Jina Embeddings V3
J
Jina Embeddings V3
Developed by arkohut
Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, specializing in sentence similarity calculation and feature extraction tasks.
Text Embedding
Transformers Supports Multiple Languages#Multilingual Embedding#Sentence Similarity Calculation#Cross-Language Retrieval

Downloads 506
Release Time : 10/23/2024
Model Overview
This model is primarily used for generating high-quality sentence embeddings, suitable for text similarity calculation, information retrieval, and semantic search tasks in multilingual environments.
Model Features
Multilingual Support
Supports sentence embedding calculation for over 100 languages, including mainstream and some niche languages
Efficient Feature Extraction
Capable of quickly generating high-quality sentence embeddings
Sentence Similarity Calculation
Specially optimized for computing semantic similarity between sentences
Model Capabilities
Multilingual text embedding
Sentence similarity calculation
Semantic feature extraction
Information retrieval
Use Cases
Information Retrieval
Cross-language document search
Implement semantic search functionality in multilingual document libraries
Text Similarity
QA system matching
Calculate semantic similarity between user questions and knowledge base questions
🚀 jina-embeddings-v3
This is a multilingual model that supports a wide range of languages and can be used for tasks such as feature extraction, sentence similarity calculation, etc. It has been tested on various datasets in the MTEB benchmark.
📚 Documentation
Model Information
Property | Details |
---|---|
Model Type | jina-embeddings-v3 |
Training Data | Not specified |
Supported Languages | 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 | cc-by-nc-4.0 |
Library Name | transformers |
Inference | false |
Evaluation Results
MTEB AFQMC (default)
- Task Type: STS
- Split: validation | Metric | Value | |--------|-------| | cosine_pearson | 41.74237700998808 | | cosine_spearman | 43.4726782647566 | | euclidean_pearson | 42.244585459479964 | | euclidean_spearman | 43.525070045169606 | | main_score | 43.4726782647566 | | manhattan_pearson | 42.04616728224863 | | manhattan_spearman | 43.308828270754645 | | pearson | 41.74237700998808 | | spearman | 43.4726782647566 |
MTEB ArguAna-PL (default)
- Task Type: Retrieval
- Split: test | Metric | Value | |--------|-------| | main_score | 50.117999999999995 | | map_at_1 | 24.253 | | map_at_10 | 40.725 | | map_at_100 | 41.699999999999996 | | map_at_1000 | 41.707 | | map_at_20 | 41.467999999999996 | | map_at_3 | 35.467 | | map_at_5 | 38.291 | | mrr_at_1 | 24.751066856330013 | | mrr_at_10 | 40.91063808169072 | | mrr_at_100 | 41.885497923928675 | | mrr_at_1000 | 41.89301098419842 | | mrr_at_20 | 41.653552355442514 | | mrr_at_3 | 35.656709340919775 | | mrr_at_5 | 38.466097676623946 | | nauc_map_at_1000_diff1 | 7.503000359807567 | | nauc_map_at_1000_max | -11.030405164830546 | | nauc_map_at_1000_std | -8.902792782585117 | | nauc_map_at_100_diff1 | 7.509899249593199 | | nauc_map_at_100_max | -11.023581259404406 | | nauc_map_at_100_std | -8.892241185067272 | | nauc_map_at_10_diff1 | 7.24369711881512 | | nauc_map_at_10_max | -10.810000200433278 | | nauc_map_at_10_std | -8.987230542165776 | | nauc_map_at_1_diff1 | 11.37175831832417 | | nauc_map_at_1_max | -13.315221903223055 | | nauc_map_at_1_std | -9.398199605510275 | | nauc_map_at_20_diff1 | 7.477364530860648 | | nauc_map_at_20_max | -10.901251218105566 | | nauc_map_at_20_std | -8.868148116405925 | | nauc_map_at_3_diff1 | 6.555548802174882 | | nauc_map_at_3_max | -12.247274800542934 | | nauc_map_at_3_std | -9.879475250984811 | | nauc_map_at_5_diff1 | 7.426588563355882 | | nauc_map_at_5_max | -11.347695686001805 | | nauc_map_at_5_std | -9.34441892203972 | | nauc_mrr_at_1000_diff1 | 5.99737552143614 | | nauc_mrr_at_1000_max | -11.327205136505727 | | nauc_mrr_at_1000_std | -8.791079115519503 | | nauc_mrr_at_100_diff1 | 6.004622525255784 | | nauc_mrr_at_100_max | -11.320336759899723 | | nauc_mrr_at_100_std | -8.780602249831777 | | nauc_mrr_at_10_diff1 | 5.783623516930227 | | nauc_mrr_at_10_max | -11.095971693467078 | | nauc_mrr_at_10_std | -8.877242032013582 | | nauc_mrr_at_1_diff1 | 9.694937537703797 | | nauc_mrr_at_1_max | -12.531905083727912 | | nauc_mrr_at_1_std | -8.903992940100146 | | nauc_mrr_at_20_diff1 | 5.984841206233873 | | nauc_mrr_at_20_max | -11.195236951048969 | | nauc_mrr_at_20_std | -8.757266039186018 | | nauc_mrr_at_3_diff1 | 5.114333824261379 | | nauc_mrr_at_3_max | -12.64809799843464 | | nauc_mrr_at_3_std | -9.791146138025184 | | nauc_mrr_at_5_diff1 | 5.88941606224512 | | nauc_mrr_at_5_max | -11.763903418071918 | | nauc_mrr_at_5_std | -9.279175712709446 | | nauc_ndcg_at_1000_diff1 | 7.076950652226086 | | nauc_ndcg_at_1000_max | -10.386482092087371 | | nauc_ndcg_at_1000_std | -8.309190917074046 | | nauc_ndcg_at_100_diff1 | 7.2329220284865245 | | nauc_ndcg_at_100_max | -10.208048403220337 | | nauc_ndcg_at_100_std | -7.997975874274613 | | nauc_ndcg_at_10_diff1 | 6.065391100006953 | | nauc_ndcg_at_10_max | -9.046164377601153 | | nauc_ndcg_at_10_std | -8.34724889697153 | | nauc_ndcg_at_1_diff1 | 11.37175831832417 | | nauc_ndcg_at_1_max | -13.315221903223055 | | nauc_ndcg_at_1_std | -9.398199605510275 | | nauc_ndcg_at_20_diff1 | 6.949389989202601 | | nauc_ndcg_at_20_max | -9.35740451760307 | | nauc_ndcg_at_20_std | -7.761295171828212 | | nauc_ndcg_at_3_diff1 | 5.051471796151364 | | nauc_ndcg_at_3_max | -12.158763333711653 | | nauc_ndcg_at_3_std | -10.078902544421926 | | nauc_ndcg_at_5_diff1 | 6.527454512611454 | | nauc_ndcg_at_5_max | -10.525118233848586 | | nauc_ndcg_at_5_std | -9.120055125584031 | | nauc_precision_at_1000_diff1 | -10.6495668199151 | | nauc_precision_at_1000_max | 12.070656425217841 | | nauc_precision_at_1000_std | 55.844551709649004 | | nauc_precision_at_100_diff1 | 19.206967129266285 | | nauc_precision_at_100_max | 16.296851020813456 | | nauc_precision_at_100_std | 45.60378984257811 | | nauc_precision_at_10_diff1 | 0.6490335354304879 | | nauc_precision_at_10_max | 0.5757198255366447 | | nauc_precision_at_10_std | -4.875847131691451 | | nauc_precision_at_1_diff1 | 11.37175831832417 | | nauc_precision_at_1_max | -13.315221903223055 | | nauc_precision_at_1_std | -9.398199605510275 | | nauc_precision_at_20_diff1 | 4.899369866929203 | | nauc_precision_at_20_max | 5.988537297189552 | | nauc_precision_at_20_std | 4.830900387582837 | | nauc_precision_at_3_diff1 | 0.8791156910997744 | | nauc_precision_at_3_max | -11.983373635905993 | | nauc_precision_at_3_std | -10.646185111581257 | | nauc_precision_at_5_diff1 | 3.9314486166548432 | | nauc_precision_at_5_max | -7.798591396895839 | | nauc_precision_at_5_std | -8.293043407234125 | | nauc_recall_at_1000_diff1 | -10.649566819918673 | | nauc_recall_at_1000_max | 12.070656425214647 | | nauc_recall_at_1000_std | 55.84455170965023 | | nauc_recall_at_100_diff1 | 19.206967129265127 | | nauc_recall_at_100_max | 16.296851020813722 | | nauc_recall_at_100_std | 45.60378984257728 | | nauc_recall_at_10_diff1 | 0.6490335354304176 | | nauc_recall_at_10_max | 0.5757198255366095 | | nauc_recall_at_10_std | -4.875847131691468 | | nauc_recall_at_1_diff1 | 11.37175831832417 | | nauc_recall_at_1_max | -13.315221903223055 | | nauc_recall_at_1_std | -9.398199605510275 | | nauc_recall_at_20_diff1 | 4.899369866929402 | | nauc_recall_at_20_max | 5.98853729718968 | | nauc_recall_at_20_std | 4.830900387582967 | | nauc_recall_at_3_diff1 | 0.8791156910997652 | | nauc_recall_at_3_max | -11.983373635905997 | | nauc_recall_at_3_std | -10.64618511158124 | | nauc_recall_at_5_diff1 | 3.9314486166548472 | | nauc_recall_at_5_max | -7.7985913968958585 | | nauc_recall_at_5_std | -8.293043407234132 | | ndcg_at_1 | 24.253 | | ndcg_at_10 | 50.117999999999995 | | ndcg_at_100 | 54.291999999999994 | | ndcg_at_1000 | 54.44799999999999 | | ndcg_at_20 | 52.771 | | ndcg_at_3 | 39.296 | | ndcg_at_5 | 44.373000000000005 | | precision_at_1 | 24.253 | | precision_at_10 | 8.016 | | precision_at_100 | 0.984 | | precision_at_1000 | 0.1 | | precision_at_20 | 4.527 | | precision_at_3 | 16.808999999999997 | | precision_at_5 | 12.546 | | recall_at_1 | 24.253 | | recall_at_10 | 80.156 | | recall_at_100 | 98.43499999999999 | | recall_at_1000 | 99.57300000000001 | | recall_at_20 | 90.54100000000001 | | recall_at_3 | 50.427 | | recall_at_5 | 62.731 |
MTEB DBPedia-PL (default)
- Task Type: Retrieval
- Split: test | Metric | Value | |--------|-------| | main_score | 34.827000000000005 | | map_at_1 | 7.049999999999999 | | map_at_10 | 14.982999999999999 | | map_at_100 | 20.816000000000003 | | map_at_1000 | 22.33 | | map_at_20 | 17.272000000000002 | | map_at_3 | 10.661 | | map_at_5 | 12.498 | | mrr_at_1 | 57.25 | | mrr_at_10 | 65.81934523809524 | | mrr_at_100 | 66.2564203928212 | | mrr_at_1000 | 66.27993662923856 | | mrr_at_20 | 66.0732139130649 | | mrr_at_3 | 64.08333333333333 | | mrr_at_5 | 65.27083333333333 | | nauc_map_at_1000_diff1 | 16.41780871174038 | | nauc_map_at_1000_max | 30.193946325654654 | | nauc_map_at_1000_std | 31.46095497039037 | | nauc_map_at_100_diff1 | 18.57903165498531 | | nauc_map_at_100_max | 29.541476938623262 | | nauc_map_at_100_std | 28.228604103301052 | | nauc_map_at_10_diff1 | 24.109434489748946 | | nauc_map_at_10_max | 21.475954208048968 | | nauc_map_at_10_std | 9.964464537806988 | | nauc_map_at_1_diff1 | 38.67437644802124 | | nauc_map_at_1_max | 14.52136658726491 | | nauc_map_at_1_std | -2.8981666782088755 | | nauc_map_at_20_diff1 | 21.42547228801935 | | nauc_map_at_20_max | 25.04510402960458 | | nauc_map_at_20_std | 16.533079346431155 | | nauc_map_at_3_diff1 | 26.63648858245477 | | nauc_map_at_3_max | 13.632235789780415 | | nauc_map_at_3_std | -0.40129174577700716 | | nauc_map_at_5_diff1 | 24.513861031197933 | | nauc_map_at_5_max | 16.599888813946688 | | nauc_map_at_5_std | 3.4448514739556346 | | nauc_mrr_at_1000_diff1 | 36.57353464537154 | | nauc_mrr_at_1000_max | 55.34763483979515 | | nauc_mrr_at_1000_std | 40.3722796438533 | | ... | ... |
📄 License
This model is licensed under the cc-by-nc-4.0 license.
Jina Embeddings V3
Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, specializing in sentence similarity and feature extraction tasks.
Text Embedding
Transformers Supports Multiple Languages

J
jinaai
3.7M
911
Ms Marco MiniLM L6 V2
Apache-2.0
A cross-encoder model trained on the MS Marco passage ranking task for query-passage relevance scoring in information retrieval
Text Embedding English
M
cross-encoder
2.5M
86
Opensearch Neural Sparse Encoding Doc V2 Distill
Apache-2.0
A sparse retrieval model based on distillation technology, optimized for OpenSearch, supporting inference-free document encoding with improved search relevance and efficiency over V1
Text Embedding
Transformers English

O
opensearch-project
1.8M
7
Sapbert From PubMedBERT Fulltext
Apache-2.0
A biomedical entity representation model based on PubMedBERT, optimized for semantic relation capture through self-aligned pre-training
Text Embedding English
S
cambridgeltl
1.7M
49
Gte Large
MIT
GTE-Large is a powerful sentence transformer model focused on sentence similarity and text embedding tasks, excelling in multiple benchmark tests.
Text Embedding English
G
thenlper
1.5M
278
Gte Base En V1.5
Apache-2.0
GTE-base-en-v1.5 is an English sentence transformer model focused on sentence similarity tasks, excelling in multiple text embedding benchmarks.
Text Embedding
Transformers Supports Multiple Languages

G
Alibaba-NLP
1.5M
63
Gte Multilingual Base
Apache-2.0
GTE Multilingual Base is a multilingual sentence embedding model supporting over 50 languages, suitable for tasks like sentence similarity calculation.
Text Embedding
Transformers Supports Multiple Languages

G
Alibaba-NLP
1.2M
246
Polybert
polyBERT is a chemical language model designed to achieve fully machine-driven ultrafast polymer informatics. It maps PSMILES strings into 600-dimensional dense fingerprints to numerically represent polymer chemical structures.
Text Embedding
Transformers

P
kuelumbus
1.0M
5
Bert Base Turkish Cased Mean Nli Stsb Tr
Apache-2.0
A sentence embedding model based on Turkish BERT, optimized for semantic similarity tasks
Text Embedding
Transformers Other

B
emrecan
1.0M
40
GIST Small Embedding V0
MIT
A text embedding model fine-tuned based on BAAI/bge-small-en-v1.5, trained with the MEDI dataset and MTEB classification task datasets, optimized for query encoding in retrieval tasks.
Text Embedding
Safetensors English
G
avsolatorio
945.68k
29
Featured Recommended AI Models