Multilingual-e5-large-pooled-Q8_0-GGUF Open-Source Model - Supports Multilingual Sentence Similarity Calculation and Feature Extraction

Multilingual E5 Large Pooled Q8 0 GGUF

Developed by falan42

Multilingual E5 large pooled model supporting sentence similarity calculation and feature extraction tasks in multiple languages.

Text Embedding Supports Multiple LanguagesOpen Source License:MIT #Multilingual embedding #Text similarity calculation #Cross-language retrieval

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Release Time : 5/13/2025

Model Overview

This is a multilingual sentence embedding model based on the E5 architecture, capable of processing texts in various languages and generating high-quality sentence embeddings.

Model Features

Multilingual support

Supports text processing in over 100 languages, including major and some less common languages

High-performance sentence embedding

Excels in sentence similarity tasks across multiple languages, capable of generating high-quality sentence vector representations

MTEB benchmark validation

Extensively evaluated on multiple tasks of the Massive Text Embedding Benchmark (MTEB), demonstrating reliable performance

Model Capabilities

Multilingual text embedding

Sentence similarity calculation

Text feature extraction

Cross-language information retrieval

Use Cases

Information retrieval

Cross-language document retrieval

Retrieving similar documents in different languages using a unified embedding space

Achieved 97-99% accuracy on the MTEB BUCC cross-language bitext mining task

Text classification

Multilingual sentiment analysis

Classifying sentiment orientation in texts of multiple languages

Achieved 46.5% accuracy on the MTEB EmotionClassification task

Product review classification

Classifying multilingual Amazon reviews

Achieved 47.56% accuracy for English on the MTEB AmazonReviewsClassification task

Question answering

Fact retrieval QA

Retrieving relevant documents from a knowledge base for questions

Achieved 84.32% MRR@10 on the MTEB HotpotQA task

🚀 Multilingual-e5-Large Model

This document presents the details of the multilingual-e5-large model, including its base model, supported languages, license, tags, and performance results on various tasks and datasets.

📚 Documentation

Model Information

Property	Details
Base Model	Hiveurban/multilingual-e5-large-pooled
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
License	mit
Tags	mteb, Sentence Transformers, sentence-similarity, feature-extraction, sentence-transformers, llama-cpp, gguf-my-repo

Model Performance Results

Model Name: multilingual-e5-large

The model has been evaluated on multiple tasks and datasets, with the following results:

Classification Tasks

Dataset Name	Accuracy	AP	F1
MTEB AmazonCounterfactualClassification (en)	79.05970149253731	43.486574390835635	73.32700092140148
MTEB AmazonCounterfactualClassification (de)	71.22055674518201	81.55756710830498	69.28271787752661
MTEB AmazonCounterfactualClassification (en-ext)	80.41979010494754	29.34879922376344	67.62475449011278
MTEB AmazonCounterfactualClassification (ja)	77.8372591006424	26.557560591210738	64.96619417368707
MTEB AmazonPolarityClassification	93.489875	90.98758636917603	93.48554819717332
MTEB AmazonReviewsClassification (en)	47.564	-	46.75122173518047
MTEB AmazonReviewsClassification (de)	45.400000000000006	-	44.17195682400632
MTEB AmazonReviewsClassification (es)	43.068	-	42.38155696855596
MTEB AmazonReviewsClassification (fr)	41.89	-	40.84407321682663
MTEB AmazonReviewsClassification (ja)	40.120000000000005	-	39.522976223819114
MTEB AmazonReviewsClassification (zh)	38.832	-	38.0392533394713
MTEB Banking77Classification	84.72727272727273	-	84.67672206031433

Retrieval Tasks

Dataset Name	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	30.725	46.055	46.900999999999996	46.911	41.548	44.297	31.152	46.231	47.07	47.08	41.738	44.468999999999994	30.725	54.379999999999995	58.138	58.389	45.156	50.123	30.725	8.087	0.9769999999999999	0.1	18.54	13.542000000000002	30.725	80.868	97.653	99.57300000000001	55.619	67.71000000000001
MTEB CQADupstackRetrieval	25.558249999999997	34.44425000000001	35.59833333333333	35.706916666666665	31.691749999999995	33.252916666666664	30.252666666666666	38.60675	39.42666666666666	39.48408333333334	36.17441666666665	37.56275	30.252666666666666	39.683	44.68541666666667	46.94316666666668	34.961749999999995	37.215666666666664	30.252666666666666	6.904166666666667	1.0989999999999995	0.14733333333333334	16.037666666666667	11.413583333333333	25.558249999999997	51.13341666666666	73.08366666666667	88.79483333333334	37.989083333333326	43.787833333333325
MTEB ClimateFEVER	10.338	18.360000000000003	19.942	20.134	15.174000000000001	16.830000000000002	23.257	33.768	34.707	34.766000000000005	30.977	32.528	23.257	25.733	-	-	-	-	23.257	-	-	-	-	-	10.338	-	-	-	-	-

Clustering Tasks

Dataset Name	V-Measure
MTEB ArxivClusteringP2P	44.30960650674069
MTEB ArxivClusteringS2S	38.427074197498996
MTEB BiorxivClusteringP2P	35.34220182511161
MTEB BiorxivClusteringS2S	33.4987096128766

Reranking Tasks

Dataset Name	MAP	MRR
MTEB AskUbuntuDupQuestions	60.28270056031872	74.38332673789738

STS Tasks

Dataset Name	Cosine Similarity Pearson	Cosine Similarity Spearman	Euclidean Pearson	Euclidean Spearman	Manhattan Pearson	Manhattan Spearman
MTEB BIOSSES	84.05942144105269	82.51212105850809	81.95639829909122	82.3717564144213	81.79273425468256	82.20066817871039

Bitext Mining Tasks

Dataset Name	Accuracy	F1	Precision	Recall
MTEB BUCC (de-en)	99.46764091858039	99.37717466945023	99.33194154488518	99.46764091858039
MTEB BUCC (fr-en)	98.29407880255337	98.11248073959938	98.02443319392472	98.29407880255337
MTEB BUCC (ru-en)	97.79009352268791	97.5176076665512	97.38136473848286	97.79009352268791
MTEB BUCC (zh-en)	99.26276987888363	99.20133403545726	99.17500438827453	99.26276987888363

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