opus-mt-tc-big-zls-en

Neural machine translation model for translating from South Slavic languages (zls) to English (en).

This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train.

Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@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",
}

Model info

Release: 2022-03-17
source language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn
target language(s): eng
model: transformer-big
data: opusTCv20210807+bt (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+bt_transformer-big_2022-03-17.zip
more information released models: OPUS-MT zls-eng README

Usage

A short example code:

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) )

# expected output:
#     Did Tom just let Mary drive the car?
#     What's the weather like today?

You can also use OPUS-MT models with the transformers pipelines, for example:

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?"))

# expected output: Did Tom just let Mary drive the car?

Benchmarks

test set translations: opusTCv20210807+bt_transformer-big_2022-03-17.test.txt
test set scores: opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

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

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

Helsinki-NLP
/

opus-mt-tc-big-zls-en