🚀 opus-mt-tc-big-zls-en
This is a neural machine translation model designed to translate from South Slavic languages (zls) to English (en). It's part of a global effort to make neural machine translation accessible for various languages.
🚀 Quick Start
This model is a part of the OPUS-MT project, aiming to make neural machine translation models widely available for many languages globally. All models are initially trained using the Marian NMT framework, an efficient NMT implementation in pure C++. They are then converted to pyTorch via the transformers library by huggingface. The training data is sourced from OPUS, and the training pipelines follow the procedures of OPUS-MT-train.
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
✨ Features
- Multilingual Support: Capable of translating from multiple South Slavic languages (bos_Latn, bul, hbs, hrv, mkd, slv, srp_Cyrl, srp_Latn) to English.
- Part of OPUS-MT Project: Leverages the resources and framework of the well - known OPUS - MT project.
📦 Installation
The README does not provide specific installation steps, so this section is skipped.
💻 Usage Examples
Basic Usage
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Да не би случайно Том да остави Мери да кара колата?",
"Какво е времето днес?"
]
model_name = "pytorch-models/opus-mt-tc-big-zls-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
Advanced Usage
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-en")
print(pipe("Да не би случайно Том да остави Мери да кара колата?"))
📚 Documentation
Model info
Benchmarks
langpair |
testset |
chr-F |
BLEU |
#sent |
#words |
bos_Latn-eng |
tatoeba-test-v2021-08-07 |
0.79339 |
66.5 |
301 |
1826 |
bul-eng |
tatoeba-test-v2021-08-07 |
0.72656 |
59.3 |
10000 |
71872 |
hbs-eng |
tatoeba-test-v2021-08-07 |
0.71783 |
57.3 |
10017 |
68934 |
hrv-eng |
tatoeba-test-v2021-08-07 |
0.74066 |
59.2 |
1480 |
10620 |
mkd-eng |
tatoeba-test-v2021-08-07 |
0.70043 |
57.4 |
10010 |
65667 |
slv-eng |
tatoeba-test-v2021-08-07 |
0.39534 |
23.5 |
2495 |
16940 |
srp_Cyrl-eng |
tatoeba-test-v2021-08-07 |
0.67628 |
47.0 |
1580 |
10181 |
srp_Latn-eng |
tatoeba-test-v2021-08-07 |
0.71878 |
58.5 |
6656 |
46307 |
bul-eng |
flores101-devtest |
0.67375 |
42.0 |
1012 |
24721 |
hrv-eng |
flores101-devtest |
0.63914 |
37.1 |
1012 |
24721 |
mkd-eng |
flores101-devtest |
0.67444 |
43.2 |
1012 |
24721 |
slv-eng |
flores101-devtest |
0.62087 |
35.2 |
1012 |
24721 |
srp_Cyrl-eng |
flores101-devtest |
0.67810 |
36.8 |
1012 |
24721 |
Acknowledgements
The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.
Model conversion info
Property |
Details |
Transformers Version |
4.16.2 |
OPUS-MT Git Hash |
3405783 |
Port Time |
Wed Apr 13 20:12:26 EEST 2022 |
Port Machine |
LM0-400-22516.local |
📄 License
The model is released under the cc - by - 4.0 license.