multilingual-e5-large-instruct-Q8_0-GGUF Open Source Multilingual Model - Supports Text Embedding and Classification, with Strong Cross-lingual Capabilities

Multilingual E5 Large Instruct Q8 0 GGUF

Developed by Gomez12

Multilingual E5 large instruction model, supporting text embedding and classification tasks in multiple languages with strong cross-language capabilities.

Large Language Model Supports Multiple LanguagesOpen Source License:MIT #Multilingual Embedding #Instruction Fine-tuning #Cross-language Retrieval

Downloads 90

Release Time : 4/13/2025

Model Overview

This model is a multilingual text embedding model based on the E5 architecture, specifically optimized for instruction tasks. It supports over 100 languages and is suitable for tasks such as text classification, information retrieval, and semantic similarity calculation.

Model Features

Multilingual Support

Supports over 100 languages with strong cross-language processing capabilities.

Instruction Optimization

Specifically optimized for instruction tasks, suitable for application scenarios requiring adherence to specific instructions.

High-performance Text Embedding

Excels in various text embedding tasks, particularly in semantic similarity calculation and information retrieval.

Model Capabilities

Text Classification

Information Retrieval

Semantic Similarity Calculation

Cross-language Text Processing

Use Cases

E-commerce

Product Review Classification

Performs sentiment analysis and classification on multilingual product reviews

Achieved 76.24% accuracy on the MTEB AmazonCounterfactualClassification task (English)

Information Retrieval

Cross-language Document Retrieval

Retrieves relevant information from documents in different languages

Achieved 49.88 map@100 on the MTEB ArguAna retrieval task

🚀 multilingual-e5-large-instruct

This is a multilingual model (multilingual-e5-large-instruct), which has achieved certain results in multiple tasks of the MTEB benchmark, such as classification, retrieval, clustering, etc. It uses the MIT license and is suitable for various natural language processing tasks.

📚 Documentation

Model Information

Property	Details
Base Model	intfloat/multilingual-e5-large-instruct
Language	Multilingual, including 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
Tags	mteb, sentence-transformers, transformers, llama-cpp, gguf-my-repo

Model Performance Results

The model has carried out a series of tests on multiple datasets, and here are the performance results on some datasets:

Classification Task

MTEB AmazonCounterfactualClassification (en):
- Accuracy: 76.23880597014924
- AP: 39.07351965022687
- F1: 70.04836733862683
MTEB AmazonPolarityClassification:
- Accuracy: 96.28742500000001
- AP: 94.8449918887462
- F1: 96.28680923610432
MTEB AmazonReviewsClassification (en):
- Accuracy: 56.716
- F1: 55.76510398266401
MTEB Banking77Classification:
- Accuracy: 85.73376623376623
- F1: 85.68480707214599
MTEB EmotionClassification:
- Accuracy: 51.51
- F1: 47.632159862049896

Retrieval Task

MTEB ArguAna:
- MAP@1: 31.721
- MAP@10: 49.221
- MAP@100: 49.884
- MAP@1000: 49.888
- MAP@3: 44.31
- MAP@5: 47.276
- MRR@1: 32.432
- MRR@10: 49.5
- MRR@100: 50.163000000000004
- MRR@1000: 50.166
- MRR@3: 44.618
- MRR@5: 47.541
- NDCG@1: 31.721
- NDCG@10: 58.384
- NDCG@100: 61.111000000000004
- NDCG@1000: 61.187999999999995
- NDCG@3: 48.386
- NDCG@5: 53.708999999999996
- Precision@1: 31.721
- Precision@10: 8.741
- Precision@100: 0.991
- Precision@1000: 0.1
- Precision@3: 20.057
- Precision@5: 14.609
- Recall@1: 31.721
- Recall@10: 87.411
- Recall@100: 99.075
- Recall@1000: 99.644
- Recall@3: 60.171
- Recall@5: 73.044
MTEB CQADupstackRetrieval:
- MAP@1: 27.764166666666668
- MAP@10: 37.298166666666674
- MAP@100: 38.530166666666666
- MAP@1000: 38.64416666666667
- MAP@3: 34.484833333333334
- MAP@5: 36.0385
- MRR@1: 32.93558333333333
- MRR@10: 41.589749999999995
- MRR@100: 42.425333333333334
- MRR@1000: 42.476333333333336
- MRR@3: 39.26825
- MRR@5: 40.567083333333336
- NDCG@1: 32.93558333333333
- NDCG@10: 42.706583333333334
- NDCG@100: 47.82483333333333
- NDCG@1000: 49.95733333333334
- NDCG@3: 38.064750000000004
- NDCG@5: 40.18158333333333
- Precision@1: 32.93558333333333
- Precision@10: 7.459833333333334
- Precision@100: 1.1830833333333335
- Precision@1000: 0.15608333333333332
- Precision@3: 17.5235
- Precision@5: 12.349833333333333
- Recall@1: 27.764166666666668
- Recall@10: 54.31775
- Recall@100: 76.74350000000001
- Recall@1000: 91.45208333333332
- Recall@3: 41.23425
- Recall@5: 46.73983333333334

Clustering Task

MTEB ArxivClusteringP2P:
- V-measure: 46.40419580759799
MTEB ArxivClusteringS2S:
- V-measure: 40.48593255007969

Reranking Task

MTEB AskUbuntuDupQuestions:
- MAP: 63.889179122289995
- MRR: 77.61146286769556

STS Task

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

BitextMining Task

MTEB BUCC (de-en):
- Accuracy: 99.65553235908142
- F1: 99.60681976339595
- Precision: 99.58246346555325
- Recall: 99.65553235908142
MTEB BUCC (fr-en):
- Accuracy: 99.26260180497468
- F1: 99.14520507740848
- Precision: 99.08650671362535
- Recall: 99.26260180497468

📄 License

This project uses the MIT license.

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