模型介绍
内容详情
替代品
模型简介
这是一个多语言的句子嵌入模型，基于E5架构，能够处理多种语言的文本并生成高质量的句子嵌入表示。
模型特点
多语言支持
支持超过100种语言的文本处理，包括主流语言和部分小众语言
高性能句子嵌入
在多种语言的句子相似度任务上表现出色，能够生成高质量的句子向量表示
MTEB基准测试验证
在MTEB(Massive Text Embedding Benchmark)多个任务上进行了广泛评估，性能可靠
模型能力
多语言文本嵌入
句子相似度计算
文本特征提取
跨语言信息检索
使用案例
信息检索
跨语言文档检索
使用统一的嵌入空间检索不同语言的相似文档
在MTEB BUCC跨语言bitext mining任务上达到97-99%的准确率
文本分类
多语言情感分析
对多种语言的文本进行情感倾向分类
在MTEB EmotionClassification任务上达到46.5%准确率
产品评论分类
对亚马逊多语言评论进行分类
在MTEB AmazonReviewsClassification任务上英语达到47.56%准确率
问答系统
事实检索问答
从知识库中检索与问题相关的文档
在MTEB HotpotQA任务上达到84.32%的MRR@10
base_model: Hiveurban/multilingual-e5-large-pooled
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
tags: - mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
- llama-cpp
- gguf-my-repo
model-index: - name: multilingual-e5-large
results:- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:- type: accuracy
value: 79.05970149253731 - type: ap
value: 43.486574390835635 - type: f1
value: 73.32700092140148
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:- type: accuracy
value: 71.22055674518201 - type: ap
value: 81.55756710830498 - type: f1
value: 69.28271787752661
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:- type: accuracy
value: 80.41979010494754 - type: ap
value: 29.34879922376344 - type: f1
value: 67.62475449011278
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:- type: accuracy
value: 77.8372591006424 - type: ap
value: 26.557560591210738 - type: f1
value: 64.96619417368707
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:- type: accuracy
value: 93.489875 - type: ap
value: 90.98758636917603 - type: f1
value: 93.48554819717332
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 47.564 - type: f1
value: 46.75122173518047
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 45.400000000000006 - type: f1
value: 44.17195682400632
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 43.068 - type: f1
value: 42.38155696855596
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 41.89 - type: f1
value: 40.84407321682663
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 40.120000000000005 - type: f1
value: 39.522976223819114
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 38.832 - type: f1
value: 38.0392533394713
- type: accuracy
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:- type: v_measure
value: 44.30960650674069
- type: v_measure
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:- type: v_measure
value: 38.427074197498996
- type: v_measure
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:- type: map
value: 60.28270056031872 - type: mrr
value: 74.38332673789738
- type: map
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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
- type: cos_sim_pearson
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:- type: accuracy
value: 99.46764091858039 - type: f1
value: 99.37717466945023 - type: precision
value: 99.33194154488518 - type: recall
value: 99.46764091858039
- type: accuracy
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:- type: accuracy
value: 98.29407880255337 - type: f1
value: 98.11248073959938 - type: precision
value: 98.02443319392472 - type: recall
value: 98.29407880255337
- type: accuracy
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:- type: accuracy
value: 97.79009352268791 - type: f1
value: 97.5176076665512 - type: precision
value: 97.38136473848286 - type: recall
value: 97.79009352268791
- type: accuracy
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:- type: accuracy
value: 99.26276987888363 - type: f1
value: 99.20133403545726 - type: precision
value: 99.17500438827453 - type: recall
value: 99.26276987888363
- type: accuracy
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:- type: accuracy
value: 84.72727272727273 - type: f1
value: 84.67672206031433
- type: accuracy
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:- type: v_measure
value: 35.34220182511161
- type: v_measure
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:- type: v_measure
value: 33.4987096128766
- type: v_measure
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:- type: accuracy
value: 46.5 - type: f1
value: 41.93833713984145
- type: accuracy
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
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
- type: map_at_1
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:- type: accuracy
value: 90.23160000000001 - type: ap
value: 85.5674856808308 - type: f1
value: 90.18033354786317
- type: accuracy
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
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
- type: map_at_1
- task:
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