origin-Helsinki-NLP-opus-mt-tc-big-gmq-en

<!-- WINDSTORM_NOTE: This README was minimally patched by Opus 4.7 1M-Context (Dr. D) on 2026-05-25T17:59:14-0400 during the Phase D upstream-archive mirror, because upstream Helsinki-NLP/opus-mt-tc-big-gmq-en ships a YAML frontmatter that HF's metadata validator rejects (non-ISO-639 entries in language[]). Specifically: - false (parsed as YAML bool) → "no" in language[] Weights (pytorch_model.bin, tf_model.h5, model.safetensors, source.spm, target.spm, etc.) are unmodified and byte-identical to upstream. Only the README YAML frontmatter is patched. Original upstream snapshot remains canonical at the Helsinki-NLP repo. -->

opus-mt-tc-big-gmq-en

Neural machine translation model for translating from North Germanic languages (gmq) 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): dan fao isl nno nob nor swe
• 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 gmq-eng README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Han var synligt nervøs.",
    "Inte ens Tom själv var övertygad."
]

model_name = "pytorch-models/opus-mt-tc-big-gmq-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:
#     He was visibly nervous.
#     Even Tom was not convinced.

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-gmq-en")
print(pipe("Han var synligt nervøs."))

# expected output: He was visibly nervous.

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

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 19:13:11 EEST 2022
• port machine: LM0-400-22516.local