| --- |
| tags: |
| - mteb |
| - sentence-transformers |
| - transformers |
| model-index: |
| - name: multilingual-e5-large-instruct |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 76.23880597014924 |
| - type: ap |
| value: 39.07351965022687 |
| - type: f1 |
| value: 70.04836733862683 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (de) |
| config: de |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 66.71306209850107 |
| - type: ap |
| value: 79.01499914759529 |
| - type: f1 |
| value: 64.81951817560703 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en-ext) |
| config: en-ext |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 73.85307346326837 |
| - type: ap |
| value: 22.447519885878737 |
| - type: f1 |
| value: 61.0162730745633 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (ja) |
| config: ja |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 76.04925053533191 |
| - type: ap |
| value: 23.44983217128922 |
| - type: f1 |
| value: 62.5723230907759 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 96.28742500000001 |
| - type: ap |
| value: 94.8449918887462 |
| - type: f1 |
| value: 96.28680923610432 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 56.716 |
| - type: f1 |
| value: 55.76510398266401 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (de) |
| config: de |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 52.99999999999999 |
| - type: f1 |
| value: 52.00829994765178 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (es) |
| config: es |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 48.806000000000004 |
| - type: f1 |
| value: 48.082345914983634 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (fr) |
| config: fr |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 48.507999999999996 |
| - type: f1 |
| value: 47.68752844642045 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (ja) |
| config: ja |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 47.709999999999994 |
| - type: f1 |
| value: 47.05870376637181 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (zh) |
| config: zh |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 44.662000000000006 |
| - type: f1 |
| value: 43.42371965372771 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 31.721 |
| - type: map_at_10 |
| value: 49.221 |
| - type: map_at_100 |
| value: 49.884 |
| - type: map_at_1000 |
| value: 49.888 |
| - type: map_at_3 |
| value: 44.31 |
| - type: map_at_5 |
| value: 47.276 |
| - type: mrr_at_1 |
| value: 32.432 |
| - type: mrr_at_10 |
| value: 49.5 |
| - type: mrr_at_100 |
| value: 50.163000000000004 |
| - type: mrr_at_1000 |
| value: 50.166 |
| - type: mrr_at_3 |
| value: 44.618 |
| - type: mrr_at_5 |
| value: 47.541 |
| - type: ndcg_at_1 |
| value: 31.721 |
| - type: ndcg_at_10 |
| value: 58.384 |
| - type: ndcg_at_100 |
| value: 61.111000000000004 |
| - type: ndcg_at_1000 |
| value: 61.187999999999995 |
| - type: ndcg_at_3 |
| value: 48.386 |
| - type: ndcg_at_5 |
| value: 53.708999999999996 |
| - type: precision_at_1 |
| value: 31.721 |
| - type: precision_at_10 |
| value: 8.741 |
| - type: precision_at_100 |
| value: 0.991 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 20.057 |
| - type: precision_at_5 |
| value: 14.609 |
| - type: recall_at_1 |
| value: 31.721 |
| - type: recall_at_10 |
| value: 87.411 |
| - type: recall_at_100 |
| value: 99.075 |
| - type: recall_at_1000 |
| value: 99.644 |
| - type: recall_at_3 |
| value: 60.171 |
| - type: recall_at_5 |
| value: 73.044 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 46.40419580759799 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 40.48593255007969 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 63.889179122289995 |
| - type: mrr |
| value: 77.61146286769556 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 88.15075203727929 |
| - type: cos_sim_spearman |
| value: 86.9622224570873 |
| - type: euclidean_pearson |
| value: 86.70473853624121 |
| - type: euclidean_spearman |
| value: 86.9622224570873 |
| - type: manhattan_pearson |
| value: 86.21089380980065 |
| - type: manhattan_spearman |
| value: 86.75318154937008 |
| - 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: 99.65553235908142 |
| - type: f1 |
| value: 99.60681976339595 |
| - type: precision |
| value: 99.58246346555325 |
| - type: recall |
| value: 99.65553235908142 |
| - 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: 99.26260180497468 |
| - type: f1 |
| value: 99.14520507740848 |
| - type: precision |
| value: 99.08650671362535 |
| - type: recall |
| value: 99.26260180497468 |
| - 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: 98.07412538967787 |
| - type: f1 |
| value: 97.86629719431936 |
| - type: precision |
| value: 97.76238309664012 |
| - type: recall |
| value: 98.07412538967787 |
| - 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: 99.42074776197998 |
| - type: f1 |
| value: 99.38564156573635 |
| - type: precision |
| value: 99.36808846761454 |
| - type: recall |
| value: 99.42074776197998 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 85.73376623376623 |
| - type: f1 |
| value: 85.68480707214599 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 40.935218072113855 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 36.276389017675264 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 27.764166666666668 |
| - type: map_at_10 |
| value: 37.298166666666674 |
| - type: map_at_100 |
| value: 38.530166666666666 |
| - type: map_at_1000 |
| value: 38.64416666666667 |
| - type: map_at_3 |
| value: 34.484833333333334 |
| - type: map_at_5 |
| value: 36.0385 |
| - type: mrr_at_1 |
| value: 32.93558333333333 |
| - type: mrr_at_10 |
| value: 41.589749999999995 |
| - type: mrr_at_100 |
| value: 42.425333333333334 |
| - type: mrr_at_1000 |
| value: 42.476333333333336 |
| - type: mrr_at_3 |
| value: 39.26825 |
| - type: mrr_at_5 |
| value: 40.567083333333336 |
| - type: ndcg_at_1 |
| value: 32.93558333333333 |
| - type: ndcg_at_10 |
| value: 42.706583333333334 |
| - type: ndcg_at_100 |
| value: 47.82483333333333 |
| - type: ndcg_at_1000 |
| value: 49.95733333333334 |
| - type: ndcg_at_3 |
| value: 38.064750000000004 |
| - type: ndcg_at_5 |
| value: 40.18158333333333 |
| - type: precision_at_1 |
| value: 32.93558333333333 |
| - type: precision_at_10 |
| value: 7.459833333333334 |
| - type: precision_at_100 |
| value: 1.1830833333333335 |
| - type: precision_at_1000 |
| value: 0.15608333333333332 |
| - type: precision_at_3 |
| value: 17.5235 |
| - type: precision_at_5 |
| value: 12.349833333333333 |
| - type: recall_at_1 |
| value: 27.764166666666668 |
| - type: recall_at_10 |
| value: 54.31775 |
| - type: recall_at_100 |
| value: 76.74350000000001 |
| - type: recall_at_1000 |
| value: 91.45208333333332 |
| - type: recall_at_3 |
| value: 41.23425 |
| - type: recall_at_5 |
| value: 46.73983333333334 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 12.969 |
| - type: map_at_10 |
| value: 21.584999999999997 |
| - type: map_at_100 |
| value: 23.3 |
| - type: map_at_1000 |
| value: 23.5 |
| - type: map_at_3 |
| value: 18.218999999999998 |
| - type: map_at_5 |
| value: 19.983 |
| - type: mrr_at_1 |
| value: 29.316 |
| - type: mrr_at_10 |
| value: 40.033 |
| - type: mrr_at_100 |
| value: 40.96 |
| - type: mrr_at_1000 |
| value: 41.001 |
| - type: mrr_at_3 |
| value: 37.123 |
| - type: mrr_at_5 |
| value: 38.757999999999996 |
| - type: ndcg_at_1 |
| value: 29.316 |
| - type: ndcg_at_10 |
| value: 29.858 |
| - type: ndcg_at_100 |
| value: 36.756 |
| - type: ndcg_at_1000 |
| value: 40.245999999999995 |
| - type: ndcg_at_3 |
| value: 24.822 |
| - type: ndcg_at_5 |
| value: 26.565 |
| - type: precision_at_1 |
| value: 29.316 |
| - type: precision_at_10 |
| value: 9.186 |
| - type: precision_at_100 |
| value: 1.6549999999999998 |
| - type: precision_at_1000 |
| value: 0.22999999999999998 |
| - type: precision_at_3 |
| value: 18.436 |
| - type: precision_at_5 |
| value: 13.876 |
| - type: recall_at_1 |
| value: 12.969 |
| - type: recall_at_10 |
| value: 35.142 |
| - type: recall_at_100 |
| value: 59.143 |
| - type: recall_at_1000 |
| value: 78.594 |
| - type: recall_at_3 |
| value: 22.604 |
| - type: recall_at_5 |
| value: 27.883000000000003 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 8.527999999999999 |
| - type: map_at_10 |
| value: 17.974999999999998 |
| - type: map_at_100 |
| value: 25.665 |
| - type: map_at_1000 |
| value: 27.406000000000002 |
| - type: map_at_3 |
| value: 13.017999999999999 |
| - type: map_at_5 |
| value: 15.137 |
| - type: mrr_at_1 |
| value: 62.5 |
| - type: mrr_at_10 |
| value: 71.891 |
| - type: mrr_at_100 |
| value: 72.294 |
| - type: mrr_at_1000 |
| value: 72.296 |
| - type: mrr_at_3 |
| value: 69.958 |
| - type: mrr_at_5 |
| value: 71.121 |
| - type: ndcg_at_1 |
| value: 50.875 |
| - type: ndcg_at_10 |
| value: 38.36 |
| - type: ndcg_at_100 |
| value: 44.235 |
| - type: ndcg_at_1000 |
| value: 52.154 |
| - type: ndcg_at_3 |
| value: 43.008 |
| - type: ndcg_at_5 |
| value: 40.083999999999996 |
| - type: precision_at_1 |
| value: 62.5 |
| - type: precision_at_10 |
| value: 30.0 |
| - type: precision_at_100 |
| value: 10.038 |
| - type: precision_at_1000 |
| value: 2.0869999999999997 |
| - type: precision_at_3 |
| value: 46.833000000000006 |
| - type: precision_at_5 |
| value: 38.800000000000004 |
| - type: recall_at_1 |
| value: 8.527999999999999 |
| - type: recall_at_10 |
| value: 23.828 |
| - type: recall_at_100 |
| value: 52.322 |
| - type: recall_at_1000 |
| value: 77.143 |
| - type: recall_at_3 |
| value: 14.136000000000001 |
| - type: recall_at_5 |
| value: 17.761 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 51.51 |
| - type: f1 |
| value: 47.632159862049896 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 60.734 |
| - type: map_at_10 |
| value: 72.442 |
| - type: map_at_100 |
| value: 72.735 |
| - type: map_at_1000 |
| value: 72.75 |
| - type: map_at_3 |
| value: 70.41199999999999 |
| - type: map_at_5 |
| value: 71.80499999999999 |
| - type: mrr_at_1 |
| value: 65.212 |
| - type: mrr_at_10 |
| value: 76.613 |
| - type: mrr_at_100 |
| value: 76.79899999999999 |
| - type: mrr_at_1000 |
| value: 76.801 |
| - type: mrr_at_3 |
| value: 74.8 |
| - type: mrr_at_5 |
| value: 76.12400000000001 |
| - type: ndcg_at_1 |
| value: 65.212 |
| - type: ndcg_at_10 |
| value: 77.988 |
| - type: ndcg_at_100 |
| value: 79.167 |
| - type: ndcg_at_1000 |
| value: 79.452 |
| - type: ndcg_at_3 |
| value: 74.362 |
| - type: ndcg_at_5 |
| value: 76.666 |
| - type: precision_at_1 |
| value: 65.212 |
| - type: precision_at_10 |
| value: 10.003 |
| - type: precision_at_100 |
| value: 1.077 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 29.518 |
| - type: precision_at_5 |
| value: 19.016 |
| - type: recall_at_1 |
| value: 60.734 |
| - type: recall_at_10 |
| value: 90.824 |
| - type: recall_at_100 |
| value: 95.71600000000001 |
| - type: recall_at_1000 |
| value: 97.577 |
| - type: recall_at_3 |
| value: 81.243 |
| - type: recall_at_5 |
| value: 86.90299999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.845 |
| - type: map_at_10 |
| value: 39.281 |
| - type: map_at_100 |
| value: 41.422 |
| - type: map_at_1000 |
| value: 41.593 |
| - type: map_at_3 |
| value: 34.467 |
| - type: map_at_5 |
| value: 37.017 |
| - type: mrr_at_1 |
| value: 47.531 |
| - type: mrr_at_10 |
| value: 56.204 |
| - type: mrr_at_100 |
| value: 56.928999999999995 |
| - type: mrr_at_1000 |
| value: 56.962999999999994 |
| - type: mrr_at_3 |
| value: 54.115 |
| - type: mrr_at_5 |
| value: 55.373000000000005 |
| - type: ndcg_at_1 |
| value: 47.531 |
| - type: ndcg_at_10 |
| value: 47.711999999999996 |
| - type: ndcg_at_100 |
| value: 54.510999999999996 |
| - type: ndcg_at_1000 |
| value: 57.103 |
| - type: ndcg_at_3 |
| value: 44.145 |
| - type: ndcg_at_5 |
| value: 45.032 |
| - type: precision_at_1 |
| value: 47.531 |
| - type: precision_at_10 |
| value: 13.194 |
| - type: precision_at_100 |
| value: 2.045 |
| - type: precision_at_1000 |
| value: 0.249 |
| - type: precision_at_3 |
| value: 29.424 |
| - type: precision_at_5 |
| value: 21.451 |
| - type: recall_at_1 |
| value: 23.845 |
| - type: recall_at_10 |
| value: 54.967 |
| - type: recall_at_100 |
| value: 79.11399999999999 |
| - type: recall_at_1000 |
| value: 94.56700000000001 |
| - type: recall_at_3 |
| value: 40.256 |
| - type: recall_at_5 |
| value: 46.215 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 37.819 |
| - type: map_at_10 |
| value: 60.889 |
| - type: map_at_100 |
| value: 61.717999999999996 |
| - type: map_at_1000 |
| value: 61.778 |
| - type: map_at_3 |
| value: 57.254000000000005 |
| - type: map_at_5 |
| value: 59.541 |
| - type: mrr_at_1 |
| value: 75.638 |
| - type: mrr_at_10 |
| value: 82.173 |
| - type: mrr_at_100 |
| value: 82.362 |
| - type: mrr_at_1000 |
| value: 82.37 |
| - type: mrr_at_3 |
| value: 81.089 |
| - type: mrr_at_5 |
| value: 81.827 |
| - type: ndcg_at_1 |
| value: 75.638 |
| - type: ndcg_at_10 |
| value: 69.317 |
| - type: ndcg_at_100 |
| value: 72.221 |
| - type: ndcg_at_1000 |
| value: 73.382 |
| - type: ndcg_at_3 |
| value: 64.14 |
| - type: ndcg_at_5 |
| value: 67.07600000000001 |
| - type: precision_at_1 |
| value: 75.638 |
| - type: precision_at_10 |
| value: 14.704999999999998 |
| - type: precision_at_100 |
| value: 1.698 |
| - type: precision_at_1000 |
| value: 0.185 |
| - type: precision_at_3 |
| value: 41.394999999999996 |
| - type: precision_at_5 |
| value: 27.162999999999997 |
| - type: recall_at_1 |
| value: 37.819 |
| - type: recall_at_10 |
| value: 73.52499999999999 |
| - type: recall_at_100 |
| value: 84.875 |
| - type: recall_at_1000 |
| value: 92.559 |
| - type: recall_at_3 |
| value: 62.092999999999996 |
| - type: recall_at_5 |
| value: 67.907 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 94.60079999999999 |
| - type: ap |
| value: 92.67396345347356 |
| - type: f1 |
| value: 94.5988098167121 |
| - task: |
| type: Retrieval |
| dataset: |
| type: msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 21.285 |
| - type: map_at_10 |
| value: 33.436 |
| - type: map_at_100 |
| value: 34.63 |
| - type: map_at_1000 |
| value: 34.681 |
| - type: map_at_3 |
| value: 29.412 |
| - type: map_at_5 |
| value: 31.715 |
| - type: mrr_at_1 |
| value: 21.848 |
| - type: mrr_at_10 |
| value: 33.979 |
| - type: mrr_at_100 |
| value: 35.118 |
| - type: mrr_at_1000 |
| value: 35.162 |
| - type: mrr_at_3 |
| value: 30.036 |
| - type: mrr_at_5 |
| value: 32.298 |
| - type: ndcg_at_1 |
| value: 21.862000000000002 |
| - type: ndcg_at_10 |
| value: 40.43 |
| - type: ndcg_at_100 |
| value: 46.17 |
| - type: ndcg_at_1000 |
| value: 47.412 |
| - type: ndcg_at_3 |
| value: 32.221 |
| - type: ndcg_at_5 |
| value: 36.332 |
| - type: precision_at_1 |
| value: 21.862000000000002 |
| - type: precision_at_10 |
| value: 6.491 |
| - type: precision_at_100 |
| value: 0.935 |
| - type: precision_at_1000 |
| value: 0.104 |
| - type: precision_at_3 |
| value: 13.744 |
| - type: precision_at_5 |
| value: 10.331999999999999 |
| - type: recall_at_1 |
| value: 21.285 |
| - type: recall_at_10 |
| value: 62.083 |
| - type: recall_at_100 |
| value: 88.576 |
| - type: recall_at_1000 |
| value: 98.006 |
| - type: recall_at_3 |
| value: 39.729 |
| - type: recall_at_5 |
| value: 49.608000000000004 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 93.92612859097127 |
| - type: f1 |
| value: 93.82370333372853 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (de) |
| config: de |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 92.67681036911807 |
| - type: f1 |
| value: 92.14191382411472 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (es) |
| config: es |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 92.26817878585723 |
| - type: f1 |
| value: 91.92824250337878 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (fr) |
| config: fr |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 89.96554963983714 |
| - type: f1 |
| value: 90.02859329630792 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (hi) |
| config: hi |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 90.02509860164935 |
| - type: f1 |
| value: 89.30665159182062 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (th) |
| config: th |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 87.55515370705244 |
| - type: f1 |
| value: 87.94449232331907 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 82.4623803009576 |
| - type: f1 |
| value: 66.06738378772725 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (de) |
| config: de |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 79.3716539870386 |
| - type: f1 |
| value: 60.37614033396853 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (es) |
| config: es |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 80.34022681787857 |
| - type: f1 |
| value: 58.302008026952 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (fr) |
| config: fr |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 76.72095208268087 |
| - type: f1 |
| value: 59.64524724009049 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (hi) |
| config: hi |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 77.87020437432773 |
| - type: f1 |
| value: 57.80202694670567 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (th) |
| config: th |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 77.73598553345387 |
| - type: f1 |
| value: 58.19628250675031 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (af) |
| config: af |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 67.6630800268998 |
| - type: f1 |
| value: 65.00996668051691 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (am) |
| config: am |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 60.7128446536651 |
| - type: f1 |
| value: 57.95860594874963 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ar) |
| config: ar |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 63.61129791526563 |
| - type: f1 |
| value: 59.75328290206483 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (az) |
| config: az |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 69.00134498991257 |
| - type: f1 |
| value: 67.0230483991802 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (bn) |
| config: bn |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 68.54068594485541 |
| - type: f1 |
| value: 65.54604628946976 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (cy) |
| config: cy |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 63.032952252858095 |
| - type: f1 |
| value: 58.715741857057104 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (da) |
| config: da |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 71.80901143241427 |
| - type: f1 |
| value: 68.33963989243877 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (de) |
| config: de |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 72.47141896435777 |
| - type: f1 |
| value: 69.56765020308262 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (el) |
| config: el |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 71.2373907195696 |
| - type: f1 |
| value: 69.04529836036467 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 77.05783456624076 |
| - type: f1 |
| value: 74.69430584708174 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (es) |
| config: es |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 72.82111634162744 |
| - type: f1 |
| value: 70.77228952803762 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fa) |
| config: fa |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 74.25353059852051 |
| - type: f1 |
| value: 71.05310103416411 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fi) |
| config: fi |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 72.28648285137861 |
| - type: f1 |
| value: 69.08020473732226 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fr) |
| config: fr |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 73.31540013449899 |
| - type: f1 |
| value: 70.9426355465791 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (he) |
| config: he |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 70.2151983860121 |
| - type: f1 |
| value: 67.52541755908858 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (hi) |
| config: hi |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 71.58372562205784 |
| - type: f1 |
| value: 69.49769064229827 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (hu) |
| config: hu |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 71.9233355749832 |
| - type: f1 |
| value: 69.36311548259593 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (hy) |
| config: hy |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 68.07330195023538 |
| - type: f1 |
| value: 64.99882022345572 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (id) |
| config: id |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 72.62273032952253 |
| - type: f1 |
| value: 70.6394885471001 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (is) |
| config: is |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 65.77000672494957 |
| - type: f1 |
| value: 62.9368944815065 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (it) |
| config: it |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 73.453261600538 |
| - type: f1 |
| value: 70.85069934666681 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ja) |
| config: ja |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 74.6906523201076 |
| - type: f1 |
| value: 72.03249740074217 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (jv) |
| config: jv |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 63.03631472763953 |
| - type: f1 |
| value: 59.3165215571852 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ka) |
| config: ka |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 58.913920645595155 |
| - type: f1 |
| value: 57.367337711611285 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (km) |
| config: km |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 54.42837928715535 |
| - type: f1 |
| value: 52.60527294970906 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (kn) |
| config: kn |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 66.33490248823135 |
| - type: f1 |
| value: 63.213340969404065 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ko) |
| config: ko |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 70.58507061197041 |
| - type: f1 |
| value: 68.40256628040486 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (lv) |
| config: lv |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 69.11230665770006 |
| - type: f1 |
| value: 66.44863577842305 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ml) |
| config: ml |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 69.70073974445192 |
| - type: f1 |
| value: 67.21291337273702 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (mn) |
| config: mn |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 66.43913920645595 |
| - type: f1 |
| value: 64.09838087422806 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ms) |
| config: ms |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 70.80026899798251 |
| - type: f1 |
| value: 68.76986742962444 |
| - task: |
| type: Classification |
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| value: 76.91201656350455 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (pt) |
| config: pt |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 77.1217215870881 |
| - type: f1 |
| value: 77.41179937912504 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ro) |
| config: ro |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 75.25891055817083 |
| - type: f1 |
| value: 75.8089244542887 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ru) |
| config: ru |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 77.70679219905851 |
| - type: f1 |
| value: 78.21459594517711 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sl) |
| config: sl |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 74.83523873570948 |
| - type: f1 |
| value: 74.86847028401978 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sq) |
| config: sq |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 74.71755211835911 |
| - type: f1 |
| value: 74.0214326485662 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sv) |
| config: sv |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 79.06523201075991 |
| - type: f1 |
| value: 79.10545620325138 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sw) |
| config: sw |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 67.91862811028918 |
| - type: f1 |
| value: 66.50386121217983 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ta) |
| config: ta |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 70.93140551445865 |
| - type: f1 |
| value: 70.755435928495 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (te) |
| config: te |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 72.40753194351042 |
| - type: f1 |
| value: 71.61816115782923 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (th) |
| config: th |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 75.1815736381977 |
| - type: f1 |
| value: 75.08016717887205 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (tl) |
| config: tl |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 72.86482851378614 |
| - type: f1 |
| value: 72.39521180006291 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (tr) |
| config: tr |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 76.46940147948891 |
| - type: f1 |
| value: 76.70044085362349 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ur) |
| config: ur |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 71.89307330195024 |
| - type: f1 |
| value: 71.5721825332298 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (vi) |
| config: vi |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 74.7511768661735 |
| - type: f1 |
| value: 75.17918654541515 |
| - 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: 78.69535978480162 |
| - type: f1 |
| value: 78.90019070153316 |
| - 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: 75.45729657027572 |
| - type: f1 |
| value: 76.19578371794672 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 36.92715354123554 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 35.53536244162518 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 33.08507884504006 |
| - type: mrr |
| value: 34.32436977159129 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 5.935 |
| - type: map_at_10 |
| value: 13.297 |
| - type: map_at_100 |
| value: 16.907 |
| - type: map_at_1000 |
| value: 18.391 |
| - type: map_at_3 |
| value: 9.626999999999999 |
| - type: map_at_5 |
| value: 11.190999999999999 |
| - type: mrr_at_1 |
| value: 46.129999999999995 |
| - type: mrr_at_10 |
| value: 54.346000000000004 |
| - type: mrr_at_100 |
| value: 55.067 |
| - type: mrr_at_1000 |
| value: 55.1 |
| - type: mrr_at_3 |
| value: 51.961 |
| - type: mrr_at_5 |
| value: 53.246 |
| - type: ndcg_at_1 |
| value: 44.118 |
| - type: ndcg_at_10 |
| value: 35.534 |
| - type: ndcg_at_100 |
| value: 32.946999999999996 |
| - type: ndcg_at_1000 |
| value: 41.599000000000004 |
| - type: ndcg_at_3 |
| value: 40.25 |
| - type: ndcg_at_5 |
| value: 37.978 |
| - type: precision_at_1 |
| value: 46.129999999999995 |
| - type: precision_at_10 |
| value: 26.842 |
| - type: precision_at_100 |
| value: 8.427 |
| - type: precision_at_1000 |
| value: 2.128 |
| - type: precision_at_3 |
| value: 37.977 |
| - type: precision_at_5 |
| value: 32.879000000000005 |
| - type: recall_at_1 |
| value: 5.935 |
| - type: recall_at_10 |
| value: 17.211000000000002 |
| - type: recall_at_100 |
| value: 34.33 |
| - type: recall_at_1000 |
| value: 65.551 |
| - type: recall_at_3 |
| value: 10.483 |
| - type: recall_at_5 |
| value: 13.078999999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 35.231 |
| - type: map_at_10 |
| value: 50.202000000000005 |
| - type: map_at_100 |
| value: 51.154999999999994 |
| - type: map_at_1000 |
| value: 51.181 |
| - type: map_at_3 |
| value: 45.774 |
| - type: map_at_5 |
| value: 48.522 |
| - type: mrr_at_1 |
| value: 39.687 |
| - type: mrr_at_10 |
| value: 52.88 |
| - type: mrr_at_100 |
| value: 53.569 |
| - type: mrr_at_1000 |
| value: 53.58500000000001 |
| - type: mrr_at_3 |
| value: 49.228 |
| - type: mrr_at_5 |
| value: 51.525 |
| - type: ndcg_at_1 |
| value: 39.687 |
| - type: ndcg_at_10 |
| value: 57.754000000000005 |
| - type: ndcg_at_100 |
| value: 61.597 |
| - type: ndcg_at_1000 |
| value: 62.18900000000001 |
| - type: ndcg_at_3 |
| value: 49.55 |
| - type: ndcg_at_5 |
| value: 54.11899999999999 |
| - type: precision_at_1 |
| value: 39.687 |
| - type: precision_at_10 |
| value: 9.313 |
| - type: precision_at_100 |
| value: 1.146 |
| - type: precision_at_1000 |
| value: 0.12 |
| - type: precision_at_3 |
| value: 22.229 |
| - type: precision_at_5 |
| value: 15.939 |
| - type: recall_at_1 |
| value: 35.231 |
| - type: recall_at_10 |
| value: 78.083 |
| - type: recall_at_100 |
| value: 94.42099999999999 |
| - type: recall_at_1000 |
| value: 98.81 |
| - type: recall_at_3 |
| value: 57.047000000000004 |
| - type: recall_at_5 |
| value: 67.637 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 71.241 |
| - type: map_at_10 |
| value: 85.462 |
| - type: map_at_100 |
| value: 86.083 |
| - type: map_at_1000 |
| value: 86.09700000000001 |
| - type: map_at_3 |
| value: 82.49499999999999 |
| - type: map_at_5 |
| value: 84.392 |
| - type: mrr_at_1 |
| value: 82.09 |
| - type: mrr_at_10 |
| value: 88.301 |
| - type: mrr_at_100 |
| value: 88.383 |
| - type: mrr_at_1000 |
| value: 88.384 |
| - type: mrr_at_3 |
| value: 87.37 |
| - type: mrr_at_5 |
| value: 88.035 |
| - type: ndcg_at_1 |
| value: 82.12 |
| - type: ndcg_at_10 |
| value: 89.149 |
| - type: ndcg_at_100 |
| value: 90.235 |
| - type: ndcg_at_1000 |
| value: 90.307 |
| - type: ndcg_at_3 |
| value: 86.37599999999999 |
| - type: ndcg_at_5 |
| value: 87.964 |
| - type: precision_at_1 |
| value: 82.12 |
| - type: precision_at_10 |
| value: 13.56 |
| - type: precision_at_100 |
| value: 1.539 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 37.88 |
| - type: precision_at_5 |
| value: 24.92 |
| - type: recall_at_1 |
| value: 71.241 |
| - type: recall_at_10 |
| value: 96.128 |
| - type: recall_at_100 |
| value: 99.696 |
| - type: recall_at_1000 |
| value: 99.994 |
| - type: recall_at_3 |
| value: 88.181 |
| - type: recall_at_5 |
| value: 92.694 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 56.59757799655151 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 64.27391998854624 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.243 |
| - type: map_at_10 |
| value: 10.965 |
| - type: map_at_100 |
| value: 12.934999999999999 |
| - type: map_at_1000 |
| value: 13.256 |
| - type: map_at_3 |
| value: 7.907 |
| - type: map_at_5 |
| value: 9.435 |
| - type: mrr_at_1 |
| value: 20.9 |
| - type: mrr_at_10 |
| value: 31.849 |
| - type: mrr_at_100 |
| value: 32.964 |
| - type: mrr_at_1000 |
| value: 33.024 |
| - type: mrr_at_3 |
| value: 28.517 |
| - type: mrr_at_5 |
| value: 30.381999999999998 |
| - type: ndcg_at_1 |
| value: 20.9 |
| - type: ndcg_at_10 |
| value: 18.723 |
| - type: ndcg_at_100 |
| value: 26.384999999999998 |
| - type: ndcg_at_1000 |
| value: 32.114 |
| - type: ndcg_at_3 |
| value: 17.753 |
| - type: ndcg_at_5 |
| value: 15.558 |
| - type: precision_at_1 |
| value: 20.9 |
| - type: precision_at_10 |
| value: 9.8 |
| - type: precision_at_100 |
| value: 2.078 |
| - type: precision_at_1000 |
| value: 0.345 |
| - type: precision_at_3 |
| value: 16.900000000000002 |
| - type: precision_at_5 |
| value: 13.88 |
| - type: recall_at_1 |
| value: 4.243 |
| - type: recall_at_10 |
| value: 19.885 |
| - type: recall_at_100 |
| value: 42.17 |
| - type: recall_at_1000 |
| value: 70.12 |
| - type: recall_at_3 |
| value: 10.288 |
| - type: recall_at_5 |
| value: 14.072000000000001 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.84209174935282 |
| - type: cos_sim_spearman |
| value: 81.73248048438833 |
| - type: euclidean_pearson |
| value: 83.02810070308149 |
| - type: euclidean_spearman |
| value: 81.73248295679514 |
| - type: manhattan_pearson |
| value: 82.95368060376002 |
| - type: manhattan_spearman |
| value: 81.60277910998718 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 88.52628804556943 |
| - type: cos_sim_spearman |
| value: 82.5713913555672 |
| - type: euclidean_pearson |
| value: 85.8796774746988 |
| - type: euclidean_spearman |
| value: 82.57137506803424 |
| - type: manhattan_pearson |
| value: 85.79671002960058 |
| - type: manhattan_spearman |
| value: 82.49445981618027 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.23682503505542 |
| - type: cos_sim_spearman |
| value: 87.15008956711806 |
| - type: euclidean_pearson |
| value: 86.79805401524959 |
| - type: euclidean_spearman |
| value: 87.15008956711806 |
| - type: manhattan_pearson |
| value: 86.65298502699244 |
| - type: manhattan_spearman |
| value: 86.97677821948562 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.63370304677802 |
| - type: cos_sim_spearman |
| value: 84.97105553540318 |
| - type: euclidean_pearson |
| value: 85.28896108687721 |
| - type: euclidean_spearman |
| value: 84.97105553540318 |
| - type: manhattan_pearson |
| value: 85.09663190337331 |
| - type: manhattan_spearman |
| value: 84.79126831644619 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 90.2614838800733 |
| - type: cos_sim_spearman |
| value: 91.0509162991835 |
| - type: euclidean_pearson |
| value: 90.33098317533373 |
| - type: euclidean_spearman |
| value: 91.05091625871644 |
| - type: manhattan_pearson |
| value: 90.26250435151107 |
| - type: manhattan_spearman |
| value: 90.97999594417519 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.80480973335091 |
| - type: cos_sim_spearman |
| value: 87.313695492969 |
| - type: euclidean_pearson |
| value: 86.49267251576939 |
| - type: euclidean_spearman |
| value: 87.313695492969 |
| - type: manhattan_pearson |
| value: 86.44019901831935 |
| - type: manhattan_spearman |
| value: 87.24205395460392 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-en) |
| config: en-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 90.05662789380672 |
| - type: cos_sim_spearman |
| value: 90.02759424426651 |
| - type: euclidean_pearson |
| value: 90.4042483422981 |
| - type: euclidean_spearman |
| value: 90.02759424426651 |
| - type: manhattan_pearson |
| value: 90.51446975000226 |
| - type: manhattan_spearman |
| value: 90.08832889933616 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (en) |
| config: en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 67.5975528273532 |
| - type: cos_sim_spearman |
| value: 67.62969861411354 |
| - type: euclidean_pearson |
| value: 69.224275734323 |
| - type: euclidean_spearman |
| value: 67.62969861411354 |
| - type: manhattan_pearson |
| value: 69.3761447059927 |
| - type: manhattan_spearman |
| value: 67.90921005611467 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.11244327231684 |
| - type: cos_sim_spearman |
| value: 88.37902438979035 |
| - type: euclidean_pearson |
| value: 87.86054279847336 |
| - type: euclidean_spearman |
| value: 88.37902438979035 |
| - type: manhattan_pearson |
| value: 87.77257757320378 |
| - type: manhattan_spearman |
| value: 88.25208966098123 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 85.87174608143563 |
| - type: mrr |
| value: 96.12836872640794 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 57.760999999999996 |
| - type: map_at_10 |
| value: 67.258 |
| - type: map_at_100 |
| value: 67.757 |
| - type: map_at_1000 |
| value: 67.78800000000001 |
| - type: map_at_3 |
| value: 64.602 |
| - type: map_at_5 |
| value: 65.64 |
| - type: mrr_at_1 |
| value: 60.667 |
| - type: mrr_at_10 |
| value: 68.441 |
| - type: mrr_at_100 |
| value: 68.825 |
| - type: mrr_at_1000 |
| value: 68.853 |
| - type: mrr_at_3 |
| value: 66.444 |
| - type: mrr_at_5 |
| value: 67.26100000000001 |
| - type: ndcg_at_1 |
| value: 60.667 |
| - type: ndcg_at_10 |
| value: 71.852 |
| - type: ndcg_at_100 |
| value: 73.9 |
| - type: ndcg_at_1000 |
| value: 74.628 |
| - type: ndcg_at_3 |
| value: 67.093 |
| - type: ndcg_at_5 |
| value: 68.58 |
| - type: precision_at_1 |
| value: 60.667 |
| - type: precision_at_10 |
| value: 9.6 |
| - type: precision_at_100 |
| value: 1.0670000000000002 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 26.111 |
| - type: precision_at_5 |
| value: 16.733 |
| - type: recall_at_1 |
| value: 57.760999999999996 |
| - type: recall_at_10 |
| value: 84.967 |
| - type: recall_at_100 |
| value: 93.833 |
| - type: recall_at_1000 |
| value: 99.333 |
| - type: recall_at_3 |
| value: 71.589 |
| - type: recall_at_5 |
| value: 75.483 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.66633663366336 |
| - type: cos_sim_ap |
| value: 91.17685358899108 |
| - type: cos_sim_f1 |
| value: 82.16818642350559 |
| - type: cos_sim_precision |
| value: 83.26488706365504 |
| - type: cos_sim_recall |
| value: 81.10000000000001 |
| - type: dot_accuracy |
| value: 99.66633663366336 |
| - type: dot_ap |
| value: 91.17663411119032 |
| - type: dot_f1 |
| value: 82.16818642350559 |
| - type: dot_precision |
| value: 83.26488706365504 |
| - type: dot_recall |
| value: 81.10000000000001 |
| - type: euclidean_accuracy |
| value: 99.66633663366336 |
| - type: euclidean_ap |
| value: 91.17685189882275 |
| - type: euclidean_f1 |
| value: 82.16818642350559 |
| - type: euclidean_precision |
| value: 83.26488706365504 |
| - type: euclidean_recall |
| value: 81.10000000000001 |
| - type: manhattan_accuracy |
| value: 99.66633663366336 |
| - type: manhattan_ap |
| value: 91.2241619496737 |
| - type: manhattan_f1 |
| value: 82.20472440944883 |
| - type: manhattan_precision |
| value: 86.51933701657458 |
| - type: manhattan_recall |
| value: 78.3 |
| - type: max_accuracy |
| value: 99.66633663366336 |
| - type: max_ap |
| value: 91.2241619496737 |
| - type: max_f1 |
| value: 82.20472440944883 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 66.85101268897951 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 42.461184054706905 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 51.44542568873886 |
| - type: mrr |
| value: 52.33656151854681 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.75982974997539 |
| - type: cos_sim_spearman |
| value: 30.385405026539914 |
| - type: dot_pearson |
| value: 30.75982433546523 |
| - type: dot_spearman |
| value: 30.385405026539914 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.22799999999999998 |
| - type: map_at_10 |
| value: 2.064 |
| - type: map_at_100 |
| value: 13.056000000000001 |
| - type: map_at_1000 |
| value: 31.747999999999998 |
| - type: map_at_3 |
| value: 0.67 |
| - type: map_at_5 |
| value: 1.097 |
| - type: mrr_at_1 |
| value: 90.0 |
| - type: mrr_at_10 |
| value: 94.667 |
| - type: mrr_at_100 |
| value: 94.667 |
| - type: mrr_at_1000 |
| value: 94.667 |
| - type: mrr_at_3 |
| value: 94.667 |
| - type: mrr_at_5 |
| value: 94.667 |
| - type: ndcg_at_1 |
| value: 86.0 |
| - type: ndcg_at_10 |
| value: 82.0 |
| - type: ndcg_at_100 |
| value: 64.307 |
| - type: ndcg_at_1000 |
| value: 57.023999999999994 |
| - type: ndcg_at_3 |
| value: 85.816 |
| - type: ndcg_at_5 |
| value: 84.904 |
| - type: precision_at_1 |
| value: 90.0 |
| - type: precision_at_10 |
| value: 85.8 |
| - type: precision_at_100 |
| value: 66.46 |
| - type: precision_at_1000 |
| value: 25.202 |
| - type: precision_at_3 |
| value: 90.0 |
| - type: precision_at_5 |
| value: 89.2 |
| - type: recall_at_1 |
| value: 0.22799999999999998 |
| - type: recall_at_10 |
| value: 2.235 |
| - type: recall_at_100 |
| value: 16.185 |
| - type: recall_at_1000 |
| value: 53.620999999999995 |
| - type: recall_at_3 |
| value: 0.7040000000000001 |
| - type: recall_at_5 |
| value: 1.172 |
| - 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: 97.39999999999999 |
| - type: f1 |
| value: 96.75 |
| - type: precision |
| value: 96.45 |
| - type: recall |
| value: 97.39999999999999 |
| - 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: 85.54913294797689 |
| - type: f1 |
| value: 82.46628131021194 |
| - type: precision |
| value: 81.1175337186898 |
| - type: recall |
| value: 85.54913294797689 |
| - 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: 81.21951219512195 |
| - type: f1 |
| value: 77.33333333333334 |
| - type: precision |
| value: 75.54878048780488 |
| - type: recall |
| value: 81.21951219512195 |
| - 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: 98.6 |
| - type: f1 |
| value: 98.26666666666665 |
| - type: precision |
| value: 98.1 |
| - type: recall |
| value: 98.6 |
| - 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: 99.5 |
| - type: f1 |
| value: 99.33333333333333 |
| - type: precision |
| value: 99.25 |
| - type: recall |
| value: 99.5 |
| - 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: 97.8 |
| - type: f1 |
| value: 97.2 |
| - type: precision |
| value: 96.89999999999999 |
| - type: recall |
| value: 97.8 |
| - 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: 97.8 |
| - type: f1 |
| value: 97.18333333333334 |
| - type: precision |
| value: 96.88333333333333 |
| - type: recall |
| value: 97.8 |
| - 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: 77.61194029850746 |
| - type: f1 |
| value: 72.81094527363183 |
| - type: precision |
| value: 70.83333333333333 |
| - type: recall |
| value: 77.61194029850746 |
| - 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: 93.7 |
| - type: f1 |
| value: 91.91666666666667 |
| - type: precision |
| value: 91.08333333333334 |
| - type: recall |
| value: 93.7 |
| - 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: 88.29268292682927 |
| - type: f1 |
| value: 85.27642276422765 |
| - type: precision |
| value: 84.01277584204414 |
| - type: recall |
| value: 88.29268292682927 |
| - 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: 96.1 |
| - type: f1 |
| value: 95.0 |
| - type: precision |
| value: 94.46666666666668 |
| - type: recall |
| value: 96.1 |
| - 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: 93.681652490887 |
| - type: f1 |
| value: 91.90765492102065 |
| - type: precision |
| value: 91.05913325232888 |
| - type: recall |
| value: 93.681652490887 |
| - 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: 92.17391304347827 |
| - type: f1 |
| value: 89.97101449275361 |
| - type: precision |
| value: 88.96811594202899 |
| - type: recall |
| value: 92.17391304347827 |
| - 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: 90.43478260869566 |
| - type: f1 |
| value: 87.72173913043478 |
| - type: precision |
| value: 86.42028985507245 |
| - type: recall |
| value: 90.43478260869566 |
| - 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: 90.4 |
| - type: f1 |
| value: 88.03 |
| - type: precision |
| value: 86.95 |
| - type: recall |
| value: 90.4 |
| - 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: 93.4 |
| - type: f1 |
| value: 91.45666666666666 |
| - type: precision |
| value: 90.525 |
| - type: recall |
| value: 93.4 |
| - 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: 81.9059107358263 |
| - type: f1 |
| value: 78.32557872364869 |
| - type: precision |
| value: 76.78260286824823 |
| - type: recall |
| value: 81.9059107358263 |
| - 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: 94.3 |
| - type: f1 |
| value: 92.58333333333333 |
| - type: precision |
| value: 91.73333333333332 |
| - type: recall |
| value: 94.3 |
| - 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: 79.10000000000001 |
| - type: f1 |
| value: 74.50500000000001 |
| - type: precision |
| value: 72.58928571428571 |
| - type: recall |
| value: 79.10000000000001 |
| - 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: 96.6 |
| - type: f1 |
| value: 95.55 |
| - type: precision |
| value: 95.05 |
| - type: recall |
| value: 96.6 |
| - 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: 82.0952380952381 |
| - type: f1 |
| value: 77.98458049886621 |
| - type: precision |
| value: 76.1968253968254 |
| - type: recall |
| value: 82.0952380952381 |
| - 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: 87.9 |
| - type: f1 |
| value: 84.99190476190476 |
| - type: precision |
| value: 83.65 |
| - type: recall |
| value: 87.9 |
| - 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: 95.7 |
| - type: f1 |
| value: 94.56666666666666 |
| - type: precision |
| value: 94.01666666666667 |
| - type: recall |
| value: 95.7 |
| - 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: 98.6 |
| - type: f1 |
| value: 98.2 |
| - type: precision |
| value: 98.0 |
| - type: recall |
| value: 98.6 |
| - 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: 95.6 |
| - type: f1 |
| value: 94.38333333333334 |
| - type: precision |
| value: 93.78333333333335 |
| - type: recall |
| value: 95.6 |
| - 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: 87.4 |
| - type: f1 |
| value: 84.10380952380952 |
| - type: precision |
| value: 82.67 |
| - type: recall |
| value: 87.4 |
| - 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: 95.5 |
| - type: f1 |
| value: 94.33333333333334 |
| - type: precision |
| value: 93.78333333333333 |
| - type: recall |
| value: 95.5 |
| - 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: 89.4 |
| - type: f1 |
| value: 86.82000000000001 |
| - type: precision |
| value: 85.64500000000001 |
| - type: recall |
| value: 89.4 |
| - 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: 95.1 |
| - type: f1 |
| value: 93.56666666666668 |
| - type: precision |
| value: 92.81666666666666 |
| - type: recall |
| value: 95.1 |
| - 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: 98.9 |
| - type: f1 |
| value: 98.6 |
| - type: precision |
| value: 98.45 |
| - type: recall |
| value: 98.9 |
| - 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: 95.01347708894879 |
| - type: f1 |
| value: 93.51752021563343 |
| - type: precision |
| value: 92.82794249775381 |
| - type: recall |
| value: 95.01347708894879 |
| - 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: 97.00854700854701 |
| - type: f1 |
| value: 96.08262108262107 |
| - type: precision |
| value: 95.65527065527067 |
| - type: recall |
| value: 97.00854700854701 |
| - 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: 96.5 |
| - type: f1 |
| value: 95.39999999999999 |
| - type: precision |
| value: 94.88333333333333 |
| - type: recall |
| value: 96.5 |
| - 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: 96.5909090909091 |
| - type: f1 |
| value: 95.49242424242425 |
| - type: precision |
| value: 94.9621212121212 |
| - type: recall |
| value: 96.5909090909091 |
| - 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: 84.90566037735849 |
| - type: f1 |
| value: 81.85883997204752 |
| - type: precision |
| value: 80.54507337526205 |
| - type: recall |
| value: 84.90566037735849 |
| - 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: 97.5 |
| - type: f1 |
| value: 96.75 |
| - type: precision |
| value: 96.38333333333333 |
| - type: recall |
| value: 97.5 |
| - 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: 86.7704280155642 |
| - type: f1 |
| value: 82.99610894941635 |
| - type: precision |
| value: 81.32295719844358 |
| - type: recall |
| value: 86.7704280155642 |
| - 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: 67.52136752136752 |
| - type: f1 |
| value: 61.89662189662191 |
| - type: precision |
| value: 59.68660968660969 |
| - type: recall |
| value: 67.52136752136752 |
| - 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: 89.2 |
| - type: f1 |
| value: 86.32 |
| - type: precision |
| value: 85.015 |
| - type: recall |
| value: 89.2 |
| - 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: 96.0 |
| - type: f1 |
| value: 94.78333333333333 |
| - type: precision |
| value: 94.18333333333334 |
| - type: recall |
| value: 96.0 |
| - 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: 83.8785046728972 |
| - type: f1 |
| value: 80.54517133956385 |
| - type: precision |
| value: 79.154984423676 |
| - type: recall |
| value: 83.8785046728972 |
| - 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: 93.60000000000001 |
| - type: f1 |
| value: 92.01333333333334 |
| - type: precision |
| value: 91.28333333333333 |
| - type: recall |
| value: 93.60000000000001 |
| - 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: 97.1 |
| - type: f1 |
| value: 96.26666666666667 |
| - type: precision |
| value: 95.85000000000001 |
| - type: recall |
| value: 97.1 |
| - 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: 84.3 |
| - type: f1 |
| value: 80.67833333333333 |
| - type: precision |
| value: 79.03928571428571 |
| - type: recall |
| value: 84.3 |
| - 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: 97.3 |
| - type: f1 |
| value: 96.48333333333332 |
| - type: precision |
| value: 96.08333333333331 |
| - type: recall |
| value: 97.3 |
| - 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: 95.7 |
| - type: f1 |
| value: 94.66666666666667 |
| - type: precision |
| value: 94.16666666666667 |
| - type: recall |
| value: 95.7 |
| - 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: 97.2 |
| - type: f1 |
| value: 96.36666666666667 |
| - type: precision |
| value: 95.96666666666668 |
| - type: recall |
| value: 97.2 |
| - 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: 94.3 |
| - type: f1 |
| value: 92.80666666666667 |
| - type: precision |
| value: 92.12833333333333 |
| - type: recall |
| value: 94.3 |
| - 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: 97.0 |
| - type: f1 |
| value: 96.22333333333334 |
| - type: precision |
| value: 95.875 |
| - type: recall |
| value: 97.0 |
| - 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: 74.33333333333333 |
| - type: f1 |
| value: 70.78174603174602 |
| - type: precision |
| value: 69.28333333333332 |
| - type: recall |
| value: 74.33333333333333 |
| - 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: 37.6 |
| - type: f1 |
| value: 32.938348952090365 |
| - type: precision |
| value: 31.2811038961039 |
| - type: recall |
| value: 37.6 |
| - 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: 91.5 |
| - type: f1 |
| value: 89.13333333333333 |
| - type: precision |
| value: 88.03333333333333 |
| - type: recall |
| value: 91.5 |
| - 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: 82.14285714285714 |
| - type: f1 |
| value: 77.67857142857143 |
| - type: precision |
| value: 75.59523809523809 |
| - type: recall |
| value: 82.14285714285714 |
| - 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: 69.0450054884742 |
| - type: f1 |
| value: 63.070409283362075 |
| - type: precision |
| value: 60.58992781824835 |
| - type: recall |
| value: 69.0450054884742 |
| - 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: 63.1 |
| - type: f1 |
| value: 57.848333333333336 |
| - type: precision |
| value: 55.69500000000001 |
| - type: recall |
| value: 63.1 |
| - 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: 96.1 |
| - type: f1 |
| value: 95.01666666666667 |
| - type: precision |
| value: 94.5 |
| - type: recall |
| value: 96.1 |
| - 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: 95.89999999999999 |
| - type: f1 |
| value: 94.90666666666667 |
| - type: precision |
| value: 94.425 |
| - type: recall |
| value: 95.89999999999999 |
| - 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: 87.6 |
| - type: f1 |
| value: 84.61333333333333 |
| - type: precision |
| value: 83.27 |
| - type: recall |
| value: 87.6 |
| - 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: 76.4 |
| - type: f1 |
| value: 71.90746031746032 |
| - type: precision |
| value: 70.07027777777778 |
| - type: recall |
| value: 76.4 |
| - 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: 97.89999999999999 |
| - type: f1 |
| value: 97.26666666666667 |
| - type: precision |
| value: 96.95 |
| - type: recall |
| value: 97.89999999999999 |
| - 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: 78.8 |
| - type: f1 |
| value: 74.39555555555555 |
| - type: precision |
| value: 72.59416666666667 |
| - type: recall |
| value: 78.8 |
| - 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: 95.19999999999999 |
| - type: f1 |
| value: 93.78999999999999 |
| - type: precision |
| value: 93.125 |
| - type: recall |
| value: 95.19999999999999 |
| - 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: 97.8 |
| - type: f1 |
| value: 97.1 |
| - type: precision |
| value: 96.75 |
| - type: recall |
| value: 97.8 |
| - 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: 95.6 |
| - type: f1 |
| value: 94.25666666666666 |
| - type: precision |
| value: 93.64166666666668 |
| - type: recall |
| value: 95.6 |
| - 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: 56.934306569343065 |
| - type: f1 |
| value: 51.461591936044485 |
| - type: precision |
| value: 49.37434827945776 |
| - type: recall |
| value: 56.934306569343065 |
| - 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: 20.200000000000003 |
| - type: f1 |
| value: 16.91799284049284 |
| - type: precision |
| value: 15.791855158730158 |
| - type: recall |
| value: 20.200000000000003 |
| - 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: 96.2 |
| - type: f1 |
| value: 95.3 |
| - type: precision |
| value: 94.85 |
| - type: recall |
| value: 96.2 |
| - 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: 96.3 |
| - type: f1 |
| value: 95.11666666666667 |
| - type: precision |
| value: 94.53333333333333 |
| - type: recall |
| value: 96.3 |
| - 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: 89.88095238095238 |
| - type: f1 |
| value: 87.14285714285714 |
| - type: precision |
| value: 85.96230158730161 |
| - type: recall |
| value: 89.88095238095238 |
| - 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: 24.099999999999998 |
| - type: f1 |
| value: 19.630969083349783 |
| - type: precision |
| value: 18.275094905094907 |
| - type: recall |
| value: 24.099999999999998 |
| - 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: 83.4368530020704 |
| - type: f1 |
| value: 79.45183870649709 |
| - type: precision |
| value: 77.7432712215321 |
| - type: recall |
| value: 83.4368530020704 |
| - 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: 95.8 |
| - type: f1 |
| value: 94.53333333333333 |
| - type: precision |
| value: 93.91666666666666 |
| - type: recall |
| value: 95.8 |
| - 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: 98.8 |
| - type: f1 |
| value: 98.48333333333332 |
| - type: precision |
| value: 98.33333333333334 |
| - type: recall |
| value: 98.8 |
| - 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: 17.5 |
| - type: f1 |
| value: 14.979285714285714 |
| - type: precision |
| value: 14.23235060690943 |
| - type: recall |
| value: 17.5 |
| - 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: 93.93939393939394 |
| - type: f1 |
| value: 91.991341991342 |
| - type: precision |
| value: 91.05339105339105 |
| - type: recall |
| value: 93.93939393939394 |
| - 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: 89.31297709923665 |
| - type: f1 |
| value: 86.76844783715012 |
| - type: precision |
| value: 85.63613231552164 |
| - type: recall |
| value: 89.31297709923665 |
| - 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: 99.12663755458514 |
| - type: f1 |
| value: 98.93255701115964 |
| - type: precision |
| value: 98.83551673944687 |
| - type: recall |
| value: 99.12663755458514 |
| - 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: 92.0 |
| - type: f1 |
| value: 89.77999999999999 |
| - type: precision |
| value: 88.78333333333333 |
| - type: recall |
| value: 92.0 |
| - 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: 96.89265536723164 |
| - type: f1 |
| value: 95.85687382297553 |
| - type: precision |
| value: 95.33898305084746 |
| - type: recall |
| value: 96.89265536723164 |
| - 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: 14.6 |
| - type: f1 |
| value: 11.820611790170615 |
| - type: precision |
| value: 11.022616224355355 |
| - type: recall |
| value: 14.6 |
| - 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: 95.89999999999999 |
| - type: f1 |
| value: 94.93333333333334 |
| - type: precision |
| value: 94.48666666666666 |
| - type: recall |
| value: 95.89999999999999 |
| - 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: 87.6 |
| - type: f1 |
| value: 84.72333333333334 |
| - type: precision |
| value: 83.44166666666666 |
| - type: recall |
| value: 87.6 |
| - 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: 94.8 |
| - type: f1 |
| value: 93.47333333333333 |
| - type: precision |
| value: 92.875 |
| - type: recall |
| value: 94.8 |
| - 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: 96.6 |
| - type: f1 |
| value: 95.71666666666665 |
| - type: precision |
| value: 95.28333333333335 |
| - type: recall |
| value: 96.6 |
| - 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: 17.8 |
| - type: f1 |
| value: 14.511074040901628 |
| - type: precision |
| value: 13.503791000666002 |
| - type: recall |
| value: 17.8 |
| - 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: 94.10187667560321 |
| - type: f1 |
| value: 92.46648793565683 |
| - type: precision |
| value: 91.71134941912423 |
| - type: recall |
| value: 94.10187667560321 |
| - 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: 97.0 |
| - type: f1 |
| value: 96.11666666666666 |
| - type: precision |
| value: 95.68333333333334 |
| - type: recall |
| value: 97.0 |
| - 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: 72.72727272727273 |
| - type: f1 |
| value: 66.58949745906267 |
| - type: precision |
| value: 63.86693017127799 |
| - type: recall |
| value: 72.72727272727273 |
| - 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: 90.14084507042254 |
| - type: f1 |
| value: 88.26291079812206 |
| - type: precision |
| value: 87.32394366197182 |
| - type: recall |
| value: 90.14084507042254 |
| - 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: 64.67065868263472 |
| - type: f1 |
| value: 58.2876627696987 |
| - type: precision |
| value: 55.79255774165953 |
| - type: recall |
| value: 64.67065868263472 |
| - 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: 95.6 |
| - type: f1 |
| value: 94.41666666666667 |
| - type: precision |
| value: 93.85 |
| - type: recall |
| value: 95.6 |
| - 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: 55.172413793103445 |
| - type: f1 |
| value: 49.63992493549144 |
| - type: precision |
| value: 47.71405113769646 |
| - type: recall |
| value: 55.172413793103445 |
| - 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: 77.46478873239437 |
| - type: f1 |
| value: 73.4417616811983 |
| - type: precision |
| value: 71.91607981220658 |
| - type: recall |
| value: 77.46478873239437 |
| - 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: 84.61538461538461 |
| - type: f1 |
| value: 80.91452991452994 |
| - type: precision |
| value: 79.33760683760683 |
| - type: recall |
| value: 84.61538461538461 |
| - 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: 98.2 |
| - type: f1 |
| value: 97.6 |
| - type: precision |
| value: 97.3 |
| - type: recall |
| value: 98.2 |
| - 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: 75.5741127348643 |
| - type: f1 |
| value: 72.00417536534445 |
| - type: precision |
| value: 70.53467872883321 |
| - type: recall |
| value: 75.5741127348643 |
| - 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: 62.2 |
| - type: f1 |
| value: 55.577460317460314 |
| - type: precision |
| value: 52.98583333333333 |
| - type: recall |
| value: 62.2 |
| - 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: 92.18241042345277 |
| - type: f1 |
| value: 90.6468124709167 |
| - type: precision |
| value: 89.95656894679696 |
| - type: recall |
| value: 92.18241042345277 |
| - 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: 96.1 |
| - type: f1 |
| value: 95.13333333333333 |
| - type: precision |
| value: 94.66666666666667 |
| - type: recall |
| value: 96.1 |
| - 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: 96.8 |
| - type: f1 |
| value: 95.85000000000001 |
| - type: precision |
| value: 95.39999999999999 |
| - type: recall |
| value: 96.8 |
| - 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: 92.1259842519685 |
| - type: f1 |
| value: 89.76377952755905 |
| - type: precision |
| value: 88.71391076115485 |
| - type: recall |
| value: 92.1259842519685 |
| - 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: 94.1 |
| - type: f1 |
| value: 92.49 |
| - type: precision |
| value: 91.725 |
| - type: recall |
| value: 94.1 |
| - 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: 77.5623268698061 |
| - type: f1 |
| value: 73.27364463791058 |
| - type: precision |
| value: 71.51947852086357 |
| - type: recall |
| value: 77.5623268698061 |
| - 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: 97.39999999999999 |
| - type: f1 |
| value: 96.56666666666666 |
| - type: precision |
| value: 96.16666666666667 |
| - type: recall |
| value: 97.39999999999999 |
| - 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: 66.34615384615384 |
| - type: f1 |
| value: 61.092032967032964 |
| - type: precision |
| value: 59.27197802197802 |
| - type: recall |
| value: 66.34615384615384 |
| - 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: 94.89999999999999 |
| - type: f1 |
| value: 93.41190476190476 |
| - type: precision |
| value: 92.7 |
| - type: recall |
| value: 94.89999999999999 |
| - 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: 93.10000000000001 |
| - type: f1 |
| value: 91.10000000000001 |
| - type: precision |
| value: 90.13333333333333 |
| - type: recall |
| value: 93.10000000000001 |
| - 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: 93.7 |
| - type: f1 |
| value: 91.97333333333334 |
| - type: precision |
| value: 91.14166666666667 |
| - type: recall |
| value: 93.7 |
| - 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: 92.21698113207547 |
| - type: f1 |
| value: 90.3796046720575 |
| - type: precision |
| value: 89.56367924528303 |
| - type: recall |
| value: 92.21698113207547 |
| - 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: 97.6 |
| - type: f1 |
| value: 96.91666666666667 |
| - type: precision |
| value: 96.6 |
| - type: recall |
| value: 97.6 |
| - 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: 97.44525547445255 |
| - type: f1 |
| value: 96.71532846715328 |
| - type: precision |
| value: 96.35036496350365 |
| - type: recall |
| value: 97.44525547445255 |
| - 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: 94.1 |
| - type: f1 |
| value: 92.34000000000002 |
| - type: precision |
| value: 91.49166666666667 |
| - type: recall |
| value: 94.1 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 3.2910000000000004 |
| - type: map_at_10 |
| value: 10.373000000000001 |
| - type: map_at_100 |
| value: 15.612 |
| - type: map_at_1000 |
| value: 17.06 |
| - type: map_at_3 |
| value: 6.119 |
| - type: map_at_5 |
| value: 7.917000000000001 |
| - type: mrr_at_1 |
| value: 44.897999999999996 |
| - type: mrr_at_10 |
| value: 56.054 |
| - type: mrr_at_100 |
| value: 56.82000000000001 |
| - type: mrr_at_1000 |
| value: 56.82000000000001 |
| - type: mrr_at_3 |
| value: 52.381 |
| - type: mrr_at_5 |
| value: 53.81 |
| - type: ndcg_at_1 |
| value: 42.857 |
| - type: ndcg_at_10 |
| value: 27.249000000000002 |
| - type: ndcg_at_100 |
| value: 36.529 |
| - type: ndcg_at_1000 |
| value: 48.136 |
| - type: ndcg_at_3 |
| value: 33.938 |
| - type: ndcg_at_5 |
| value: 29.951 |
| - type: precision_at_1 |
| value: 44.897999999999996 |
| - type: precision_at_10 |
| value: 22.653000000000002 |
| - type: precision_at_100 |
| value: 7.000000000000001 |
| - type: precision_at_1000 |
| value: 1.48 |
| - type: precision_at_3 |
| value: 32.653 |
| - type: precision_at_5 |
| value: 27.755000000000003 |
| - type: recall_at_1 |
| value: 3.2910000000000004 |
| - type: recall_at_10 |
| value: 16.16 |
| - type: recall_at_100 |
| value: 43.908 |
| - type: recall_at_1000 |
| value: 79.823 |
| - type: recall_at_3 |
| value: 7.156 |
| - type: recall_at_5 |
| value: 10.204 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 71.05879999999999 |
| - type: ap |
| value: 14.609748142799111 |
| - type: f1 |
| value: 54.878956295843096 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 64.61799660441426 |
| - type: f1 |
| value: 64.8698191961434 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 51.32860036611885 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.34714192048638 |
| - type: cos_sim_ap |
| value: 80.26732975975634 |
| - type: cos_sim_f1 |
| value: 73.53415148134374 |
| - type: cos_sim_precision |
| value: 69.34767360299276 |
| - type: cos_sim_recall |
| value: 78.25857519788919 |
| - type: dot_accuracy |
| value: 88.34714192048638 |
| - type: dot_ap |
| value: 80.26733698491206 |
| - type: dot_f1 |
| value: 73.53415148134374 |
| - type: dot_precision |
| value: 69.34767360299276 |
| - type: dot_recall |
| value: 78.25857519788919 |
| - type: euclidean_accuracy |
| value: 88.34714192048638 |
| - type: euclidean_ap |
| value: 80.26734337771738 |
| - type: euclidean_f1 |
| value: 73.53415148134374 |
| - type: euclidean_precision |
| value: 69.34767360299276 |
| - type: euclidean_recall |
| value: 78.25857519788919 |
| - type: manhattan_accuracy |
| value: 88.30541813196639 |
| - type: manhattan_ap |
| value: 80.19415808104145 |
| - type: manhattan_f1 |
| value: 73.55143870713441 |
| - type: manhattan_precision |
| value: 73.25307511122743 |
| - type: manhattan_recall |
| value: 73.85224274406332 |
| - type: max_accuracy |
| value: 88.34714192048638 |
| - type: max_ap |
| value: 80.26734337771738 |
| - type: max_f1 |
| value: 73.55143870713441 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 89.81061047075717 |
| - type: cos_sim_ap |
| value: 87.11747055081017 |
| - type: cos_sim_f1 |
| value: 80.04355498817256 |
| - type: cos_sim_precision |
| value: 78.1165262000733 |
| - type: cos_sim_recall |
| value: 82.06806282722513 |
| - type: dot_accuracy |
| value: 89.81061047075717 |
| - type: dot_ap |
| value: 87.11746902745236 |
| - type: dot_f1 |
| value: 80.04355498817256 |
| - type: dot_precision |
| value: 78.1165262000733 |
| - type: dot_recall |
| value: 82.06806282722513 |
| - type: euclidean_accuracy |
| value: 89.81061047075717 |
| - type: euclidean_ap |
| value: 87.11746919324248 |
| - type: euclidean_f1 |
| value: 80.04355498817256 |
| - type: euclidean_precision |
| value: 78.1165262000733 |
| - type: euclidean_recall |
| value: 82.06806282722513 |
| - type: manhattan_accuracy |
| value: 89.79508673885202 |
| - type: manhattan_ap |
| value: 87.11074390832218 |
| - type: manhattan_f1 |
| value: 80.13002540726349 |
| - type: manhattan_precision |
| value: 77.83826945412311 |
| - type: manhattan_recall |
| value: 82.56082537727133 |
| - type: max_accuracy |
| value: 89.81061047075717 |
| - type: max_ap |
| value: 87.11747055081017 |
| - type: max_f1 |
| value: 80.13002540726349 |
| language: |
| - multilingual |
| - af |
| - am |
| - ar |
| - as |
| - az |
| - be |
| - bg |
| - bn |
| - br |
| - bs |
| - ca |
| - cs |
| - cy |
| - da |
| - de |
| - el |
| - en |
| - eo |
| - es |
| - et |
| - eu |
| - fa |
| - fi |
| - fr |
| - fy |
| - ga |
| - gd |
| - gl |
| - gu |
| - ha |
| - he |
| - hi |
| - hr |
| - hu |
| - hy |
| - id |
| - is |
| - it |
| - ja |
| - jv |
| - ka |
| - kk |
| - km |
| - kn |
| - ko |
| - ku |
| - ky |
| - la |
| - lo |
| - lt |
| - lv |
| - mg |
| - mk |
| - ml |
| - mn |
| - mr |
| - ms |
| - my |
| - ne |
| - nl |
| - 'no' |
| - om |
| - or |
| - pa |
| - pl |
| - ps |
| - pt |
| - ro |
| - ru |
| - sa |
| - sd |
| - si |
| - sk |
| - sl |
| - so |
| - sq |
| - sr |
| - su |
| - sv |
| - sw |
| - ta |
| - te |
| - th |
| - tl |
| - tr |
| - ug |
| - uk |
| - ur |
| - uz |
| - vi |
| - xh |
| - yi |
| - zh |
| license: mit |
| --- |
| |
| ## Multilingual-E5-large-instruct |
|
|
| [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). |
| Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 |
|
|
| [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/abs/2402.05672). |
| Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 |
|
|
| This model has 24 layers and the embedding size is 1024. |
|
|
| ## Usage |
|
|
| Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset. |
|
|
| ### Transformers |
|
|
| ```python |
| import torch.nn.functional as F |
| |
| from torch import Tensor |
| from transformers import AutoTokenizer, AutoModel |
| |
| |
| def average_pool(last_hidden_states: Tensor, |
| attention_mask: Tensor) -> Tensor: |
| last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
| return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
| |
| def get_detailed_instruct(task_description: str, query: str) -> str: |
| return f'Instruct: {task_description}\nQuery: {query}' |
| |
| # Each query must come with a one-sentence instruction that describes the task |
| task = 'Given a web search query, retrieve relevant passages that answer the query' |
| queries = [ |
| get_detailed_instruct(task, 'how much protein should a female eat'), |
| get_detailed_instruct(task, '南瓜的家常做法') |
| ] |
| # No need to add instruction for retrieval documents |
| documents = [ |
| "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| "1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" |
| ] |
| input_texts = queries + documents |
| |
| tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct') |
| model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct') |
| |
| # Tokenize the input texts |
| batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
| |
| outputs = model(**batch_dict) |
| embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
| |
| # normalize embeddings |
| embeddings = F.normalize(embeddings, p=2, dim=1) |
| scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
| print(scores.tolist()) |
| # => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]] |
| ``` |
|
|
| ### Sentence Transformers |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| def get_detailed_instruct(task_description: str, query: str) -> str: |
| return f'Instruct: {task_description}\nQuery: {query}' |
| |
| # Each query must come with a one-sentence instruction that describes the task |
| task = 'Given a web search query, retrieve relevant passages that answer the query' |
| queries = [ |
| get_detailed_instruct(task, 'how much protein should a female eat'), |
| get_detailed_instruct(task, '南瓜的家常做法') |
| ] |
| # No need to add instruction for retrieval documents |
| documents = [ |
| "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| "1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" |
| ] |
| input_texts = queries + documents |
| |
| model = SentenceTransformer('intfloat/multilingual-e5-large-instruct') |
| |
| embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True) |
| scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
| print(scores.tolist()) |
| # [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]] |
| ``` |
|
|
| ## Supported Languages |
|
|
| This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) |
| and continually trained on a mixture of multilingual datasets. |
| It supports 100 languages from xlm-roberta, |
| but low-resource languages may see performance degradation. |
|
|
| ## Training Details |
|
|
| **Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) |
|
|
| **First stage**: contrastive pre-training with 1 billion weakly supervised text pairs. |
|
|
| **Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper. |
|
|
| ## MTEB Benchmark Evaluation |
|
|
| Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
| on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
|
|
| ## FAQ |
|
|
| **1. Do I need to add instructions to the query?** |
|
|
| Yes, this is how the model is trained, otherwise you will see a performance degradation. |
| The task definition should be a one-sentence instruction that describes the task. |
| This is a way to customize text embeddings for different scenarios through natural language instructions. |
|
|
| Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation. |
|
|
| On the other hand, there is no need to add instructions to the document side. |
|
|
| **2. Why are my reproduced results slightly different from reported in the model card?** |
|
|
| Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
|
|
| **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
|
|
| This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
|
|
| For text embedding tasks like text retrieval or semantic similarity, |
| what matters is the relative order of the scores instead of the absolute values, |
| so this should not be an issue. |
|
|
| ## Citation |
|
|
| If you find our paper or models helpful, please consider cite as follows: |
|
|
| ``` |
| @article{wang2022text, |
| title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, |
| author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, |
| journal={arXiv preprint arXiv:2212.03533}, |
| year={2022} |
| } |
| ``` |
|
|
| ## Limitations |
|
|
| Long texts will be truncated to at most 512 tokens. |
|
|
