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EN
Multilingual E5 Large Instruct Q5 0 GGUF
M
Multilingual E5 Large Instruct Q5 0 GGUF
Developed by yoeven
Multilingual E5 large instruction model supporting text embedding and classification tasks in multiple languages
Downloads 14
Release Time : 1/6/2025
Model Overview
A multilingual text embedding model based on intfloat/multilingual-e5-large-instruct, supporting a wide range of classification, clustering, and retrieval tasks
Model Features
Multilingual Support
Supports text processing in over 50 languages, including major and some lesser-known languages
High-Performance Classification
Outstanding performance in MTEB benchmarks, achieving over 96% accuracy in English classification tasks
Powerful Retrieval Capability
Excellent performance in multiple retrieval tasks, especially in bilingual text mining with accuracy up to 99%
Broad Applicability
Supports various NLP tasks such as classification, clustering, retrieval, and reranking
Model Capabilities
Text Classification
Text Clustering
Information Retrieval
Bilingual Text Mining
Semantic Similarity Calculation
Text Reranking
Use Cases
E-Commerce
Product Review Classification
Sentiment analysis and classification of multilingual product reviews
Achieved 56.7% accuracy in English Amazon review classification tasks
Counterfactual Review Detection
Identifying potentially misleading product reviews
Achieved 76.2% accuracy in English Amazon counterfactual classification tasks
Information Retrieval
Document Retrieval
Retrieving relevant information from large document collections
Achieved 49.2% average precision@10 on the ArguAna dataset
Bilingual Document Alignment
Automatically matching similar documents in different languages
Achieved 99.6% accuracy in BUCC bilingual mining tasks
Academic Research
Paper Clustering
Topic clustering of academic papers
Achieved 46.4% v_measure in Arxiv paper clustering tasks
🚀 multilingual-e5-large-instruct
This is a multilingual model based on intfloat/multilingual-e5-large-instruct, which demonstrates excellent performance across various tasks in multiple languages.
📚 Documentation
Model Information
| Property | Details |
|---|---|
| Model Type | multilingual-e5-large-instruct |
| Base Model | intfloat/multilingual-e5-large-instruct |
| License | MIT |
| Supported Languages | 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 |
Model Performance
Classification Tasks
| Task | Dataset | Metrics | Value |
|---|---|---|---|
| Classification | MTEB AmazonCounterfactualClassification (en) | accuracy | 76.23880597014924 |
| Classification | MTEB AmazonCounterfactualClassification (en) | ap | 39.07351965022687 |
| Classification | MTEB AmazonCounterfactualClassification (en) | f1 | 70.04836733862683 |
| Classification | MTEB AmazonCounterfactualClassification (de) | accuracy | 66.71306209850107 |
| Classification | MTEB AmazonCounterfactualClassification (de) | ap | 79.01499914759529 |
| Classification | MTEB AmazonCounterfactualClassification (de) | f1 | 64.81951817560703 |
| Classification | MTEB AmazonCounterfactualClassification (en-ext) | accuracy | 73.85307346326837 |
| Classification | MTEB AmazonCounterfactualClassification (en-ext) | ap | 22.447519885878737 |
| Classification | MTEB AmazonCounterfactualClassification (en-ext) | f1 | 61.0162730745633 |
| Classification | MTEB AmazonCounterfactualClassification (ja) | accuracy | 76.04925053533191 |
| Classification | MTEB AmazonCounterfactualClassification (ja) | ap | 23.44983217128922 |
| Classification | MTEB AmazonCounterfactualClassification (ja) | f1 | 62.5723230907759 |
| Classification | MTEB AmazonPolarityClassification | accuracy | 96.28742500000001 |
| Classification | MTEB AmazonPolarityClassification | ap | 94.8449918887462 |
| Classification | MTEB AmazonPolarityClassification | f1 | 96.28680923610432 |
| Classification | MTEB AmazonReviewsClassification (en) | accuracy | 56.716 |
| Classification | MTEB AmazonReviewsClassification (en) | f1 | 55.76510398266401 |
| Classification | MTEB AmazonReviewsClassification (de) | accuracy | 52.99999999999999 |
| Classification | MTEB AmazonReviewsClassification (de) | f1 | 52.00829994765178 |
| Classification | MTEB AmazonReviewsClassification (es) | accuracy | 48.806000000000004 |
| Classification | MTEB AmazonReviewsClassification (es) | f1 | 48.082345914983634 |
| Classification | MTEB AmazonReviewsClassification (fr) | accuracy | 48.507999999999996 |
| Classification | MTEB AmazonReviewsClassification (fr) | f1 | 47.68752844642045 |
| Classification | MTEB AmazonReviewsClassification (ja) | accuracy | 47.709999999999994 |
| Classification | MTEB AmazonReviewsClassification (ja) | f1 | 47.05870376637181 |
| Classification | MTEB AmazonReviewsClassification (zh) | accuracy | 44.662000000000006 |
| Classification | MTEB AmazonReviewsClassification (zh) | f1 | 43.42371965372771 |
| Classification | MTEB Banking77Classification | accuracy | 85.73376623376623 |
| Classification | MTEB Banking77Classification | f1 | 85.68480707214599 |
| Classification | MTEB EmotionClassification | accuracy | 51.51 |
| Classification | MTEB EmotionClassification | f1 | 47.632159862049896 |
Retrieval Tasks
| Task | Dataset | Metrics | Value |
|---|---|---|---|
| Retrieval | MTEB ArguAna | map_at_1 | 31.721 |
| Retrieval | MTEB ArguAna | map_at_10 | 49.221 |
| Retrieval | MTEB ArguAna | map_at_100 | 49.884 |
| Retrieval | MTEB ArguAna | map_at_1000 | 49.888 |
| Retrieval | MTEB ArguAna | map_at_3 | 44.31 |
| Retrieval | MTEB ArguAna | map_at_5 | 47.276 |
| Retrieval | MTEB ArguAna | mrr_at_1 | 32.432 |
| Retrieval | MTEB ArguAna | mrr_at_10 | 49.5 |
| Retrieval | MTEB ArguAna | mrr_at_100 | 50.163000000000004 |
| Retrieval | MTEB ArguAna | mrr_at_1000 | 50.166 |
| Retrieval | MTEB ArguAna | mrr_at_3 | 44.618 |
| Retrieval | MTEB ArguAna | mrr_at_5 | 47.541 |
| Retrieval | MTEB ArguAna | ndcg_at_1 | 31.721 |
| Retrieval | MTEB ArguAna | ndcg_at_10 | 58.384 |
| Retrieval | MTEB ArguAna | ndcg_at_100 | 61.111000000000004 |
| Retrieval | MTEB ArguAna | ndcg_at_1000 | 61.187999999999995 |
| Retrieval | MTEB ArguAna | ndcg_at_3 | 48.386 |
| Retrieval | MTEB ArguAna | ndcg_at_5 | 53.708999999999996 |
| Retrieval | MTEB ArguAna | precision_at_1 | 31.721 |
| Retrieval | MTEB ArguAna | precision_at_10 | 8.741 |
| Retrieval | MTEB ArguAna | precision_at_100 | 0.991 |
| Retrieval | MTEB ArguAna | precision_at_1000 | 0.1 |
| Retrieval | MTEB ArguAna | precision_at_3 | 20.057 |
| Retrieval | MTEB ArguAna | precision_at_5 | 14.609 |
| Retrieval | MTEB ArguAna | recall_at_1 | 31.721 |
| Retrieval | MTEB ArguAna | recall_at_10 | 87.411 |
| Retrieval | MTEB ArguAna | recall_at_100 | 99.075 |
| Retrieval | MTEB ArguAna | recall_at_1000 | 99.644 |
| Retrieval | MTEB ArguAna | recall_at_3 | 60.171 |
| Retrieval | MTEB ArguAna | recall_at_5 | 73.044 |
| Retrieval | MTEB CQADupstackRetrieval | map_at_1 | 27.764166666666668 |
| Retrieval | MTEB CQADupstackRetrieval | map_at_10 | 37.298166666666674 |
| Retrieval | MTEB CQADupstackRetrieval | map_at_100 | 38.530166666666666 |
| Retrieval | MTEB CQADupstackRetrieval | map_at_1000 | 38.64416666666667 |
| Retrieval | MTEB CQADupstackRetrieval | map_at_3 | 34.484833333333334 |
| Retrieval | MTEB CQADupstackRetrieval | map_at_5 | 36.0385 |
| Retrieval | MTEB CQADupstackRetrieval | mrr_at_1 | 32.93558333333333 |
| Retrieval | MTEB CQADupstackRetrieval | mrr_at_10 | 41.589749999999995 |
| Retrieval | MTEB CQADupstackRetrieval | mrr_at_100 | 42.425333333333334 |
| Retrieval | MTEB CQADupstackRetrieval | mrr_at_1000 | 42.476333333333336 |
| Retrieval | MTEB CQADupstackRetrieval | mrr_at_3 | 39.26825 |
| Retrieval | MTEB CQADupstackRetrieval | mrr_at_5 | 40.567083333333336 |
| Retrieval | MTEB CQADupstackRetrieval | ndcg_at_1 | 32.93558333333333 |
| Retrieval | MTEB CQADupstackRetrieval | ndcg_at_10 | 42.706583333333334 |
| Retrieval | MTEB CQADupstackRetrieval | ndcg_at_100 | 47.82483333333333 |
| Retrieval | MTEB CQADupstackRetrieval | ndcg_at_1000 | 49.95733333333334 |
| Retrieval | MTEB CQADupstackRetrieval | ndcg_at_3 | 38.064750000000004 |
| Retrieval | MTEB CQADupstackRetrieval | ndcg_at_5 | 40.18158333333333 |
| Retrieval | MTEB CQADupstackRetrieval | precision_at_1 | 32.93558333333333 |
| Retrieval | MTEB CQADupstackRetrieval | precision_at_10 | 7.459833333333334 |
| Retrieval | MTEB CQADupstackRetrieval | precision_at_100 | 1.1830833333333335 |
| Retrieval | MTEB CQADupstackRetrieval | precision_at_1000 | 0.15608333333333332 |
| Retrieval | MTEB CQADupstackRetrieval | precision_at_3 | 17.5235 |
| Retrieval | MTEB CQADupstackRetrieval | precision_at_5 | 12.349833333333333 |
| Retrieval | MTEB CQADupstackRetrieval | recall_at_1 | 27.764166666666668 |
| Retrieval | MTEB CQADupstackRetrieval | recall_at_10 | 54.31775 |
| Retrieval | MTEB CQADupstackRetrieval | recall_at_100 | 76.74350000000001 |
| Retrieval | MTEB CQADupstackRetrieval | recall_at_1000 | 91.45208333333332 |
| Retrieval | MTEB CQADupstackRetrieval | recall_at_3 | 41.23425 |
| Retrieval | MTEB CQADupstackRetrieval | recall_at_5 | 46.73983333333334 |
| Retrieval | MTEB ClimateFEVER | map_at_1 | 12.969 |
| Retrieval | MTEB ClimateFEVER | map_at_10 | 21.584999999999997 |
| Retrieval | MTEB ClimateFEVER | map_at_100 | 23.3 |
| Retrieval | MTEB ClimateFEVER | map_at_1000 | 23.5 |
| Retrieval | MTEB ClimateFEVER | map_at_3 | 18.218999999999998 |
| Retrieval | MTEB ClimateFEVER | map_at_5 | 19.983 |
| Retrieval | MTEB ClimateFEVER | mrr_at_1 | 29.316 |
| Retrieval | MTEB ClimateFEVER | mrr_at_10 | 40.033 |
| Retrieval | MTEB ClimateFEVER | mrr_at_100 | 40.96 |
| Retrieval | MTEB ClimateFEVER | mrr_at_1000 | 41.001 |
| Retrieval | MTEB ClimateFEVER | mrr_at_3 | 37.123 |
| Retrieval | MTEB ClimateFEVER | mrr_at_5 | 38.757999999999996 |
| Retrieval | MTEB ClimateFEVER | ndcg_at_1 | 29.316 |
| Retrieval | MTEB ClimateFEVER | ndcg_at_10 | 29.858 |
| Retrieval | MTEB ClimateFEVER | ndcg_at_100 | 36.756 |
| Retrieval | MTEB ClimateFEVER | ndcg_at_1000 | 40.245999999999995 |
| Retrieval | MTEB ClimateFEVER | ndcg_at_3 | 24.822 |
| Retrieval | MTEB ClimateFEVER | ndcg_at_5 | 26.565 |
| Retrieval | MTEB ClimateFEVER | precision_at_1 | 29.316 |
| Retrieval | MTEB ClimateFEVER | precision_at_10 | 9.186 |
| Retrieval | MTEB ClimateFEVER | precision_at_100 | 1.6549999999999998 |
| Retrieval | MTEB ClimateFEVER | precision_at_1000 | 0.22999999999999998 |
| Retrieval | MTEB ClimateFEVER | precision_at_3 | 18.436 |
| Retrieval | MTEB ClimateFEVER | precision_at_5 | 13.876 |
| Retrieval | MTEB ClimateFEVER | recall_at_1 | 12.969 |
| Retrieval | MTEB ClimateFEVER | recall_at_10 | 35.142 |
| Retrieval | MTEB ClimateFEVER | recall_at_100 | 59.143 |
| Retrieval | MTEB ClimateFEVER | recall_at_1000 | 78.594 |
| Retrieval | MTEB ClimateFEVER | recall_at_3 | 22.604 |
| Retrieval | MTEB ClimateFEVER | recall_at_5 | 27.883000000000003 |
| Retrieval | MTEB DBPedia | map_at_1 | 8.527999999999999 |
| Retrieval | MTEB DBPedia | map_at_10 | 17.974999999999998 |
| Retrieval | MTEB DBPedia | map_at_100 | 25.665 |
| Retrieval | MTEB DBPedia | map_at_1000 | 27.406000000000002 |
| Retrieval | MTEB DBPedia | map_at_3 | 13.017999999999999 |
| Retrieval | MTEB DBPedia | map_at_5 | 15.137 |
| Retrieval | MTEB DBPedia | mrr_at_1 | 62.5 |
| Retrieval | MTEB DBPedia | mrr_at_10 | 71.891 |
| Retrieval | MTEB DBPedia | mrr_at_100 | 72.294 |
| Retrieval | MTEB DBPedia | mrr_at_1000 | 72.296 |
| Retrieval | MTEB DBPedia | mrr_at_3 | 69.958 |
| Retrieval | MTEB DBPedia | mrr_at_5 | 71.121 |
| Retrieval | MTEB DBPedia | ndcg_at_1 | 50.875 |
| Retrieval | MTEB DBPedia | ndcg_at_10 | 38.36 |
| Retrieval | MTEB DBPedia | ndcg_at_100 | 44.235 |
| Retrieval | MTEB DBPedia | ndcg_at_1000 | 52.154 |
| Retrieval | MTEB DBPedia | ndcg_at_3 | 43.008 |
| Retrieval | MTEB DBPedia | ndcg_at_5 | 40.083999999999996 |
| Retrieval | MTEB DBPedia | precision_at_1 | 62.5 |
| Retrieval | MTEB DBPedia | precision_at_10 | 30.0 |
| Retrieval | MTEB DBPedia | precision_at_100 | 10.038 |
| Retrieval | MTEB DBPedia | precision_at_1000 | 2.0869999999999997 |
| Retrieval | MTEB DBPedia | precision_at_3 | 46.833000000000006 |
| Retrieval | MTEB DBPedia | precision_at_5 | 38.800000000000004 |
| Retrieval | MTEB DBPedia | recall_at_1 | 8.527999999999999 |
| Retrieval | MTEB DBPedia | recall_at_10 | 23.828 |
| Retrieval | MTEB DBPedia | recall_at_100 | 52.322 |
| Retrieval | MTEB DBPedia | recall_at_1000 | 77.143 |
| Retrieval | MTEB DBPedia | recall_at_3 | 14.136000000000001 |
| Retrieval | MTEB DBPedia | recall_at_5 | 17.761 |
| Retrieval | MTEB FEVER | map_at_1 | 60.734 |
| Retrieval | MTEB FEVER | map_at_10 | 72.442 |
| Retrieval | MTEB FEVER | map_at_100 | 72.735 |
| Retrieval | MTEB FEVER | map_at_1000 | 72.75 |
| Retrieval | MTEB FEVER | map_at_3 | 70.41199999999999 |
| Retrieval | MTEB FEVER | map_at_5 | 71.80499999999999 |
| Retrieval | MTEB FEVER | mrr_at_1 | 65.212 |
| Retrieval | MTEB FEVER | mrr_at_10 | 76.613 |
| Retrieval | MTEB FEVER | mrr_at_100 | 76.79899999999999 |
| Retrieval | MTEB FEVER | mrr_at_1000 | 76.801 |
| Retrieval | MTEB FEVER | mrr_at_3 | 74.8 |
| Retrieval | MTEB FEVER | mrr_at_5 | 76.12400000000001 |
| Retrieval | MTEB FEVER | ndcg_at_1 | 65.212 |
| Retrieval | MTEB FEVER | ndcg_at_10 | 77.988 |
| Retrieval | MTEB FEVER | ndcg_at_100 | 79.167 |
| Retrieval | MTEB FEVER | ndcg_at_1000 | 79.452 |
| Retrieval | MTEB FEVER | ndcg_at_3 | 74.362 |
| Retrieval | MTEB FEVER | ndcg_at_5 | 76.666 |
| Retrieval | MTEB FEVER | precision_at_1 | 65.212 |
| Retrieval | MTEB FEVER | precision_at_10 | 10.003 |
| Retrieval | MTEB FEVER | precision_at_100 | 1.077 |
| Retrieval | MTEB FEVER | precision_at_1000 | 0.11199999999999999 |
| Retrieval | MTEB FEVER | precision_at_3 | 29.518 |
| Retrieval | MTEB FEVER | precision_at_5 | 19.016 |
| Retrieval | MTEB FEVER | recall_at_1 | 60.734 |
| Retrieval | MTEB FEVER | recall_at_10 | 90.824 |
| Retrieval | MTEB FEVER | recall_at_100 | 95.71600000000001 |
| Retrieval | MTEB FEVER | recall_at_1000 | 97.577 |
| Retrieval | MTEB FEVER | recall_at_3 | 81.243 |
| Retrieval | MTEB FEVER | recall_at_5 | 86.90299999999999 |
| Retrieval | MTEB FiQA2018 | map_at_1 | 23.845 |
| Retrieval | MTEB FiQA2018 | map_at_10 | 39.281 |
| Retrieval | MTEB FiQA2018 | map_at_100 | 41.422 |
| Retrieval | MTEB FiQA2018 | map_at_1000 | 41.593 |
| Retrieval | MTEB FiQA2018 | map_at_3 | 34.467 |
| Retrieval | MTEB FiQA2018 | map_at_5 | 37.017 |
| Retrieval | MTEB FiQA2018 | mrr_at_1 | 47.531 |
| Retrieval | MTEB FiQA2018 | mrr_at_10 | 56.204 |
| Retrieval | MTEB FiQA2018 | mrr_at_100 | 56.928999999999995 |
| Retrieval | MTEB FiQA2018 | mrr_at_1000 | 56.962999999999994 |
| Retrieval | MTEB FiQA2018 | mrr_at_3 | 54.115 |
| Retrieval | MTEB FiQA2018 | mrr_at_5 | 55.373000000000005 |
| Retrieval | MTEB FiQA2018 | ndcg_at_1 | 47.531 |
| Retrieval | MTEB FiQA2018 | ndcg_at_10 | 47.711999999999996 |
| Retrieval | MTEB FiQA2018 | ndcg_at_100 | 54.510999999999996 |
| Retrieval | MTEB FiQA2018 | ndcg_at_1000 | 57.103 |
| Retrieval | MTEB FiQA2018 | ndcg_at_3 | 44.145 |
| Retrieval | MTEB FiQA2018 | ndcg_at_5 | 45.032 |
| Retrieval | MTEB FiQA2018 | precision_at_1 | 47.531 |
| Retrieval | MTEB FiQA2018 | precision_at_10 | 13.194 |
| Retrieval | MTEB FiQA2018 | precision_at_100 | 2.045 |
| Retrieval | MTEB FiQA2018 | precision_at_1000 | 0.249 |
| Retrieval | MTEB FiQA2018 | precision_at_3 | 29.424 |
| Retrieval | MTEB FiQA2018 | precision_at_5 | 21.451 |
| Retrieval | MTEB FiQA2018 | recall_at_1 | 23.845 |
| Retrieval | MTEB FiQA2018 | recall_at_10 | 54.967 |
| Retrieval | MTEB FiQA2018 | recall_at_100 | 79.11399999999999 |
| Retrieval | MTEB FiQA2018 | recall_at_1000 | 94.56700000000001 |
| Retrieval | MTEB FiQA2018 | recall_at_3 | 40.256 |
| Retrieval | MTEB FiQA2018 | recall_at_5 | 46.215 |
| Retrieval | MTEB HotpotQA | map_at_1 | 37.819 |
| Retrieval | MTEB HotpotQA | map_at_10 | 60.889 |
| Retrieval | MTEB HotpotQA | map_at_100 | 61.717999999999996 |
| Retrieval | MTEB HotpotQA | map_at_1000 | 61.778 |
| Retrieval | MTEB HotpotQA | map_at_3 | 57.254000000000005 |
| Retrieval | MTEB HotpotQA | map_at_5 | 59.541 |
| Retrieval | MTEB HotpotQA | mrr_at_1 | 75.638 |
| Retrieval | MTEB HotpotQA | mrr_at_10 | 82.173 |
| Retrieval | MTEB HotpotQA | mrr_at_100 | 82.362 |
| Retrieval | MTEB HotpotQA | mrr_at_1000 | 82.37 |
| Retrieval | MTEB HotpotQA | mrr_at_3 | 81.089 |
| Retrieval | MTEB HotpotQA | mrr_at_5 | 81.827 |
| Retrieval | MTEB HotpotQA | ndcg_at_1 | 75.638 |
| Retrieval | MTEB HotpotQA | ndcg_at_10 | 69.317 |
| Retrieval | MTEB HotpotQA | ndcg_at_100 | 72.221 |
| Retrieval | MTEB HotpotQA | ndcg_at_1000 | 73.382 |
| Retrieval | MTEB HotpotQA | ndcg_at_3 | 64.14 |
| Retrieval | MTEB HotpotQA | ndcg_at_5 | 67.07600000000001 |
| Retrieval | MTEB HotpotQA | precision_at_1 | 75.638 |
| Retrieval | MTEB HotpotQA | precision_at_10 | 14.704999999999998 |
| Retrieval | MTEB HotpotQA | precision_at_100 | 1.698 |
| Retrieval | MTEB HotpotQA | precision_at_1000 | 0.185 |
| Retrieval | MTEB HotpotQA | precision_at_3 | 41.394999999999996 |
| Retrieval | MTEB HotpotQA | precision_at_5 | 27.162999999999997 |
Clustering Tasks
| Task | Dataset | Metrics | Value |
|---|---|---|---|
| Clustering | MTEB ArxivClusteringP2P | v_measure | 46.40419580759799 |
| Clustering | MTEB ArxivClusteringS2S | v_measure | 40.48593255007969 |
| Clustering | MTEB BiorxivClusteringP2P | v_measure | 40.935218072113855 |
| Clustering | MTEB BiorxivClusteringS2S | v_measure | 36.276389017675264 |
Reranking Tasks
| Task | Dataset | Metrics | Value |
|---|---|---|---|
| Reranking | MTEB AskUbuntuDupQuestions | map | 63.889179122289995 |
| Reranking | MTEB AskUbuntuDupQuestions | mrr | 77.61146286769556 |
STS Tasks
| Task | Dataset | Metrics | Value |
|---|---|---|---|
| STS | MTEB BIOSSES | cos_sim_pearson | 88.15075203727929 |
| STS | MTEB BIOSSES | cos_sim_spearman | 86.9622224570873 |
| STS | MTEB BIOSSES | euclidean_pearson | 86.70473853624121 |
| STS | MTEB BIOSSES | euclidean_spearman | 86.9622224570873 |
| STS | MTEB BIOSSES | manhattan_pearson | 86.21089380980065 |
| STS | MTEB BIOSSES | manhattan_spearman | 86.75318154937008 |
BitextMining Tasks
| Task | Dataset | Metrics | Value |
|---|---|---|---|
| BitextMining | MTEB BUCC (de-en) | accuracy | 99.65553235908142 |
| BitextMining | MTEB BUCC (de-en) | f1 | 99.60681976339595 |
| BitextMining | MTEB BUCC (de-en) | precision | 99.58246346555325 |
| BitextMining | MTEB BUCC (de-en) | recall | 99.65553235908142 |
| BitextMining | MTEB BUCC (fr-en) | accuracy | 99.26260180497468 |
| BitextMining | MTEB BUCC (fr-en) | f1 | 99.14520507740848 |
| BitextMining | MTEB BUCC (fr-en) | precision | 99.08650671362535 |
| BitextMining | MTEB BUCC (fr-en) | recall | 99.26260180497468 |
| BitextMining | MTEB BUCC (ru-en) | accuracy | 98.07412538967787 |
| BitextMining | MTEB BUCC (ru-en) | f1 | 97.86629719431936 |
| BitextMining | MTEB BUCC (ru-en) | precision | 97.76238309664012 |
| BitextMining | MTEB BUCC (ru-en) | recall | 98.07412538967787 |
| BitextMining | MTEB BUCC (zh-en) | accuracy | 99.42074776197998 |
| BitextMining | MTEB BUCC (zh-en) | f1 | 99.38564156573635 |
| BitextMining | MTEB BUCC (zh-en) | precision | 99.36808846761454 |
| BitextMining | MTEB BUCC (zh-en) | recall | 99.42074776197998 |
📄 License
This project is licensed under the MIT license.
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