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--- |
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language: |
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- da |
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- en |
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- fo |
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- gmq |
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- is |
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- nb |
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- nn |
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- false |
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- sv |
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tags: |
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- translation |
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- opus-mt-tc |
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license: cc-by-4.0 |
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model-index: |
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- name: opus-mt-tc-big-gmq-en |
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results: |
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- task: |
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name: Translation dan-eng |
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type: translation |
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args: dan-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: dan eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 49.3 |
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- task: |
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name: Translation isl-eng |
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type: translation |
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args: isl-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: isl eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 34.2 |
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- task: |
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name: Translation nob-eng |
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type: translation |
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args: nob-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: nob eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 44.2 |
|
- task: |
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name: Translation swe-eng |
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type: translation |
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args: swe-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: swe eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 49.8 |
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- task: |
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name: Translation isl-eng |
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type: translation |
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args: isl-eng |
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dataset: |
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name: newsdev2021.is-en |
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type: newsdev2021.is-en |
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args: isl-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 30.4 |
|
- task: |
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name: Translation dan-eng |
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type: translation |
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args: dan-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: dan-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 65.9 |
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- task: |
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name: Translation fao-eng |
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type: translation |
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args: fao-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: fao-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 30.1 |
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- task: |
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name: Translation isl-eng |
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type: translation |
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args: isl-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: isl-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 53.3 |
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- task: |
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name: Translation nno-eng |
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type: translation |
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args: nno-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: nno-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 56.1 |
|
- task: |
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name: Translation nob-eng |
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type: translation |
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args: nob-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: nob-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 60.2 |
|
- task: |
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name: Translation swe-eng |
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type: translation |
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args: swe-eng |
|
dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: swe-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 66.4 |
|
- task: |
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name: Translation isl-eng |
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type: translation |
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args: isl-eng |
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dataset: |
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name: newstest2021.is-en |
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type: wmt-2021-news |
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args: isl-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 34.4 |
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--- |
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# opus-mt-tc-big-gmq-en |
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|
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Neural machine translation model for translating from North Germanic languages (gmq) to English (en). |
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This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), 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](https://marian-nmt.github.io/), 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](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). |
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* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) |
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``` |
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@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
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url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
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} |
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|
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@inproceedings{tiedemann-2020-tatoeba, |
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
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author = {Tiedemann, J{\"o}rg}, |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.wmt-1.139", |
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pages = "1174--1182", |
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} |
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``` |
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## Model info |
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* Release: 2022-03-09 |
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* source language(s): dan fao isl nno nob nor swe |
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* target language(s): eng |
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* model: transformer-big |
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* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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* tokenization: SentencePiece (spm32k,spm32k) |
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* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip) |
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* more information released models: [OPUS-MT gmq-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-eng/README.md) |
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## Usage |
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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"Han var synligt nervøs.", |
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"Inte ens Tom själv var övertygad." |
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] |
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|
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model_name = "pytorch-models/opus-mt-tc-big-gmq-en" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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for t in translated: |
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print( tokenizer.decode(t, skip_special_tokens=True) ) |
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# expected output: |
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# He was visibly nervous. |
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# Even Tom was not convinced. |
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``` |
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|
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-en") |
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print(pipe("Han var synligt nervøs.")) |
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|
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# expected output: He was visibly nervous. |
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``` |
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## Benchmarks |
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* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) |
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* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
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|
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| langpair | testset | chr-F | BLEU | #sent | #words | |
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|----------|---------|-------|-------|-------|--------| |
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| dan-eng | tatoeba-test-v2021-08-07 | 0.78292 | 65.9 | 10795 | 79684 | |
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| fao-eng | tatoeba-test-v2021-08-07 | 0.47467 | 30.1 | 294 | 1984 | |
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| isl-eng | tatoeba-test-v2021-08-07 | 0.68346 | 53.3 | 2503 | 19788 | |
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| nno-eng | tatoeba-test-v2021-08-07 | 0.69788 | 56.1 | 460 | 3524 | |
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| nob-eng | tatoeba-test-v2021-08-07 | 0.73524 | 60.2 | 4539 | 36823 | |
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| swe-eng | tatoeba-test-v2021-08-07 | 0.77665 | 66.4 | 10362 | 68513 | |
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| dan-eng | flores101-devtest | 0.72322 | 49.3 | 1012 | 24721 | |
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| isl-eng | flores101-devtest | 0.59616 | 34.2 | 1012 | 24721 | |
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| nob-eng | flores101-devtest | 0.68224 | 44.2 | 1012 | 24721 | |
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| swe-eng | flores101-devtest | 0.72042 | 49.8 | 1012 | 24721 | |
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| isl-eng | newsdev2021.is-en | 0.56709 | 30.4 | 2004 | 46383 | |
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| isl-eng | newstest2021.is-en | 0.57756 | 34.4 | 1000 | 22529 | |
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## Acknowledgements |
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The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), 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](https://memad.eu/), 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](https://www.csc.fi/), Finland. |
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## Model conversion info |
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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 3405783 |
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* port time: Wed Apr 13 19:13:11 EEST 2022 |
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* port machine: LM0-400-22516.local |
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|
