--- 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 model-index: - name: multilingual-e5-large results: - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 task: type: Classification - dataset: config: de name: MTEB AmazonCounterfactualClassification (de) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 task: type: Classification - dataset: config: en-ext name: MTEB AmazonCounterfactualClassification (en-ext) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 task: type: Classification - dataset: config: ja name: MTEB AmazonCounterfactualClassification (ja) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 task: type: Classification - dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 task: type: Classification - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 task: type: Classification - dataset: config: de name: MTEB AmazonReviewsClassification (de) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 task: type: Classification - dataset: config: es name: MTEB AmazonReviewsClassification (es) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 task: type: Classification - dataset: config: fr name: MTEB AmazonReviewsClassification (fr) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 task: type: Classification - dataset: config: ja name: MTEB AmazonReviewsClassification (ja) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 task: type: Classification - dataset: config: zh name: MTEB AmazonReviewsClassification (zh) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 task: type: Classification - dataset: config: default name: MTEB ArguAna revision: None split: test type: arguana 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: Retrieval - dataset: config: default name: MTEB ArxivClusteringP2P revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics: - type: v_measure value: 44.30960650674069 task: type: Clustering - dataset: config: default name: MTEB ArxivClusteringS2S revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics: - type: v_measure value: 38.427074197498996 task: type: Clustering - dataset: config: default name: MTEB AskUbuntuDupQuestions revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics: - type: map value: 60.28270056031872 - type: mrr value: 74.38332673789738 task: type: Reranking - dataset: config: default name: MTEB BIOSSES revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts 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: STS - dataset: config: de-en name: MTEB BUCC (de-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining 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: config: fr-en name: MTEB BUCC (fr-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining 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: config: ru-en name: MTEB BUCC (ru-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining 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: config: zh-en name: MTEB BUCC (zh-en) revision: d51519689f32196a32af33b075a01d0e7c51e252 split: test type: mteb/bucc-bitext-mining metrics: - type: accuracy value: 99.26276987888363 - type: f1 value: 99.20133403545726 - type: precision value: 99.17500438827453 - type: recall value: 99.26276987888363 task: type: BitextMining - dataset: config: default name: MTEB Banking77Classification revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics: - type: accuracy value: 84.72727272727273 - type: f1 value: 84.67672206031433 task: type: Classification - dataset: config: default name: MTEB BiorxivClusteringP2P revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics: - type: v_measure value: 35.34220182511161 task: type: Clustering - dataset: config: default name: MTEB BiorxivClusteringS2S revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics: - type: v_measure value: 33.4987096128766 task: type: Clustering - dataset: config: default name: MTEB CQADupstackRetrieval revision: None split: test type: BeIR/cqadupstack 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: config: default name: MTEB ClimateFEVER revision: None split: test type: climate-fever 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: config: default name: MTEB DBPedia revision: None split: test type: dbpedia-entity 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: Retrieval - dataset: config: default name: MTEB EmotionClassification revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 split: test type: mteb/emotion metrics: - type: accuracy value: 46.5 - type: f1 value: 41.93833713984145 task: type: Classification - dataset: config: default name: MTEB FEVER revision: None split: test type: fever 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: config: default name: MTEB FiQA2018 revision: None split: test type: fiqa 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: config: default name: MTEB HotpotQA revision: None split: test type: hotpotqa 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: Retrieval - dataset: config: default name: MTEB ImdbClassification revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics: - type: accuracy value: 90.23160000000001 - type: ap value: 85.5674856808308 - type: f1 value: 90.18033354786317 task: type: Classification - dataset: config: default name: MTEB MSMARCO revision: None split: dev type: msmarco 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: Retrieval - dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 93.66621067031465 - type: f1 value: 93.49622853272142 task: type: Classification - dataset: config: de name: MTEB MTOPDomainClassification (de) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 91.94702733164272 - type: f1 value: 91.17043441745282 task: type: Classification - dataset: config: es name: MTEB MTOPDomainClassification (es) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 92.20146764509674 - type: f1 value: 91.98359080555608 task: type: Classification - dataset: config: fr name: MTEB MTOPDomainClassification (fr) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 88.99780770435328 - type: f1 value: 89.19746342724068 task: type: Classification - dataset: config: hi name: MTEB MTOPDomainClassification (hi) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 89.78486912871998 - type: f1 value: 89.24578823628642 task: type: Classification - dataset: config: th name: MTEB MTOPDomainClassification (th) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 88.74502712477394 - type: f1 value: 89.00297573881542 task: type: Classification - dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 77.9046967624259 - type: f1 value: 59.36787125785957 task: type: Classification - dataset: config: de name: MTEB MTOPIntentClassification (de) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 74.5280360664976 - type: f1 value: 57.17723440888718 task: type: Classification - dataset: config: es name: MTEB MTOPIntentClassification (es) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 75.44029352901934 - type: f1 value: 54.052855531072964 task: type: Classification - dataset: config: fr name: MTEB MTOPIntentClassification (fr) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 70.5606013153774 - type: f1 value: 52.62215934386531 task: type: Classification - dataset: config: hi name: MTEB MTOPIntentClassification (hi) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 73.11581211903908 - type: f1 value: 52.341291845645465 task: type: Classification - dataset: config: th name: MTEB MTOPIntentClassification (th) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 74.28933092224233 - type: f1 value: 57.07918745504911 task: type: Classification - dataset: config: af name: MTEB MassiveIntentClassification (af) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 62.38063214525892 - type: f1 value: 59.46463723443009 task: type: Classification - dataset: config: am name: MTEB MassiveIntentClassification (am) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 56.06926698049766 - type: f1 value: 52.49084283283562 task: type: Classification - dataset: config: ar name: MTEB MassiveIntentClassification (ar) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 60.74983187626093 - type: f1 value: 56.960640620165904 task: type: Classification - dataset: config: az name: MTEB MassiveIntentClassification (az) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - 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type: accuracy value: 76.09952925353059 - type: f1 value: 76.07992707688408 task: type: Classification - dataset: config: sw name: MTEB MassiveScenarioClassification (sw) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 63.140551445864155 - type: f1 value: 61.73855010331415 task: type: Classification - dataset: config: ta name: MTEB MassiveScenarioClassification (ta) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 66.27774041694687 - type: f1 value: 64.83664868894539 task: type: Classification - dataset: config: te name: MTEB MassiveScenarioClassification (te) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 66.69468728984533 - type: f1 value: 64.76239666920868 task: type: Classification - dataset: config: th name: MTEB MassiveScenarioClassification (th) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 73.44653665097512 - type: f1 value: 73.14646052013873 task: type: Classification - dataset: config: tl name: MTEB MassiveScenarioClassification (tl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 67.71351714862139 - type: f1 value: 66.67212180163382 task: type: Classification - dataset: config: tr name: MTEB MassiveScenarioClassification (tr) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 73.9946200403497 - type: f1 value: 73.87348793725525 task: type: Classification - dataset: config: ur name: MTEB MassiveScenarioClassification (ur) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 68.15400134498992 - type: f1 value: 67.09433241421094 task: type: Classification - dataset: config: vi name: MTEB MassiveScenarioClassification (vi) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 73.11365164761264 - type: f1 value: 73.59502539433753 task: type: Classification - dataset: config: zh-CN name: MTEB MassiveScenarioClassification (zh-CN) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 76.82582380632145 - type: f1 value: 76.89992945316313 task: type: Classification - dataset: config: zh-TW name: MTEB MassiveScenarioClassification (zh-TW) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 71.81237390719569 - type: f1 value: 72.36499770986265 task: type: Classification - dataset: config: default name: MTEB MedrxivClusteringP2P revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics: - type: v_measure value: 31.480506569594695 task: type: Clustering - dataset: config: default name: MTEB MedrxivClusteringS2S revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 split: test type: mteb/medrxiv-clustering-s2s metrics: - type: v_measure value: 29.71252128004552 task: type: Clustering - dataset: config: default name: MTEB MindSmallReranking revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 split: test type: mteb/mind_small metrics: - type: map value: 31.421396787056548 - type: mrr value: 32.48155274872267 task: type: Reranking - dataset: config: default name: MTEB NFCorpus revision: None split: test type: nfcorpus 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: config: default name: MTEB NQ revision: None split: test type: nq 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: config: default name: MTEB QuoraRetrieval revision: None split: test type: quora 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: Retrieval - dataset: config: default name: MTEB RedditClustering revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics: - type: v_measure value: 46.54068723291946 task: type: Clustering - dataset: config: default name: MTEB RedditClusteringP2P revision: 282350215ef01743dc01b456c7f5241fa8937f16 split: test type: mteb/reddit-clustering-p2p metrics: - type: v_measure value: 63.216287629895994 task: type: Clustering - dataset: config: default name: MTEB SCIDOCS revision: None split: test type: scidocs 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: Retrieval - dataset: config: default name: MTEB SICK-R revision: a6ea5a8cab320b040a23452cc28066d9beae2cee split: test type: mteb/sickr-sts 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: config: default name: MTEB STS12 revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts 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: config: default name: MTEB STS13 revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts 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: config: default name: MTEB STS14 revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts 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: config: default name: MTEB STS15 revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts 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: config: default name: MTEB STS16 revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts 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: config: ko-ko name: MTEB STS17 (ko-ko) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: ar-ar name: MTEB STS17 (ar-ar) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: en-ar name: MTEB STS17 (en-ar) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: en-de name: MTEB STS17 (en-de) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: en-en name: MTEB STS17 (en-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: en-tr name: MTEB STS17 (en-tr) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: es-en name: MTEB STS17 (es-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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: config: es-es name: MTEB STS17 (es-es) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts 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 - 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dataset: config: default name: MTEB SciDocsRR revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics: - type: map value: 82.03549357197389 - type: mrr value: 95.05437645143527 task: type: Reranking - dataset: config: default name: MTEB SciFact revision: None split: test type: scifact 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: Retrieval - dataset: config: default name: MTEB SprintDuplicateQuestions revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification 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: PairClassification - dataset: config: default name: MTEB StackExchangeClustering revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: v_measure value: 57.525863806168566 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - 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type: max_f1 value: 78.62997658079624 task: type: PairClassification tags: - mteb - sentence-similarity - feature-extraction - onnx - teradata --- ***See Disclaimer below*** ---- # A Teradata Vantage compatible Embeddings Model # intfloat/multilingual-e5-large ## Overview of this Model An Embedding Model which maps text (sentence/ paragraphs) into a vector. The [intfloat/multilingual-e5-large](https://huggingface.co./intfloat/multilingual-e5-large) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings. - 559.89M params (Sizes in ONNX format - "int8": 535.01MB, "uint8": 535.01MB) - 514 maximum input tokens - 1024 dimensions of output vector - Licence: mit. The released models can be used for commercial purposes free of charge. - Reference to Original Model: https://huggingface.co./intfloat/multilingual-e5-large ## Quickstart: Deploying this Model in Teradata Vantage We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion. **Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed. To get started, clone the pre-converted model directly from the Teradata HuggingFace repository. ```python import teradataml as tdml import getpass from huggingface_hub import hf_hub_download model_name = "multilingual-e5-large" number_dimensions_output = 1024 model_file_name = "model_int8.onnx" # Step 1: Download Model from Teradata HuggingFace Page hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./") hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./") # Step 2: Create Connection to Vantage tdml.create_context(host = input('enter your hostname'), username=input('enter your username'), password = getpass.getpass("enter your password")) # Step 3: Load Models into Vantage # a) Embedding model tdml.save_byom(model_id = model_name, # must be unique in the models table model_file = f"onnx/{model_file_name}", table_name = 'embeddings_models' ) # b) Tokenizer tdml.save_byom(model_id = model_name, # must be unique in the models table model_file = 'tokenizer.json', table_name = 'embeddings_tokenizers') # Step 4: Test ONNXEmbeddings Function # Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. # If it has got a different name, just rename it in a subquery/CTE. input_table = "emails.emails" embeddings_query = f""" SELECT * from mldb.ONNXEmbeddings( on {input_table} as InputTable on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION using Accumulate('id', 'txt') ModelOutputTensor('sentence_embedding') EnableMemoryCheck('false') OutputFormat('FLOAT32({number_dimensions_output})') OverwriteCachedModel('true') ) a """ DF_embeddings = tdml.DataFrame.from_query(embeddings_query) DF_embeddings ``` ## What Can I Do with the Embeddings? Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples: - **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb) - **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb) - **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb) ## Deep Dive into Model Conversion to ONNX **The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.** You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format. ### Part 1. Importing and Converting Model using optimum We start by importing the pre-trained [intfloat/multilingual-e5-large](https://huggingface.co./intfloat/multilingual-e5-large) model from Hugging Face. To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format. After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage We are generating ONNX files for multiple different precisions: int8, uint8 You can find the detailed conversion steps in the file [convert.py](./convert.py) ### Part 2. Running the model in Python with onnxruntime & compare results Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results. If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database. ```python import onnxruntime as rt from sentence_transformers.util import cos_sim from sentence_transformers import SentenceTransformer import transformers sentences_1 = 'How is the weather today?' sentences_2 = 'What is the current weather like today?' # Calculate ONNX result tokenizer = transformers.AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large") predef_sess = rt.InferenceSession("onnx/model_int8.onnx") enc1 = tokenizer(sentences_1) embeddings_1_onnx = predef_sess.run(None, {"input_ids": [enc1.input_ids], "attention_mask": [enc1.attention_mask]}) enc2 = tokenizer(sentences_2) embeddings_2_onnx = predef_sess.run(None, {"input_ids": [enc2.input_ids], "attention_mask": [enc2.attention_mask]}) # Calculate embeddings with SentenceTransformer model = SentenceTransformer(model_id, trust_remote_code=True) embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True) embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True) # Compare results print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0]))) print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer))) ``` You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py) ----- DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. 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