diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,6061 @@ +--- +tags: +- mteb +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: 50.0 + - 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: + - 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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: + - 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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: + - 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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 + - 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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: 89.0 + - type: f1 + value: 86.23190476190476 + - type: precision + value: 85.035 + - type: recall + value: 89.0 + - 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 + +[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). +Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +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']) + +# (Optionally) 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/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). + +## 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). + +## Citation + +If you find our paper or models helpful, please consider cite as follows: + +``` +@article{wang2022text, + title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, + author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, + journal={arXiv preprint arXiv:2212.03533}, + year={2022} +} +``` + +## Limitations + +Long texts will be truncated to at most 512 tokens. +