NothingSoftware/opus-mt-tc-big-fr-zle-ct2-int8

opus-mt-tc-big-fr-zle — CTranslate2, int8

French → Russian (and the other East Slavic targets of the original model), converted to CTranslate2 with int8 weights. This is a format conversion of Helsinki-NLP/opus-mt-tc-big-fr-zle by the Language Technology Research Group at the University of Helsinki (OPUS-MT, Jörg Tiedemann and colleagues), released 2022-03-23. Nothing was retrained or fine-tuned. All credit for the model belongs to its authors.

It is the French-to-Russian model the NTranscript app downloads for its offline translation.

License

CC-BY 4.0, the license of the original model. The conversion is shared under the same terms.

Usage

Each source sentence starts with the target-language token >>rus<< (>>ukr<< and >>bel<< also work), tokenized with source.spm; the output is decoded with target.spm.

import ctranslate2, sentencepiece as spm

translator = ctranslate2.Translator(".", compute_type="int8")
src = spm.SentencePieceProcessor(model_file="source.spm")
tgt = spm.SentencePieceProcessor(model_file="target.spm")

tokens = [">>rus<<"] + src.encode("…", out_type=str) + ["</s>"]
result = translator.translate_batch([tokens], beam_size=2)
print(tgt.decode(result[0].hypotheses[0]))

Conversion

ct2-transformers-converter --model Helsinki-NLP/opus-mt-tc-big-fr-zle \
    --output_dir opus-mt-tc-big-fr-zle-int8 --quantization int8 \
    --copy_files source.spm target.spm

CTranslate2 4.8.2, transformers 5.17.0. int8 scores the same as float32 on the test we ran (WMT20 en-ru, first 500 sentences: BLEU 28.0 both), at half the size and about twice the speed on a CPU.

Files

File	Bytes	SHA-256
config.json	233	72901fbd8abd89fb5cf4a388f26fc681f5c4c58a1e1a88b30b879f107270e7ee
model.bin	242,439,853	742263b38109178d319fb67df179e54fe6ce9bed135f80940ee0ee78e440f7ad
shared_vocabulary.json	2,259,838	926e4fc129bdaa0d94d2c37371c7de1bab9547be728963447798bf61116794de
source.spm	824,258	a552ab37e1013e599492a306a13f3bc6e163549d494ac162efc252f9d201a71f
target.spm	1,035,951	90e397a3ef99dc67d483405e86144399218afb25355aeb8caea483aef9b650b6

Original evaluation (fra-rus)

language pair	test set	chr-F	BLEU	#sent	#words
fra-rus	tatoeba-test-v2021-08-07	0.66502	46.1	11490	70123
fra-rus	flores101-devtest	0.54106	25.8	1012	23295
fra-rus	newstest2012	0.51254	23.1	3003	64790
fra-rus	newstest2013	0.52342	24.8	3000	58560

Citation

@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",
}

Acknowledgements (from the original model card)

The original 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, with computational resources provided by CSC — IT Center for Science, Finland.