π§
Multilingual E5 Large model by intfloat
β 35
π§
Model
Multilingual E5 Large
by intfloat
ID: hf-model--intfloat--multilingual-e5-large
Scale 0.56B
Context Window 4.096K
Downloads 3.3M
FNI Rank 35
Percentile Top 0%
Activity
β 0.0%
--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: ...
Audited 35 FNI Score
Tiny 0.56B Params
4k Context
Hot 3.3M Downloads
8G GPU ~2GB Est. VRAM
Dense XLMROBERTAMODEL Architecture
| Entity Passport | |
| Registry ID | hf-model--intfloat--multilingual-e5-large |
| Provider | huggingface |
πΎ
Compute Threshold
~1.7GB VRAM
Interactive
Analyze Hardware
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* Static estimation for 4-Bit Quantization.
π
Cite this model
Academic & Research Attribution
BibTeX
@misc{hf_model__intfloat__multilingual_e5_large,
author = {intfloat},
title = {Multilingual E5 Large Model},
year = {2026},
howpublished = {\url{https://huggingface.co/intfloat/multilingual-e5-large}},
note = {Accessed via Free2AITools Knowledge Fortress}
} APA Style
intfloat. (2026). Multilingual E5 Large [Model]. Free2AITools. https://huggingface.co/intfloat/multilingual-e5-large
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β‘ Quick Commands
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ollama run multilingual-e5-large π€ HF Download
huggingface-cli download intfloat/multilingual-e5-large 35.0
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π₯ Popularity (P) 0
π Velocity (V) 0
π‘οΈ Credibility (C) 0
π§ Utility (U) 0
Nexus Verified Data
π¬ Why this score?
The Nexus Index for Multilingual E5 Large aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.
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tags:
- mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
model-index: - name: multilingual-e5-large
results:- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
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:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
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:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
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:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
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:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
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:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 47.564 - type: f1
value: 46.75122173518047
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 45.400000000000006 - type: f1
value: 44.17195682400632
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 43.068 - type: f1
value: 42.38155696855596
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 41.89 - type: f1
value: 40.84407321682663
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 40.120000000000005 - type: f1
value: 39.522976223819114
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 38.832 - type: f1
value: 38.0392533394713
- type: accuracy
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB 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:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:- type: v_measure
value: 44.30960650674069
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:- type: v_measure
value: 38.427074197498996
- type: v_measure
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:- type: map
value: 60.28270056031872 - type: mrr
value: 74.38332673789738
- type: map
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
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:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
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:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
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:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
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:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
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:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:- type: accuracy
value: 84.72727272727273 - type: f1
value: 84.67672206031433
- type: accuracy
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:- type: v_measure
value: 35.34220182511161
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:- type: v_measure
value: 33.4987096128766
- type: v_measure
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
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:
type: climate-fever
name: MTEB ClimateFEVER
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:
type: dbpedia-entity
name: MTEB DBPedia
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:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:- type: accuracy
value: 46.5 - type: f1
value: 41.93833713984145
- type: accuracy
- task:
type: Retrieval
dataset:
type: fever
name: MTEB 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:
type: fiqa
name: MTEB FiQA2018
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:
type: hotpotqa
name: MTEB 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:
type: mteb/imdb
name: MTEB ImdbClassification
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:
type: msmarco
name: MTEB 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
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value: 38.391999999999996 - type: mrr_at_1000
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metrics:- type: map_at_1
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dataset:
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metrics:- type: map_at_1
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value: 86.443 - type: mrr_at_5
value: 87.10799999999999 - type: ndcg_at_1
value: 81.04 - type: ndcg_at_10
value: 88.181 - type: ndcg_at_100
value: 89.411 - type: ndcg_at_1000
value: 89.507 - type: ndcg_at_3
value: 85.28099999999999 - type: ndcg_at_5
value: 86.888 - type: precision_at_1
value: 81.04 - type: precision_at_10
value: 13.406 - type: precision_at_100
value: 1.5350000000000001 - type: precision_at_1000
value: 0.157 - type: precision_at_3
value: 37.31 - type: precision_at_5
value: 24.54 - type: recall_at_1
value: 70.352 - type: recall_at_10
value: 95.358 - type: recall_at_100
value: 99.541 - type: recall_at_1000
value: 99.984 - type: recall_at_3
value: 87.111 - type: recall_at_5
value: 91.643
- type: map_at_1
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:- type: v_measure
value: 46.54068723291946
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:- type: v_measure
value: 63.216287629895994
- type: v_measure
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 4.023000000000001 - type: map_at_10
value: 10.071 - type: map_at_100
value: 11.892 - type: map_at_1000
value: 12.196 - type: map_at_3
value: 7.234 - type: map_at_5
value: 8.613999999999999 - type: mrr_at_1
value: 19.900000000000002 - type: mrr_at_10
value: 30.516 - type: mrr_at_100
value: 31.656000000000002 - type: mrr_at_1000
value: 31.723000000000003 - type: mrr_at_3
value: 27.400000000000002 - type: mrr_at_5
value: 29.270000000000003 - type: ndcg_at_1
value: 19.900000000000002 - type: ndcg_at_10
value: 17.474 - type: ndcg_at_100
value: 25.020999999999997 - type: ndcg_at_1000
value: 30.728 - type: ndcg_at_3
value: 16.588 - type: ndcg_at_5
value: 14.498 - type: precision_at_1
value: 19.900000000000002 - type: precision_at_10
value: 9.139999999999999 - type: precision_at_100
value: 2.011 - type: precision_at_1000
value: 0.33899999999999997 - type: precision_at_3
value: 15.667 - type: precision_at_5
value: 12.839999999999998 - type: recall_at_1
value: 4.023000000000001 - type: recall_at_10
value: 18.497 - type: recall_at_100
value: 40.8 - type: recall_at_1000
value: 68.812 - type: recall_at_3
value: 9.508 - type: recall_at_5
value: 12.983
- type: map_at_1
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:- type: cos_sim_pearson
value: 83.967008785134 - type: cos_sim_spearman
value: 80.23142141101837 - type: euclidean_pearson
value: 81.20166064704539 - type: euclidean_spearman
value: 80.18961335654585 - type: manhattan_pearson
value: 81.13925443187625 - type: manhattan_spearman
value: 80.07948723044424
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:- type: cos_sim_pearson
value: 86.94262461316023 - type: cos_sim_spearman
value: 80.01596278563865 - type: euclidean_pearson
value: 83.80799622922581 - type: euclidean_spearman
value: 79.94984954947103 - type: manhattan_pearson
value: 83.68473841756281 - type: manhattan_spearman
value: 79.84990707951822
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:- type: cos_sim_pearson
value: 80.57346443146068 - type: cos_sim_spearman
value: 81.54689837570866 - type: euclidean_pearson
value: 81.10909881516007 - type: euclidean_spearman
value: 81.56746243261762 - type: manhattan_pearson
value: 80.87076036186582 - type: manhattan_spearman
value: 81.33074987964402
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:- type: cos_sim_pearson
value: 79.54733787179849 - type: cos_sim_spearman
value: 77.72202105610411 - type: euclidean_pearson
value: 78.9043595478849 - type: euclidean_spearman
value: 77.93422804309435 - type: manhattan_pearson
value: 78.58115121621368 - type: manhattan_spearman
value: 77.62508135122033
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:- type: cos_sim_pearson
value: 88.59880017237558 - type: cos_sim_spearman
value: 89.31088630824758 - type: euclidean_pearson
value: 88.47069261564656 - type: euclidean_spearman
value: 89.33581971465233 - type: manhattan_pearson
value: 88.40774264100956 - type: manhattan_spearman
value: 89.28657485627835
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:- type: cos_sim_pearson
value: 84.08055117917084 - type: cos_sim_spearman
value: 85.78491813080304 - type: euclidean_pearson
value: 84.99329155500392 - type: euclidean_spearman
value: 85.76728064677287 - type: manhattan_pearson
value: 84.87947428989587 - type: manhattan_spearman
value: 85.62429454917464
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 82.14190939287384 - type: cos_sim_spearman
value: 82.27331573306041 - type: euclidean_pearson
value: 81.891896953716 - type: euclidean_spearman
value: 82.37695542955998 - type: manhattan_pearson
value: 81.73123869460504 - type: manhattan_spearman
value: 82.19989168441421
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 76.84695301843362 - type: cos_sim_spearman
value: 77.87790986014461 - type: euclidean_pearson
value: 76.91981583106315 - type: euclidean_spearman
value: 77.88154772749589 - type: manhattan_pearson
value: 76.94953277451093 - type: manhattan_spearman
value: 77.80499230728604
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 75.44657840482016 - type: cos_sim_spearman
value: 75.05531095119674 - type: euclidean_pearson
value: 75.88161755829299 - type: euclidean_spearman
value: 74.73176238219332 - type: manhattan_pearson
value: 75.63984765635362 - type: manhattan_spearman
value: 74.86476440770737
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 85.64700140524133 - type: cos_sim_spearman
value: 86.16014210425672 - type: euclidean_pearson
value: 86.49086860843221 - type: euclidean_spearman
value: 86.09729326815614 - type: manhattan_pearson
value: 86.43406265125513 - type: manhattan_spearman
value: 86.17740150939994
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 87.91170098764921 - type: cos_sim_spearman
value: 88.12437004058931 - type: euclidean_pearson
value: 88.81828254494437 - type: euclidean_spearman
value: 88.14831794572122 - type: manhattan_pearson
value: 88.93442183448961 - type: manhattan_spearman
value: 88.15254630778304
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 72.91390577997292 - type: cos_sim_spearman
value: 71.22979457536074 - type: euclidean_pearson
value: 74.40314008106749 - type: euclidean_spearman
value: 72.54972136083246 - type: manhattan_pearson
value: 73.85687539530218 - type: manhattan_spearman
value: 72.09500771742637
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 80.9301067983089 - type: cos_sim_spearman
value: 80.74989828346473 - type: euclidean_pearson
value: 81.36781301814257 - type: euclidean_spearman
value: 80.9448819964426 - type: manhattan_pearson
value: 81.0351322685609 - type: manhattan_spearman
value: 80.70192121844177
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 87.13820465980005 - type: cos_sim_spearman
value: 86.73532498758757 - type: euclidean_pearson
value: 87.21329451846637 - type: euclidean_spearman
value: 86.57863198601002 - type: manhattan_pearson
value: 87.06973713818554 - type: manhattan_spearman
value: 86.47534918791499
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 85.48720108904415 - type: cos_sim_spearman
value: 85.62221757068387 - type: euclidean_pearson
value: 86.1010129512749 - type: euclidean_spearman
value: 85.86580966509942 - type: manhattan_pearson
value: 86.26800938808971 - type: manhattan_spearman
value: 85.88902721678429
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 83.98021347333516 - type: cos_sim_spearman
value: 84.53806553803501 - type: euclidean_pearson
value: 84.61483347248364 - type: euclidean_spearman
value: 85.14191408011702 - type: manhattan_pearson
value: 84.75297588825967 - type: manhattan_spearman
value: 85.33176753669242
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 84.51856644893233 - type: cos_sim_spearman
value: 85.27510748506413 - type: euclidean_pearson
value: 85.09886861540977 - type: euclidean_spearman
value: 85.62579245860887 - type: manhattan_pearson
value: 84.93017860464607 - type: manhattan_spearman
value: 85.5063988898453
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 62.581573200584195 - type: cos_sim_spearman
value: 63.05503590247928 - type: euclidean_pearson
value: 63.652564812602094 - type: euclidean_spearman
value: 62.64811520876156 - type: manhattan_pearson
value: 63.506842893061076 - type: manhattan_spearman
value: 62.51289573046917
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 48.2248801729127 - type: cos_sim_spearman
value: 56.5936604678561 - type: euclidean_pearson
value: 43.98149464089 - type: euclidean_spearman
value: 56.108561882423615 - type: manhattan_pearson
value: 43.86880305903564 - type: manhattan_spearman
value: 56.04671150510166
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 55.17564527009831 - type: cos_sim_spearman
value: 64.57978560979488 - type: euclidean_pearson
value: 58.8818330154583 - type: euclidean_spearman
value: 64.99214839071281 - type: manhattan_pearson
value: 58.72671436121381 - type: manhattan_spearman
value: 65.10713416616109
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 26.772131864023297 - type: cos_sim_spearman
value: 34.68200792408681 - type: euclidean_pearson
value: 16.68082419005441 - type: euclidean_spearman
value: 34.83099932652166 - type: manhattan_pearson
value: 16.52605949659529 - type: manhattan_spearman
value: 34.82075801399475
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 54.42415189043831 - type: cos_sim_spearman
value: 63.54594264576758 - type: euclidean_pearson
value: 57.36577498297745 - type: euclidean_spearman
value: 63.111466379158074 - type: manhattan_pearson
value: 57.584543715873885 - type: manhattan_spearman
value: 63.22361054139183
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 47.55216762405518 - type: cos_sim_spearman
value: 56.98670142896412 - type: euclidean_pearson
value: 50.15318757562699 - type: euclidean_spearman
value: 56.524941926541906 - type: manhattan_pearson
value: 49.955618528674904 - type: manhattan_spearman
value: 56.37102209240117
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 49.20540980338571 - type: cos_sim_spearman
value: 59.9009453504406 - type: euclidean_pearson
value: 49.557749853620535 - type: euclidean_spearman
value: 59.76631621172456 - type: manhattan_pearson
value: 49.62340591181147 - type: manhattan_spearman
value: 59.94224880322436
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 51.508169956576985 - type: cos_sim_spearman
value: 66.82461565306046 - type: euclidean_pearson
value: 56.2274426480083 - type: euclidean_spearman
value: 66.6775323848333 - type: manhattan_pearson
value: 55.98277796300661 - type: manhattan_spearman
value: 66.63669848497175
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 72.86478788045507 - type: cos_sim_spearman
value: 76.7946552053193 - type: euclidean_pearson
value: 75.01598530490269 - type: euclidean_spearman
value: 76.83618917858281 - type: manhattan_pearson
value: 74.68337628304332 - type: manhattan_spearman
value: 76.57480204017773
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 55.922619099401984 - type: cos_sim_spearman
value: 56.599362477240774 - type: euclidean_pearson
value: 56.68307052369783 - type: euclidean_spearman
value: 54.28760436777401 - type: manhattan_pearson
value: 56.67763566500681 - type: manhattan_spearman
value: 53.94619541711359
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 66.74357206710913 - type: cos_sim_spearman
value: 72.5208244925311 - type: euclidean_pearson
value: 67.49254562186032 - type: euclidean_spearman
value: 72.02469076238683 - type: manhattan_pearson
value: 67.45251772238085 - type: manhattan_spearman
value: 72.05538819984538
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 71.25734330033191 - type: cos_sim_spearman
value: 76.98349083946823 - type: euclidean_pearson
value: 73.71642838667736 - type: euclidean_spearman
value: 77.01715504651384 - type: manhattan_pearson
value: 73.61712711868105 - type: manhattan_spearman
value: 77.01392571153896
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 63.18215462781212 - type: cos_sim_spearman
value: 65.54373266117607 - type: euclidean_pearson
value: 64.54126095439005 - type: euclidean_spearman
value: 65.30410369102711 - type: manhattan_pearson
value: 63.50332221148234 - type: manhattan_spearman
value: 64.3455878104313
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 62.30509221440029 - type: cos_sim_spearman
value: 65.99582704642478 - type: euclidean_pearson
value: 63.43818859884195 - type: euclidean_spearman
value: 66.83172582815764 - type: manhattan_pearson
value: 63.055779168508764 - type: manhattan_spearman
value: 65.49585020501449
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 59.587830825340404 - type: cos_sim_spearman
value: 68.93467614588089 - type: euclidean_pearson
value: 62.3073527367404 - type: euclidean_spearman
value: 69.69758171553175 - type: manhattan_pearson
value: 61.9074580815789 - type: manhattan_spearman
value: 69.57696375597865
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 57.143220125577066 - type: cos_sim_spearman
value: 67.78857859159226 - type: euclidean_pearson
value: 55.58225107923733 - type: euclidean_spearman
value: 67.80662907184563 - type: manhattan_pearson
value: 56.24953502726514 - type: manhattan_spearman
value: 67.98262125431616
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 21.826928900322066 - type: cos_sim_spearman
value: 49.578506634400405 - type: euclidean_pearson
value: 27.939890138843214 - type: euclidean_spearman
value: 52.71950519136242 - type: manhattan_pearson
value: 26.39878683847546 - type: manhattan_spearman
value: 47.54609580342499
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 57.27603854632001 - type: cos_sim_spearman
value: 50.709255283710995 - type: euclidean_pearson
value: 59.5419024445929 - type: euclidean_spearman
value: 50.709255283710995 - type: manhattan_pearson
value: 59.03256832438492 - type: manhattan_spearman
value: 61.97797868009122
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:- type: cos_sim_pearson
value: 85.00757054859712 - type: cos_sim_spearman
value: 87.29283629622222 - type: euclidean_pearson
value: 86.54824171775536 - type: euclidean_spearman
value: 87.24364730491402 - type: manhattan_pearson
value: 86.5062156915074 - type: manhattan_spearman
value: 87.15052170378574
- type: cos_sim_pearson
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:- type: map
value: 82.03549357197389 - type: mrr
value: 95.05437645143527
- type: map
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 57.260999999999996 - type: map_at_10
value: 66.259 - type: map_at_100
value: 66.884 - type: map_at_1000
value: 66.912 - type: map_at_3
value: 63.685 - type: map_at_5
value: 65.35499999999999 - type: mrr_at_1
value: 60.333000000000006 - type: mrr_at_10
value: 67.5 - type: mrr_at_100
value: 68.013 - type: mrr_at_1000
value: 68.038 - type: mrr_at_3
value: 65.61099999999999 - type: mrr_at_5
value: 66.861 - type: ndcg_at_1
value: 60.333000000000006 - type: ndcg_at_10
value: 70.41 - type: ndcg_at_100
value: 73.10600000000001 - type: ndcg_at_1000
value: 73.846 - type: ndcg_at_3
value: 66.133 - type: ndcg_at_5
value: 68.499 - type: precision_at_1
value: 60.333000000000006 - type: precision_at_10
value: 9.232999999999999 - type: precision_at_100
value: 1.0630000000000002 - type: precision_at_1000
value: 0.11299999999999999 - type: precision_at_3
value: 25.667 - type: precision_at_5
value: 17.067 - type: recall_at_1
value: 57.260999999999996 - type: recall_at_10
value: 81.94399999999999 - type: recall_at_100
value: 93.867 - type: recall_at_1000
value: 99.667 - type: recall_at_3
value: 70.339 - type: recall_at_5
value: 76.25
- type: map_at_1
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:- type: cos_sim_accuracy
value: 99.74356435643564 - type: cos_sim_ap
value: 93.13411948212683 - type: cos_sim_f1
value: 86.80521991300147 - type: cos_sim_precision
value: 84.00374181478017 - type: cos_sim_recall
value: 89.8 - type: dot_accuracy
value: 99.67920792079208 - type: dot_ap
value: 89.27277565444479 - type: dot_f1
value: 83.9276990718124 - type: dot_precision
value: 82.04393505253104 - type: dot_recall
value: 85.9 - type: euclidean_accuracy
value: 99.74257425742574 - type: euclidean_ap
value: 93.17993008259062 - type: euclidean_f1
value: 86.69396110542476 - type: euclidean_precision
value: 88.78406708595388 - type: euclidean_recall
value: 84.7 - type: manhattan_accuracy
value: 99.74257425742574 - type: manhattan_ap
value: 93.14413755550099 - type: manhattan_f1
value: 86.82483594144371 - type: manhattan_precision
value: 87.66564729867483 - type: manhattan_recall
value: 86 - type: max_accuracy
value: 99.74356435643564 - type: max_ap
value: 93.17993008259062 - type: max_f1
value: 86.82483594144371
- type: cos_sim_accuracy
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:- type: v_measure
value: 57.525863806168566
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:- type: v_measure
value: 32.68850574423839
- type: v_measure
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:- type: map
value: 49.71580650644033 - type: mrr
value: 50.50971903913081
- type: map
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:- type: cos_sim_pearson
value: 29.152190498799484 - type: cos_sim_spearman
value: 29.686180371952727 - type: dot_pearson
value: 27.248664793816342 - type: dot_spearman
value: 28.37748983721745
- type: cos_sim_pearson
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 0.20400000000000001 - type: map_at_10
value: 1.6209999999999998 - type: map_at_100
value: 9.690999999999999 - type: map_at_1000
value: 23.733 - type: map_at_3
value: 0.575 - type: map_at_5
value: 0.885 - type: mrr_at_1
value: 78 - type: mrr_at_10
value: 86.56700000000001 - type: mrr_at_100
value: 86.56700000000001 - type: mrr_at_1000
value: 86.56700000000001 - type: mrr_at_3
value: 85.667 - type: mrr_at_5
value: 86.56700000000001 - type: ndcg_at_1
value: 76 - type: ndcg_at_10
value: 71.326 - type: ndcg_at_100
value: 54.208999999999996 - type: ndcg_at_1000
value: 49.252 - type: ndcg_at_3
value: 74.235 - type: ndcg_at_5
value: 73.833 - type: precision_at_1
value: 78 - type: precision_at_10
value: 74.8 - type: precision_at_100
value: 55.50000000000001 - type: precision_at_1000
value: 21.836 - type: precision_at_3
value: 78 - type: precision_at_5
value: 78 - type: recall_at_1
value: 0.20400000000000001 - type: recall_at_10
value: 1.894 - type: recall_at_100
value: 13.245999999999999 - type: recall_at_1000
value: 46.373 - type: recall_at_3
value: 0.613 - type: recall_at_5
value: 0.991
- type: map_at_1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 95.89999999999999 - type: f1
value: 94.69999999999999 - type: precision
value: 94.11666666666667 - type: recall
value: 95.89999999999999
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 68.20809248554913 - type: f1
value: 63.431048720066066 - type: precision
value: 61.69143958161298 - type: recall
value: 68.20809248554913
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 71.21951219512195 - type: f1
value: 66.82926829268293 - type: precision
value: 65.1260162601626 - type: recall
value: 71.21951219512195
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 97.2 - type: f1
value: 96.26666666666667 - type: precision
value: 95.8 - type: recall
value: 97.2
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 99.3 - type: f1
value: 99.06666666666666 - type: precision
value: 98.95 - type: recall
value: 99.3
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 97.39999999999999 - type: f1
value: 96.63333333333333 - type: precision
value: 96.26666666666668 - type: recall
value: 97.39999999999999
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 96 - type: f1
value: 94.86666666666666 - type: precision
value: 94.31666666666668 - type: recall
value: 96
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 47.01492537313433 - type: f1
value: 40.178867566927266 - type: precision
value: 38.179295828549556 - type: recall
value: 47.01492537313433
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 86.5 - type: f1
value: 83.62537480063796 - type: precision
value: 82.44555555555554 - type: recall
value: 86.5
- type: accuracy
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:- type: accuracy
value: 80.48780487804879 - type: f1
value: 75.45644599303138 - type: precision
value: 73.37398373983739 - type: recall
value: 80.48780487804879
- type: accuracy
- task:
type: BitextMining
dataset:
- task:
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π Limitations & Considerations
- β’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
- β’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
- β’ FNI scores are relative rankings and may change as new models are added.
- β’ Source: Unknown
Top Tier
Social Proof
HuggingFace Hub
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π Daily sync (03:00 UTC)
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π‘οΈ Model Transparency Report
Verified data manifest for traceability and transparency.
100% Data Disclosure Active
π Identity & Source
- id
- hf-model--intfloat--multilingual-e5-large
- source
- huggingface
- author
- intfloat
- tags
- sentence-transformerspytorchonnxsafetensorsopenvinoxlm-robertamtebsentence transformerssentence-similarityfeature-extractionmultilingualafamarasazbebgbnbrbscacscydadeeleneoeseteufafifrfygagdglguhahehihrhuhyidisitjajvkakkkmknkokukylaloltlvmgmkmlmnmrmsmynenlnoomorpaplpsptrorusasdsiskslsosqsrsusvswtatethtltrugukuruzvixhyizharxiv:2402.05672arxiv:2108.08787arxiv:2104.08663arxiv:2210.07316license:mitmodel-indextext-embeddings-inferenceendpoints_compatibledeploy:azureregion:us
βοΈ Technical Specs
- architecture
- XLMRobertaModel
- params billions
- 0.56
- context length
- 4,096
- pipeline tag
- feature-extraction
- vram gb
- 1.7
- vram is estimated
- true
- vram formula
- VRAM β (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)
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