Multilingual - e5 - large - instruct - Q6_K - GGUF Open - Source Model: Supports Text Embedding and Classification for Over 100 Languages

Multilingual E5 Large Instruct Q6 K GGUF

Developed by kcccat

Multilingual E5 large instruction model supporting text embedding and classification tasks for over 100 languages

Large Language Model Supports Multiple LanguagesOpen Source License:MIT #Multilingual Text Embedding #Large-scale Semantic Retrieval #Cross-lingual Classification

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Release Time : 2/21/2025

Model Overview

A multilingual text embedding model based on intfloat/multilingual-e5-large-instruct, excelling in cross-lingual text classification, retrieval, and clustering tasks

Model Features

Multilingual Support

Supports text processing in over 100 languages, including major languages and some low-resource languages

High-performance Text Embedding

Outstanding performance on MTEB benchmarks, particularly excelling in text classification and retrieval tasks

Instruction Optimization

Specially optimized for instruction tasks, better understanding task requirements

Model Capabilities

Multilingual Text Classification

Cross-lingual Text Retrieval

Document Clustering Analysis

Bilingual Text Mining

Semantic Similarity Calculation

Text Re-ranking

Use Cases

E-commerce

Multilingual Product Review Classification

Sentiment analysis and classification of product reviews on platforms like Amazon

Achieved 96.29% accuracy on MTEB AmazonPolarityClassification task

Information Retrieval

Cross-lingual Document Retrieval

Finding relevant content across document collections in different languages

Achieved 99.66% accuracy on MTEB BUCC bilingual mining task

Academic Research

Academic Paper Clustering

Topic clustering of academic papers on arXiv and Biorxiv

Achieved 46.40 v_measure score on MTEB ArxivClusteringP2P task

🚀 multilingual-e5-large-instruct

This is a multilingual model based on intfloat/multilingual-e5-large-instruct. It provides performance metrics across multiple tasks and datasets, demonstrating its effectiveness in various natural language processing scenarios.

📚 Documentation

Model Information

Property	Details
Model Type	multilingual-e5-large-instruct
Base Model	intfloat/multilingual-e5-large-instruct
License	MIT
Supported Languages	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

Performance Metrics

The model has been evaluated on various tasks and datasets, with the following performance metrics:

Classification Tasks

Dataset	Accuracy	AP	F1
MTEB AmazonCounterfactualClassification (en)	76.23880597014924	39.07351965022687	70.04836733862683
MTEB AmazonCounterfactualClassification (de)	66.71306209850107	79.01499914759529	64.81951817560703
MTEB AmazonCounterfactualClassification (en-ext)	73.85307346326837	22.447519885878737	61.0162730745633
MTEB AmazonCounterfactualClassification (ja)	76.04925053533191	23.44983217128922	62.5723230907759
MTEB AmazonPolarityClassification	96.28742500000001	94.8449918887462	96.28680923610432
MTEB AmazonReviewsClassification (en)	56.716	N/A	55.76510398266401
MTEB AmazonReviewsClassification (de)	52.99999999999999	N/A	52.00829994765178
MTEB AmazonReviewsClassification (es)	48.806000000000004	N/A	48.082345914983634
MTEB AmazonReviewsClassification (fr)	48.507999999999996	N/A	47.68752844642045
MTEB AmazonReviewsClassification (ja)	47.709999999999994	N/A	47.05870376637181
MTEB AmazonReviewsClassification (zh)	44.662000000000006	N/A	43.42371965372771
MTEB Banking77Classification	85.73376623376623	N/A	85.68480707214599
MTEB EmotionClassification	51.51	N/A	47.632159862049896

Retrieval Tasks

| Dataset | MAP@1 | MAP@10 | MAP@100 | MAP@1000 | MAP@3 | MAP@5 | MRR@1 | MRR@10 | MRR@100 | MRR@1000 | MRR@3 | MRR@5 | NDCG@1 | NDCG@10 | NDCG@100 | NDCG@1000 | NDCG@3 | NDCG@5 | Precision@1 | Precision@10 | Precision@100 | Precision@1000 | Precision@3 | Precision@5 | Recall@1 | Recall@10 | Recall@100 | Recall@1000 | Recall@3 | Recall@5 | |---------|-------|--------|---------|----------|-------|-------|--------|---------|----------|-----------|--------|-------|--------|---------|----------|-----------|--------|-------|-----------|------------|-------------|--------------|-----------|---------|---------|----------|-----------|------------|------------|---------|---------| | MTEB ArguAna | 31.721 | 49.221 | 49.884 | 49.888 | 44.31 | 47.276 | 32.432 | 49.5 | 50.163000000000004 | 50.166 | 44.618 | 47.541 | 31.721 | 58.384 | 61.111000000000004 | 61.187999999999995 | 48.386 | 53.708999999999996 | 31.721 | 8.741 | 0.991 | 0.1 | 20.057 | 14.609 | 31.721 | 87.411 | 99.075 | 99.644 | 60.171 | 73.044 | | MTEB CQADupstackRetrieval | 27.764166666666668 | 37.298166666666674 | 38.530166666666666 | 38.64416666666667 | 34.484833333333334 | 36.0385 | 32.93558333333333 | 41.589749999999995 | 42.425333333333334 | 42.476333333333336 | 39.26825 | 40.567083333333336 | 32.93558333333333 | 42.706583333333334 | 47.82483333333333 | 49.95733333333334 | 38.064750000000004 | 40.18158333333333 | 32.93558333333333 | 7.459833333333334 | 1.1830833333333335 | 0.15608333333333332 | 17.5235 | 12.349833333333333 | 27.764166666666668 | 54.31775 | 76.74350000000001 | 91.45208333333332 | 41.23425 | 46.73983333333334 | | MTEB ClimateFEVER | 12.969 | 21.584999999999997 | 23.3 | 23.5 | 18.218999999999998 | 19.983 | 29.316 | 40.033 | 40.96 | 41.001 | 37.123 | 38.757999999999996 | 29.316 | 29.858 | 36.756 | 40.245999999999995 | 24.822 | 26.565 | 29.316 | 9.186 | 1.6549999999999998 | 0.22999999999999998 | 18.436 | 13.876 | 12.969 | 35.142 | 59.143 | 78.594 | 22.604 | 27.883000000000003 | | MTEB DBPedia | 8.527999999999999 | 17.974999999999998 | 25.665 | 27.406000000000002 | 13.017999999999999 | 15.137 | 62.5 | 71.891 | 72.294 | 72.296 | 69.958 | 71.121 | 50.875 | 38.36 | 44.235 | 52.154 | 43.008 | 40.083999999999996 | 62.5 | 30.0 | 10.038 | 2.0869999999999997 | 46.833000000000006 | 38.800000000000004 | 8.527999999999999 | 23.828 | 52.322 | 77.143 | 14.136000000000001 | 17.761 | | MTEB FEVER | 60.734 | 72.442 | 72.735 | 72.75 | 70.41199999999999 | 71.80499999999999 | 65.212 | 76.613 | 76.79899999999999 | 76.801 | 74.8 | 76.12400000000001 | 65.212 | 77.988 | 79.167 | 79.452 | 74.362 | 76.666 | 65.212 | 10.003 | 1.077 | 0.11199999999999999 | 29.518 | 19.016 | 60.734 | 90.824 | 95.71600000000001 | 97.577 | 81.243 | 86.90299999999999 | | MTEB FiQA2018 | 23.845 | 39.281 | 41.422 | 41.593 | 34.467 | 37.017 | 47.531 | 56.204 | 56.928999999999995 | 56.962999999999994 | 54.115 | 55.373000000000005 | 47.531 | 47.711999999999996 | 54.510999999999996 | 57.103 | 44.145 | 45.032 | 47.531 | 13.194 | 2.045 | 0.249 | 29.424 | 21.451 | 23.845 | 54.967 | 79.11399999999999 | 94.56700000000001 | 40.256 | 46.215 | | MTEB HotpotQA | 37.819 | 60.889 | 61.717999999999996 | 61.778 | 57.254000000000005 | 59.541 | 75.638 | 82.173 | 82.362 | 82.37 | 81.089 | 81.827 | 75.638 | 69.317 | 72.221 | 73.382 | 64.14 | 67.07600000000001 | 75.638 | 14.704999999999998 | 1.698 | 0.185 | 41.394999999999996 | 27.162999999999997 | 37.819 | 60.889 | 61.717999999999996 | 61.778 | 57.254000000000005 | 59.541 |

Clustering Tasks

Dataset	V-Measure
MTEB ArxivClusteringP2P	46.40419580759799
MTEB ArxivClusteringS2S	40.48593255007969
MTEB BiorxivClusteringP2P	40.935218072113855
MTEB BiorxivClusteringS2S	36.276389017675264

Reranking Tasks

Dataset	MAP	MRR
MTEB AskUbuntuDupQuestions	63.889179122289995	77.61146286769556

STS Tasks

Dataset	Cosine Similarity Pearson	Cosine Similarity Spearman	Euclidean Pearson	Euclidean Spearman	Manhattan Pearson	Manhattan Spearman
MTEB BIOSSES	88.15075203727929	86.9622224570873	86.70473853624121	86.9622224570873	86.21089380980065	86.75318154937008

Bitext Mining Tasks

Dataset	Accuracy	F1	Precision	Recall
MTEB BUCC (de-en)	99.65553235908142	99.60681976339595	99.58246346555325	99.65553235908142
MTEB BUCC (fr-en)	99.26260180497468	99.14520507740848	99.08650671362535	99.26260180497468
MTEB BUCC (ru-en)	98.07412538967787	97.86629719431936	97.76238309664012	98.07412538967787
MTEB BUCC (zh-en)	99.42074776197998	99.38564156573635	99.36808846761454	99.42074776197998

📄 License

This project is licensed under the MIT license.

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