🚀 inc-eng Translation Model
This model is designed for translating Indic languages to English, offering a reliable solution for multilingual communication.
🚀 Quick Start
This inc-eng model is a powerful tool for translating Indic languages to English. You can download the original weights and test set translations from the provided links.
✨ Features
- Multilingual Support: Supports multiple Indic languages including asm, awa, ben, etc.
- Transformer Model: Utilizes the transformer architecture for efficient translation.
- Pre - processing: Applies normalization and SentencePiece (spm32k, spm32k) for better performance.
📦 Installation
No specific installation steps are provided in the original document.
📚 Documentation
Model Information
| Property |
Details |
| Model Type |
Transformer |
| Source Language(s) |
asm, awa, ben, bho, gom, guj, hif_Latn, hin, mai, mar, npi, ori, pan_Guru, pnb, rom, san_Deva, sin, snd_Arab, urd |
| Target Language(s) |
eng |
| Pre - processing |
normalization + SentencePiece (spm32k, spm32k) |
| Download Original Weights |
opus2m - 2020 - 08 - 01.zip |
| Test Set Translations |
opus2m - 2020 - 08 - 01.test.txt |
| Test Set Scores |
opus2m - 2020 - 08 - 01.eval.txt |
Benchmarks
| testset |
BLEU |
chr - F |
| newsdev2014 - hineng.hin.eng |
8.9 |
0.341 |
| newsdev2019 - engu - gujeng.guj.eng |
8.7 |
0.321 |
| newstest2014 - hien - hineng.hin.eng |
13.1 |
0.396 |
| newstest2019 - guen - gujeng.guj.eng |
6.5 |
0.290 |
| Tatoeba - test.asm - eng.asm.eng |
18.1 |
0.363 |
| Tatoeba - test.awa - eng.awa.eng |
6.2 |
0.222 |
| Tatoeba - test.ben - eng.ben.eng |
44.7 |
0.595 |
| Tatoeba - test.bho - eng.bho.eng |
29.4 |
0.458 |
| Tatoeba - test.guj - eng.guj.eng |
19.3 |
0.383 |
| Tatoeba - test.hif - eng.hif.eng |
3.7 |
0.220 |
| Tatoeba - test.hin - eng.hin.eng |
38.6 |
0.564 |
| Tatoeba - test.kok - eng.kok.eng |
6.6 |
0.287 |
| Tatoeba - test.lah - eng.lah.eng |
16.0 |
0.272 |
| Tatoeba - test.mai - eng.mai.eng |
75.6 |
0.796 |
| Tatoeba - test.mar - eng.mar.eng |
25.9 |
0.497 |
| Tatoeba - test.multi.eng |
29.0 |
0.502 |
| Tatoeba - test.nep - eng.nep.eng |
4.5 |
0.198 |
| Tatoeba - test.ori - eng.ori.eng |
5.0 |
0.226 |
| Tatoeba - test.pan - eng.pan.eng |
17.4 |
0.375 |
| Tatoeba - test.rom - eng.rom.eng |
1.7 |
0.174 |
| Tatoeba - test.san - eng.san.eng |
5.0 |
0.173 |
| Tatoeba - test.sin - eng.sin.eng |
31.2 |
0.511 |
| Tatoeba - test.snd - eng.snd.eng |
45.7 |
0.670 |
| Tatoeba - test.urd - eng.urd.eng |
25.6 |
0.456 |
System Info
- hf_name: inc - eng
- source_languages: inc
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/inc-eng/README.md
- original_repo: Tatoeba - Challenge
- tags: ['translation']
- languages: ['bn', 'or', 'gu', 'mr', 'ur', 'hi', 'as', 'si', 'inc', 'en']
- src_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'bho', 'hin', 'san_Deva', 'asm', 'rom', 'mai', 'awa', 'sin'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k, spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/inc-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/inc-eng/opus2m-2020-08-01.test.txt
- src_alpha3: inc
- tgt_alpha3: eng
- short_pair: inc - en
- chrF2_score: 0.502
- bleu: 29.0
- brevity_penalty: 1.0
- ref_len: 64706.0
- src_name: Indic languages
- tgt_name: English
- train_date: 2020 - 08 - 01
- src_alpha2: inc
- tgt_alpha2: en
- prefer_old: False
- long_pair: inc - eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020 - 08 - 21 - 14:41
📄 License
This project is licensed under the Apache - 2.0 license.
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
