opus-mt-tc-big-zle-en

Neural machine translation model for translating from East Slavic languages (zle) 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): bel rus ukr
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 zle-eng README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Скільки мені слід купити пива?",
    "Я клієнтка."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-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:
#     How much beer should I buy?
#     I'm a client.

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-zle-en")
print(pipe("Скільки мені слід купити пива?"))

# expected output: How much beer should I buy?

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
bel-eng	tatoeba-test-v2021-08-07	0.65221	48.1	2500	18571
rus-eng	tatoeba-test-v2021-08-07	0.71452	57.4	19425	147872
ukr-eng	tatoeba-test-v2021-08-07	0.71162	56.9	13127	88607
bel-eng	flores101-devtest	0.51689	18.1	1012	24721
rus-eng	flores101-devtest	0.62581	35.2	1012	24721
ukr-eng	flores101-devtest	0.65001	39.2	1012	24721
rus-eng	newstest2012	0.63724	39.2	3003	72812
rus-eng	newstest2013	0.57641	31.3	3000	64505
rus-eng	newstest2014	0.65667	40.5	3003	69190
rus-eng	newstest2015	0.61747	36.1	2818	64428
rus-eng	newstest2016	0.61414	35.7	2998	69278
rus-eng	newstest2017	0.65365	40.8	3001	69025
rus-eng	newstest2018	0.61386	35.2	3000	71291
rus-eng	newstest2019	0.65476	41.6	2000	42642
rus-eng	newstest2020	0.64878	36.9	991	20217
rus-eng	newstestB2020	0.65685	39.3	991	20423
rus-eng	tico19-test	0.63280	33.3	2100	56323

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: 1bdabf7
port time: Wed Mar 23 22:17:11 EET 2022
port machine: LM0-400-22516.local

Helsinki-NLP
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opus-mt-tc-big-zle-en