🚀 opus-mt-tc-big-it-en
A neural machine translation model designed to translate text from Italian (it) to English (en).
This model is an integral part of the OPUS-MT project. The OPUS-MT project aims to make neural machine translation models accessible for numerous languages worldwide. All models are initially trained using the Marian NMT framework, an efficient NMT implementation written in pure C++. These 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",
}
📚 Documentation
Model Info
Usage
💻 Usage Examples
Basic Usage
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"So chi è il mio nemico.",
"Tom è illetterato; non capisce assolutamente nulla."
]
model_name = "pytorch-models/opus-mt-tc-big-it-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-it-en")
print(pipe("So chi è il mio nemico."))
Benchmarks
langpair |
testset |
chr-F |
BLEU |
#sent |
#words |
ita-eng |
tatoeba-test-v2021-08-07 |
0.82288 |
72.1 |
17320 |
119214 |
ita-eng |
flores101-devtest |
0.62115 |
32.8 |
1012 |
24721 |
ita-eng |
newssyscomb2009 |
0.59822 |
34.4 |
502 |
11818 |
ita-eng |
newstest2009 |
0.59646 |
34.3 |
2525 |
65399 |
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 19:40:08 EEST 2022 |
Port Machine |
LM0-400-22516.local |
📄 License
This model is licensed under cc-by-4.0.