--- 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-560m results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 25.170024237678657 - type: cos_sim_spearman value: 25.32025098111752 - type: euclidean_pearson value: 25.34284673812859 - type: euclidean_spearman value: 25.52812937004611 - type: manhattan_pearson value: 25.734179522960822 - type: manhattan_spearman value: 25.92247507041032 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 32.3359541791282 - type: cos_sim_spearman value: 33.45815274836323 - type: euclidean_pearson value: 35.14748229440635 - type: euclidean_spearman value: 33.377829932851334 - type: manhattan_pearson value: 35.359130773295625 - type: manhattan_spearman value: 33.524469762932426 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.35820895522389 - type: ap value: 35.45566303125099 - type: f1 value: 66.49474786522534 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.423982869379 - type: ap value: 78.32781372746805 - type: f1 value: 64.24959400774807 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.65817091454274 - type: ap value: 21.73416645163647 - type: f1 value: 60.52120070712094 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 56.86295503211991 - type: ap value: 12.906256075113513 - type: f1 value: 46.68625513679152 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 83.8095 - type: ap value: 78.5195717101614 - type: f1 value: 83.74169093676316 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.97 - type: f1 value: 38.57853211177342 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 26.846000000000004 - type: f1 value: 26.473886891677306 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.974 - type: f1 value: 38.31719230291287 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.38799999999999 - type: f1 value: 37.53319978613875 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 28.311999999999998 - type: f1 value: 27.988313617729755 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 35.704 - type: f1 value: 34.863182924437254 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 21.053 - type: map_at_10 value: 35.811 - type: map_at_100 value: 37.035000000000004 - type: map_at_1000 value: 37.055 - type: map_at_3 value: 30.666 - type: map_at_5 value: 33.525 - type: mrr_at_1 value: 21.266 - type: mrr_at_10 value: 35.906 - type: mrr_at_100 value: 37.122 - type: mrr_at_1000 value: 37.141999999999996 - type: mrr_at_3 value: 30.714000000000002 - type: mrr_at_5 value: 33.576 - type: ndcg_at_1 value: 21.053 - type: ndcg_at_10 value: 44.545 - type: ndcg_at_100 value: 49.844 - type: ndcg_at_1000 value: 50.298 - type: ndcg_at_3 value: 33.889 - type: ndcg_at_5 value: 39.059 - type: precision_at_1 value: 21.053 - type: precision_at_10 value: 7.269 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.414 - type: precision_at_5 value: 11.166 - type: recall_at_1 value: 21.053 - type: recall_at_10 value: 72.688 - type: recall_at_100 value: 96.017 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 43.242999999999995 - type: recall_at_5 value: 55.832 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.26646269393896 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.00218289816601 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 57.381567373603424 - type: mrr value: 70.09431473420392 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.14803223261677 - type: cos_sim_spearman value: 84.43626128689064 - type: euclidean_pearson value: 85.03130036472703 - type: euclidean_spearman value: 84.05974668365359 - type: manhattan_pearson value: 85.59339889467545 - type: manhattan_spearman value: 83.86938090025696 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 44.19468290937555 - type: cos_sim_spearman value: 43.93025426799595 - type: euclidean_pearson value: 45.273900549350735 - type: euclidean_spearman value: 45.07419415738924 - type: manhattan_pearson value: 45.469211385235376 - type: manhattan_spearman value: 45.27440191151001 - 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: 11.440501043841337 - type: f1 value: 11.295895880968951 - type: precision value: 11.237446950317073 - type: recall value: 11.440501043841337 - 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: 96.53312788906008 - type: f1 value: 96.18093770636143 - type: precision value: 96.00667693888035 - type: recall value: 96.53312788906008 - 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: 1.6972635954277795 - type: f1 value: 1.5885146938143124 - type: precision value: 1.5581125970067466 - type: recall value: 1.6972635954277795 - 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: 96.31384939441811 - type: f1 value: 96.15587151132175 - type: precision value: 96.07688256977357 - type: recall value: 96.31384939441811 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 80.97402597402598 - type: f1 value: 80.88177660652944 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.266950159712465 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 28.65092446021672 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 35.21075820650184 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 35.121931960714484 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 63.41256934884578 - type: mrr value: 68.6492857142857 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 63.663067375541104 - type: mrr value: 68.92075396825396 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.997 - type: map_at_10 value: 35.477 - type: map_at_100 value: 36.722 - type: map_at_1000 value: 36.849 - type: map_at_3 value: 32.083 - type: map_at_5 value: 33.884 - type: mrr_at_1 value: 32.046 - type: mrr_at_10 value: 41.455999999999996 - type: mrr_at_100 value: 42.214 - type: mrr_at_1000 value: 42.268 - type: mrr_at_3 value: 38.722 - type: mrr_at_5 value: 40.266999999999996 - type: ndcg_at_1 value: 32.046 - type: ndcg_at_10 value: 41.705999999999996 - type: ndcg_at_100 value: 46.695 - type: ndcg_at_1000 value: 49.128 - type: ndcg_at_3 value: 36.6 - type: ndcg_at_5 value: 38.725 - type: precision_at_1 value: 32.046 - type: precision_at_10 value: 8.197000000000001 - type: precision_at_100 value: 1.323 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 18.073 - type: precision_at_5 value: 13.047 - type: recall_at_1 value: 24.997 - type: recall_at_10 value: 54.013 - type: recall_at_100 value: 75.29400000000001 - type: recall_at_1000 value: 91.611 - type: recall_at_3 value: 38.627 - type: recall_at_5 value: 45.019999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.194 - type: map_at_10 value: 30.076000000000004 - type: map_at_100 value: 31.0 - type: map_at_1000 value: 31.125999999999998 - type: map_at_3 value: 28.137 - type: map_at_5 value: 29.206 - type: mrr_at_1 value: 28.535 - type: mrr_at_10 value: 34.833999999999996 - type: mrr_at_100 value: 35.504999999999995 - type: mrr_at_1000 value: 35.57 - type: mrr_at_3 value: 33.089 - type: mrr_at_5 value: 34.115 - type: ndcg_at_1 value: 28.535 - type: ndcg_at_10 value: 34.285 - type: ndcg_at_100 value: 38.286 - type: ndcg_at_1000 value: 41.007 - type: ndcg_at_3 value: 31.395 - type: ndcg_at_5 value: 32.687 - type: precision_at_1 value: 28.535 - type: precision_at_10 value: 6.166 - type: precision_at_100 value: 1.042 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 14.862 - type: precision_at_5 value: 10.331 - type: recall_at_1 value: 23.194 - type: recall_at_10 value: 41.648 - type: recall_at_100 value: 58.999 - type: recall_at_1000 value: 77.46300000000001 - type: recall_at_3 value: 32.931 - type: recall_at_5 value: 36.736999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.899 - type: map_at_10 value: 42.657000000000004 - type: map_at_100 value: 43.717 - type: map_at_1000 value: 43.79 - type: map_at_3 value: 39.635 - type: map_at_5 value: 41.538000000000004 - type: mrr_at_1 value: 36.864999999999995 - type: mrr_at_10 value: 46.137 - type: mrr_at_100 value: 46.946 - type: mrr_at_1000 value: 46.986 - type: mrr_at_3 value: 43.469 - type: mrr_at_5 value: 45.262 - type: ndcg_at_1 value: 36.864999999999995 - type: ndcg_at_10 value: 48.164 - type: ndcg_at_100 value: 52.769999999999996 - type: ndcg_at_1000 value: 54.393 - type: ndcg_at_3 value: 42.887 - type: ndcg_at_5 value: 45.871 - type: precision_at_1 value: 36.864999999999995 - type: precision_at_10 value: 7.843 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 19.352 - type: precision_at_5 value: 13.618 - type: recall_at_1 value: 31.899 - type: recall_at_10 value: 61.131 - type: recall_at_100 value: 81.504 - type: recall_at_1000 value: 93.146 - type: recall_at_3 value: 46.971000000000004 - type: recall_at_5 value: 54.42399999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.621000000000002 - type: map_at_10 value: 23.621 - type: map_at_100 value: 24.636 - type: map_at_1000 value: 24.739 - type: map_at_3 value: 21.623 - type: map_at_5 value: 22.511 - type: mrr_at_1 value: 19.096 - type: mrr_at_10 value: 25.288 - type: mrr_at_100 value: 26.238 - type: mrr_at_1000 value: 26.314 - type: mrr_at_3 value: 23.202 - type: mrr_at_5 value: 24.213 - type: ndcg_at_1 value: 19.096 - type: ndcg_at_10 value: 27.529999999999998 - type: ndcg_at_100 value: 32.763 - type: ndcg_at_1000 value: 35.538 - type: ndcg_at_3 value: 23.362 - type: ndcg_at_5 value: 24.961 - type: precision_at_1 value: 19.096 - type: precision_at_10 value: 4.417999999999999 - type: precision_at_100 value: 0.739 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 9.981 - type: precision_at_5 value: 6.959999999999999 - type: recall_at_1 value: 17.621000000000002 - type: recall_at_10 value: 38.079 - type: recall_at_100 value: 62.499 - type: recall_at_1000 value: 83.783 - type: recall_at_3 value: 26.687 - type: recall_at_5 value: 30.459000000000003 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.019 - type: map_at_10 value: 15.869 - type: map_at_100 value: 17.078 - type: map_at_1000 value: 17.205000000000002 - type: map_at_3 value: 13.794 - type: map_at_5 value: 14.814 - type: mrr_at_1 value: 13.930000000000001 - type: mrr_at_10 value: 19.172 - type: mrr_at_100 value: 20.325 - type: mrr_at_1000 value: 20.415 - type: mrr_at_3 value: 17.122999999999998 - type: mrr_at_5 value: 18.124000000000002 - type: ndcg_at_1 value: 13.930000000000001 - type: ndcg_at_10 value: 19.646 - type: ndcg_at_100 value: 25.684 - type: ndcg_at_1000 value: 29.14 - type: ndcg_at_3 value: 15.614 - type: ndcg_at_5 value: 17.247 - type: precision_at_1 value: 13.930000000000001 - type: precision_at_10 value: 3.868 - type: precision_at_100 value: 0.8 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 7.420999999999999 - type: precision_at_5 value: 5.672 - type: recall_at_1 value: 11.019 - type: recall_at_10 value: 28.116000000000003 - type: recall_at_100 value: 54.794 - type: recall_at_1000 value: 79.838 - type: recall_at_3 value: 17.124 - type: recall_at_5 value: 21.086 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.791 - type: map_at_10 value: 33.442 - type: map_at_100 value: 34.719 - type: map_at_1000 value: 34.849000000000004 - type: map_at_3 value: 30.885 - type: map_at_5 value: 32.245000000000005 - type: mrr_at_1 value: 30.606 - type: mrr_at_10 value: 38.922000000000004 - type: mrr_at_100 value: 39.822 - type: mrr_at_1000 value: 39.881 - type: mrr_at_3 value: 36.622 - type: mrr_at_5 value: 37.907000000000004 - type: ndcg_at_1 value: 30.606 - type: ndcg_at_10 value: 38.867000000000004 - type: ndcg_at_100 value: 44.364 - type: ndcg_at_1000 value: 47.073 - type: ndcg_at_3 value: 34.63 - type: ndcg_at_5 value: 36.479 - type: precision_at_1 value: 30.606 - type: precision_at_10 value: 7.0360000000000005 - type: precision_at_100 value: 1.174 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 16.522000000000002 - type: precision_at_5 value: 11.588 - type: recall_at_1 value: 24.791 - type: recall_at_10 value: 49.736000000000004 - type: recall_at_100 value: 72.67099999999999 - type: recall_at_1000 value: 91.29599999999999 - type: recall_at_3 value: 37.345 - type: recall_at_5 value: 42.400999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.669999999999998 - type: map_at_10 value: 28.605000000000004 - type: map_at_100 value: 29.769000000000002 - type: map_at_1000 value: 29.881999999999998 - type: map_at_3 value: 25.886 - type: map_at_5 value: 27.317999999999998 - type: mrr_at_1 value: 25.457 - type: mrr_at_10 value: 33.423 - type: mrr_at_100 value: 34.269 - type: mrr_at_1000 value: 34.336 - type: mrr_at_3 value: 30.974 - type: mrr_at_5 value: 32.23 - type: ndcg_at_1 value: 25.457 - type: ndcg_at_10 value: 33.785 - type: ndcg_at_100 value: 39.145 - type: ndcg_at_1000 value: 41.772 - type: ndcg_at_3 value: 29.014 - type: ndcg_at_5 value: 31.019999999999996 - type: precision_at_1 value: 25.457 - type: precision_at_10 value: 6.2330000000000005 - type: precision_at_100 value: 1.045 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 13.813 - type: precision_at_5 value: 9.863 - type: recall_at_1 value: 20.669999999999998 - type: recall_at_10 value: 44.651 - type: recall_at_100 value: 68.037 - type: recall_at_1000 value: 86.282 - type: recall_at_3 value: 31.381999999999998 - type: recall_at_5 value: 36.778 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.796583333333338 - type: map_at_10 value: 26.900166666666664 - type: map_at_100 value: 27.956583333333334 - type: map_at_1000 value: 28.08083333333333 - type: map_at_3 value: 24.598416666666665 - type: map_at_5 value: 25.81791666666667 - type: mrr_at_1 value: 23.68591666666667 - type: mrr_at_10 value: 30.65558333333333 - type: mrr_at_100 value: 31.503583333333335 - type: mrr_at_1000 value: 31.576083333333333 - type: mrr_at_3 value: 28.50525 - type: mrr_at_5 value: 29.690666666666665 - type: ndcg_at_1 value: 23.68591666666667 - type: ndcg_at_10 value: 31.425000000000004 - type: ndcg_at_100 value: 36.34316666666666 - type: ndcg_at_1000 value: 39.164249999999996 - type: ndcg_at_3 value: 27.330083333333338 - type: ndcg_at_5 value: 29.14408333333333 - type: precision_at_1 value: 23.68591666666667 - type: precision_at_10 value: 5.5862500000000015 - type: precision_at_100 value: 0.9571666666666666 - type: precision_at_1000 value: 0.13866666666666666 - type: precision_at_3 value: 12.663499999999999 - type: precision_at_5 value: 9.035333333333332 - type: recall_at_1 value: 19.796583333333338 - type: recall_at_10 value: 41.289416666666675 - type: recall_at_100 value: 63.251250000000006 - type: recall_at_1000 value: 83.4515 - type: recall_at_3 value: 29.727916666666665 - type: recall_at_5 value: 34.45824999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.121 - type: map_at_10 value: 22.104 - type: map_at_100 value: 23.003 - type: map_at_1000 value: 23.108 - type: map_at_3 value: 20.233 - type: map_at_5 value: 21.186 - type: mrr_at_1 value: 18.865000000000002 - type: mrr_at_10 value: 24.951 - type: mrr_at_100 value: 25.779000000000003 - type: mrr_at_1000 value: 25.863999999999997 - type: mrr_at_3 value: 23.083000000000002 - type: mrr_at_5 value: 24.049 - type: ndcg_at_1 value: 18.865000000000002 - type: ndcg_at_10 value: 26.031 - type: ndcg_at_100 value: 30.589 - type: ndcg_at_1000 value: 33.565 - type: ndcg_at_3 value: 22.369 - type: ndcg_at_5 value: 23.932000000000002 - type: precision_at_1 value: 18.865000000000002 - type: precision_at_10 value: 4.324999999999999 - type: precision_at_100 value: 0.722 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 10.072000000000001 - type: precision_at_5 value: 7.086 - type: recall_at_1 value: 16.121 - type: recall_at_10 value: 35.577 - type: recall_at_100 value: 56.298 - type: recall_at_1000 value: 79.089 - type: recall_at_3 value: 25.239 - type: recall_at_5 value: 29.242 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 10.968 - type: map_at_10 value: 15.639 - type: map_at_100 value: 16.459 - type: map_at_1000 value: 16.584 - type: map_at_3 value: 14.127 - type: map_at_5 value: 14.911 - type: mrr_at_1 value: 13.73 - type: mrr_at_10 value: 18.822 - type: mrr_at_100 value: 19.592000000000002 - type: mrr_at_1000 value: 19.683999999999997 - type: mrr_at_3 value: 17.223 - type: mrr_at_5 value: 18.082 - type: ndcg_at_1 value: 13.73 - type: ndcg_at_10 value: 18.881999999999998 - type: ndcg_at_100 value: 23.182 - type: ndcg_at_1000 value: 26.479000000000003 - type: ndcg_at_3 value: 16.067999999999998 - type: ndcg_at_5 value: 17.265 - type: precision_at_1 value: 13.73 - type: precision_at_10 value: 3.544 - type: precision_at_100 value: 0.679 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 7.674 - type: precision_at_5 value: 5.561 - type: recall_at_1 value: 10.968 - type: recall_at_10 value: 25.596000000000004 - type: recall_at_100 value: 45.411 - type: recall_at_1000 value: 69.555 - type: recall_at_3 value: 17.582 - type: recall_at_5 value: 20.785 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.886 - type: map_at_10 value: 27.029999999999998 - type: map_at_100 value: 27.968 - type: map_at_1000 value: 28.108 - type: map_at_3 value: 25.001 - type: map_at_5 value: 26.185000000000002 - type: mrr_at_1 value: 24.067 - type: mrr_at_10 value: 30.756 - type: mrr_at_100 value: 31.593 - type: mrr_at_1000 value: 31.685999999999996 - type: mrr_at_3 value: 28.793999999999997 - type: mrr_at_5 value: 29.997 - type: ndcg_at_1 value: 24.067 - type: ndcg_at_10 value: 31.095 - type: ndcg_at_100 value: 35.893 - type: ndcg_at_1000 value: 39.158 - type: ndcg_at_3 value: 27.321 - type: ndcg_at_5 value: 29.247 - type: precision_at_1 value: 24.067 - type: precision_at_10 value: 5.103 - type: precision_at_100 value: 0.8460000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.601 - type: recall_at_1 value: 20.886 - type: recall_at_10 value: 39.797 - type: recall_at_100 value: 61.399 - type: recall_at_1000 value: 84.555 - type: recall_at_3 value: 29.721999999999998 - type: recall_at_5 value: 34.455999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.394 - type: map_at_10 value: 28.303 - type: map_at_100 value: 29.726000000000003 - type: map_at_1000 value: 29.955 - type: map_at_3 value: 25.705 - type: map_at_5 value: 26.989 - type: mrr_at_1 value: 25.691999999999997 - type: mrr_at_10 value: 32.495000000000005 - type: mrr_at_100 value: 33.461999999999996 - type: mrr_at_1000 value: 33.534000000000006 - type: mrr_at_3 value: 30.137999999999998 - type: mrr_at_5 value: 31.383 - type: ndcg_at_1 value: 25.691999999999997 - type: ndcg_at_10 value: 33.300000000000004 - type: ndcg_at_100 value: 39.062000000000005 - type: ndcg_at_1000 value: 42.176 - type: ndcg_at_3 value: 28.859 - type: ndcg_at_5 value: 30.805 - type: precision_at_1 value: 25.691999999999997 - type: precision_at_10 value: 6.383 - type: precision_at_100 value: 1.387 - type: precision_at_1000 value: 0.22899999999999998 - type: precision_at_3 value: 13.439 - type: precision_at_5 value: 9.959999999999999 - type: recall_at_1 value: 21.394 - type: recall_at_10 value: 42.853 - type: recall_at_100 value: 69.284 - type: recall_at_1000 value: 89.646 - type: recall_at_3 value: 29.786 - type: recall_at_5 value: 34.797 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.999 - type: map_at_10 value: 19.979 - type: map_at_100 value: 20.682000000000002 - type: map_at_1000 value: 20.775 - type: map_at_3 value: 18.072 - type: map_at_5 value: 19.028 - type: mrr_at_1 value: 15.342 - type: mrr_at_10 value: 21.611 - type: mrr_at_100 value: 22.298000000000002 - type: mrr_at_1000 value: 22.375 - type: mrr_at_3 value: 19.624 - type: mrr_at_5 value: 20.659 - type: ndcg_at_1 value: 15.342 - type: ndcg_at_10 value: 23.809 - type: ndcg_at_100 value: 27.685 - type: ndcg_at_1000 value: 30.542 - type: ndcg_at_3 value: 19.842000000000002 - type: ndcg_at_5 value: 21.490000000000002 - type: precision_at_1 value: 15.342 - type: precision_at_10 value: 3.9190000000000005 - type: precision_at_100 value: 0.627 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 8.688 - type: precision_at_5 value: 6.1370000000000005 - type: recall_at_1 value: 13.999 - type: recall_at_10 value: 34.276 - type: recall_at_100 value: 52.825 - type: recall_at_1000 value: 75.154 - type: recall_at_3 value: 23.339 - type: recall_at_5 value: 27.314 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 8.27 - type: map_at_10 value: 14.161999999999999 - type: map_at_100 value: 15.775 - type: map_at_1000 value: 15.947 - type: map_at_3 value: 11.701 - type: map_at_5 value: 12.952 - type: mrr_at_1 value: 18.632 - type: mrr_at_10 value: 28.871000000000002 - type: mrr_at_100 value: 29.985 - type: mrr_at_1000 value: 30.037999999999997 - type: mrr_at_3 value: 25.451 - type: mrr_at_5 value: 27.366 - type: ndcg_at_1 value: 18.632 - type: ndcg_at_10 value: 21.017 - type: ndcg_at_100 value: 28.022999999999996 - type: ndcg_at_1000 value: 31.518 - type: ndcg_at_3 value: 16.611 - type: ndcg_at_5 value: 18.149 - type: precision_at_1 value: 18.632 - type: precision_at_10 value: 6.736000000000001 - type: precision_at_100 value: 1.414 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 12.313 - type: precision_at_5 value: 9.759 - type: recall_at_1 value: 8.27 - type: recall_at_10 value: 26.218999999999998 - type: recall_at_100 value: 50.77 - type: recall_at_1000 value: 70.8 - type: recall_at_3 value: 15.526000000000002 - type: recall_at_5 value: 19.724 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 10.598 - type: map_at_10 value: 15.869 - type: map_at_100 value: 17.081 - type: map_at_1000 value: 17.267 - type: map_at_3 value: 13.877 - type: map_at_5 value: 14.884 - type: mrr_at_1 value: 17.279 - type: mrr_at_10 value: 22.554 - type: mrr_at_100 value: 23.521 - type: mrr_at_1000 value: 23.619 - type: mrr_at_3 value: 20.647 - type: mrr_at_5 value: 21.625 - type: ndcg_at_1 value: 17.279 - type: ndcg_at_10 value: 20.029 - type: ndcg_at_100 value: 25.968000000000004 - type: ndcg_at_1000 value: 30.158 - type: ndcg_at_3 value: 16.947000000000003 - type: ndcg_at_5 value: 18.069 - type: precision_at_1 value: 17.279 - type: precision_at_10 value: 4.704 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 9.777 - type: precision_at_5 value: 7.207 - type: recall_at_1 value: 10.598 - type: recall_at_10 value: 26.034000000000002 - type: recall_at_100 value: 51.385999999999996 - type: recall_at_1000 value: 80.49 - type: recall_at_3 value: 16.834 - type: recall_at_5 value: 20.317 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 70.40288634996993 - type: cos_sim_ap value: 78.43387766087626 - type: cos_sim_f1 value: 73.09982840415867 - type: cos_sim_precision value: 64.31616341030195 - type: cos_sim_recall value: 84.66214636427402 - type: dot_accuracy value: 65.52014431749849 - type: dot_ap value: 70.89507344960353 - type: dot_f1 value: 70.7030509759333 - type: dot_precision value: 59.43922255854708 - type: dot_recall value: 87.2340425531915 - type: euclidean_accuracy value: 69.84966927239927 - type: euclidean_ap value: 78.08825177727368 - type: euclidean_f1 value: 72.68394399761692 - type: euclidean_precision value: 63.16879530548844 - type: euclidean_recall value: 85.57400046761748 - type: manhattan_accuracy value: 69.9579073962718 - type: manhattan_ap value: 78.38355697667261 - type: manhattan_f1 value: 73.06507508663844 - type: manhattan_precision value: 62.10112911143839 - type: manhattan_recall value: 88.73041851765257 - type: max_accuracy value: 70.40288634996993 - type: max_ap value: 78.43387766087626 - type: max_f1 value: 73.09982840415867 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 23.973 - type: map_at_10 value: 30.074 - type: map_at_100 value: 31.05 - type: map_at_1000 value: 31.147000000000002 - type: map_at_3 value: 27.977 - type: map_at_5 value: 29.247 - type: mrr_at_1 value: 24.025 - type: mrr_at_10 value: 30.093999999999998 - type: mrr_at_100 value: 31.068 - type: mrr_at_1000 value: 31.165 - type: mrr_at_3 value: 27.994000000000003 - type: mrr_at_5 value: 29.243000000000002 - type: ndcg_at_1 value: 24.025 - type: ndcg_at_10 value: 33.566 - type: ndcg_at_100 value: 38.818999999999996 - type: ndcg_at_1000 value: 41.477000000000004 - type: ndcg_at_3 value: 29.293000000000003 - type: ndcg_at_5 value: 31.564999999999998 - type: precision_at_1 value: 24.025 - type: precision_at_10 value: 4.489 - type: precision_at_100 value: 0.709 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 11.064 - type: precision_at_5 value: 7.734000000000001 - type: recall_at_1 value: 23.973 - type: recall_at_10 value: 44.731 - type: recall_at_100 value: 70.52199999999999 - type: recall_at_1000 value: 91.491 - type: recall_at_3 value: 33.087 - type: recall_at_5 value: 38.567 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.950000000000001 - type: map_at_10 value: 13.236999999999998 - type: map_at_100 value: 16.137 - type: map_at_1000 value: 16.785 - type: map_at_3 value: 10.378 - type: map_at_5 value: 11.62 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 61.861 - type: mrr_at_100 value: 62.436 - type: mrr_at_1000 value: 62.456 - type: mrr_at_3 value: 60.458 - type: mrr_at_5 value: 61.208 - type: ndcg_at_1 value: 43.75 - type: ndcg_at_10 value: 28.224 - type: ndcg_at_100 value: 29.244999999999997 - type: ndcg_at_1000 value: 34.410000000000004 - type: ndcg_at_3 value: 33.955 - type: ndcg_at_5 value: 30.597 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 20.825 - type: precision_at_100 value: 5.462 - type: precision_at_1000 value: 1.1320000000000001 - type: precision_at_3 value: 37.0 - type: precision_at_5 value: 28.849999999999998 - type: recall_at_1 value: 6.950000000000001 - type: recall_at_10 value: 17.159 - type: recall_at_100 value: 31.657999999999998 - type: recall_at_1000 value: 49.155 - type: recall_at_3 value: 11.393 - type: recall_at_5 value: 13.568 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 16.333000000000002 - type: map_at_10 value: 44.080999999999996 - type: map_at_100 value: 47.958 - type: map_at_1000 value: 48.183 - type: map_at_3 value: 31.468 - type: map_at_5 value: 38.213 - type: mrr_at_1 value: 63.0 - type: mrr_at_10 value: 72.006 - type: mrr_at_100 value: 72.299 - type: mrr_at_1000 value: 72.313 - type: mrr_at_3 value: 70.375 - type: mrr_at_5 value: 71.33 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 56.044000000000004 - type: ndcg_at_100 value: 63.629999999999995 - type: ndcg_at_1000 value: 66.156 - type: ndcg_at_3 value: 55.85 - type: ndcg_at_5 value: 53.559 - type: precision_at_1 value: 63.0 - type: precision_at_10 value: 27.279999999999998 - type: precision_at_100 value: 4.005 - type: precision_at_1000 value: 0.462 - type: precision_at_3 value: 49.633 - type: precision_at_5 value: 40.6 - type: recall_at_1 value: 16.333000000000002 - type: recall_at_10 value: 57.152 - type: recall_at_100 value: 80.231 - type: recall_at_1000 value: 92.95400000000001 - type: recall_at_3 value: 34.793 - type: recall_at_5 value: 44.989000000000004 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 33.7 - type: map_at_10 value: 42.327999999999996 - type: map_at_100 value: 43.230000000000004 - type: map_at_1000 value: 43.274 - type: map_at_3 value: 39.883 - type: map_at_5 value: 41.178 - type: mrr_at_1 value: 33.7 - type: mrr_at_10 value: 42.327999999999996 - type: mrr_at_100 value: 43.230000000000004 - type: mrr_at_1000 value: 43.274 - type: mrr_at_3 value: 39.883 - type: mrr_at_5 value: 41.178 - type: ndcg_at_1 value: 33.7 - type: ndcg_at_10 value: 46.996 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 52.823 - type: ndcg_at_3 value: 41.891 - type: ndcg_at_5 value: 44.232 - type: precision_at_1 value: 33.7 - type: precision_at_10 value: 6.1899999999999995 - type: precision_at_100 value: 0.8410000000000001 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 15.9 - type: precision_at_5 value: 10.68 - type: recall_at_1 value: 33.7 - type: recall_at_10 value: 61.9 - type: recall_at_100 value: 84.1 - type: recall_at_1000 value: 93.60000000000001 - type: recall_at_3 value: 47.699999999999996 - type: recall_at_5 value: 53.400000000000006 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.76500000000001 - type: f1 value: 40.46330006682868 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 45.078 - type: map_at_10 value: 55.443 - type: map_at_100 value: 56.03900000000001 - type: map_at_1000 value: 56.067 - type: map_at_3 value: 53.174 - type: map_at_5 value: 54.510999999999996 - type: mrr_at_1 value: 48.575 - type: mrr_at_10 value: 59.194 - type: mrr_at_100 value: 59.760999999999996 - type: mrr_at_1000 value: 59.784000000000006 - type: mrr_at_3 value: 56.896 - type: mrr_at_5 value: 58.282000000000004 - type: ndcg_at_1 value: 48.575 - type: ndcg_at_10 value: 61.096 - type: ndcg_at_100 value: 63.94800000000001 - type: ndcg_at_1000 value: 64.68199999999999 - type: ndcg_at_3 value: 56.58 - type: ndcg_at_5 value: 58.928000000000004 - type: precision_at_1 value: 48.575 - type: precision_at_10 value: 8.18 - type: precision_at_100 value: 0.968 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 22.662 - type: precision_at_5 value: 14.881 - type: recall_at_1 value: 45.078 - type: recall_at_10 value: 75.057 - type: recall_at_100 value: 88.05199999999999 - type: recall_at_1000 value: 93.58999999999999 - type: recall_at_3 value: 62.77700000000001 - type: recall_at_5 value: 68.50699999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 11.097999999999999 - type: map_at_10 value: 18.288 - type: map_at_100 value: 19.903000000000002 - type: map_at_1000 value: 20.108 - type: map_at_3 value: 15.576 - type: map_at_5 value: 16.997999999999998 - type: mrr_at_1 value: 23.302 - type: mrr_at_10 value: 30.978 - type: mrr_at_100 value: 32.072 - type: mrr_at_1000 value: 32.15 - type: mrr_at_3 value: 28.549000000000003 - type: mrr_at_5 value: 29.931 - type: ndcg_at_1 value: 23.302 - type: ndcg_at_10 value: 24.488 - type: ndcg_at_100 value: 31.052999999999997 - type: ndcg_at_1000 value: 35.124 - type: ndcg_at_3 value: 21.215999999999998 - type: ndcg_at_5 value: 22.314999999999998 - type: precision_at_1 value: 23.302 - type: precision_at_10 value: 7.13 - type: precision_at_100 value: 1.3559999999999999 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 14.198 - type: precision_at_5 value: 10.895000000000001 - type: recall_at_1 value: 11.097999999999999 - type: recall_at_10 value: 30.352 - type: recall_at_100 value: 54.937999999999995 - type: recall_at_1000 value: 79.586 - type: recall_at_3 value: 19.486 - type: recall_at_5 value: 23.860999999999997 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 28.325 - type: map_at_10 value: 37.305 - type: map_at_100 value: 38.0 - type: map_at_1000 value: 38.065 - type: map_at_3 value: 35.219 - type: map_at_5 value: 36.466 - type: mrr_at_1 value: 56.650999999999996 - type: mrr_at_10 value: 63.574 - type: mrr_at_100 value: 63.966 - type: mrr_at_1000 value: 63.992000000000004 - type: mrr_at_3 value: 62.107 - type: mrr_at_5 value: 62.976 - type: ndcg_at_1 value: 56.650999999999996 - type: ndcg_at_10 value: 46.046 - type: ndcg_at_100 value: 48.916 - type: ndcg_at_1000 value: 50.410999999999994 - type: ndcg_at_3 value: 42.516999999999996 - type: ndcg_at_5 value: 44.374 - type: precision_at_1 value: 56.650999999999996 - type: precision_at_10 value: 9.392 - type: precision_at_100 value: 1.166 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 26.068 - type: precision_at_5 value: 17.11 - type: recall_at_1 value: 28.325 - type: recall_at_10 value: 46.961999999999996 - type: recall_at_100 value: 58.318999999999996 - type: recall_at_1000 value: 68.298 - type: recall_at_3 value: 39.102 - type: recall_at_5 value: 42.775 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 40.461716044632546 - type: f1 value: 33.890745966734315 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 72.21000000000001 - type: ap value: 66.59963731769069 - type: f1 value: 71.97616824840041 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 78.25515947467167 - type: ap value: 38.265118237185064 - type: f1 value: 70.73962826410575 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 63.98362797180168 - type: cos_sim_spearman value: 71.97575564053473 - type: euclidean_pearson value: 70.56052438394708 - type: euclidean_spearman value: 72.48267176371337 - type: manhattan_pearson value: 70.7156268448442 - type: manhattan_spearman value: 72.61065396802094 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 55.775 - type: map_at_10 value: 65.074 - type: map_at_100 value: 65.596 - type: map_at_1000 value: 65.618 - type: map_at_3 value: 62.92 - type: map_at_5 value: 64.277 - type: mrr_at_1 value: 57.708000000000006 - type: mrr_at_10 value: 65.824 - type: mrr_at_100 value: 66.286 - type: mrr_at_1000 value: 66.306 - type: mrr_at_3 value: 63.871 - type: mrr_at_5 value: 65.093 - type: ndcg_at_1 value: 57.708000000000006 - type: ndcg_at_10 value: 69.309 - type: ndcg_at_100 value: 71.723 - type: ndcg_at_1000 value: 72.313 - type: ndcg_at_3 value: 65.134 - type: ndcg_at_5 value: 67.476 - type: precision_at_1 value: 57.708000000000006 - type: precision_at_10 value: 8.668 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 24.837999999999997 - type: precision_at_5 value: 16.128999999999998 - type: recall_at_1 value: 55.775 - type: recall_at_10 value: 81.702 - type: recall_at_100 value: 92.785 - type: recall_at_1000 value: 97.425 - type: recall_at_3 value: 70.587 - type: recall_at_5 value: 76.199 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 17.771 - type: map_at_10 value: 28.16 - type: map_at_100 value: 29.363 - type: map_at_1000 value: 29.431 - type: map_at_3 value: 24.767 - type: map_at_5 value: 26.706999999999997 - type: mrr_at_1 value: 18.252 - type: mrr_at_10 value: 28.666000000000004 - type: mrr_at_100 value: 29.837000000000003 - type: mrr_at_1000 value: 29.898999999999997 - type: mrr_at_3 value: 25.308000000000003 - type: mrr_at_5 value: 27.226 - type: ndcg_at_1 value: 18.252 - type: ndcg_at_10 value: 34.176 - type: ndcg_at_100 value: 40.138 - type: ndcg_at_1000 value: 41.923 - type: ndcg_at_3 value: 27.214 - type: ndcg_at_5 value: 30.695 - type: precision_at_1 value: 18.252 - type: precision_at_10 value: 5.503 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 11.667 - type: precision_at_5 value: 8.754000000000001 - type: recall_at_1 value: 17.771 - type: recall_at_10 value: 52.781 - type: recall_at_100 value: 80.638 - type: recall_at_1000 value: 94.46 - type: recall_at_3 value: 33.767 - type: recall_at_5 value: 42.172 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.93388052895577 - type: f1 value: 89.55553145791954 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 68.42490842490842 - type: f1 value: 67.01398674117826 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.2121414276184 - type: f1 value: 87.61981627763988 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.49013466958974 - type: f1 value: 85.09758510104221 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.22732162065257 - type: f1 value: 83.24580378090367 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 53.171790235081374 - type: f1 value: 51.93028909966765 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 66.5640674874601 - type: f1 value: 49.856876973153966 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 49.171597633136095 - type: f1 value: 32.166022205347545 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) config: es split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 65.71714476317545 - type: f1 value: 45.748971341625136 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 62.65267773253993 - type: f1 value: 45.904472624086026 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (hi) config: hi split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.8752240946576 - type: f1 value: 40.7359613185448 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (th) config: th split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 41.67088607594936 - type: f1 value: 28.12210726419673 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (af) config: af split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 43.29186281102892 - type: f1 value: 41.83461350696014 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (am) config: am split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 23.214525891055814 - type: f1 value: 22.364131190189962 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ar) config: ar split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 53.38264963012777 - type: f1 value: 50.74546702709091 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (az) config: az split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 39.55951580363147 - type: f1 value: 39.07769075741216 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (bn) config: bn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 56.73839946200403 - type: f1 value: 54.36728741542025 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (cy) config: cy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 39.99663752521857 - type: f1 value: 38.709817953652596 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (da) config: da split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 46.933422999327504 - type: f1 value: 45.32022679895763 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (de) config: de split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 45.820443846671154 - type: f1 value: 42.853155158197886 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (el) config: el split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 37.874915938130464 - type: f1 value: 35.9849010888881 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.08944182918628 - type: f1 value: 64.5039080809391 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (es) config: es split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.17350369872226 - type: f1 value: 60.0792530132073 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fa) config: fa split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 45.652320107599195 - type: f1 value: 44.28182554287625 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fi) config: fi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 40.282447881640884 - type: f1 value: 38.79927524886836 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fr) config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.60591795561533 - type: f1 value: 61.01451309609411 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (he) config: he split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 32.225958305312716 - type: f1 value: 30.903299940417906 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hi) config: hi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 59.46200403496974 - type: f1 value: 57.34556231956785 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hu) config: hu split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 40.907868190988566 - type: f1 value: 39.74702259997524 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hy) config: hy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 29.939475453934094 - type: f1 value: 28.462353413371353 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (id) config: id split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 59.14256893073302 - type: f1 value: 57.24600767871435 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (is) config: is split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 39.620040349697376 - type: f1 value: 38.414866180464735 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (it) config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.772024209818426 - type: f1 value: 51.05050942366993 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ja) config: ja split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 53.749159381304636 - type: f1 value: 52.04563008527909 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (jv) config: jv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 46.29455279085406 - type: f1 value: 43.84047527739209 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ka) config: ka split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.107599193006045 - type: f1 value: 24.58731463875415 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (km) config: km split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 27.21923335574984 - type: f1 value: 25.964338481976796 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (kn) config: kn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 47.96906523201077 - type: f1 value: 45.32239408435578 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ko) config: ko split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 40.53799596503026 - type: f1 value: 39.15655510771227 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (lv) config: lv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 43.140551445864155 - type: f1 value: 42.12232733095163 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ml) config: ml split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 53.69199731002017 - type: f1 value: 50.67085509122796 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (mn) config: mn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 33.37256220578346 - type: f1 value: 33.39335560955231 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ms) config: ms split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.94014794889038 - type: f1 value: 50.6207021226521 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (my) config: my split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.322797579018157 - type: f1 value: 23.94164121951907 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (nb) config: nb split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 44.11903160726294 - type: f1 value: 43.016752983579536 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (nl) config: nl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 44.03496973772697 - type: f1 value: 42.322828283176754 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pl) config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 41.63080026899798 - type: f1 value: 39.58824644978166 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pt) config: pt split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.7350369872226 - type: f1 value: 59.956752206079386 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ro) config: ro split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 45.72629455279086 - type: f1 value: 44.731249269647826 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ru) config: ru split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 47.61264290517822 - type: f1 value: 45.5280995218491 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sl) config: sl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 42.82784129119032 - type: f1 value: 41.37165985220223 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sq) config: sq split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 43.61466039004707 - type: f1 value: 43.164498227815535 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sv) config: sv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 44.64021519838602 - type: f1 value: 43.04775030948548 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sw) config: sw split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 45.54808338937458 - type: f1 value: 44.011677633779975 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ta) config: ta split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.2441156691325 - type: f1 value: 48.73592932403811 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (te) config: te split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 47.43443174176195 - type: f1 value: 45.08686598891457 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (th) config: th split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 36.87962340282448 - type: f1 value: 36.50540864756967 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (tl) config: tl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 45.9280430396772 - type: f1 value: 44.57216865343283 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (tr) config: tr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 38.591123066577 - type: f1 value: 37.886312373767446 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ur) config: ur split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.85272360457296 - type: f1 value: 49.43461566216979 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (vi) config: vi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.72225958305313 - type: f1 value: 56.95500715299434 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.74915938130464 - type: f1 value: 62.35543158488615 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-TW) config: zh-TW split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 59.95292535305985 - type: f1 value: 59.73499569346673 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (af) config: af split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 47.42098184263618 - type: f1 value: 45.22541854557743 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (am) config: am split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 24.707464694014796 - type: f1 value: 24.033506081882468 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ar) config: ar split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 62.09145931405515 - type: f1 value: 62.22048940230962 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (az) config: az split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 39.25016812373907 - type: f1 value: 38.35431952425269 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (bn) config: bn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.37256220578345 - type: f1 value: 63.12728180326932 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (cy) config: cy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 39.172831203765966 - type: f1 value: 37.078841372640234 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (da) config: da split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 49.11230665770006 - type: f1 value: 46.489580286547245 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (de) config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 50.7128446536651 - type: f1 value: 48.27782602378952 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (el) config: el split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 39.46536650975118 - type: f1 value: 37.4365280056047 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.26160053799597 - type: f1 value: 73.4478249967817 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (es) config: es split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.31203765971756 - type: f1 value: 68.70554437788068 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fa) config: fa split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 45.652320107599195 - type: f1 value: 44.55357745265521 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fi) config: fi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 38.94754539340955 - type: f1 value: 36.48927336173062 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fr) config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.69872225958305 - type: f1 value: 68.81347966311543 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (he) config: he split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.131809011432416 - type: f1 value: 30.212230946937474 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hi) config: hi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 65.57498318762609 - type: f1 value: 65.16084751135229 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hu) config: hu split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 42.965702757229316 - type: f1 value: 40.575896627739105 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hy) config: hy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 32.125084061869536 - type: f1 value: 30.708056882129476 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (id) config: id split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 65.10759919300607 - type: f1 value: 64.5007800119315 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (is) config: is split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 40.83725622057834 - type: f1 value: 37.855774705520886 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (it) config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 54.55279085406859 - type: f1 value: 52.73318944173822 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ja) config: ja split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 57.14525891055817 - type: f1 value: 55.96714177558203 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (jv) config: jv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 49.30060524546065 - type: f1 value: 47.82999154670342 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ka) config: ka split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 25.85743106926698 - type: f1 value: 24.974946990729716 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (km) config: km split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 31.180228648285137 - type: f1 value: 28.22387838219335 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (kn) config: kn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 53.00941492938802 - type: f1 value: 52.39610045092559 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ko) config: ko split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 40.24546065904505 - type: f1 value: 38.99779773215032 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (lv) config: lv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 41.88298587760592 - type: f1 value: 39.53867071594289 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ml) config: ml split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.078681909885674 - type: f1 value: 58.47368723772022 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (mn) config: mn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 33.33893745796907 - type: f1 value: 32.113466354321226 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ms) config: ms split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 57.454606590450574 - type: f1 value: 56.13075383338251 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (my) config: my split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 27.19569603227976 - type: f1 value: 26.300773160344015 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nb) config: nb split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 46.78547410894418 - type: f1 value: 44.233771335183015 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nl) config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 48.4196368527236 - type: f1 value: 45.55838648206857 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pl) config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 41.63080026899798 - type: f1 value: 40.77775839499525 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pt) config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.408876933423 - type: f1 value: 66.7358693871042 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ro) config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 50.077336919973106 - type: f1 value: 48.572749739090014 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ru) config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 49.942837928715534 - type: f1 value: 49.34771836662566 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sl) config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 43.43308675184936 - type: f1 value: 41.818008297000986 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sq) config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 44.082044384667114 - type: f1 value: 43.25002746432129 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sv) config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 46.45258910558171 - type: f1 value: 44.00958237591922 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sw) config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 49.53261600537996 - type: f1 value: 48.01969699634672 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ta) config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 56.792199058507066 - type: f1 value: 56.54421925671813 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (te) config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 54.0114324142569 - type: f1 value: 52.29830350891558 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (th) config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 38.584398117014125 - type: f1 value: 36.551426239639575 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tl) config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 48.07330195023538 - type: f1 value: 46.463553675519975 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tr) config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 40.645595158036315 - type: f1 value: 40.21280676607986 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ur) config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 57.74714189643577 - type: f1 value: 56.8673027258351 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (vi) config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 65.83389374579693 - type: f1 value: 66.11273939782248 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.38735709482181 - type: f1 value: 72.89481650271512 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-TW) config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.63685272360458 - type: f1 value: 70.72285841806938 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 30.8 - type: map_at_10 value: 34.782000000000004 - type: map_at_100 value: 35.333999999999996 - type: map_at_1000 value: 35.405 - type: map_at_3 value: 34.0 - type: map_at_5 value: 34.345 - type: mrr_at_1 value: 30.8 - type: mrr_at_10 value: 34.782000000000004 - type: mrr_at_100 value: 35.333999999999996 - type: mrr_at_1000 value: 35.405 - type: mrr_at_3 value: 34.0 - type: mrr_at_5 value: 34.345 - type: ndcg_at_1 value: 30.8 - type: ndcg_at_10 value: 36.675000000000004 - type: ndcg_at_100 value: 39.633 - type: ndcg_at_1000 value: 41.904 - type: ndcg_at_3 value: 35.028 - type: ndcg_at_5 value: 35.648 - type: precision_at_1 value: 30.8 - type: precision_at_10 value: 4.26 - type: precision_at_100 value: 0.571 - type: precision_at_1000 value: 0.076 - type: precision_at_3 value: 12.667 - type: precision_at_5 value: 7.9 - type: recall_at_1 value: 30.8 - type: recall_at_10 value: 42.6 - type: recall_at_100 value: 57.099999999999994 - type: recall_at_1000 value: 75.8 - type: recall_at_3 value: 38.0 - type: recall_at_5 value: 39.5 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.84536559870361 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.714921841841605 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.52145905910035 - type: mrr value: 31.551577344311845 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 23.6853605350459 - type: mrr value: 22.341269841269842 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 63.16666666666666 - type: f1 value: 63.09453591106835 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 3.7060000000000004 - type: map_at_10 value: 9.032 - type: map_at_100 value: 11.395 - type: map_at_1000 value: 12.713 - type: map_at_3 value: 6.502 - type: map_at_5 value: 7.8100000000000005 - type: mrr_at_1 value: 37.461 - type: mrr_at_10 value: 45.839999999999996 - type: mrr_at_100 value: 46.513 - type: mrr_at_1000 value: 46.571 - type: mrr_at_3 value: 43.55 - type: mrr_at_5 value: 44.773 - type: ndcg_at_1 value: 35.913000000000004 - type: ndcg_at_10 value: 27.340999999999998 - type: ndcg_at_100 value: 25.197000000000003 - type: ndcg_at_1000 value: 34.632000000000005 - type: ndcg_at_3 value: 31.952 - type: ndcg_at_5 value: 30.244 - type: precision_at_1 value: 37.461 - type: precision_at_10 value: 20.495 - type: precision_at_100 value: 6.551 - type: precision_at_1000 value: 1.966 - type: precision_at_3 value: 30.753000000000004 - type: precision_at_5 value: 26.935 - type: recall_at_1 value: 3.7060000000000004 - type: recall_at_10 value: 12.958 - type: recall_at_100 value: 26.582 - type: recall_at_1000 value: 59.724 - type: recall_at_3 value: 7.503 - type: recall_at_5 value: 9.808 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 22.201999999999998 - type: map_at_10 value: 33.76 - type: map_at_100 value: 34.867 - type: map_at_1000 value: 34.92 - type: map_at_3 value: 30.233999999999998 - type: map_at_5 value: 32.291 - type: mrr_at_1 value: 25.232 - type: mrr_at_10 value: 36.239 - type: mrr_at_100 value: 37.119 - type: mrr_at_1000 value: 37.162 - type: mrr_at_3 value: 33.213 - type: mrr_at_5 value: 35.02 - type: ndcg_at_1 value: 25.232 - type: ndcg_at_10 value: 40.046 - type: ndcg_at_100 value: 45.025 - type: ndcg_at_1000 value: 46.459 - type: ndcg_at_3 value: 33.343 - type: ndcg_at_5 value: 36.801 - type: precision_at_1 value: 25.232 - type: precision_at_10 value: 6.796 - type: precision_at_100 value: 0.959 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 15.276 - type: precision_at_5 value: 11.17 - type: recall_at_1 value: 22.201999999999998 - type: recall_at_10 value: 56.733 - type: recall_at_100 value: 79.041 - type: recall_at_1000 value: 90.08500000000001 - type: recall_at_3 value: 39.412000000000006 - type: recall_at_5 value: 47.352 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 62.53383865728208 - type: cos_sim_ap value: 66.3197921045625 - type: cos_sim_f1 value: 69.3385214007782 - type: cos_sim_precision value: 54.89833641404805 - type: cos_sim_recall value: 94.08658922914466 - type: dot_accuracy value: 59.7184623714131 - type: dot_ap value: 61.53586693000539 - type: dot_f1 value: 68.26923076923077 - type: dot_precision value: 52.53272623790552 - type: dot_recall value: 97.46568109820485 - type: euclidean_accuracy value: 62.912831618841366 - type: euclidean_ap value: 67.15479155849464 - type: euclidean_f1 value: 70.64071370640713 - type: euclidean_precision value: 57.34035549703752 - type: euclidean_recall value: 91.97465681098205 - type: manhattan_accuracy value: 63.50839198700595 - type: manhattan_ap value: 67.55807251483273 - type: manhattan_f1 value: 70.58356490670901 - type: manhattan_precision value: 56.55216284987278 - type: manhattan_recall value: 93.8753959873284 - type: max_accuracy value: 63.50839198700595 - type: max_ap value: 67.55807251483273 - type: max_f1 value: 70.64071370640713 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 87.11 - type: ap value: 84.20351278644551 - type: f1 value: 87.10043002123766 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 13.050279647770473 - type: cos_sim_spearman value: 14.227909232579874 - type: euclidean_pearson value: 16.372629300358096 - type: euclidean_spearman value: 14.68140021547196 - type: manhattan_pearson value: 16.266960163157336 - type: manhattan_spearman value: 14.627750758965616 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 30.56036276943463 - type: cos_sim_spearman value: 32.918859292204 - type: euclidean_pearson value: 31.679745438037195 - type: euclidean_spearman value: 33.68461814972644 - type: manhattan_pearson value: 31.994557954084563 - type: manhattan_spearman value: 33.97758185204816 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 68.327 - type: map_at_10 value: 81.938 - type: map_at_100 value: 82.581 - type: map_at_1000 value: 82.60300000000001 - type: map_at_3 value: 78.89399999999999 - type: map_at_5 value: 80.816 - type: mrr_at_1 value: 78.75 - type: mrr_at_10 value: 85.302 - type: mrr_at_100 value: 85.432 - type: mrr_at_1000 value: 85.434 - type: mrr_at_3 value: 84.128 - type: mrr_at_5 value: 84.91199999999999 - type: ndcg_at_1 value: 78.74 - type: ndcg_at_10 value: 86.042 - type: ndcg_at_100 value: 87.468 - type: ndcg_at_1000 value: 87.641 - type: ndcg_at_3 value: 82.799 - type: ndcg_at_5 value: 84.603 - type: precision_at_1 value: 78.74 - type: precision_at_10 value: 13.071 - type: precision_at_100 value: 1.508 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.08 - type: precision_at_5 value: 23.87 - type: recall_at_1 value: 68.327 - type: recall_at_10 value: 93.962 - type: recall_at_100 value: 99.054 - type: recall_at_1000 value: 99.9 - type: recall_at_3 value: 84.788 - type: recall_at_5 value: 89.73 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 41.337989152483956 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 51.2046136625677 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.763 - type: map_at_10 value: 8.785 - type: map_at_100 value: 10.266 - type: map_at_1000 value: 10.506 - type: map_at_3 value: 6.551 - type: map_at_5 value: 7.670000000000001 - type: mrr_at_1 value: 18.5 - type: mrr_at_10 value: 27.771 - type: mrr_at_100 value: 28.842000000000002 - type: mrr_at_1000 value: 28.913 - type: mrr_at_3 value: 24.767 - type: mrr_at_5 value: 26.457000000000004 - type: ndcg_at_1 value: 18.5 - type: ndcg_at_10 value: 15.312000000000001 - type: ndcg_at_100 value: 21.599 - type: ndcg_at_1000 value: 26.473999999999997 - type: ndcg_at_3 value: 14.821000000000002 - type: ndcg_at_5 value: 12.836 - type: precision_at_1 value: 18.5 - type: precision_at_10 value: 7.779999999999999 - type: precision_at_100 value: 1.69 - type: precision_at_1000 value: 0.28700000000000003 - type: precision_at_3 value: 13.667000000000002 - type: precision_at_5 value: 11.08 - type: recall_at_1 value: 3.763 - type: recall_at_10 value: 15.798000000000002 - type: recall_at_100 value: 34.313 - type: recall_at_1000 value: 58.318000000000005 - type: recall_at_3 value: 8.312999999999999 - type: recall_at_5 value: 11.238 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.33402689861924 - type: cos_sim_spearman value: 78.52738315932625 - type: euclidean_pearson value: 80.800678573052 - type: euclidean_spearman value: 77.86666946799137 - type: manhattan_pearson value: 81.03106755866989 - type: manhattan_spearman value: 78.0676393879487 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.86998503723257 - type: cos_sim_spearman value: 74.07437934108376 - type: euclidean_pearson value: 80.91626452869946 - type: euclidean_spearman value: 76.88419802521403 - type: manhattan_pearson value: 81.50196980117957 - type: manhattan_spearman value: 77.2456891009073 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.19616084290932 - type: cos_sim_spearman value: 81.80834431353927 - type: euclidean_pearson value: 81.25429737195789 - type: euclidean_spearman value: 82.00934127307355 - type: manhattan_pearson value: 81.67403556759655 - type: manhattan_spearman value: 82.42359818976753 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.50884725941148 - type: cos_sim_spearman value: 77.0493522248929 - type: euclidean_pearson value: 79.15856111178543 - type: euclidean_spearman value: 77.24292975474096 - type: manhattan_pearson value: 79.22641788874807 - type: manhattan_spearman value: 77.37101663798234 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 83.75652767224308 - type: cos_sim_spearman value: 84.61113973428688 - type: euclidean_pearson value: 83.73646379542737 - type: euclidean_spearman value: 84.47126779405652 - type: manhattan_pearson value: 83.89617307570857 - type: manhattan_spearman value: 84.6073703393468 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.16302763567215 - type: cos_sim_spearman value: 83.08923353997561 - type: euclidean_pearson value: 80.08338016232464 - type: euclidean_spearman value: 80.40181608724076 - type: manhattan_pearson value: 80.02358856208708 - type: manhattan_spearman value: 80.30032329982274 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ko-ko) config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 56.45965932801117 - type: cos_sim_spearman value: 57.28270045199294 - type: euclidean_pearson value: 57.3615782157595 - type: euclidean_spearman value: 56.94348399074146 - type: manhattan_pearson value: 57.9426531718209 - type: manhattan_spearman value: 57.61844831263504 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ar-ar) config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 80.2973366536596 - type: cos_sim_spearman value: 80.60259304741632 - type: euclidean_pearson value: 78.30266089843892 - type: euclidean_spearman value: 78.06065126709282 - type: manhattan_pearson value: 78.61370380599344 - type: manhattan_spearman value: 78.45738598619143 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-ar) config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 72.35020162217042 - type: cos_sim_spearman value: 72.59857902847162 - type: euclidean_pearson value: 65.03547299350457 - type: euclidean_spearman value: 64.16617373109685 - type: manhattan_pearson value: 65.68996569454929 - type: manhattan_spearman value: 64.88542254595046 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-de) config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 39.766484883595425 - type: cos_sim_spearman value: 40.3429946300341 - type: euclidean_pearson value: 39.47427150040957 - type: euclidean_spearman value: 39.072525589079696 - type: manhattan_pearson value: 40.56345338078474 - type: manhattan_spearman value: 40.444629078138036 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.83798941013089 - type: cos_sim_spearman value: 89.15159294402415 - type: euclidean_pearson value: 87.9810618414505 - type: euclidean_spearman value: 87.90818542026535 - type: manhattan_pearson value: 88.06116863048229 - type: manhattan_spearman value: 88.00182442010694 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-tr) config: en-tr split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 7.416028059666332 - type: cos_sim_spearman value: 6.792945857606915 - type: euclidean_pearson value: 11.485332917116061 - type: euclidean_spearman value: 9.793932873423419 - type: manhattan_pearson value: 9.148469412558393 - type: manhattan_spearman value: 7.803450524017845 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-en) config: es-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 80.16381852152489 - type: cos_sim_spearman value: 81.80324089694928 - type: euclidean_pearson value: 76.41433274302783 - type: euclidean_spearman value: 77.15238726996526 - type: manhattan_pearson value: 77.08610108551368 - type: manhattan_spearman value: 77.99971298324311 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-es) config: es-es split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.11032272383456 - type: cos_sim_spearman value: 85.64528002839239 - type: euclidean_pearson value: 85.54301672487198 - type: euclidean_spearman value: 84.21727806530393 - type: manhattan_pearson value: 85.57145576255618 - type: manhattan_spearman value: 84.07127479487694 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (fr-en) config: fr-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 79.73703272230806 - type: cos_sim_spearman value: 79.9424510113259 - type: euclidean_pearson value: 77.64485173960838 - type: euclidean_spearman value: 77.54693014468836 - type: manhattan_pearson value: 77.96911553781774 - type: manhattan_spearman value: 77.87266778206842 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (it-en) config: it-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 37.260672179617515 - type: cos_sim_spearman value: 34.80434004457536 - type: euclidean_pearson value: 38.55806751295782 - type: euclidean_spearman value: 36.129700913023115 - type: manhattan_pearson value: 40.74316244582763 - type: manhattan_spearman value: 38.60667540883322 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (nl-en) config: nl-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 38.038311386574456 - type: cos_sim_spearman value: 33.576193063894195 - type: euclidean_pearson value: 33.712663568034316 - type: euclidean_spearman value: 32.560617375956916 - type: manhattan_pearson value: 35.60457167895616 - type: manhattan_spearman value: 34.63036216555931 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 61.01583638162472 - type: cos_sim_spearman value: 62.92281428893316 - type: euclidean_pearson value: 62.939630289711815 - type: euclidean_spearman value: 64.15209661725994 - type: manhattan_pearson value: 64.24261705090608 - type: manhattan_spearman value: 64.78283158164017 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de) config: de split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 21.529440799555704 - type: cos_sim_spearman value: 26.62727800620091 - type: euclidean_pearson value: 16.837244578590123 - type: euclidean_spearman value: 25.012107525591425 - type: manhattan_pearson value: 18.445531476179454 - type: manhattan_spearman value: 27.070240480795153 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es) config: es split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 49.655500043363624 - type: cos_sim_spearman value: 56.31248457847469 - type: euclidean_pearson value: 48.787154598246616 - type: euclidean_spearman value: 52.90454409579225 - type: manhattan_pearson value: 55.392327232639836 - type: manhattan_spearman value: 57.3726886727899 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl) config: pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 2.9137753115190304 - type: cos_sim_spearman value: 15.062114976486532 - type: euclidean_pearson value: -2.034404984782681 - type: euclidean_spearman value: 14.683481835467338 - type: manhattan_pearson value: -0.22204468354050833 - type: manhattan_spearman value: 15.526420635759743 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (tr) config: tr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 4.3616620418459915 - type: cos_sim_spearman value: 22.11078316878173 - type: euclidean_pearson value: 15.111514877123403 - type: euclidean_spearman value: 21.232869644925973 - type: manhattan_pearson value: 19.71276925909529 - type: manhattan_spearman value: 25.704469862313466 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (ar) config: ar split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 44.25888840250496 - type: cos_sim_spearman value: 54.82352971568842 - type: euclidean_pearson value: 48.00261414068268 - type: euclidean_spearman value: 53.3721608428832 - type: manhattan_pearson value: 50.6442021864215 - type: manhattan_spearman value: 55.352339945631954 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (ru) config: ru split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 0.08233514100531068 - type: cos_sim_spearman value: 28.771721168834276 - type: euclidean_pearson value: 10.783524938899138 - type: euclidean_spearman value: 24.67831010432439 - type: manhattan_pearson value: 16.98415610436092 - type: manhattan_spearman value: 25.81670115913176 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 36.86678706245425 - type: cos_sim_spearman value: 40.9736918674032 - type: euclidean_pearson value: 26.42365971768556 - type: euclidean_spearman value: 30.479818788692054 - type: manhattan_pearson value: 41.08694658968258 - type: manhattan_spearman value: 45.080877435751084 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (fr) config: fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 75.98114217777062 - type: cos_sim_spearman value: 78.7295845730892 - type: euclidean_pearson value: 76.99433076522276 - type: euclidean_spearman value: 79.71421663258973 - type: manhattan_pearson value: 78.65656344143478 - type: manhattan_spearman value: 80.60968909615123 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-en) config: de-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 47.33261398683554 - type: cos_sim_spearman value: 49.547954534754666 - type: euclidean_pearson value: 48.23362592012922 - type: euclidean_spearman value: 49.17277986369927 - type: manhattan_pearson value: 49.06792311033889 - type: manhattan_spearman value: 51.27529282708198 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es-en) config: es-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.10070360470756 - type: cos_sim_spearman value: 71.03150249855938 - type: euclidean_pearson value: 67.05372897033872 - type: euclidean_spearman value: 69.73291838049877 - type: manhattan_pearson value: 70.34740916239467 - type: manhattan_spearman value: 72.40053406658815 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (it) config: it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 56.581317404418904 - type: cos_sim_spearman value: 62.61318021096797 - type: euclidean_pearson value: 57.4403074342031 - type: euclidean_spearman value: 60.04897783631694 - type: manhattan_pearson value: 58.441729285803014 - type: manhattan_spearman value: 60.70510326005463 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl-en) config: pl-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 47.064414464023905 - type: cos_sim_spearman value: 43.716659075869465 - type: euclidean_pearson value: 43.81699490724336 - type: euclidean_spearman value: 43.784380306563726 - type: manhattan_pearson value: 53.664583329563264 - type: manhattan_spearman value: 45.399271192350135 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh-en) config: zh-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.585903017365055 - type: cos_sim_spearman value: 63.90147651068459 - type: euclidean_pearson value: 50.21918146173064 - type: euclidean_spearman value: 53.02530618040754 - type: manhattan_pearson value: 62.7472089813117 - type: manhattan_spearman value: 63.90440606248973 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es-it) config: es-it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 59.06715980430013 - type: cos_sim_spearman value: 61.2993294424547 - type: euclidean_pearson value: 53.67335552456426 - type: euclidean_spearman value: 55.32940583953816 - type: manhattan_pearson value: 58.08097600675386 - type: manhattan_spearman value: 57.1966250850173 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-fr) config: de-fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 18.94271219818519 - type: cos_sim_spearman value: 22.355519793818935 - type: euclidean_pearson value: 14.336479135636187 - type: euclidean_spearman value: 18.862751864788684 - type: manhattan_pearson value: 14.481730447681057 - type: manhattan_spearman value: 17.572142526671563 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-pl) config: de-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 20.644357537446464 - type: cos_sim_spearman value: 35.32083671407284 - type: euclidean_pearson value: 28.24720906134992 - type: euclidean_spearman value: 46.437508077438395 - type: manhattan_pearson value: 42.09834718968137 - type: manhattan_spearman value: 53.02744622635869 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (fr-pl) config: fr-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 71.84986730523782 - type: cos_sim_spearman value: 73.24670207647144 - type: euclidean_pearson value: 62.450055500805604 - type: euclidean_spearman value: 61.97797868009122 - type: manhattan_pearson value: 56.32083882980946 - type: manhattan_spearman value: 39.440531887330785 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 78.11479317838469 - type: cos_sim_spearman value: 77.7709743500025 - type: euclidean_pearson value: 78.83834281752932 - type: euclidean_spearman value: 78.21978829646487 - type: manhattan_pearson value: 79.36075578990533 - type: manhattan_spearman value: 78.72958965446072 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.92539499228975 - type: cos_sim_spearman value: 83.63025944536395 - type: euclidean_pearson value: 81.54744230098872 - type: euclidean_spearman value: 81.08707735758752 - type: manhattan_pearson value: 81.50252353111375 - type: manhattan_spearman value: 81.00641210322735 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 75.12690809334019 - type: mrr value: 92.28846951886169 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 47.15 - type: map_at_10 value: 56.748 - type: map_at_100 value: 57.528999999999996 - type: map_at_1000 value: 57.56400000000001 - type: map_at_3 value: 53.691 - type: map_at_5 value: 55.656000000000006 - type: mrr_at_1 value: 49.667 - type: mrr_at_10 value: 58.24700000000001 - type: mrr_at_100 value: 58.855000000000004 - type: mrr_at_1000 value: 58.888 - type: mrr_at_3 value: 55.72200000000001 - type: mrr_at_5 value: 57.272 - type: ndcg_at_1 value: 49.667 - type: ndcg_at_10 value: 61.739 - type: ndcg_at_100 value: 65.17399999999999 - type: ndcg_at_1000 value: 66.122 - type: ndcg_at_3 value: 56.266000000000005 - type: ndcg_at_5 value: 59.357000000000006 - type: precision_at_1 value: 49.667 - type: precision_at_10 value: 8.5 - type: precision_at_100 value: 1.04 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 22.111 - type: precision_at_5 value: 15.133 - type: recall_at_1 value: 47.15 - type: recall_at_10 value: 75.52799999999999 - type: recall_at_100 value: 91.167 - type: recall_at_1000 value: 98.667 - type: recall_at_3 value: 60.978 - type: recall_at_5 value: 68.839 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71188118811881 - type: cos_sim_ap value: 92.0858173884619 - type: cos_sim_f1 value: 85.48864758144126 - type: cos_sim_precision value: 84.40545808966861 - type: cos_sim_recall value: 86.6 - type: dot_accuracy value: 99.57722772277228 - type: dot_ap value: 83.92226742515372 - type: dot_f1 value: 78.85091629519565 - type: dot_precision value: 78.11579980372915 - type: dot_recall value: 79.60000000000001 - type: euclidean_accuracy value: 99.6970297029703 - type: euclidean_ap value: 91.69378964699095 - type: euclidean_f1 value: 85.08771929824562 - type: euclidean_precision value: 82.98479087452472 - type: euclidean_recall value: 87.3 - type: manhattan_accuracy value: 99.7019801980198 - type: manhattan_ap value: 92.00969741996086 - type: manhattan_f1 value: 84.95752123938031 - type: manhattan_precision value: 84.91508491508492 - type: manhattan_recall value: 85.0 - type: max_accuracy value: 99.71188118811881 - type: max_ap value: 92.0858173884619 - type: max_f1 value: 85.48864758144126 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 54.50675991473899 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 31.12415042272221 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 47.37961638353922 - type: mrr value: 48.04425558102029 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.358583236464177 - type: cos_sim_spearman value: 32.06044850511017 - type: dot_pearson value: 30.36343303587471 - type: dot_spearman value: 30.303932242144704 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 63.73951666189072 - type: mrr value: 73.54706021429108 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 16.892 - type: map_at_10 value: 40.215 - type: map_at_100 value: 43.9 - type: map_at_1000 value: 44.185 - type: map_at_3 value: 30.008000000000003 - type: map_at_5 value: 35.465 - type: mrr_at_1 value: 63.931000000000004 - type: mrr_at_10 value: 70.35 - type: mrr_at_100 value: 70.762 - type: mrr_at_1000 value: 70.784 - type: mrr_at_3 value: 68.863 - type: mrr_at_5 value: 69.758 - type: ndcg_at_1 value: 63.931000000000004 - type: ndcg_at_10 value: 51.573 - type: ndcg_at_100 value: 59.067 - type: ndcg_at_1000 value: 62.388 - type: ndcg_at_3 value: 55.422000000000004 - type: ndcg_at_5 value: 52.322 - type: precision_at_1 value: 63.931000000000004 - type: precision_at_10 value: 25.373 - type: precision_at_100 value: 3.894 - type: precision_at_1000 value: 0.47400000000000003 - type: precision_at_3 value: 48.083 - type: precision_at_5 value: 38.513 - type: recall_at_1 value: 16.892 - type: recall_at_10 value: 49.945 - type: recall_at_100 value: 73.41499999999999 - type: recall_at_1000 value: 89.776 - type: recall_at_3 value: 32.544000000000004 - type: recall_at_5 value: 40.501 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 44.153999999999996 - type: f1 value: 42.69123774230511 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22300000000000003 - type: map_at_10 value: 1.7999999999999998 - type: map_at_100 value: 9.098 - type: map_at_1000 value: 20.59 - type: map_at_3 value: 0.6459999999999999 - type: map_at_5 value: 1.006 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 91.5 - type: mrr_at_100 value: 91.5 - type: mrr_at_1000 value: 91.5 - type: mrr_at_3 value: 91.0 - type: mrr_at_5 value: 91.5 - type: ndcg_at_1 value: 80.0 - type: ndcg_at_10 value: 72.992 - type: ndcg_at_100 value: 51.778999999999996 - type: ndcg_at_1000 value: 44.473 - type: ndcg_at_3 value: 77.531 - type: ndcg_at_5 value: 74.685 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 78.60000000000001 - type: precision_at_100 value: 52.800000000000004 - type: precision_at_1000 value: 19.736 - type: precision_at_3 value: 83.333 - type: precision_at_5 value: 80.0 - type: recall_at_1 value: 0.22300000000000003 - type: recall_at_10 value: 2.016 - type: recall_at_100 value: 12.21 - type: recall_at_1000 value: 41.427 - type: recall_at_3 value: 0.6839999999999999 - type: recall_at_5 value: 1.083 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (sqi-eng) config: sqi-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 11.0 - type: f1 value: 8.487309997179562 - type: precision value: 7.935185890268856 - type: recall value: 11.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fry-eng) config: fry-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 23.699421965317917 - type: f1 value: 18.09982567208001 - type: precision value: 16.582017825552963 - type: recall value: 23.699421965317917 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kur-eng) config: kur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.780487804878048 - type: f1 value: 6.484836753129436 - type: precision value: 5.916220801747723 - type: recall value: 8.780487804878048 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tur-eng) config: tur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.0 - type: f1 value: 3.493223480735001 - type: precision value: 3.1492116349139385 - type: recall value: 5.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (deu-eng) config: deu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 33.6 - type: f1 value: 29.339340352229065 - type: precision value: 27.997920626374693 - type: recall value: 33.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nld-eng) config: nld-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 20.200000000000003 - type: f1 value: 16.330981736231458 - type: precision value: 15.250949969794044 - type: recall value: 20.200000000000003 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ron-eng) config: ron-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 19.6 - type: f1 value: 14.951120083366323 - type: precision value: 13.617335362707001 - type: recall value: 19.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ang-eng) config: ang-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 20.149253731343283 - type: f1 value: 13.312899786780385 - type: precision value: 11.979388770433545 - type: recall value: 20.149253731343283 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ido-eng) config: ido-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 31.4 - type: f1 value: 26.21323201417634 - type: precision value: 24.607830064672168 - type: recall value: 31.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (jav-eng) config: jav-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 18.048780487804876 - type: f1 value: 14.347798542920492 - type: precision value: 13.301672920575362 - type: recall value: 18.048780487804876 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (isl-eng) config: isl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.2 - type: f1 value: 3.2713297295122503 - type: precision value: 2.978548911585725 - type: recall value: 5.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (slv-eng) config: slv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 7.411907654921021 - type: f1 value: 5.412915976323278 - type: precision value: 4.975402373122839 - type: recall value: 7.411907654921021 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cym-eng) config: cym-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.521739130434783 - type: f1 value: 5.871393789897329 - type: precision value: 5.350472658912557 - type: recall value: 8.521739130434783 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kaz-eng) config: kaz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 1.565217391304348 - type: f1 value: 0.7422394530145001 - type: precision value: 0.7201734373569025 - type: recall value: 1.565217391304348 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (est-eng) config: est-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.3 - type: f1 value: 3.0838354401589694 - type: precision value: 2.709942839090994 - type: recall value: 5.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (heb-eng) config: heb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.8 - type: f1 value: 0.24583802742178057 - type: precision value: 0.18710578268453032 - type: recall value: 0.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (gla-eng) config: gla-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.945717732207479 - type: f1 value: 2.7266734043909437 - type: precision value: 2.3247505400014186 - type: recall value: 4.945717732207479 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mar-eng) config: mar-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 54.2 - type: f1 value: 47.22780366692132 - type: precision value: 44.740178571428565 - type: recall value: 54.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lat-eng) config: lat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 25.8 - type: f1 value: 19.547406382656526 - type: precision value: 17.80766233766234 - type: recall value: 25.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bel-eng) config: bel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.9 - type: f1 value: 3.283031457969928 - type: precision value: 3.0361515007649467 - type: recall value: 4.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pms-eng) config: pms-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 22.476190476190478 - type: f1 value: 17.494204011570957 - type: precision value: 16.16236240785113 - type: recall value: 22.476190476190478 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (gle-eng) config: gle-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 6.3 - type: f1 value: 3.461898170471662 - type: precision value: 2.975546957350575 - type: recall value: 6.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pes-eng) config: pes-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.6 - type: f1 value: 5.874235156578609 - type: precision value: 5.201352547725499 - type: recall value: 8.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nob-eng) config: nob-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 15.2 - type: f1 value: 11.908986787697534 - type: precision value: 11.090628985937808 - type: recall value: 15.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bul-eng) config: bul-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 6.9 - type: f1 value: 4.58348360335125 - type: precision value: 4.183620994869927 - type: recall value: 6.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cbk-eng) config: cbk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 62.1 - type: f1 value: 55.70845598845599 - type: precision value: 53.22281746031747 - type: recall value: 62.1 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hun-eng) config: hun-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.8 - type: f1 value: 3.246932234432234 - type: precision value: 2.9738765839703265 - type: recall value: 4.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (uig-eng) config: uig-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.8999999999999999 - type: f1 value: 0.5331481481481481 - type: precision value: 0.4918990604783396 - type: recall value: 0.8999999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (rus-eng) config: rus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 31.7 - type: f1 value: 25.22406237037816 - type: precision value: 23.27273155929038 - type: recall value: 31.7 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (spa-eng) config: spa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.5 - type: f1 value: 95.48333333333333 - type: precision value: 95.0 - type: recall value: 96.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hye-eng) config: hye-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.40431266846361186 - type: f1 value: 0.22521185350542844 - type: precision value: 0.20245384171411912 - type: recall value: 0.40431266846361186 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tel-eng) config: tel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 43.162393162393165 - type: f1 value: 35.83662064431295 - type: precision value: 33.66590199923534 - type: recall value: 43.162393162393165 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (afr-eng) config: afr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 12.2 - type: f1 value: 9.007009351120605 - type: precision value: 8.26509907921979 - type: recall value: 12.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mon-eng) config: mon-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 2.0454545454545454 - type: f1 value: 0.846869670733307 - type: precision value: 0.719285857023819 - type: recall value: 2.0454545454545454 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (arz-eng) config: arz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 56.18448637316562 - type: f1 value: 49.41850369523325 - type: precision value: 46.84486373165618 - type: recall value: 56.18448637316562 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hrv-eng) config: hrv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.4 - type: f1 value: 6.274306734742452 - type: precision value: 5.854786915151029 - type: recall value: 8.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nov-eng) config: nov-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 45.13618677042802 - type: f1 value: 38.784818726452976 - type: precision value: 36.65848310789945 - type: recall value: 45.13618677042802 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (gsw-eng) config: gsw-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 23.076923076923077 - type: f1 value: 17.501757501757503 - type: precision value: 16.06289721674337 - type: recall value: 23.076923076923077 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nds-eng) config: nds-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 15.8 - type: f1 value: 11.834682187321722 - type: precision value: 10.871016304088595 - type: recall value: 15.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ukr-eng) config: ukr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 7.3 - type: f1 value: 4.929314970921539 - type: precision value: 4.427714750128542 - type: recall value: 7.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (uzb-eng) config: uzb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.14018691588785 - type: f1 value: 2.543797914741945 - type: precision value: 2.1476927403586066 - type: recall value: 5.14018691588785 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lit-eng) config: lit-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.0 - type: f1 value: 3.173243817101591 - type: precision value: 2.8643206769285485 - type: recall value: 5.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ina-eng) config: ina-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 69.5 - type: f1 value: 63.89614902641219 - type: precision value: 61.628650793650785 - type: recall value: 69.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lfn-eng) config: lfn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 41.8 - type: f1 value: 37.523909714712914 - type: precision value: 36.054581750900766 - type: recall value: 41.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (zsm-eng) config: zsm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 79.2 - type: f1 value: 74.88805555555554 - type: precision value: 73.05083333333333 - type: recall value: 79.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ita-eng) config: ita-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 43.5 - type: f1 value: 37.28660019590605 - type: precision value: 35.18067447433519 - type: recall value: 43.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cmn-eng) config: cmn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.5 - type: f1 value: 92.95 - type: precision value: 92.2 - type: recall value: 94.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lvs-eng) config: lvs-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.2 - type: f1 value: 3.5297755651484026 - type: precision value: 3.190013722690584 - type: recall value: 5.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (glg-eng) config: glg-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.7 - type: f1 value: 69.2602380952381 - type: precision value: 67.03261904761905 - type: recall value: 74.7 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ceb-eng) config: ceb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.0 - type: f1 value: 5.639611303143687 - type: precision value: 5.209856824277429 - type: recall value: 8.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bre-eng) config: bre-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 6.1 - type: f1 value: 3.847611167634209 - type: precision value: 3.3324923687423693 - type: recall value: 6.1 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ben-eng) config: ben-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 75.5 - type: f1 value: 70.14214285714286 - type: precision value: 67.88761904761904 - type: recall value: 75.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (swg-eng) config: swg-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 20.535714285714285 - type: f1 value: 16.437074829931973 - type: precision value: 15.459837781266353 - type: recall value: 20.535714285714285 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (arq-eng) config: arq-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 21.405049396267835 - type: f1 value: 16.162968480476714 - type: precision value: 14.506603642481391 - type: recall value: 21.405049396267835 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kab-eng) config: kab-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 1.4000000000000001 - type: f1 value: 0.8861559696342305 - type: precision value: 0.7898232323232323 - type: recall value: 1.4000000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fra-eng) config: fra-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.5 - type: f1 value: 91.65333333333334 - type: precision value: 90.80833333333332 - type: recall value: 93.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (por-eng) config: por-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.8 - type: f1 value: 92.08333333333333 - type: precision value: 91.23333333333333 - type: recall value: 93.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tat-eng) config: tat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 1.3 - type: f1 value: 0.9654912597950575 - type: precision value: 0.911237853823405 - type: recall value: 1.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (oci-eng) config: oci-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 35.5 - type: f1 value: 29.385868020868024 - type: precision value: 27.38218614718615 - type: recall value: 35.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pol-eng) config: pol-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.3 - type: f1 value: 5.625495291471218 - type: precision value: 5.006352187769519 - type: recall value: 8.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (war-eng) config: war-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 9.3 - type: f1 value: 7.188871139201601 - type: precision value: 6.68110313042221 - type: recall value: 9.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (aze-eng) config: aze-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.9 - type: f1 value: 3.4368196711816386 - type: precision value: 3.1516575755476186 - type: recall value: 4.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (vie-eng) config: vie-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.5 - type: f1 value: 92.85666666666667 - type: precision value: 92.07499999999999 - type: recall value: 94.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nno-eng) config: nno-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 10.9 - type: f1 value: 8.052880589619718 - type: precision value: 7.2833020438680816 - type: recall value: 10.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cha-eng) config: cha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 21.897810218978105 - type: f1 value: 16.459096459096457 - type: precision value: 14.99391727493917 - type: recall value: 21.897810218978105 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mhr-eng) config: mhr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.8 - type: f1 value: 0.43900258600589265 - type: precision value: 0.42151473277789064 - type: recall value: 0.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (dan-eng) config: dan-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 14.899999999999999 - type: f1 value: 11.403181682754628 - type: precision value: 10.506373051667312 - type: recall value: 14.899999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ell-eng) config: ell-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 1.9 - type: f1 value: 0.8872641689515834 - type: precision value: 0.7857231069685399 - type: recall value: 1.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (amh-eng) config: amh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 1.1904761904761905 - type: f1 value: 0.20847048496818082 - type: precision value: 0.11904761904761904 - type: recall value: 1.1904761904761905 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pam-eng) config: pam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.3 - type: f1 value: 3.784571880595977 - type: precision value: 3.4556477020719782 - type: recall value: 5.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hsb-eng) config: hsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 9.316770186335404 - type: f1 value: 6.80343720685027 - type: precision value: 6.316650292717499 - type: recall value: 9.316770186335404 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (srp-eng) config: srp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.8999999999999995 - type: f1 value: 4.5486926228313695 - type: precision value: 4.311121913612427 - type: recall value: 5.8999999999999995 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (epo-eng) config: epo-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 18.099999999999998 - type: f1 value: 13.4170874831821 - type: precision value: 12.178193046524806 - type: recall value: 18.099999999999998 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kzj-eng) config: kzj-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.3999999999999995 - type: f1 value: 3.3905735425765524 - type: precision value: 3.2588935800436625 - type: recall value: 4.3999999999999995 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (awa-eng) config: awa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 37.66233766233766 - type: f1 value: 30.539579468150897 - type: precision value: 28.60288100547841 - type: recall value: 37.66233766233766 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fao-eng) config: fao-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 12.213740458015266 - type: f1 value: 8.297822182308039 - type: precision value: 7.463649581970193 - type: recall value: 12.213740458015266 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mal-eng) config: mal-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 78.31149927219796 - type: f1 value: 73.35759340126152 - type: precision value: 71.26394953905871 - type: recall value: 78.31149927219796 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ile-eng) config: ile-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 51.800000000000004 - type: f1 value: 44.24010323010323 - type: precision value: 41.450707972582975 - type: recall value: 51.800000000000004 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bos-eng) config: bos-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 13.27683615819209 - type: f1 value: 9.167320569156727 - type: precision value: 8.200402665583079 - type: recall value: 13.27683615819209 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cor-eng) config: cor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.8 - type: f1 value: 3.1268763352790283 - type: precision value: 2.84393718699601 - type: recall value: 4.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cat-eng) config: cat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.1 - type: f1 value: 81.55 - type: precision value: 79.98166666666665 - type: recall value: 85.1 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (eus-eng) config: eus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 48.3 - type: f1 value: 42.347894491129786 - type: precision value: 40.36040404040404 - type: recall value: 48.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (yue-eng) config: yue-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 78.8 - type: f1 value: 74.35484848484847 - type: precision value: 72.43277777777777 - type: recall value: 78.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (swe-eng) config: swe-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 13.900000000000002 - type: f1 value: 10.718252991153888 - type: precision value: 9.835761434404196 - type: recall value: 13.900000000000002 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (dtp-eng) config: dtp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.9 - type: f1 value: 3.371714825002496 - type: precision value: 3.085928254003479 - type: recall value: 4.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kat-eng) config: kat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.5361930294906166 - type: f1 value: 0.40389703692021933 - type: precision value: 0.40302666854804575 - type: recall value: 0.5361930294906166 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (jpn-eng) config: jpn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 55.300000000000004 - type: f1 value: 48.83353113553113 - type: precision value: 46.48630659536542 - type: recall value: 55.300000000000004 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (csb-eng) config: csb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.300395256916996 - type: f1 value: 5.261552988548536 - type: precision value: 4.724388115499655 - type: recall value: 8.300395256916996 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (xho-eng) config: xho-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.450704225352112 - type: f1 value: 4.829974470478787 - type: precision value: 4.337585798478816 - type: recall value: 8.450704225352112 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (orv-eng) config: orv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 1.0778443113772456 - type: f1 value: 0.5373251562068135 - type: precision value: 0.5107640721914694 - type: recall value: 1.0778443113772456 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ind-eng) config: ind-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.5 - type: f1 value: 85.46333333333334 - type: precision value: 84.1 - type: recall value: 88.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tuk-eng) config: tuk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.41871921182266 - type: f1 value: 2.8063639248802965 - type: precision value: 2.2699550039451513 - type: recall value: 5.41871921182266 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (max-eng) config: max-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 40.49295774647887 - type: f1 value: 33.455454951933824 - type: precision value: 31.4339393461183 - type: recall value: 40.49295774647887 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (swh-eng) config: swh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 18.974358974358974 - type: f1 value: 14.517578026097205 - type: precision value: 13.3510327465177 - type: recall value: 18.974358974358974 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hin-eng) config: hin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.5 - type: f1 value: 85.34666666666666 - type: precision value: 83.89999999999999 - type: recall value: 88.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (dsb-eng) config: dsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.1419624217119 - type: f1 value: 5.830783012763732 - type: precision value: 5.4408714223116545 - type: recall value: 8.1419624217119 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ber-eng) config: ber-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.800000000000001 - type: f1 value: 3.9245687335866406 - type: precision value: 3.5535667824951584 - type: recall value: 5.800000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tam-eng) config: tam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 68.40390879478826 - type: f1 value: 62.25738069386277 - type: precision value: 60.10935318752908 - type: recall value: 68.40390879478826 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (slk-eng) config: slk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 7.1 - type: f1 value: 5.4876787833762135 - type: precision value: 5.126663482701374 - type: recall value: 7.1 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tgl-eng) config: tgl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.9 - type: f1 value: 6.519531004112515 - type: precision value: 5.987707404636394 - type: recall value: 8.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ast-eng) config: ast-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 66.92913385826772 - type: f1 value: 59.96062992125984 - type: precision value: 57.13348331458567 - type: recall value: 66.92913385826772 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mkd-eng) config: mkd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.3 - type: f1 value: 2.765805343607201 - type: precision value: 2.5247851243177144 - type: recall value: 4.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (khm-eng) config: khm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.41551246537396125 - type: f1 value: 0.1497838495760933 - type: precision value: 0.14429034844729552 - type: recall value: 0.41551246537396125 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ces-eng) config: ces-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 5.800000000000001 - type: f1 value: 3.761224995516873 - type: precision value: 3.2689210175496086 - type: recall value: 5.800000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tzl-eng) config: tzl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 16.346153846153847 - type: f1 value: 14.524291497975709 - type: precision value: 13.995726495726496 - type: recall value: 16.346153846153847 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (urd-eng) config: urd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 67.80000000000001 - type: f1 value: 61.615800865800864 - type: precision value: 59.12333333333334 - type: recall value: 67.80000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ara-eng) config: ara-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.8 - type: f1 value: 80.08857142857143 - type: precision value: 78.46666666666667 - type: recall value: 83.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kor-eng) config: kor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.2 - type: f1 value: 2.6507751588440254 - type: precision value: 2.335273168189835 - type: recall value: 4.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (yid-eng) config: yid-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.4716981132075472 - type: f1 value: 0.19293763102725367 - type: precision value: 0.1622040325564188 - type: recall value: 0.4716981132075472 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fin-eng) config: fin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 4.9 - type: f1 value: 3.5001791555125235 - type: precision value: 3.277940522301425 - type: recall value: 4.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tha-eng) config: tha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 0.9124087591240875 - type: f1 value: 0.5083420229405631 - type: precision value: 0.4674562188049969 - type: recall value: 0.9124087591240875 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (wuu-eng) config: wuu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 79.4 - type: f1 value: 74.62333333333333 - type: precision value: 72.52333333333334 - type: recall value: 79.4 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 51.02719281751054 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 48.31885339280247 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.426 - type: map_at_10 value: 9.029 - type: map_at_100 value: 14.299999999999999 - type: map_at_1000 value: 15.798000000000002 - type: map_at_3 value: 4.626 - type: map_at_5 value: 6.221 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 46.608 - type: mrr_at_100 value: 47.195 - type: mrr_at_1000 value: 47.208 - type: mrr_at_3 value: 41.837 - type: mrr_at_5 value: 43.673 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 23.354 - type: ndcg_at_100 value: 33.875 - type: ndcg_at_1000 value: 45.369 - type: ndcg_at_3 value: 25.734 - type: ndcg_at_5 value: 23.873 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 21.224 - type: precision_at_100 value: 7.122000000000001 - type: precision_at_1000 value: 1.459 - type: precision_at_3 value: 26.531 - type: precision_at_5 value: 24.082 - type: recall_at_1 value: 2.426 - type: recall_at_10 value: 15.622 - type: recall_at_100 value: 44.318999999999996 - type: recall_at_1000 value: 78.632 - type: recall_at_3 value: 5.798 - type: recall_at_5 value: 8.927 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 67.9606 - type: ap value: 12.665547829558923 - type: f1 value: 52.10043478110198 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.601018675721576 - type: f1 value: 59.91486569196274 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 37.881729581540135 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.68003814746379 - type: cos_sim_ap value: 65.95659315362258 - type: cos_sim_f1 value: 61.94669484560291 - type: cos_sim_precision value: 55.80706579225725 - type: cos_sim_recall value: 69.6042216358839 - type: dot_accuracy value: 81.97532335936103 - type: dot_ap value: 58.99091918849294 - type: dot_f1 value: 57.098765432098766 - type: dot_precision value: 51.8990073370738 - type: dot_recall value: 63.45646437994723 - type: euclidean_accuracy value: 83.18531322644095 - type: euclidean_ap value: 64.5631762106556 - type: euclidean_f1 value: 61.150808574652125 - type: euclidean_precision value: 58.25173155003582 - type: euclidean_recall value: 64.35356200527704 - type: manhattan_accuracy value: 83.14358943792097 - type: manhattan_ap value: 64.73090464118813 - type: manhattan_f1 value: 61.228384019081695 - type: manhattan_precision value: 55.86507072905332 - type: manhattan_recall value: 67.73087071240106 - type: max_accuracy value: 83.68003814746379 - type: max_ap value: 65.95659315362258 - type: max_f1 value: 61.94669484560291 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.7161873714441 - type: cos_sim_ap value: 85.10870963707444 - type: cos_sim_f1 value: 77.88396923766146 - type: cos_sim_precision value: 75.59791274097695 - type: cos_sim_recall value: 80.31259624268556 - type: dot_accuracy value: 87.74595412737222 - type: dot_ap value: 81.22910623983562 - type: dot_f1 value: 76.08511889448344 - type: dot_precision value: 72.78672385908163 - type: dot_recall value: 79.69664305512781 - type: euclidean_accuracy value: 88.13404742500097 - type: euclidean_ap value: 84.03032098854915 - type: euclidean_f1 value: 76.3909440662918 - type: euclidean_precision value: 73.51894047279977 - type: euclidean_recall value: 79.49645826917154 - type: manhattan_accuracy value: 88.13598789148911 - type: manhattan_ap value: 84.13258714083858 - type: manhattan_f1 value: 76.44922164566346 - type: manhattan_precision value: 73.70640365923384 - type: manhattan_recall value: 79.40406529103788 - type: max_accuracy value: 88.7161873714441 - type: max_ap value: 85.10870963707444 - type: max_f1 value: 77.88396923766146 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 41.8 - type: map_at_10 value: 50.57000000000001 - type: map_at_100 value: 51.271 - type: map_at_1000 value: 51.31099999999999 - type: map_at_3 value: 48.283 - type: map_at_5 value: 49.633 - type: mrr_at_1 value: 41.8 - type: mrr_at_10 value: 50.57000000000001 - type: mrr_at_100 value: 51.271 - type: mrr_at_1000 value: 51.31099999999999 - type: mrr_at_3 value: 48.283 - type: mrr_at_5 value: 49.633 - type: ndcg_at_1 value: 41.8 - type: ndcg_at_10 value: 55.071999999999996 - type: ndcg_at_100 value: 58.604 - type: ndcg_at_1000 value: 59.679 - type: ndcg_at_3 value: 50.394000000000005 - type: ndcg_at_5 value: 52.825 - type: precision_at_1 value: 41.8 - type: precision_at_10 value: 6.93 - type: precision_at_100 value: 0.861 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 18.833 - type: precision_at_5 value: 12.479999999999999 - type: recall_at_1 value: 41.8 - type: recall_at_10 value: 69.3 - type: recall_at_100 value: 86.1 - type: recall_at_1000 value: 94.6 - type: recall_at_3 value: 56.49999999999999 - type: recall_at_5 value: 62.4 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 80.65 - type: ap value: 59.927241826012924 - type: f1 value: 78.72456184299979 --- # Model Card for udever-bloom `udever-bloom-560m` is finetuned from [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) 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-560m#training-data) - **Finetuned from model :** [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) ### 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 ## 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-560m') model = BloomModel.from_pretrained('izhx/udever-bloom-560m') 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-560m](https://huggingface.co/bigscience/bloom-560m). - 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} } ```