Helsinki-NLP_opus-mt-tc-big-fi-en

---

language:

• en
• fi tags:
• translation
• opus-mt-tc license: cc-by-4.0 model-index:
• name: opus-mt-tc-big-fi-en results:
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: flores101-devtest type: flores_101 args: fin eng devtest metrics:
    • name: BLEU type: bleu value: 35.4
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: newsdev2015 type: newsdev2015 args: fin-eng metrics:
    • name: BLEU type: bleu value: 28.6
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fin-eng metrics:
    • name: BLEU type: bleu value: 57.4
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: newstest2015 type: wmt-2015-news args: fin-eng metrics:
    • name: BLEU type: bleu value: 29.9
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: newstest2016 type: wmt-2016-news args: fin-eng metrics:
    • name: BLEU type: bleu value: 34.3
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: newstest2017 type: wmt-2017-news args: fin-eng metrics:
    • name: BLEU type: bleu value: 37.3
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: newstest2018 type: wmt-2018-news args: fin-eng metrics:
    • name: BLEU type: bleu value: 27.1
  • task: name: Translation fin-eng type: translation args: fin-eng dataset: name: newstest2019 type: wmt-2019-news args: fin-eng metrics:
    • name: BLEU type: bleu value: 32.7

---

opus-mt-tc-big-fi-en

Neural machine translation model for translating from Finnish (fi) 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: 2021-12-08
• source language(s): fin
• target language(s): eng
• model: transformer (big)
• data: opusTCv20210807+bt (source)
• tokenization: SentencePiece (spm32k,spm32k)
• original model: opusTCv20210807+bt-2021-12-08.zip
• more information released models: OPUS-MT fin-eng README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Kolme kolmanteen on kaksikymmentäseitsemän.",
    "Heille syntyi poikavauva."
]

model_name = "pytorch-models/opus-mt-tc-big-fi-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) )

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-fi-en")
print(pipe("Kolme kolmanteen on kaksikymmentäseitsemän."))

Benchmarks

• test set translations: opusTCv20210807+bt-2021-12-08.test.txt
• test set scores: opusTCv20210807+bt-2021-12-08.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
fin-eng	tatoeba-test-v2021-08-07	0.72298	57.4	10690	80552
fin-eng	flores101-devtest	0.62521	35.4	1012	24721
fin-eng	newsdev2015	0.56232	28.6	1500	32012
fin-eng	newstest2015	0.57469	29.9	1370	27270
fin-eng	newstest2016	0.60715	34.3	3000	62945
fin-eng	newstest2017	0.63050	37.3	3002	61846
fin-eng	newstest2018	0.54199	27.1	3000	62325
fin-eng	newstest2019	0.59620	32.7	1996	36215
fin-eng	newstestB2016	0.55472	27.9	3000	62945
fin-eng	newstestB2017	0.58847	31.1	3002	61846

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: f084bad
• port time: Tue Mar 22 14:52:19 EET 2022
• port machine: LM0-400-22516.local