metadata

language:
  - en
  - ro
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-big-en-ro
    results:
      - task:
          name: Translation eng-ron
          type: translation
          args: eng-ron
        dataset:
          name: flores101-devtest
          type: flores_101
          args: eng ron devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 40.4
      - task:
          name: Translation eng-ron
          type: translation
          args: eng-ron
        dataset:
          name: newsdev2016
          type: newsdev2016
          args: eng-ron
        metrics:
          - name: BLEU
            type: bleu
            value: 36.4
      - task:
          name: Translation eng-ron
          type: translation
          args: eng-ron
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: eng-ron
        metrics:
          - name: BLEU
            type: bleu
            value: 48.6
      - task:
          name: Translation eng-ron
          type: translation
          args: eng-ron
        dataset:
          name: newstest2016
          type: wmt-2016-news
          args: eng-ron
        metrics:
          - name: BLEU
            type: bleu
            value: 34

opus-mt-tc-big-en-ro

Neural machine translation model for translating from English (en) to Romanian (ro).

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: 2022-02-25
source language(s): eng
target language(s): ron
model: transformer-big
data: opusTCv20210807+bt (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+bt_transformer-big_2022-02-25.zip
more information released models: OPUS-MT eng-ron README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>ron<< A bad writer's prose is full of hackneyed phrases.",
    ">>ron<< Zero is a special number."
]

model_name = "pytorch-models/opus-mt-tc-big-en-ro"
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:
#     Proza unui scriitor prost este plină de fraze tocite.
#     Zero este un număr special.

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-en-ro")
print(pipe(">>ron<< A bad writer's prose is full of hackneyed phrases."))

# expected output: Proza unui scriitor prost este plină de fraze tocite.

Benchmarks

test set translations: opusTCv20210807+bt_transformer-big_2022-02-25.test.txt
test set scores: opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
eng-ron	tatoeba-test-v2021-08-07	0.68606	48.6	5508	40367
eng-ron	flores101-devtest	0.64876	40.4	1012	26799
eng-ron	newsdev2016	0.62682	36.4	1999	51300
eng-ron	newstest2016	0.60702	34.0	1999	48945

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 17:55:46 EEST 2022
port machine: LM0-400-22516.local