| --- |
| tags: |
| - mteb |
| - Sentence Transformers |
| - sentence-similarity |
| - feature-extraction |
| - sentence-transformers |
| model-index: |
| - name: multilingual-e5-large |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 79.05970149253731 |
| - type: ap |
| value: 43.486574390835635 |
| - type: f1 |
| value: 73.32700092140148 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (de) |
| config: de |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 71.22055674518201 |
| - type: ap |
| value: 81.55756710830498 |
| - type: f1 |
| value: 69.28271787752661 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en-ext) |
| config: en-ext |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 80.41979010494754 |
| - type: ap |
| value: 29.34879922376344 |
| - type: f1 |
| value: 67.62475449011278 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (ja) |
| config: ja |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 77.8372591006424 |
| - type: ap |
| value: 26.557560591210738 |
| - type: f1 |
| value: 64.96619417368707 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 93.489875 |
| - type: ap |
| value: 90.98758636917603 |
| - type: f1 |
| value: 93.48554819717332 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 47.564 |
| - type: f1 |
| value: 46.75122173518047 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (de) |
| config: de |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 45.400000000000006 |
| - type: f1 |
| value: 44.17195682400632 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (es) |
| config: es |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 43.068 |
| - type: f1 |
| value: 42.38155696855596 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (fr) |
| config: fr |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 41.89 |
| - type: f1 |
| value: 40.84407321682663 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (ja) |
| config: ja |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 40.120000000000005 |
| - type: f1 |
| value: 39.522976223819114 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (zh) |
| config: zh |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 38.832 |
| - type: f1 |
| value: 38.0392533394713 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 30.725 |
| - type: map_at_10 |
| value: 46.055 |
| - type: map_at_100 |
| value: 46.900999999999996 |
| - type: map_at_1000 |
| value: 46.911 |
| - type: map_at_3 |
| value: 41.548 |
| - type: map_at_5 |
| value: 44.297 |
| - type: mrr_at_1 |
| value: 31.152 |
| - type: mrr_at_10 |
| value: 46.231 |
| - type: mrr_at_100 |
| value: 47.07 |
| - type: mrr_at_1000 |
| value: 47.08 |
| - type: mrr_at_3 |
| value: 41.738 |
| - type: mrr_at_5 |
| value: 44.468999999999994 |
| - type: ndcg_at_1 |
| value: 30.725 |
| - type: ndcg_at_10 |
| value: 54.379999999999995 |
| - type: ndcg_at_100 |
| value: 58.138 |
| - type: ndcg_at_1000 |
| value: 58.389 |
| - type: ndcg_at_3 |
| value: 45.156 |
| - type: ndcg_at_5 |
| value: 50.123 |
| - type: precision_at_1 |
| value: 30.725 |
| - type: precision_at_10 |
| value: 8.087 |
| - type: precision_at_100 |
| value: 0.9769999999999999 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 18.54 |
| - type: precision_at_5 |
| value: 13.542000000000002 |
| - type: recall_at_1 |
| value: 30.725 |
| - type: recall_at_10 |
| value: 80.868 |
| - type: recall_at_100 |
| value: 97.653 |
| - type: recall_at_1000 |
| value: 99.57300000000001 |
| - type: recall_at_3 |
| value: 55.619 |
| - type: recall_at_5 |
| value: 67.71000000000001 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 44.30960650674069 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 38.427074197498996 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 60.28270056031872 |
| - type: mrr |
| value: 74.38332673789738 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.05942144105269 |
| - type: cos_sim_spearman |
| value: 82.51212105850809 |
| - type: euclidean_pearson |
| value: 81.95639829909122 |
| - type: euclidean_spearman |
| value: 82.3717564144213 |
| - type: manhattan_pearson |
| value: 81.79273425468256 |
| - type: manhattan_spearman |
| value: 82.20066817871039 |
| - 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: 99.46764091858039 |
| - type: f1 |
| value: 99.37717466945023 |
| - type: precision |
| value: 99.33194154488518 |
| - type: recall |
| value: 99.46764091858039 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/bucc-bitext-mining |
| name: MTEB BUCC (fr-en) |
| config: fr-en |
| split: test |
| revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| metrics: |
| - type: accuracy |
| value: 98.29407880255337 |
| - type: f1 |
| value: 98.11248073959938 |
| - type: precision |
| value: 98.02443319392472 |
| - type: recall |
| value: 98.29407880255337 |
| - 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: 97.79009352268791 |
| - type: f1 |
| value: 97.5176076665512 |
| - type: precision |
| value: 97.38136473848286 |
| - type: recall |
| value: 97.79009352268791 |
| - 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: 99.26276987888363 |
| - type: f1 |
| value: 99.20133403545726 |
| - type: precision |
| value: 99.17500438827453 |
| - type: recall |
| value: 99.26276987888363 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 84.72727272727273 |
| - type: f1 |
| value: 84.67672206031433 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 35.34220182511161 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 33.4987096128766 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.558249999999997 |
| - type: map_at_10 |
| value: 34.44425000000001 |
| - type: map_at_100 |
| value: 35.59833333333333 |
| - type: map_at_1000 |
| value: 35.706916666666665 |
| - type: map_at_3 |
| value: 31.691749999999995 |
| - type: map_at_5 |
| value: 33.252916666666664 |
| - type: mrr_at_1 |
| value: 30.252666666666666 |
| - type: mrr_at_10 |
| value: 38.60675 |
| - type: mrr_at_100 |
| value: 39.42666666666666 |
| - type: mrr_at_1000 |
| value: 39.48408333333334 |
| - type: mrr_at_3 |
| value: 36.17441666666665 |
| - type: mrr_at_5 |
| value: 37.56275 |
| - type: ndcg_at_1 |
| value: 30.252666666666666 |
| - type: ndcg_at_10 |
| value: 39.683 |
| - type: ndcg_at_100 |
| value: 44.68541666666667 |
| - type: ndcg_at_1000 |
| value: 46.94316666666668 |
| - type: ndcg_at_3 |
| value: 34.961749999999995 |
| - type: ndcg_at_5 |
| value: 37.215666666666664 |
| - type: precision_at_1 |
| value: 30.252666666666666 |
| - type: precision_at_10 |
| value: 6.904166666666667 |
| - type: precision_at_100 |
| value: 1.0989999999999995 |
| - type: precision_at_1000 |
| value: 0.14733333333333334 |
| - type: precision_at_3 |
| value: 16.037666666666667 |
| - type: precision_at_5 |
| value: 11.413583333333333 |
| - type: recall_at_1 |
| value: 25.558249999999997 |
| - type: recall_at_10 |
| value: 51.13341666666666 |
| - type: recall_at_100 |
| value: 73.08366666666667 |
| - type: recall_at_1000 |
| value: 88.79483333333334 |
| - type: recall_at_3 |
| value: 37.989083333333326 |
| - type: recall_at_5 |
| value: 43.787833333333325 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 10.338 |
| - type: map_at_10 |
| value: 18.360000000000003 |
| - type: map_at_100 |
| value: 19.942 |
| - type: map_at_1000 |
| value: 20.134 |
| - type: map_at_3 |
| value: 15.174000000000001 |
| - type: map_at_5 |
| value: 16.830000000000002 |
| - type: mrr_at_1 |
| value: 23.257 |
| - type: mrr_at_10 |
| value: 33.768 |
| - type: mrr_at_100 |
| value: 34.707 |
| - type: mrr_at_1000 |
| value: 34.766000000000005 |
| - type: mrr_at_3 |
| value: 30.977 |
| - type: mrr_at_5 |
| value: 32.528 |
| - type: ndcg_at_1 |
| value: 23.257 |
| - type: ndcg_at_10 |
| value: 25.733 |
| - type: ndcg_at_100 |
| value: 32.288 |
| - type: ndcg_at_1000 |
| value: 35.992000000000004 |
| - type: ndcg_at_3 |
| value: 20.866 |
| - type: ndcg_at_5 |
| value: 22.612 |
| - type: precision_at_1 |
| value: 23.257 |
| - type: precision_at_10 |
| value: 8.124 |
| - type: precision_at_100 |
| value: 1.518 |
| - type: precision_at_1000 |
| value: 0.219 |
| - type: precision_at_3 |
| value: 15.679000000000002 |
| - type: precision_at_5 |
| value: 12.117 |
| - type: recall_at_1 |
| value: 10.338 |
| - type: recall_at_10 |
| value: 31.154 |
| - type: recall_at_100 |
| value: 54.161 |
| - type: recall_at_1000 |
| value: 75.21900000000001 |
| - type: recall_at_3 |
| value: 19.427 |
| - type: recall_at_5 |
| value: 24.214 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 8.498 |
| - type: map_at_10 |
| value: 19.103 |
| - type: map_at_100 |
| value: 27.375 |
| - type: map_at_1000 |
| value: 28.981 |
| - type: map_at_3 |
| value: 13.764999999999999 |
| - type: map_at_5 |
| value: 15.950000000000001 |
| - type: mrr_at_1 |
| value: 65.5 |
| - type: mrr_at_10 |
| value: 74.53800000000001 |
| - type: mrr_at_100 |
| value: 74.71799999999999 |
| - type: mrr_at_1000 |
| value: 74.725 |
| - type: mrr_at_3 |
| value: 72.792 |
| - type: mrr_at_5 |
| value: 73.554 |
| - type: ndcg_at_1 |
| value: 53.37499999999999 |
| - type: ndcg_at_10 |
| value: 41.286 |
| - type: ndcg_at_100 |
| value: 45.972 |
| - type: ndcg_at_1000 |
| value: 53.123 |
| - type: ndcg_at_3 |
| value: 46.172999999999995 |
| - type: ndcg_at_5 |
| value: 43.033 |
| - type: precision_at_1 |
| value: 65.5 |
| - type: precision_at_10 |
| value: 32.725 |
| - type: precision_at_100 |
| value: 10.683 |
| - type: precision_at_1000 |
| value: 1.978 |
| - type: precision_at_3 |
| value: 50 |
| - type: precision_at_5 |
| value: 41.349999999999994 |
| - type: recall_at_1 |
| value: 8.498 |
| - type: recall_at_10 |
| value: 25.070999999999998 |
| - type: recall_at_100 |
| value: 52.383 |
| - type: recall_at_1000 |
| value: 74.91499999999999 |
| - type: recall_at_3 |
| value: 15.207999999999998 |
| - type: recall_at_5 |
| value: 18.563 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 46.5 |
| - type: f1 |
| value: 41.93833713984145 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 67.914 |
| - type: map_at_10 |
| value: 78.10000000000001 |
| - type: map_at_100 |
| value: 78.333 |
| - type: map_at_1000 |
| value: 78.346 |
| - type: map_at_3 |
| value: 76.626 |
| - type: map_at_5 |
| value: 77.627 |
| - type: mrr_at_1 |
| value: 72.74199999999999 |
| - type: mrr_at_10 |
| value: 82.414 |
| - type: mrr_at_100 |
| value: 82.511 |
| - type: mrr_at_1000 |
| value: 82.513 |
| - type: mrr_at_3 |
| value: 81.231 |
| - type: mrr_at_5 |
| value: 82.065 |
| - type: ndcg_at_1 |
| value: 72.74199999999999 |
| - type: ndcg_at_10 |
| value: 82.806 |
| - type: ndcg_at_100 |
| value: 83.677 |
| - type: ndcg_at_1000 |
| value: 83.917 |
| - type: ndcg_at_3 |
| value: 80.305 |
| - type: ndcg_at_5 |
| value: 81.843 |
| - type: precision_at_1 |
| value: 72.74199999999999 |
| - type: precision_at_10 |
| value: 10.24 |
| - type: precision_at_100 |
| value: 1.089 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_3 |
| value: 31.268 |
| - type: precision_at_5 |
| value: 19.706000000000003 |
| - type: recall_at_1 |
| value: 67.914 |
| - type: recall_at_10 |
| value: 92.889 |
| - type: recall_at_100 |
| value: 96.42699999999999 |
| - type: recall_at_1000 |
| value: 97.92 |
| - type: recall_at_3 |
| value: 86.21 |
| - type: recall_at_5 |
| value: 90.036 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.166 |
| - type: map_at_10 |
| value: 35.57 |
| - type: map_at_100 |
| value: 37.405 |
| - type: map_at_1000 |
| value: 37.564 |
| - type: map_at_3 |
| value: 30.379 |
| - type: map_at_5 |
| value: 33.324 |
| - type: mrr_at_1 |
| value: 43.519000000000005 |
| - type: mrr_at_10 |
| value: 51.556000000000004 |
| - type: mrr_at_100 |
| value: 52.344 |
| - type: mrr_at_1000 |
| value: 52.373999999999995 |
| - type: mrr_at_3 |
| value: 48.868 |
| - type: mrr_at_5 |
| value: 50.319 |
| - type: ndcg_at_1 |
| value: 43.519000000000005 |
| - type: ndcg_at_10 |
| value: 43.803 |
| - type: ndcg_at_100 |
| value: 50.468999999999994 |
| - type: ndcg_at_1000 |
| value: 53.111 |
| - type: ndcg_at_3 |
| value: 38.893 |
| - type: ndcg_at_5 |
| value: 40.653 |
| - type: precision_at_1 |
| value: 43.519000000000005 |
| - type: precision_at_10 |
| value: 12.253 |
| - type: precision_at_100 |
| value: 1.931 |
| - type: precision_at_1000 |
| value: 0.242 |
| - type: precision_at_3 |
| value: 25.617 |
| - type: precision_at_5 |
| value: 19.383 |
| - type: recall_at_1 |
| value: 22.166 |
| - type: recall_at_10 |
| value: 51.6 |
| - type: recall_at_100 |
| value: 76.574 |
| - type: recall_at_1000 |
| value: 92.192 |
| - type: recall_at_3 |
| value: 34.477999999999994 |
| - type: recall_at_5 |
| value: 41.835 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 39.041 |
| - type: map_at_10 |
| value: 62.961999999999996 |
| - type: map_at_100 |
| value: 63.79899999999999 |
| - type: map_at_1000 |
| value: 63.854 |
| - type: map_at_3 |
| value: 59.399 |
| - type: map_at_5 |
| value: 61.669 |
| - type: mrr_at_1 |
| value: 78.082 |
| - type: mrr_at_10 |
| value: 84.321 |
| - type: mrr_at_100 |
| value: 84.49600000000001 |
| - type: mrr_at_1000 |
| value: 84.502 |
| - type: mrr_at_3 |
| value: 83.421 |
| - type: mrr_at_5 |
| value: 83.977 |
| - type: ndcg_at_1 |
| value: 78.082 |
| - type: ndcg_at_10 |
| value: 71.229 |
| - type: ndcg_at_100 |
| value: 74.10900000000001 |
| - type: ndcg_at_1000 |
| value: 75.169 |
| - type: ndcg_at_3 |
| value: 66.28699999999999 |
| - type: ndcg_at_5 |
| value: 69.084 |
| - type: precision_at_1 |
| value: 78.082 |
| - type: precision_at_10 |
| value: 14.993 |
| - type: precision_at_100 |
| value: 1.7239999999999998 |
| - type: precision_at_1000 |
| value: 0.186 |
| - type: precision_at_3 |
| value: 42.737 |
| - type: precision_at_5 |
| value: 27.843 |
| - type: recall_at_1 |
| value: 39.041 |
| - type: recall_at_10 |
| value: 74.96300000000001 |
| - type: recall_at_100 |
| value: 86.199 |
| - type: recall_at_1000 |
| value: 93.228 |
| - type: recall_at_3 |
| value: 64.105 |
| - type: recall_at_5 |
| value: 69.608 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 90.23160000000001 |
| - type: ap |
| value: 85.5674856808308 |
| - type: f1 |
| value: 90.18033354786317 |
| - task: |
| type: Retrieval |
| dataset: |
| type: msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.091 |
| - type: map_at_10 |
| value: 36.753 |
| - type: map_at_100 |
| value: 37.913000000000004 |
| - type: map_at_1000 |
| value: 37.958999999999996 |
| - type: map_at_3 |
| value: 32.818999999999996 |
| - type: map_at_5 |
| value: 35.171 |
| - type: mrr_at_1 |
| value: 24.742 |
| - type: mrr_at_10 |
| value: 37.285000000000004 |
| - type: mrr_at_100 |
| value: 38.391999999999996 |
| - type: mrr_at_1000 |
| value: 38.431 |
| - type: mrr_at_3 |
| value: 33.440999999999995 |
| - type: mrr_at_5 |
| value: 35.75 |
| - type: ndcg_at_1 |
| value: 24.742 |
| - type: ndcg_at_10 |
| value: 43.698 |
| - type: ndcg_at_100 |
| value: 49.145 |
| - type: ndcg_at_1000 |
| value: 50.23800000000001 |
| - type: ndcg_at_3 |
| value: 35.769 |
| - type: ndcg_at_5 |
| value: 39.961999999999996 |
| - type: precision_at_1 |
| value: 24.742 |
| - type: precision_at_10 |
| value: 6.7989999999999995 |
| - type: precision_at_100 |
| value: 0.95 |
| - type: precision_at_1000 |
| value: 0.104 |
| - type: precision_at_3 |
| value: 15.096000000000002 |
| - type: precision_at_5 |
| value: 11.183 |
| - type: recall_at_1 |
| value: 24.091 |
| - type: recall_at_10 |
| value: 65.068 |
| - type: recall_at_100 |
| value: 89.899 |
| - type: recall_at_1000 |
| value: 98.16 |
| - type: recall_at_3 |
| value: 43.68 |
| - type: recall_at_5 |
| value: 53.754999999999995 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 93.66621067031465 |
| - type: f1 |
| value: 93.49622853272142 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (de) |
| config: de |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 91.94702733164272 |
| - type: f1 |
| value: 91.17043441745282 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (es) |
| config: es |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 92.20146764509674 |
| - type: f1 |
| value: 91.98359080555608 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (fr) |
| config: fr |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 88.99780770435328 |
| - type: f1 |
| value: 89.19746342724068 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (hi) |
| config: hi |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 89.78486912871998 |
| - type: f1 |
| value: 89.24578823628642 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (th) |
| config: th |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 88.74502712477394 |
| - type: f1 |
| value: 89.00297573881542 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 77.9046967624259 |
| - type: f1 |
| value: 59.36787125785957 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (de) |
| config: de |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 74.5280360664976 |
| - type: f1 |
| value: 57.17723440888718 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (es) |
| config: es |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 75.44029352901934 |
| - type: f1 |
| value: 54.052855531072964 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (fr) |
| config: fr |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 70.5606013153774 |
| - type: f1 |
| value: 52.62215934386531 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (hi) |
| config: hi |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 73.11581211903908 |
| - type: f1 |
| value: 52.341291845645465 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (th) |
| config: th |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 74.28933092224233 |
| - type: f1 |
| value: 57.07918745504911 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (af) |
| config: af |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 62.38063214525892 |
| - type: f1 |
| value: 59.46463723443009 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (am) |
| config: am |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 56.06926698049766 |
| - type: f1 |
| value: 52.49084283283562 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ar) |
| config: ar |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 60.74983187626093 |
| - type: f1 |
| value: 56.960640620165904 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (az) |
| config: az |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 64.86550100874243 |
| - type: f1 |
| value: 62.47370548140688 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (bn) |
| config: bn |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 61.03812421957381 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (cy) |
| config: cy |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 51.51207916008392 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (da) |
| config: da |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 69.45527908540686 |
| - type: f1 |
| value: 66.16631905400318 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (de) |
| config: de |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 66.16755288646591 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (el) |
| config: el |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 66.95654394661507 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 73.75588433086752 |
| - type: f1 |
| value: 71.79973779656923 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (es) |
| config: es |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 68.37494379215734 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fa) |
| config: fa |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 66.79517376481645 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fi) |
| config: fi |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 70.12104909213181 |
| - type: f1 |
| value: 67.29448842879584 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fr) |
| config: fr |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 67.01134288992947 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (he) |
| config: he |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 64.68730512660757 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (hi) |
| config: hi |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 65.24938539425598 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (hu) |
| config: hu |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 69.68728984532616 |
| - type: f1 |
| value: 67.0487169762553 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (hy) |
| config: hy |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 62.07464694014795 |
| - type: f1 |
| value: 59.183532276789286 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (id) |
| config: id |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 70.04707464694015 |
| - type: f1 |
| value: 67.66829629003848 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (is) |
| config: is |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 62.42434431741762 |
| - type: f1 |
| value: 59.01617226544757 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (it) |
| config: it |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 68.10033760906255 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ja) |
| config: ja |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 69.74931103158923 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (jv) |
| config: jv |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 57.91190316072628 |
| - type: f1 |
| value: 54.05551136648796 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ka) |
| config: ka |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 51.78211163416275 |
| - type: f1 |
| value: 49.874888544058535 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (km) |
| config: km |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 47.017484868863484 |
| - type: f1 |
| value: 44.53364263352014 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (kn) |
| config: kn |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| - type: f1 |
| value: 59.01185692320829 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ko) |
| config: ko |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 69.42501681237391 |
| - type: f1 |
| value: 67.13169450166086 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (lv) |
| config: lv |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 67.0780094149294 |
| - type: f1 |
| value: 64.41720167850707 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ml) |
| config: ml |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
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| value: 65.57162071284466 |
| - type: f1 |
| value: 62.414138683804424 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (mn) |
| config: mn |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 61.71149966375252 |
| - type: f1 |
| value: 58.594805125087234 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (ms) |
| config: ms |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 66.03900470746471 |
| - type: f1 |
| value: 63.87937257883887 |
| - task: |
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| value: 74.3982040999179 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (pt) |
| config: pt |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 73.49024882313383 |
| - type: f1 |
| value: 73.82153848368573 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ro) |
| config: ro |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 71.72158708809684 |
| - type: f1 |
| value: 71.85049433180541 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ru) |
| config: ru |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 75.137861466039 |
| - type: f1 |
| value: 75.37628348188467 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sl) |
| config: sl |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 71.86953597848016 |
| - type: f1 |
| value: 71.87537624521661 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sq) |
| config: sq |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 70.27572293207801 |
| - type: f1 |
| value: 68.80017302344231 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sv) |
| config: sv |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 76.09952925353059 |
| - type: f1 |
| value: 76.07992707688408 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sw) |
| config: sw |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 63.140551445864155 |
| - type: f1 |
| value: 61.73855010331415 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ta) |
| config: ta |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 66.27774041694687 |
| - type: f1 |
| value: 64.83664868894539 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (te) |
| config: te |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 66.69468728984533 |
| - type: f1 |
| value: 64.76239666920868 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (th) |
| config: th |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 73.44653665097512 |
| - type: f1 |
| value: 73.14646052013873 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (tl) |
| config: tl |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 67.71351714862139 |
| - type: f1 |
| value: 66.67212180163382 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (tr) |
| config: tr |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 73.9946200403497 |
| - type: f1 |
| value: 73.87348793725525 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ur) |
| config: ur |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 68.15400134498992 |
| - type: f1 |
| value: 67.09433241421094 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (vi) |
| config: vi |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 73.11365164761264 |
| - type: f1 |
| value: 73.59502539433753 |
| - 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: 76.82582380632145 |
| - type: f1 |
| value: 76.89992945316313 |
| - 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: 71.81237390719569 |
| - type: f1 |
| value: 72.36499770986265 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 31.480506569594695 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 29.71252128004552 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 31.421396787056548 |
| - type: mrr |
| value: 32.48155274872267 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 5.595 |
| - type: map_at_10 |
| value: 12.642000000000001 |
| - type: map_at_100 |
| value: 15.726 |
| - type: map_at_1000 |
| value: 17.061999999999998 |
| - type: map_at_3 |
| value: 9.125 |
| - type: map_at_5 |
| value: 10.866000000000001 |
| - type: mrr_at_1 |
| value: 43.344 |
| - type: mrr_at_10 |
| value: 52.227999999999994 |
| - type: mrr_at_100 |
| value: 52.898999999999994 |
| - type: mrr_at_1000 |
| value: 52.944 |
| - type: mrr_at_3 |
| value: 49.845 |
| - type: mrr_at_5 |
| value: 51.115 |
| - type: ndcg_at_1 |
| value: 41.949999999999996 |
| - type: ndcg_at_10 |
| value: 33.995 |
| - type: ndcg_at_100 |
| value: 30.869999999999997 |
| - type: ndcg_at_1000 |
| value: 39.487 |
| - type: ndcg_at_3 |
| value: 38.903999999999996 |
| - type: ndcg_at_5 |
| value: 37.236999999999995 |
| - type: precision_at_1 |
| value: 43.344 |
| - type: precision_at_10 |
| value: 25.480000000000004 |
| - type: precision_at_100 |
| value: 7.672 |
| - type: precision_at_1000 |
| value: 2.028 |
| - type: precision_at_3 |
| value: 36.636 |
| - type: precision_at_5 |
| value: 32.632 |
| - type: recall_at_1 |
| value: 5.595 |
| - type: recall_at_10 |
| value: 16.466 |
| - type: recall_at_100 |
| value: 31.226 |
| - type: recall_at_1000 |
| value: 62.778999999999996 |
| - type: recall_at_3 |
| value: 9.931 |
| - type: recall_at_5 |
| value: 12.884 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 40.414 |
| - type: map_at_10 |
| value: 56.754000000000005 |
| - type: map_at_100 |
| value: 57.457 |
| - type: map_at_1000 |
| value: 57.477999999999994 |
| - type: map_at_3 |
| value: 52.873999999999995 |
| - type: map_at_5 |
| value: 55.175 |
| - type: mrr_at_1 |
| value: 45.278 |
| - type: mrr_at_10 |
| value: 59.192 |
| - type: mrr_at_100 |
| value: 59.650000000000006 |
| - type: mrr_at_1000 |
| value: 59.665 |
| - type: mrr_at_3 |
| value: 56.141 |
| - type: mrr_at_5 |
| value: 57.998000000000005 |
| - type: ndcg_at_1 |
| value: 45.278 |
| - type: ndcg_at_10 |
| value: 64.056 |
| - type: ndcg_at_100 |
| value: 66.89 |
| - type: ndcg_at_1000 |
| value: 67.364 |
| - type: ndcg_at_3 |
| value: 56.97 |
| - type: ndcg_at_5 |
| value: 60.719 |
| - type: precision_at_1 |
| value: 45.278 |
| - type: precision_at_10 |
| value: 9.994 |
| - type: precision_at_100 |
| value: 1.165 |
| - type: precision_at_1000 |
| value: 0.121 |
| - type: precision_at_3 |
| value: 25.512 |
| - type: precision_at_5 |
| value: 17.509 |
| - type: recall_at_1 |
| value: 40.414 |
| - type: recall_at_10 |
| value: 83.596 |
| - type: recall_at_100 |
| value: 95.72 |
| - type: recall_at_1000 |
| value: 99.24 |
| - type: recall_at_3 |
| value: 65.472 |
| - type: recall_at_5 |
| value: 74.039 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 70.352 |
| - type: map_at_10 |
| value: 84.369 |
| - type: map_at_100 |
| value: 85.02499999999999 |
| - type: map_at_1000 |
| value: 85.04 |
| - type: map_at_3 |
| value: 81.42399999999999 |
| - type: map_at_5 |
| value: 83.279 |
| - type: mrr_at_1 |
| value: 81.05 |
| - type: mrr_at_10 |
| value: 87.401 |
| - type: mrr_at_100 |
| value: 87.504 |
| - type: mrr_at_1000 |
| value: 87.505 |
| - type: mrr_at_3 |
| value: 86.443 |
| - type: mrr_at_5 |
| value: 87.10799999999999 |
| - type: ndcg_at_1 |
| value: 81.04 |
| - type: ndcg_at_10 |
| value: 88.181 |
| - type: ndcg_at_100 |
| value: 89.411 |
| - type: ndcg_at_1000 |
| value: 89.507 |
| - type: ndcg_at_3 |
| value: 85.28099999999999 |
| - type: ndcg_at_5 |
| value: 86.888 |
| - type: precision_at_1 |
| value: 81.04 |
| - type: precision_at_10 |
| value: 13.406 |
| - type: precision_at_100 |
| value: 1.5350000000000001 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 37.31 |
| - type: precision_at_5 |
| value: 24.54 |
| - type: recall_at_1 |
| value: 70.352 |
| - type: recall_at_10 |
| value: 95.358 |
| - type: recall_at_100 |
| value: 99.541 |
| - type: recall_at_1000 |
| value: 99.984 |
| - type: recall_at_3 |
| value: 87.111 |
| - type: recall_at_5 |
| value: 91.643 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 46.54068723291946 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 63.216287629895994 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.023000000000001 |
| - type: map_at_10 |
| value: 10.071 |
| - type: map_at_100 |
| value: 11.892 |
| - type: map_at_1000 |
| value: 12.196 |
| - type: map_at_3 |
| value: 7.234 |
| - type: map_at_5 |
| value: 8.613999999999999 |
| - type: mrr_at_1 |
| value: 19.900000000000002 |
| - type: mrr_at_10 |
| value: 30.516 |
| - type: mrr_at_100 |
| value: 31.656000000000002 |
| - type: mrr_at_1000 |
| value: 31.723000000000003 |
| - type: mrr_at_3 |
| value: 27.400000000000002 |
| - type: mrr_at_5 |
| value: 29.270000000000003 |
| - type: ndcg_at_1 |
| value: 19.900000000000002 |
| - type: ndcg_at_10 |
| value: 17.474 |
| - type: ndcg_at_100 |
| value: 25.020999999999997 |
| - type: ndcg_at_1000 |
| value: 30.728 |
| - type: ndcg_at_3 |
| value: 16.588 |
| - type: ndcg_at_5 |
| value: 14.498 |
| - type: precision_at_1 |
| value: 19.900000000000002 |
| - type: precision_at_10 |
| value: 9.139999999999999 |
| - type: precision_at_100 |
| value: 2.011 |
| - type: precision_at_1000 |
| value: 0.33899999999999997 |
| - type: precision_at_3 |
| value: 15.667 |
| - type: precision_at_5 |
| value: 12.839999999999998 |
| - type: recall_at_1 |
| value: 4.023000000000001 |
| - type: recall_at_10 |
| value: 18.497 |
| - type: recall_at_100 |
| value: 40.8 |
| - type: recall_at_1000 |
| value: 68.812 |
| - type: recall_at_3 |
| value: 9.508 |
| - type: recall_at_5 |
| value: 12.983 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.967008785134 |
| - type: cos_sim_spearman |
| value: 80.23142141101837 |
| - type: euclidean_pearson |
| value: 81.20166064704539 |
| - type: euclidean_spearman |
| value: 80.18961335654585 |
| - type: manhattan_pearson |
| value: 81.13925443187625 |
| - type: manhattan_spearman |
| value: 80.07948723044424 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.94262461316023 |
| - type: cos_sim_spearman |
| value: 80.01596278563865 |
| - type: euclidean_pearson |
| value: 83.80799622922581 |
| - type: euclidean_spearman |
| value: 79.94984954947103 |
| - type: manhattan_pearson |
| value: 83.68473841756281 |
| - type: manhattan_spearman |
| value: 79.84990707951822 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 80.57346443146068 |
| - type: cos_sim_spearman |
| value: 81.54689837570866 |
| - type: euclidean_pearson |
| value: 81.10909881516007 |
| - type: euclidean_spearman |
| value: 81.56746243261762 |
| - type: manhattan_pearson |
| value: 80.87076036186582 |
| - type: manhattan_spearman |
| value: 81.33074987964402 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 79.54733787179849 |
| - type: cos_sim_spearman |
| value: 77.72202105610411 |
| - type: euclidean_pearson |
| value: 78.9043595478849 |
| - type: euclidean_spearman |
| value: 77.93422804309435 |
| - type: manhattan_pearson |
| value: 78.58115121621368 |
| - type: manhattan_spearman |
| value: 77.62508135122033 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 88.59880017237558 |
| - type: cos_sim_spearman |
| value: 89.31088630824758 |
| - type: euclidean_pearson |
| value: 88.47069261564656 |
| - type: euclidean_spearman |
| value: 89.33581971465233 |
| - type: manhattan_pearson |
| value: 88.40774264100956 |
| - type: manhattan_spearman |
| value: 89.28657485627835 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.08055117917084 |
| - type: cos_sim_spearman |
| value: 85.78491813080304 |
| - type: euclidean_pearson |
| value: 84.99329155500392 |
| - type: euclidean_spearman |
| value: 85.76728064677287 |
| - type: manhattan_pearson |
| value: 84.87947428989587 |
| - type: manhattan_spearman |
| value: 85.62429454917464 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (ko-ko) |
| config: ko-ko |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.14190939287384 |
| - type: cos_sim_spearman |
| value: 82.27331573306041 |
| - type: euclidean_pearson |
| value: 81.891896953716 |
| - type: euclidean_spearman |
| value: 82.37695542955998 |
| - type: manhattan_pearson |
| value: 81.73123869460504 |
| - type: manhattan_spearman |
| value: 82.19989168441421 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (ar-ar) |
| config: ar-ar |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 76.84695301843362 |
| - type: cos_sim_spearman |
| value: 77.87790986014461 |
| - type: euclidean_pearson |
| value: 76.91981583106315 |
| - type: euclidean_spearman |
| value: 77.88154772749589 |
| - type: manhattan_pearson |
| value: 76.94953277451093 |
| - type: manhattan_spearman |
| value: 77.80499230728604 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-ar) |
| config: en-ar |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 75.44657840482016 |
| - type: cos_sim_spearman |
| value: 75.05531095119674 |
| - type: euclidean_pearson |
| value: 75.88161755829299 |
| - type: euclidean_spearman |
| value: 74.73176238219332 |
| - type: manhattan_pearson |
| value: 75.63984765635362 |
| - type: manhattan_spearman |
| value: 74.86476440770737 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-de) |
| config: en-de |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.64700140524133 |
| - type: cos_sim_spearman |
| value: 86.16014210425672 |
| - type: euclidean_pearson |
| value: 86.49086860843221 |
| - type: euclidean_spearman |
| value: 86.09729326815614 |
| - type: manhattan_pearson |
| value: 86.43406265125513 |
| - type: manhattan_spearman |
| value: 86.17740150939994 |
| - 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: 87.91170098764921 |
| - type: cos_sim_spearman |
| value: 88.12437004058931 |
| - type: euclidean_pearson |
| value: 88.81828254494437 |
| - type: euclidean_spearman |
| value: 88.14831794572122 |
| - type: manhattan_pearson |
| value: 88.93442183448961 |
| - type: manhattan_spearman |
| value: 88.15254630778304 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-tr) |
| config: en-tr |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 72.91390577997292 |
| - type: cos_sim_spearman |
| value: 71.22979457536074 |
| - type: euclidean_pearson |
| value: 74.40314008106749 |
| - type: euclidean_spearman |
| value: 72.54972136083246 |
| - type: manhattan_pearson |
| value: 73.85687539530218 |
| - type: manhattan_spearman |
| value: 72.09500771742637 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (es-en) |
| config: es-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 80.9301067983089 |
| - type: cos_sim_spearman |
| value: 80.74989828346473 |
| - type: euclidean_pearson |
| value: 81.36781301814257 |
| - type: euclidean_spearman |
| value: 80.9448819964426 |
| - type: manhattan_pearson |
| value: 81.0351322685609 |
| - type: manhattan_spearman |
| value: 80.70192121844177 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (es-es) |
| config: es-es |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.13820465980005 |
| - type: cos_sim_spearman |
| value: 86.73532498758757 |
| - type: euclidean_pearson |
| value: 87.21329451846637 |
| - type: euclidean_spearman |
| value: 86.57863198601002 |
| - type: manhattan_pearson |
| value: 87.06973713818554 |
| - type: manhattan_spearman |
| value: 86.47534918791499 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (fr-en) |
| config: fr-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.48720108904415 |
| - type: cos_sim_spearman |
| value: 85.62221757068387 |
| - type: euclidean_pearson |
| value: 86.1010129512749 |
| - type: euclidean_spearman |
| value: 85.86580966509942 |
| - type: manhattan_pearson |
| value: 86.26800938808971 |
| - type: manhattan_spearman |
| value: 85.88902721678429 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (it-en) |
| config: it-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.98021347333516 |
| - type: cos_sim_spearman |
| value: 84.53806553803501 |
| - type: euclidean_pearson |
| value: 84.61483347248364 |
| - type: euclidean_spearman |
| value: 85.14191408011702 |
| - type: manhattan_pearson |
| value: 84.75297588825967 |
| - type: manhattan_spearman |
| value: 85.33176753669242 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (nl-en) |
| config: nl-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.51856644893233 |
| - type: cos_sim_spearman |
| value: 85.27510748506413 |
| - type: euclidean_pearson |
| value: 85.09886861540977 |
| - type: euclidean_spearman |
| value: 85.62579245860887 |
| - type: manhattan_pearson |
| value: 84.93017860464607 |
| - type: manhattan_spearman |
| value: 85.5063988898453 |
| - 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: 62.581573200584195 |
| - type: cos_sim_spearman |
| value: 63.05503590247928 |
| - type: euclidean_pearson |
| value: 63.652564812602094 |
| - type: euclidean_spearman |
| value: 62.64811520876156 |
| - type: manhattan_pearson |
| value: 63.506842893061076 |
| - type: manhattan_spearman |
| value: 62.51289573046917 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de) |
| config: de |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 48.2248801729127 |
| - type: cos_sim_spearman |
| value: 56.5936604678561 |
| - type: euclidean_pearson |
| value: 43.98149464089 |
| - type: euclidean_spearman |
| value: 56.108561882423615 |
| - type: manhattan_pearson |
| value: 43.86880305903564 |
| - type: manhattan_spearman |
| value: 56.04671150510166 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (es) |
| config: es |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 55.17564527009831 |
| - type: cos_sim_spearman |
| value: 64.57978560979488 |
| - type: euclidean_pearson |
| value: 58.8818330154583 |
| - type: euclidean_spearman |
| value: 64.99214839071281 |
| - type: manhattan_pearson |
| value: 58.72671436121381 |
| - type: manhattan_spearman |
| value: 65.10713416616109 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (pl) |
| config: pl |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 26.772131864023297 |
| - type: cos_sim_spearman |
| value: 34.68200792408681 |
| - type: euclidean_pearson |
| value: 16.68082419005441 |
| - type: euclidean_spearman |
| value: 34.83099932652166 |
| - type: manhattan_pearson |
| value: 16.52605949659529 |
| - type: manhattan_spearman |
| value: 34.82075801399475 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (tr) |
| config: tr |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 54.42415189043831 |
| - type: cos_sim_spearman |
| value: 63.54594264576758 |
| - type: euclidean_pearson |
| value: 57.36577498297745 |
| - type: euclidean_spearman |
| value: 63.111466379158074 |
| - type: manhattan_pearson |
| value: 57.584543715873885 |
| - type: manhattan_spearman |
| value: 63.22361054139183 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (ar) |
| config: ar |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 47.55216762405518 |
| - type: cos_sim_spearman |
| value: 56.98670142896412 |
| - type: euclidean_pearson |
| value: 50.15318757562699 |
| - type: euclidean_spearman |
| value: 56.524941926541906 |
| - type: manhattan_pearson |
| value: 49.955618528674904 |
| - type: manhattan_spearman |
| value: 56.37102209240117 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (ru) |
| config: ru |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 49.20540980338571 |
| - type: cos_sim_spearman |
| value: 59.9009453504406 |
| - type: euclidean_pearson |
| value: 49.557749853620535 |
| - type: euclidean_spearman |
| value: 59.76631621172456 |
| - type: manhattan_pearson |
| value: 49.62340591181147 |
| - type: manhattan_spearman |
| value: 59.94224880322436 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (zh) |
| config: zh |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 51.508169956576985 |
| - type: cos_sim_spearman |
| value: 66.82461565306046 |
| - type: euclidean_pearson |
| value: 56.2274426480083 |
| - type: euclidean_spearman |
| value: 66.6775323848333 |
| - type: manhattan_pearson |
| value: 55.98277796300661 |
| - type: manhattan_spearman |
| value: 66.63669848497175 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (fr) |
| config: fr |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 72.86478788045507 |
| - type: cos_sim_spearman |
| value: 76.7946552053193 |
| - type: euclidean_pearson |
| value: 75.01598530490269 |
| - type: euclidean_spearman |
| value: 76.83618917858281 |
| - type: manhattan_pearson |
| value: 74.68337628304332 |
| - type: manhattan_spearman |
| value: 76.57480204017773 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de-en) |
| config: de-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 55.922619099401984 |
| - type: cos_sim_spearman |
| value: 56.599362477240774 |
| - type: euclidean_pearson |
| value: 56.68307052369783 |
| - type: euclidean_spearman |
| value: 54.28760436777401 |
| - type: manhattan_pearson |
| value: 56.67763566500681 |
| - type: manhattan_spearman |
| value: 53.94619541711359 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (es-en) |
| config: es-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 66.74357206710913 |
| - type: cos_sim_spearman |
| value: 72.5208244925311 |
| - type: euclidean_pearson |
| value: 67.49254562186032 |
| - type: euclidean_spearman |
| value: 72.02469076238683 |
| - type: manhattan_pearson |
| value: 67.45251772238085 |
| - type: manhattan_spearman |
| value: 72.05538819984538 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (it) |
| config: it |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 71.25734330033191 |
| - type: cos_sim_spearman |
| value: 76.98349083946823 |
| - type: euclidean_pearson |
| value: 73.71642838667736 |
| - type: euclidean_spearman |
| value: 77.01715504651384 |
| - type: manhattan_pearson |
| value: 73.61712711868105 |
| - type: manhattan_spearman |
| value: 77.01392571153896 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (pl-en) |
| config: pl-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 63.18215462781212 |
| - type: cos_sim_spearman |
| value: 65.54373266117607 |
| - type: euclidean_pearson |
| value: 64.54126095439005 |
| - type: euclidean_spearman |
| value: 65.30410369102711 |
| - type: manhattan_pearson |
| value: 63.50332221148234 |
| - type: manhattan_spearman |
| value: 64.3455878104313 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (zh-en) |
| config: zh-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 62.30509221440029 |
| - type: cos_sim_spearman |
| value: 65.99582704642478 |
| - type: euclidean_pearson |
| value: 63.43818859884195 |
| - type: euclidean_spearman |
| value: 66.83172582815764 |
| - type: manhattan_pearson |
| value: 63.055779168508764 |
| - type: manhattan_spearman |
| value: 65.49585020501449 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (es-it) |
| config: es-it |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 59.587830825340404 |
| - type: cos_sim_spearman |
| value: 68.93467614588089 |
| - type: euclidean_pearson |
| value: 62.3073527367404 |
| - type: euclidean_spearman |
| value: 69.69758171553175 |
| - type: manhattan_pearson |
| value: 61.9074580815789 |
| - type: manhattan_spearman |
| value: 69.57696375597865 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de-fr) |
| config: de-fr |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 57.143220125577066 |
| - type: cos_sim_spearman |
| value: 67.78857859159226 |
| - type: euclidean_pearson |
| value: 55.58225107923733 |
| - type: euclidean_spearman |
| value: 67.80662907184563 |
| - type: manhattan_pearson |
| value: 56.24953502726514 |
| - type: manhattan_spearman |
| value: 67.98262125431616 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de-pl) |
| config: de-pl |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 21.826928900322066 |
| - type: cos_sim_spearman |
| value: 49.578506634400405 |
| - type: euclidean_pearson |
| value: 27.939890138843214 |
| - type: euclidean_spearman |
| value: 52.71950519136242 |
| - type: manhattan_pearson |
| value: 26.39878683847546 |
| - type: manhattan_spearman |
| value: 47.54609580342499 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (fr-pl) |
| config: fr-pl |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 57.27603854632001 |
| - type: cos_sim_spearman |
| value: 50.709255283710995 |
| - type: euclidean_pearson |
| value: 59.5419024445929 |
| - type: euclidean_spearman |
| value: 50.709255283710995 |
| - type: manhattan_pearson |
| value: 59.03256832438492 |
| - type: manhattan_spearman |
| value: 61.97797868009122 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.00757054859712 |
| - type: cos_sim_spearman |
| value: 87.29283629622222 |
| - type: euclidean_pearson |
| value: 86.54824171775536 |
| - type: euclidean_spearman |
| value: 87.24364730491402 |
| - type: manhattan_pearson |
| value: 86.5062156915074 |
| - type: manhattan_spearman |
| value: 87.15052170378574 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 82.03549357197389 |
| - type: mrr |
| value: 95.05437645143527 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 57.260999999999996 |
| - type: map_at_10 |
| value: 66.259 |
| - type: map_at_100 |
| value: 66.884 |
| - type: map_at_1000 |
| value: 66.912 |
| - type: map_at_3 |
| value: 63.685 |
| - type: map_at_5 |
| value: 65.35499999999999 |
| - type: mrr_at_1 |
| value: 60.333000000000006 |
| - type: mrr_at_10 |
| value: 67.5 |
| - type: mrr_at_100 |
| value: 68.013 |
| - type: mrr_at_1000 |
| value: 68.038 |
| - type: mrr_at_3 |
| value: 65.61099999999999 |
| - type: mrr_at_5 |
| value: 66.861 |
| - type: ndcg_at_1 |
| value: 60.333000000000006 |
| - type: ndcg_at_10 |
| value: 70.41 |
| - type: ndcg_at_100 |
| value: 73.10600000000001 |
| - type: ndcg_at_1000 |
| value: 73.846 |
| - type: ndcg_at_3 |
| value: 66.133 |
| - type: ndcg_at_5 |
| value: 68.499 |
| - type: precision_at_1 |
| value: 60.333000000000006 |
| - type: precision_at_10 |
| value: 9.232999999999999 |
| - type: precision_at_100 |
| value: 1.0630000000000002 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_3 |
| value: 25.667 |
| - type: precision_at_5 |
| value: 17.067 |
| - type: recall_at_1 |
| value: 57.260999999999996 |
| - type: recall_at_10 |
| value: 81.94399999999999 |
| - type: recall_at_100 |
| value: 93.867 |
| - type: recall_at_1000 |
| value: 99.667 |
| - type: recall_at_3 |
| value: 70.339 |
| - type: recall_at_5 |
| value: 76.25 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.74356435643564 |
| - type: cos_sim_ap |
| value: 93.13411948212683 |
| - type: cos_sim_f1 |
| value: 86.80521991300147 |
| - type: cos_sim_precision |
| value: 84.00374181478017 |
| - type: cos_sim_recall |
| value: 89.8 |
| - type: dot_accuracy |
| value: 99.67920792079208 |
| - type: dot_ap |
| value: 89.27277565444479 |
| - type: dot_f1 |
| value: 83.9276990718124 |
| - type: dot_precision |
| value: 82.04393505253104 |
| - type: dot_recall |
| value: 85.9 |
| - type: euclidean_accuracy |
| value: 99.74257425742574 |
| - type: euclidean_ap |
| value: 93.17993008259062 |
| - type: euclidean_f1 |
| value: 86.69396110542476 |
| - type: euclidean_precision |
| value: 88.78406708595388 |
| - type: euclidean_recall |
| value: 84.7 |
| - type: manhattan_accuracy |
| value: 99.74257425742574 |
| - type: manhattan_ap |
| value: 93.14413755550099 |
| - type: manhattan_f1 |
| value: 86.82483594144371 |
| - type: manhattan_precision |
| value: 87.66564729867483 |
| - type: manhattan_recall |
| value: 86 |
| - type: max_accuracy |
| value: 99.74356435643564 |
| - type: max_ap |
| value: 93.17993008259062 |
| - type: max_f1 |
| value: 86.82483594144371 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 57.525863806168566 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 32.68850574423839 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 49.71580650644033 |
| - type: mrr |
| value: 50.50971903913081 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 29.152190498799484 |
| - type: cos_sim_spearman |
| value: 29.686180371952727 |
| - type: dot_pearson |
| value: 27.248664793816342 |
| - type: dot_spearman |
| value: 28.37748983721745 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.20400000000000001 |
| - type: map_at_10 |
| value: 1.6209999999999998 |
| - type: map_at_100 |
| value: 9.690999999999999 |
| - type: map_at_1000 |
| value: 23.733 |
| - type: map_at_3 |
| value: 0.575 |
| - type: map_at_5 |
| value: 0.885 |
| - type: mrr_at_1 |
| value: 78 |
| - type: mrr_at_10 |
| value: 86.56700000000001 |
| - type: mrr_at_100 |
| value: 86.56700000000001 |
| - type: mrr_at_1000 |
| value: 86.56700000000001 |
| - type: mrr_at_3 |
| value: 85.667 |
| - type: mrr_at_5 |
| value: 86.56700000000001 |
| - type: ndcg_at_1 |
| value: 76 |
| - type: ndcg_at_10 |
| value: 71.326 |
| - type: ndcg_at_100 |
| value: 54.208999999999996 |
| - type: ndcg_at_1000 |
| value: 49.252 |
| - type: ndcg_at_3 |
| value: 74.235 |
| - type: ndcg_at_5 |
| value: 73.833 |
| - type: precision_at_1 |
| value: 78 |
| - type: precision_at_10 |
| value: 74.8 |
| - type: precision_at_100 |
| value: 55.50000000000001 |
| - type: precision_at_1000 |
| value: 21.836 |
| - type: precision_at_3 |
| value: 78 |
| - type: precision_at_5 |
| value: 78 |
| - type: recall_at_1 |
| value: 0.20400000000000001 |
| - type: recall_at_10 |
| value: 1.894 |
| - type: recall_at_100 |
| value: 13.245999999999999 |
| - type: recall_at_1000 |
| value: 46.373 |
| - type: recall_at_3 |
| value: 0.613 |
| - type: recall_at_5 |
| value: 0.991 |
| - 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: 95.89999999999999 |
| - type: f1 |
| value: 94.69999999999999 |
| - type: precision |
| value: 94.11666666666667 |
| - type: recall |
| value: 95.89999999999999 |
| - 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: 68.20809248554913 |
| - type: f1 |
| value: 63.431048720066066 |
| - type: precision |
| value: 61.69143958161298 |
| - type: recall |
| value: 68.20809248554913 |
| - 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: 71.21951219512195 |
| - type: f1 |
| value: 66.82926829268293 |
| - type: precision |
| value: 65.1260162601626 |
| - type: recall |
| value: 71.21951219512195 |
| - 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: 97.2 |
| - type: f1 |
| value: 96.26666666666667 |
| - type: precision |
| value: 95.8 |
| - type: recall |
| value: 97.2 |
| - 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: 99.3 |
| - type: f1 |
| value: 99.06666666666666 |
| - type: precision |
| value: 98.95 |
| - type: recall |
| value: 99.3 |
| - 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: 97.39999999999999 |
| - type: f1 |
| value: 96.63333333333333 |
| - type: precision |
| value: 96.26666666666668 |
| - type: recall |
| value: 97.39999999999999 |
| - 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: 96 |
| - type: f1 |
| value: 94.86666666666666 |
| - type: precision |
| value: 94.31666666666668 |
| - type: recall |
| value: 96 |
| - 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: 47.01492537313433 |
| - type: f1 |
| value: 40.178867566927266 |
| - type: precision |
| value: 38.179295828549556 |
| - type: recall |
| value: 47.01492537313433 |
| - 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: 86.5 |
| - type: f1 |
| value: 83.62537480063796 |
| - type: precision |
| value: 82.44555555555554 |
| - type: recall |
| value: 86.5 |
| - 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: 80.48780487804879 |
| - type: f1 |
| value: 75.45644599303138 |
| - type: precision |
| value: 73.37398373983739 |
| - type: recall |
| value: 80.48780487804879 |
| - 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: 93.7 |
| - type: f1 |
| value: 91.95666666666666 |
| - type: precision |
| value: 91.125 |
| - type: recall |
| value: 93.7 |
| - 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: 91.73754556500607 |
| - type: f1 |
| value: 89.65168084244632 |
| - type: precision |
| value: 88.73025516403402 |
| - type: recall |
| value: 91.73754556500607 |
| - 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: 81.04347826086956 |
| - type: f1 |
| value: 76.2128364389234 |
| - type: precision |
| value: 74.2 |
| - type: recall |
| value: 81.04347826086956 |
| - 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: 83.65217391304348 |
| - type: f1 |
| value: 79.4376811594203 |
| - type: precision |
| value: 77.65797101449274 |
| - type: recall |
| value: 83.65217391304348 |
| - 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: 87.5 |
| - type: f1 |
| value: 85.02690476190476 |
| - type: precision |
| value: 83.96261904761904 |
| - type: recall |
| value: 87.5 |
| - 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: 89.3 |
| - type: f1 |
| value: 86.52333333333333 |
| - type: precision |
| value: 85.22833333333332 |
| - type: recall |
| value: 89.3 |
| - 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: 65.01809408926418 |
| - type: f1 |
| value: 59.00594446432805 |
| - type: precision |
| value: 56.827215807915444 |
| - type: recall |
| value: 65.01809408926418 |
| - 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: 91.2 |
| - type: f1 |
| value: 88.58 |
| - type: precision |
| value: 87.33333333333334 |
| - type: recall |
| value: 91.2 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (lat-eng) |
| config: lat-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 59.199999999999996 |
| - type: f1 |
| value: 53.299166276284915 |
| - type: precision |
| value: 51.3383908045977 |
| - type: recall |
| value: 59.199999999999996 |
| - 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: 93.2 |
| - type: f1 |
| value: 91.2 |
| - type: precision |
| value: 90.25 |
| - type: recall |
| value: 93.2 |
| - 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: 64.76190476190476 |
| - type: f1 |
| value: 59.867110667110666 |
| - type: precision |
| value: 58.07390192653351 |
| - type: recall |
| value: 64.76190476190476 |
| - 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: 76.2 |
| - type: f1 |
| value: 71.48147546897547 |
| - type: precision |
| value: 69.65409090909091 |
| - type: recall |
| value: 76.2 |
| - 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: 93.8 |
| - type: f1 |
| value: 92.14 |
| - type: precision |
| value: 91.35833333333333 |
| - type: recall |
| value: 93.8 |
| - 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: 97.89999999999999 |
| - type: f1 |
| value: 97.2 |
| - type: precision |
| value: 96.85000000000001 |
| - type: recall |
| value: 97.89999999999999 |
| - 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: 94.6 |
| - type: f1 |
| value: 92.93333333333334 |
| - type: precision |
| value: 92.13333333333333 |
| - type: recall |
| value: 94.6 |
| - 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: 74.1 |
| - type: f1 |
| value: 69.14817460317461 |
| - type: precision |
| value: 67.2515873015873 |
| - type: recall |
| value: 74.1 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (hun-eng) |
| config: hun-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 95.19999999999999 |
| - type: f1 |
| value: 94.01333333333335 |
| - type: precision |
| value: 93.46666666666667 |
| - type: recall |
| value: 95.19999999999999 |
| - 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: 76.9 |
| - type: f1 |
| value: 72.07523809523809 |
| - type: precision |
| value: 70.19777777777779 |
| - type: recall |
| value: 76.9 |
| - 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: 94.1 |
| - type: f1 |
| value: 92.31666666666666 |
| - type: precision |
| value: 91.43333333333332 |
| - type: recall |
| value: 94.1 |
| - 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: 97.8 |
| - type: f1 |
| value: 97.1 |
| - type: precision |
| value: 96.76666666666668 |
| - type: recall |
| value: 97.8 |
| - 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: 92.85714285714286 |
| - type: f1 |
| value: 90.92093441150045 |
| - type: precision |
| value: 90.00449236298293 |
| - type: recall |
| value: 92.85714285714286 |
| - 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: 93.16239316239316 |
| - type: f1 |
| value: 91.33903133903132 |
| - type: precision |
| value: 90.56267806267806 |
| - type: recall |
| value: 93.16239316239316 |
| - 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: 92.4 |
| - type: f1 |
| value: 90.25666666666666 |
| - type: precision |
| value: 89.25833333333334 |
| - type: recall |
| value: 92.4 |
| - 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: 90.22727272727272 |
| - type: f1 |
| value: 87.53030303030303 |
| - type: precision |
| value: 86.37121212121211 |
| - type: recall |
| value: 90.22727272727272 |
| - 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: 79.03563941299791 |
| - type: f1 |
| value: 74.7349505840072 |
| - type: precision |
| value: 72.9035639412998 |
| - type: recall |
| value: 79.03563941299791 |
| - 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 |
| - type: f1 |
| value: 96.15 |
| - type: precision |
| value: 95.76666666666668 |
| - type: recall |
| value: 97 |
| - 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: 76.26459143968872 |
| - type: f1 |
| value: 71.55642023346303 |
| - type: precision |
| value: 69.7544932369835 |
| - type: recall |
| value: 76.26459143968872 |
| - 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: 58.119658119658126 |
| - type: f1 |
| value: 51.65242165242165 |
| - type: precision |
| value: 49.41768108434775 |
| - type: recall |
| value: 58.119658119658126 |
| - 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: 74.3 |
| - type: f1 |
| value: 69.52055555555555 |
| - type: precision |
| value: 67.7574938949939 |
| - type: recall |
| value: 74.3 |
| - 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: 94.8 |
| - type: f1 |
| value: 93.31666666666666 |
| - type: precision |
| value: 92.60000000000001 |
| - type: recall |
| value: 94.8 |
| - 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: 76.63551401869158 |
| - type: f1 |
| value: 72.35202492211837 |
| - type: precision |
| value: 70.60358255451713 |
| - type: recall |
| value: 76.63551401869158 |
| - 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: 90.4 |
| - type: f1 |
| value: 88.4811111111111 |
| - type: precision |
| value: 87.7452380952381 |
| - type: recall |
| value: 90.4 |
| - 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: 95 |
| - type: f1 |
| value: 93.60666666666667 |
| - type: precision |
| value: 92.975 |
| - type: recall |
| value: 95 |
| - 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: 67.2 |
| - type: f1 |
| value: 63.01595782872099 |
| - type: precision |
| value: 61.596587301587306 |
| - type: recall |
| value: 67.2 |
| - 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: 95.7 |
| - type: f1 |
| value: 94.52999999999999 |
| - type: precision |
| value: 94 |
| - type: recall |
| value: 95.7 |
| - 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: 94.6 |
| - type: f1 |
| value: 93.28999999999999 |
| - type: precision |
| value: 92.675 |
| - type: recall |
| value: 94.6 |
| - 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: 96.39999999999999 |
| - type: f1 |
| value: 95.28333333333333 |
| - type: precision |
| value: 94.75 |
| - type: recall |
| value: 96.39999999999999 |
| - 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: 91.9 |
| - type: f1 |
| value: 89.83 |
| - type: precision |
| value: 88.92 |
| - type: recall |
| value: 91.9 |
| - 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: 94.69999999999999 |
| - type: f1 |
| value: 93.34222222222223 |
| - type: precision |
| value: 92.75416666666668 |
| - type: recall |
| value: 94.69999999999999 |
| - 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: 60.333333333333336 |
| - type: f1 |
| value: 55.31203703703703 |
| - type: precision |
| value: 53.39971108326371 |
| - type: recall |
| value: 60.333333333333336 |
| - 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: 12.9 |
| - type: f1 |
| value: 11.099861903031458 |
| - type: precision |
| value: 10.589187932631877 |
| - type: recall |
| value: 12.9 |
| - 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: 86.7 |
| - type: f1 |
| value: 83.0152380952381 |
| - type: precision |
| value: 81.37833333333333 |
| - type: recall |
| value: 86.7 |
| - 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: 63.39285714285714 |
| - type: f1 |
| value: 56.832482993197274 |
| - type: precision |
| value: 54.56845238095237 |
| - type: recall |
| value: 63.39285714285714 |
| - 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: 48.73765093304062 |
| - type: f1 |
| value: 41.555736920720456 |
| - type: precision |
| value: 39.06874531737319 |
| - type: recall |
| value: 48.73765093304062 |
| - 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: 41.099999999999994 |
| - type: f1 |
| value: 36.540165945165946 |
| - type: precision |
| value: 35.05175685425686 |
| - type: recall |
| value: 41.099999999999994 |
| - 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: 94.89999999999999 |
| - type: f1 |
| value: 93.42333333333333 |
| - type: precision |
| value: 92.75833333333333 |
| - type: recall |
| value: 94.89999999999999 |
| - 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: 94.89999999999999 |
| - type: f1 |
| value: 93.63333333333334 |
| - type: precision |
| value: 93.01666666666665 |
| - type: recall |
| value: 94.89999999999999 |
| - 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: 77.9 |
| - type: f1 |
| value: 73.64833333333334 |
| - type: precision |
| value: 71.90282106782105 |
| - type: recall |
| value: 77.9 |
| - 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: 59.4 |
| - type: f1 |
| value: 54.90521367521367 |
| - type: precision |
| value: 53.432840025471606 |
| - type: recall |
| value: 59.4 |
| - 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: 97.39999999999999 |
| - type: f1 |
| value: 96.6 |
| - type: precision |
| value: 96.2 |
| - type: recall |
| value: 97.39999999999999 |
| - 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: 67.2 |
| - type: f1 |
| value: 62.25926129426129 |
| - type: precision |
| value: 60.408376623376626 |
| - type: recall |
| value: 67.2 |
| - 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: 90.2 |
| - type: f1 |
| value: 87.60666666666667 |
| - type: precision |
| value: 86.45277777777778 |
| - type: recall |
| value: 90.2 |
| - 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: 97.7 |
| - type: f1 |
| value: 97 |
| - type: precision |
| value: 96.65 |
| - type: recall |
| value: 97.7 |
| - 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: 93.2 |
| - type: f1 |
| value: 91.39746031746031 |
| - type: precision |
| value: 90.6125 |
| - type: recall |
| value: 93.2 |
| - 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: 32.11678832116788 |
| - type: f1 |
| value: 27.210415386260234 |
| - type: precision |
| value: 26.20408990846947 |
| - type: recall |
| value: 32.11678832116788 |
| - 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: 8.5 |
| - type: f1 |
| value: 6.787319277832475 |
| - type: precision |
| value: 6.3452094433344435 |
| - type: recall |
| value: 8.5 |
| - 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: 96.1 |
| - type: f1 |
| value: 95.08 |
| - type: precision |
| value: 94.61666666666667 |
| - type: recall |
| value: 96.1 |
| - 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: 95.3 |
| - type: f1 |
| value: 93.88333333333333 |
| - type: precision |
| value: 93.18333333333332 |
| - type: recall |
| value: 95.3 |
| - 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: 85.11904761904762 |
| - type: f1 |
| value: 80.69444444444444 |
| - type: precision |
| value: 78.72023809523809 |
| - type: recall |
| value: 85.11904761904762 |
| - 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: 11.1 |
| - type: f1 |
| value: 9.276381801735853 |
| - type: precision |
| value: 8.798174603174601 |
| - type: recall |
| value: 11.1 |
| - 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: 63.56107660455487 |
| - type: f1 |
| value: 58.70433569191332 |
| - type: precision |
| value: 56.896926581464015 |
| - type: recall |
| value: 63.56107660455487 |
| - 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: 94.69999999999999 |
| - type: f1 |
| value: 93.10000000000001 |
| - type: precision |
| value: 92.35 |
| - type: recall |
| value: 94.69999999999999 |
| - 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: 96.8 |
| - type: f1 |
| value: 96.01222222222222 |
| - type: precision |
| value: 95.67083333333332 |
| - type: recall |
| value: 96.8 |
| - 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: 9.2 |
| - type: f1 |
| value: 7.911555250305249 |
| - type: precision |
| value: 7.631246556216846 |
| - type: recall |
| value: 9.2 |
| - 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: 77.48917748917748 |
| - type: f1 |
| value: 72.27375798804371 |
| - type: precision |
| value: 70.14430014430013 |
| - type: recall |
| value: 77.48917748917748 |
| - 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: 77.09923664122137 |
| - type: f1 |
| value: 72.61541257724463 |
| - type: precision |
| value: 70.8998380754106 |
| - type: recall |
| value: 77.09923664122137 |
| - 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: 98.2532751091703 |
| - type: f1 |
| value: 97.69529354682193 |
| - type: precision |
| value: 97.42843279961184 |
| - type: recall |
| value: 98.2532751091703 |
| - 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: 82.8 |
| - type: f1 |
| value: 79.14672619047619 |
| - type: precision |
| value: 77.59489247311828 |
| - type: recall |
| value: 82.8 |
| - 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: 94.35028248587571 |
| - type: f1 |
| value: 92.86252354048965 |
| - type: precision |
| value: 92.2080979284369 |
| - type: recall |
| value: 94.35028248587571 |
| - 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: 8.5 |
| - type: f1 |
| value: 6.282429263935621 |
| - type: precision |
| value: 5.783274240739785 |
| - type: recall |
| value: 8.5 |
| - 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: 92.7 |
| - type: f1 |
| value: 91.025 |
| - type: precision |
| value: 90.30428571428571 |
| - type: recall |
| value: 92.7 |
| - 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: 81 |
| - type: f1 |
| value: 77.8232380952381 |
| - type: precision |
| value: 76.60194444444444 |
| - type: recall |
| value: 81 |
| - 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: 91 |
| - type: f1 |
| value: 88.70857142857142 |
| - type: precision |
| value: 87.7 |
| - type: recall |
| value: 91 |
| - 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: 96.39999999999999 |
| - type: f1 |
| value: 95.3 |
| - type: precision |
| value: 94.76666666666667 |
| - type: recall |
| value: 96.39999999999999 |
| - 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: 8.1 |
| - type: f1 |
| value: 7.001008218834307 |
| - type: precision |
| value: 6.708329562594269 |
| - type: recall |
| value: 8.1 |
| - 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: 87.1313672922252 |
| - type: f1 |
| value: 84.09070598748882 |
| - type: precision |
| value: 82.79171454104429 |
| - type: recall |
| value: 87.1313672922252 |
| - 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: 96.39999999999999 |
| - type: f1 |
| value: 95.28333333333333 |
| - type: precision |
| value: 94.73333333333332 |
| - type: recall |
| value: 96.39999999999999 |
| - 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: 42.29249011857708 |
| - type: f1 |
| value: 36.981018542283365 |
| - type: precision |
| value: 35.415877813576024 |
| - type: recall |
| value: 42.29249011857708 |
| - 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: 83.80281690140845 |
| - type: f1 |
| value: 80.86854460093896 |
| - type: precision |
| value: 79.60093896713614 |
| - type: recall |
| value: 83.80281690140845 |
| - 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: 45.26946107784431 |
| - type: f1 |
| value: 39.80235464678088 |
| - type: precision |
| value: 38.14342660001342 |
| - type: recall |
| value: 45.26946107784431 |
| - 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: 94.3 |
| - type: f1 |
| value: 92.9 |
| - type: precision |
| value: 92.26666666666668 |
| - type: recall |
| value: 94.3 |
| - 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: 37.93103448275862 |
| - type: f1 |
| value: 33.15192743764172 |
| - type: precision |
| value: 31.57456528146183 |
| - type: recall |
| value: 37.93103448275862 |
| - 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: 69.01408450704226 |
| - type: f1 |
| value: 63.41549295774648 |
| - type: precision |
| value: 61.342778895595806 |
| - type: recall |
| value: 69.01408450704226 |
| - 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: 76.66666666666667 |
| - type: f1 |
| value: 71.60705960705961 |
| - type: precision |
| value: 69.60683760683762 |
| - type: recall |
| value: 76.66666666666667 |
| - 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: 95.8 |
| - type: f1 |
| value: 94.48333333333333 |
| - type: precision |
| value: 93.83333333333333 |
| - type: recall |
| value: 95.8 |
| - 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: 52.81837160751566 |
| - type: f1 |
| value: 48.435977731384824 |
| - type: precision |
| value: 47.11291973845539 |
| - type: recall |
| value: 52.81837160751566 |
| - 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: 44.9 |
| - type: f1 |
| value: 38.88962621607783 |
| - type: precision |
| value: 36.95936507936508 |
| - type: recall |
| value: 44.9 |
| - 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: 90.55374592833876 |
| - type: f1 |
| value: 88.22553125484721 |
| - type: precision |
| value: 87.26927252985884 |
| - type: recall |
| value: 90.55374592833876 |
| - 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: 94.6 |
| - type: f1 |
| value: 93.13333333333333 |
| - type: precision |
| value: 92.45333333333333 |
| - type: recall |
| value: 94.6 |
| - 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: 93.7 |
| - type: f1 |
| value: 91.99666666666667 |
| - type: precision |
| value: 91.26666666666668 |
| - type: recall |
| value: 93.7 |
| - 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: 85.03937007874016 |
| - type: f1 |
| value: 81.75853018372703 |
| - type: precision |
| value: 80.34120734908137 |
| - type: recall |
| value: 85.03937007874016 |
| - 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: 88.3 |
| - type: f1 |
| value: 85.5 |
| - type: precision |
| value: 84.25833333333334 |
| - type: recall |
| value: 88.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (khm-eng) |
| config: khm-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 65.51246537396122 |
| - type: f1 |
| value: 60.02297410192148 |
| - type: precision |
| value: 58.133467727289236 |
| - type: recall |
| value: 65.51246537396122 |
| - 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: 96 |
| - type: f1 |
| value: 94.89 |
| - type: precision |
| value: 94.39166666666667 |
| - type: recall |
| value: 96 |
| - 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: 57.692307692307686 |
| - type: f1 |
| value: 53.162393162393165 |
| - type: precision |
| value: 51.70673076923077 |
| - type: recall |
| value: 57.692307692307686 |
| - 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: 91.60000000000001 |
| - type: f1 |
| value: 89.21190476190475 |
| - type: precision |
| value: 88.08666666666667 |
| - type: recall |
| value: 91.60000000000001 |
| - 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: 88 |
| - type: f1 |
| value: 85.47 |
| - type: precision |
| value: 84.43266233766234 |
| - type: recall |
| value: 88 |
| - 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: 92.7 |
| - type: f1 |
| value: 90.64999999999999 |
| - type: precision |
| value: 89.68333333333332 |
| - type: recall |
| value: 92.7 |
| - 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: 80.30660377358491 |
| - type: f1 |
| value: 76.33044137466307 |
| - type: precision |
| value: 74.78970125786164 |
| - type: recall |
| value: 80.30660377358491 |
| - 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: 96.39999999999999 |
| - type: f1 |
| value: 95.44 |
| - type: precision |
| value: 94.99166666666666 |
| - type: recall |
| value: 96.39999999999999 |
| - 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: 96.53284671532847 |
| - type: f1 |
| value: 95.37712895377129 |
| - type: precision |
| value: 94.7992700729927 |
| - type: recall |
| value: 96.53284671532847 |
| - 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: 89 |
| - type: f1 |
| value: 86.23190476190476 |
| - type: precision |
| value: 85.035 |
| - type: recall |
| value: 89 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 2.585 |
| - type: map_at_10 |
| value: 9.012 |
| - type: map_at_100 |
| value: 14.027000000000001 |
| - type: map_at_1000 |
| value: 15.565000000000001 |
| - type: map_at_3 |
| value: 5.032 |
| - type: map_at_5 |
| value: 6.657 |
| - type: mrr_at_1 |
| value: 28.571 |
| - type: mrr_at_10 |
| value: 45.377 |
| - type: mrr_at_100 |
| value: 46.119 |
| - type: mrr_at_1000 |
| value: 46.127 |
| - type: mrr_at_3 |
| value: 41.156 |
| - type: mrr_at_5 |
| value: 42.585 |
| - type: ndcg_at_1 |
| value: 27.551 |
| - type: ndcg_at_10 |
| value: 23.395 |
| - type: ndcg_at_100 |
| value: 33.342 |
| - type: ndcg_at_1000 |
| value: 45.523 |
| - type: ndcg_at_3 |
| value: 25.158 |
| - type: ndcg_at_5 |
| value: 23.427 |
| - type: precision_at_1 |
| value: 28.571 |
| - type: precision_at_10 |
| value: 21.429000000000002 |
| - type: precision_at_100 |
| value: 6.714 |
| - type: precision_at_1000 |
| value: 1.473 |
| - type: precision_at_3 |
| value: 27.211000000000002 |
| - type: precision_at_5 |
| value: 24.490000000000002 |
| - type: recall_at_1 |
| value: 2.585 |
| - type: recall_at_10 |
| value: 15.418999999999999 |
| - type: recall_at_100 |
| value: 42.485 |
| - type: recall_at_1000 |
| value: 79.536 |
| - type: recall_at_3 |
| value: 6.239999999999999 |
| - type: recall_at_5 |
| value: 8.996 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 71.3234 |
| - type: ap |
| value: 14.361688653847423 |
| - type: f1 |
| value: 54.819068624319044 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 61.97792869269949 |
| - type: f1 |
| value: 62.28965628513728 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 38.90540145385218 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 86.53513739047506 |
| - type: cos_sim_ap |
| value: 75.27741586677557 |
| - type: cos_sim_f1 |
| value: 69.18792902473774 |
| - type: cos_sim_precision |
| value: 67.94708725515136 |
| - type: cos_sim_recall |
| value: 70.47493403693932 |
| - type: dot_accuracy |
| value: 84.7052512368123 |
| - type: dot_ap |
| value: 69.36075482849378 |
| - type: dot_f1 |
| value: 64.44688376631296 |
| - type: dot_precision |
| value: 59.92288500793831 |
| - type: dot_recall |
| value: 69.70976253298153 |
| - type: euclidean_accuracy |
| value: 86.60666388508076 |
| - type: euclidean_ap |
| value: 75.47512772621097 |
| - type: euclidean_f1 |
| value: 69.413872536473 |
| - type: euclidean_precision |
| value: 67.39562624254472 |
| - type: euclidean_recall |
| value: 71.55672823218997 |
| - type: manhattan_accuracy |
| value: 86.52917684925792 |
| - type: manhattan_ap |
| value: 75.34000110496703 |
| - type: manhattan_f1 |
| value: 69.28489190226429 |
| - type: manhattan_precision |
| value: 67.24608889992551 |
| - type: manhattan_recall |
| value: 71.45118733509234 |
| - type: max_accuracy |
| value: 86.60666388508076 |
| - type: max_ap |
| value: 75.47512772621097 |
| - type: max_f1 |
| value: 69.413872536473 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 89.01695967710637 |
| - type: cos_sim_ap |
| value: 85.8298270742901 |
| - type: cos_sim_f1 |
| value: 78.46988128389272 |
| - type: cos_sim_precision |
| value: 74.86017897091722 |
| - type: cos_sim_recall |
| value: 82.44533415460425 |
| - type: dot_accuracy |
| value: 88.19420188613343 |
| - type: dot_ap |
| value: 83.82679165901324 |
| - type: dot_f1 |
| value: 76.55833777304208 |
| - type: dot_precision |
| value: 75.6884875846501 |
| - type: dot_recall |
| value: 77.44841392054204 |
| - type: euclidean_accuracy |
| value: 89.03054294252338 |
| - type: euclidean_ap |
| value: 85.89089555185325 |
| - type: euclidean_f1 |
| value: 78.62997658079624 |
| - type: euclidean_precision |
| value: 74.92329149232914 |
| - type: euclidean_recall |
| value: 82.72251308900523 |
| - type: manhattan_accuracy |
| value: 89.0266620095471 |
| - type: manhattan_ap |
| value: 85.86458997929147 |
| - type: manhattan_f1 |
| value: 78.50685331000291 |
| - type: manhattan_precision |
| value: 74.5499861534201 |
| - type: manhattan_recall |
| value: 82.90729904527257 |
| - type: max_accuracy |
| value: 89.03054294252338 |
| - type: max_ap |
| value: 85.89089555185325 |
| - type: max_f1 |
| value: 78.62997658079624 |
| 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 |
|
|
| [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/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 is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
|
|
| ```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] |
| |
| |
| # Each input text should start with "query: " or "passage: ", even for non-English texts. |
| # For tasks other than retrieval, you can simply use the "query: " prefix. |
| input_texts = ['query: how much protein should a female eat', |
| 'query: 南瓜的家常做法', |
| "passage: 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.", |
| "passage: 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"] |
| |
| tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large') |
| model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') |
| |
| # 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()) |
| ``` |
|
|
| ## 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 weak supervision |
|
|
| | Dataset | Weak supervision | # of text pairs | |
| |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| |
| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | |
| | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | |
| | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | |
| | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | |
| | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | |
| | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | |
| | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | |
| | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | |
| | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | |
|
|
| **Second stage**: supervised fine-tuning |
|
|
| | Dataset | Language | # of text pairs | |
| |----------------------------------------------------------------------------------------|--------------|-----------------| |
| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | |
| | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | |
| | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | |
| | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | |
| | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | |
| | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | |
| | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | |
| | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | |
| | [Quora](https://huggingface.co/datasets/quora) | English | 150k | |
| | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | |
| | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | |
|
|
| For all labeled datasets, we only use its training set for fine-tuning. |
|
|
| For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). |
|
|
| ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) |
|
|
| | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |
| |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | |
| | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | |
| | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | |
| | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | |
| | | | |
| | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | |
| | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | |
| | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | |
|
|
| ## 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). |
|
|
| ## Support for Sentence Transformers |
|
|
| Below is an example for usage with sentence_transformers. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| model = SentenceTransformer('intfloat/multilingual-e5-large') |
| input_texts = [ |
| 'query: how much protein should a female eat', |
| 'query: 南瓜的家常做法', |
| "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
| "passage: 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" |
| ] |
| embeddings = model.encode(input_texts, normalize_embeddings=True) |
| ``` |
| |
| Package requirements |
| |
| `pip install sentence_transformers~=2.2.2` |
|
|
| Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
|
|
| ## FAQ |
|
|
| **1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
|
|
| Yes, this is how the model is trained, otherwise you will see a performance degradation. |
|
|
| Here are some rules of thumb: |
| - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
|
|
| - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. |
|
|
| - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
|
|
| **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{wang2024multilingual, |
| title={Multilingual E5 Text Embeddings: A Technical Report}, |
| author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, |
| journal={arXiv preprint arXiv:2402.05672}, |
| year={2024} |
| } |
| ``` |
|
|
| ## Limitations |
|
|
| Long texts will be truncated to at most 512 tokens. |
|
|
