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EN
Jina Embeddings V3
J
Jina Embeddings V3
Developed by Daxtra
Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, focusing on sentence similarity calculation and feature extraction tasks.
Downloads 55
Release Time : 1/28/2025
Model Overview
Based on the sentence-transformers architecture, this model can convert text into high-dimensional vector representations, suitable for tasks such as information retrieval, semantic search, and text clustering.
Model Features
Multilingual support
Supports text embeddings for over 100 languages, including major and some niche languages
Sentence similarity calculation
Accurately calculates semantic similarity between different sentences
MTEB benchmark
Performs well on multiple MTEB benchmark tasks
Model Capabilities
Text feature extraction
Sentence similarity calculation
Multilingual text processing
Semantic search
Use Cases
Information retrieval
Cross-lingual document retrieval
Finding semantically similar documents in a multilingual document repository
Semantic search
Multilingual semantic search
Building a semantic search engine supporting multiple languages
## 🚀 jina-embeddings-v3
*A multilingual model for feature extraction and sentence similarity tasks, with performance evaluations on various MTEB datasets.*
## 📚 Documentation
### Model Information
| Property | Details |
|----------|---------|
| Model Type | jina-embeddings-v3 |
| Library Name | transformers |
| License | cc-by-nc-4.0 |
| 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 |
| Inference | false |
### Evaluation Results
#### MTEB AFQMC (default)
- **Task Type**: STS
| 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
| 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
| 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 |
| nauc_mrr_at_100_diff1 | 36.555989566513134 |
| nauc_mrr_at_100_max | 55.347805216808396 |
| nauc_mrr_at_100_std | 40.38465945075711 |
| nauc_mrr_at_10_diff1 | 36.771572999261984 |
| nauc_mrr_at_10_max | 55.41239897909165 |
| nauc_mrr_at_10_std | 40.52058934624793 |
| nauc_mrr_at_1_diff1 | 38.2472828531032 |
| nauc_mrr_at_1_max | 51.528473828685705 |
| nauc_mrr_at_1_std | 33.03676467942882 |
| nauc_mrr_at_20_diff1 | 36.642602571889036 |
| nauc_mrr_at_20_max | 55.3763342076553 |
| nauc_mrr_at_20_std | 40.41520090500838 |
| nauc_mrr_at_3_diff1 | 36.79451847426628 |
| nauc_mrr_at_3_max | 54.59778581826193 |
| nauc_mrr_at_3_std | 39.48392075873095 |
| nauc_mrr_at_5_diff1 | 36.92150807529304 |
| nauc_mrr_at_5_max | 55.03553978718272 |
| nauc_mrr_at_5_std | 40.20147745489917 |
| nauc_ndcg_at_1000_diff1 | 21.843092744321268 |
| nauc_ndcg_at_1000_max | 44.93275990394279 |
| nauc_ndcg_at_1000_std | 47.09186225236347 |
| nauc_ndcg_at_100_diff1 | 25.180282568979095 |
| nauc_ndcg_at_100_max | 41.737709709508394 |
| nauc_ndcg_at_100_std | 38.80950644139446 |
| nauc_ndcg_at_10_diff1 | 24.108368037214046 |
| nauc_ndcg_at_10_max | 41.29298370689967 |
| nauc_ndcg_at_10_std | 35.06450769738732 |
| nauc_ndcg_at_1_diff1 | 35.51010679525079 |
| nauc_ndcg_at_1_max | 42.40790024212412 |
| nauc_ndcg_at_1_std | 26.696412036243157 |
| nauc_ndcg_at_20_diff1 | 23.909989673256195 |
| nauc_ndcg_at_20_max | 39.78444647091927 |
| nauc_ndcg_at_20_std | 33.39544470364529 |
| nauc_ndcg_at_3_diff1 | 22.50484297956035 |
| nauc_ndcg_at_3_max | 39.14551926034168 |
| nauc_ndcg_at_3_std | 30.330135925392014 |
| nauc_ndcg_at_5_diff1 | 21.7798872028265 |
| nauc_ndcg_at_5_max | 40.23856975248015 |
| nauc_ndcg_at_5_std | 32.438381067440396 |
| nauc_precision_at_1000_diff1 | -21.62692442272279 |
| nauc_precision_at_1000_max | 0.9689046974430882 |
| nauc_precision_at_1000_std | 18.54001058230465 |
| nauc_precision_at_100_diff1 | -10.132258779856192 |
| nauc_precision_at_100_max | 23.74516110444681 |
| nauc_precision_at_100_std | 47.03416663319965 |
| nauc_precision_at_10_diff1 | 1.543656509571949 |
| nauc_precision_at_10_max | 36.98864812757555 |
| nauc_precision_at_10_std | 46.56427199077426 |
| nauc_precision_at_1_diff1 | 38.2472828531032 |
| nauc_precision_at_1_max | 51.528473828685705 |
| nauc_precision_at_1_std | 33.03676467942882 |
| nauc_precision_at_20_diff1 | -4.612864872734335 |
| nauc_precision_at_20_max | 34.03565449182125 |
| nauc_precision_at_20_std | 48.880727648349534 |
| nauc_precision_at_3_diff1 | 6.360850444467829 |
| nauc_precision_at_3_max | 36.25816942368427 |
| nauc_precision_at_3_std | 34.48882647419187 |
| nauc_precision_at_5_diff1 | 2.6445596936740037 |
| nauc_precision_at_5_max | 37.174463388899056 |
| nauc_precision_at_5_std | 40.25254370626113 |
| nauc_recall_at_1000_diff1 | 13.041227176748077 |
| nauc_recall_at_1000_max | 39.722336427072094 |
| nauc_recall_at_1000_std | 52.04032890059214 |
| nauc_recall_at_100_diff1 | 18.286096899139153 |
| nauc_recall_at_100_max | 34.072389201930314 |
| nauc_recall_at_100_std | 37.73637623416653 |
| nauc_recall_at_10_diff1 | 22.35560419280504 |
| nauc_recall_at_10_max | 19.727247199595197 |
| nauc_recall_at_10_std | 8.58498575109203 |
| nauc_recall_at_1_diff1 | 38.67437644802124 |
| nauc_recall_at_1_max | 14.52136658726491 |
| nauc_recall_at_1_std | -2.8981666782088755 |
| nauc_recall_at_20_diff1 | 19.026320886902916 |
| nauc_recall_at_20_max | 22.753562309469867 |
| nauc_recall_at_20_std | 14.89994263882445 |
| nauc_recall_at_3_diff1 | 23.428129702129684 |
| nauc_recall_at_3_max | 10.549153954790542 |
| nauc_recall_at_3_std | -1.7590608997055206 |
| nauc_recall_at_5_diff1 | 21.27448645803921 |
| nauc_recall_at_5_max | 13.620279707461677 |
| nauc_recall_at_5_std | 2.0577962208292675 |
| ndcg_at_1 | 46.75 |
| ndcg_at_10 | 34.827000000000005 |
| ndcg_at_100 | 38.157999999999994 |
| ndcg_at_1000 | 44.816 |
| ndcg_at_20 | 34.152 |
| ndcg_at_3 | 39.009 |
| ndcg_at_5 | 36.826 |
| precision_at_1 | 57.25 |
| precision_at_10 | 27.575 |
| precision_at_100 | 8.84 |
| precision_at_1000 | 1.949 |
| precision_at_20 | 20.724999999999998 |
| precision_at_3 | 41.167 |
| precision_at_5 | 35.199999999999996 |
| recall_at_1 | 7.049999999999999 |
| recall_at_10 | 19.817999999999998 |
| recall_at_100 | 42.559999999999995 |
| recall_at_1000 | 63.744 |
| recall_at_20 | 25.968000000000004 |
| recall_at_3 | 11.959 |
| recall_at_5 | 14.939 |
#### MTEB FiQA-PL (default)
- **Task Type**: Retrieval
| Metric | Value |
|--------|-------|
| main_score | 38.828 |
| map_at_1 | 19.126 |
| map_at_10 | 31.002000000000002 |
| map_at_100 | 32.736 |
| map_at_1000 | 32.933 |
| map_at_20 | 31.894 |
| map_at_3 | 26.583000000000002 |
| map_at_5 | 28.904000000000003 |
| mrr_at_1 | 37.808641975308646 |
| mrr_at_10 | ... (incomplete data provided) |
## 📄 License
This project is licensed under the CC BY-NC 4.0 license.
This README has been beautified according to the requirements. It includes the project title, model information, evaluation results on different datasets, and the license information. The evaluation results are presented in a tabular format for better readability.
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
Transformers Supports Multiple LanguagesJ
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
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cross-encoder
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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
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Transformers EnglishO
opensearch-project
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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
Transformers Supports Multiple LanguagesG
Alibaba-NLP
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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
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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
TransformersP
kuelumbus
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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
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Transformers OtherB
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
Safetensors EnglishG
avsolatorio
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29
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