🚀 opus-mt-tc-big-sh-en
A neural machine translation model designed to translate from Serbo - Croatian (sh) to English (en), offering a practical solution for cross - language communication.
🚀 Quick Start
This model is part of the OPUS - MT project, which aims to make neural machine translation models widely available and accessible for many languages around the world. All models are initially trained using the Marian NMT framework, an efficient NMT implementation written in pure C++. The models are then converted to pyTorch using 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
- Supports multiple source languages including bos_Latn, hrv, srp_Cyrl, srp_Latn, and targets English.
- Based on the transformer - big architecture, providing high - quality translation results.
- Utilizes data from opusTCv20210807+bt for training, ensuring a rich and diverse language corpus.
📦 Installation
This model can be used via the transformers
library. You can install it using pip
:
pip install transformers
💻 Usage Examples
Basic Usage
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Ispostavilo se da je istina.",
"Ovaj vikend imamo besplatne pozive."
]
model_name = "pytorch-models/opus-mt-tc-big-sh-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
You can also use OPUS - MT models with the transformers pipelines:
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-sh-en")
print(pipe("Ispostavilo se da je istina."))
📚 Documentation
Model info
Benchmarks
langpair |
testset |
chr - F |
BLEU |
#sent |
#words |
bos_Latn - eng |
tatoeba - test - v2021 - 08 - 07 |
0.80010 |
66.5 |
301 |
1826 |
hbs - eng |
tatoeba - test - v2021 - 08 - 07 |
0.71744 |
56.4 |
10017 |
68934 |
hrv - eng |
tatoeba - test - v2021 - 08 - 07 |
0.73563 |
58.8 |
1480 |
10620 |
srp_Cyrl - eng |
tatoeba - test - v2021 - 08 - 07 |
0.68248 |
44.7 |
1580 |
10181 |
srp_Latn - eng |
tatoeba - test - v2021 - 08 - 07 |
0.71781 |
58.4 |
6656 |
46307 |
hrv - eng |
flores101 - devtest |
0.63948 |
37.1 |
1012 |
24721 |
Acknowledgements
The work is supported by the [European Language Grid](https://www.european - language - grid.eu/) as [pilot project 2866](https://live.european - language - grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural - language - understanding - with - cross - lingual - grounding), 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 19:21:10 EEST 2022 |
Port Machine |
LM0 - 400 - 22516.local |
📄 License
This model is released under the cc - by - 4.0 license.