Helsinki-NLP_opus-mt-tc-big-ar-en

---

language:

• ar
• en tags:
• translation
• opus-mt-tc license: cc-by-4.0 model-index:
• name: opus-mt-tc-big-ar-en results:
  • task: name: Translation ara-eng type: translation args: ara-eng dataset: name: flores101-devtest type: flores_101 args: ara eng devtest metrics:
    • name: BLEU type: bleu value: 42.6
  • task: name: Translation ara-eng type: translation args: ara-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ara-eng metrics:
    • name: BLEU type: bleu value: 47.3
  • task: name: Translation ara-eng type: translation args: ara-eng dataset: name: tico19-test type: tico19-test args: ara-eng metrics:
    • name: BLEU type: bleu value: 44.4

---

opus-mt-tc-big-ar-en

Neural machine translation model for translating from Arabic (ar) 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: 2022-03-09
• source language(s): afb ara arz
• target language(s): eng
• model: transformer-big
• data: opusTCv20210807+bt (source)
• tokenization: SentencePiece (spm32k,spm32k)
• original model: opusTCv20210807+bt_transformer-big_2022-03-09.zip
• more information released models: OPUS-MT ara-eng README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "اتبع قلبك فحسب.",
    "وين راهي دّوش؟"
]

model_name = "pytorch-models/opus-mt-tc-big-ar-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:
#     Just follow your heart.
#     Wayne Rahi Dosh?

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-ar-en")
print(pipe("اتبع قلبك فحسب."))

# expected output: Just follow your heart.

Benchmarks

• test set translations: opusTCv20210807+bt_transformer-big_2022-03-09.test.txt
• test set scores: opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
ara-eng	tatoeba-test-v2021-08-07	0.63477	47.3	10305	76975
ara-eng	flores101-devtest	0.66987	42.6	1012	24721
ara-eng	tico19-test	0.68521	44.4	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: 3405783
• port time: Wed Apr 13 18:17:57 EEST 2022
• port machine: LM0-400-22516.local