.11| 54.28| 45.27| 81.52| 31.10 | | Udever-bloom-3b | 59.86| 71.91| 40.74| 84.06| 54.90| 47.67| 82.37| 30.62 | | Udever-bloom-7b1 | 60.63 | 72.13| 40.81| 85.40| 55.91| 49.34| 83.01| 30.97 | ### Table 2: [CodeSearchNet](https://github.com/github/CodeSearchNet) | CodeSearchNet | Go | Ruby | Python | Java | JS | PHP | Avg. | |-|-|-|-|-|-|-|-| | CodeBERT | 69.3 | 70.6 | 84.0 | 86.8 | 74.8 | 70.6 | 76.0 | | GraphCodeBERT | 84.1 | 73.2 | 87.9 | 75.7 | 71.1 | 72.5 | 77.4 | | cpt-code S | **97.7** | **86.3** | 99.8 | 94.0 | 86.0 | 96.7 | 93.4 | | cpt-code M | 97.5 | 85.5 | **99.9** | **94.4** | **86.5** | **97.2** | **93.5** | | sgpt-bloom-7b1-msmarco | 76.79 | 69.25 | 95.68 | 77.93 | 70.35 | 73.45 | 77.24 | || | Udever-bloom-560m | 75.38 | 66.67 | 96.23 | 78.99 | 69.39 | 73.69 | 76.73 | | Udever-bloom-1b1 | 78.76 | 72.85 | 97.67 | 82.77 | 74.38 | 78.97 | 80.90 | | Udever-bloom-3b | 80.63 | 75.40 | 98.02 | 83.88 | 76.18 | 79.67 | 82.29 | | Udever-bloom-7b1 | 79.37 | 76.59 | 98.38 | 84.68 | 77.49 | 80.03 | 82.76 | ### Table 3: Chinese multi-domain retrieval [Multi-cpr](https://dl.acm.org/doi/10.1145/3477495.3531736) | | | |E-commerce | | Entertainment video | | Medical | | |--|--|--|--|--|--|--|--|--| | Model | Train | Backbone | MRR@10 | Recall@1k | MRR@10 | Recall@1k | MRR@10 | Recall@1k | || | BM25 | - | - | 0.225 | 0.815 | 0.225 | 0.780 | 0.187 | 0.482 | | Doc2Query | - | - | 0.239 | 0.826 | 0.238 | 0.794 | 0.210 | 0.505 | | DPR-1 | In-Domain | BERT | 0.270 | 0.921 | 0.254 | 0.934 | 0.327 | 0.747 | | DPR-2 | In-Domain | BERT-CT | 0.289 | **0.926** | 0.263 | **0.935** | 0.339 | **0.769** | | text-embedding-ada-002 | General | GPT | 0.183 | 0.825 | 0.159 | 0.786 | 0.245 | 0.593 | | sgpt-bloom-7b1-msmarco | General | BLOOM | 0.242 | 0.840 | 0.227 | 0.829 | 0.311 | 0.675 | || | Udever-bloom-560m | General | BLOOM | 0.156 | 0.802 | 0.149 | 0.749 | 0.245 | 0.571 | | Udever-bloom-1b1 | General | BLOOM | 0.244 | 0.863 | 0.208 | 0.815 | 0.241 | 0.557 | | Udever-bloom-3b | General | BLOOM | 0.267 | 0.871 | 0.228 | 0.836 | 0.288 | 0.619 | | Udever-bloom-7b1 | General | BLOOM | **0.296** | 0.889 | **0.267** | 0.907 | **0.343** | 0.705 | #### More results refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 3. ## Technical Specifications ### Model Architecture and Objective - Model: [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1). - Objective: Constrastive loss with hard negatives (refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 2.2). ### Compute Infrastructure - Nvidia A100 SXM4 80GB. - torch 2.0.0, transformers 4.29.2. ## Citation **BibTeX:** ```BibTeX @article{zhang2023language, title={Language Models are Universal Embedders}, author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min}, journal={arXiv preprint arXiv:2310.08232}, year={2023} } ```