|
--- |
|
language: |
|
- af |
|
- de |
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- en |
|
- fy |
|
- gmw |
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- gos |
|
- hrx |
|
- lb |
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- nds |
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- nl |
|
- pdc |
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- yi |
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- multilingual |
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license: cc-by-4.0 |
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tags: |
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- translation |
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- opus-mt-tc |
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model-index: |
|
- name: opus-mt-tc-base-gmw-gmw |
|
results: |
|
- task: |
|
type: translation |
|
name: Translation afr-deu |
|
dataset: |
|
name: flores101-devtest |
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type: flores_101 |
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args: afr deu devtest |
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metrics: |
|
- type: bleu |
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value: 21.6 |
|
name: BLEU |
|
- type: bleu |
|
value: 46.8 |
|
name: BLEU |
|
- type: bleu |
|
value: 21.4 |
|
name: BLEU |
|
- type: bleu |
|
value: 33.8 |
|
name: BLEU |
|
- type: bleu |
|
value: 33.8 |
|
name: BLEU |
|
- type: bleu |
|
value: 29.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 21.0 |
|
name: BLEU |
|
- type: bleu |
|
value: 25.6 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
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dataset: |
|
name: multi30k_test_2016_flickr |
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type: multi30k-2016_flickr |
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args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 32.2 |
|
name: BLEU |
|
- type: bleu |
|
value: 28.8 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: multi30k_test_2017_flickr |
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type: multi30k-2017_flickr |
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args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 32.7 |
|
name: BLEU |
|
- type: bleu |
|
value: 27.6 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: multi30k_test_2017_mscoco |
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type: multi30k-2017_mscoco |
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args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 25.5 |
|
name: BLEU |
|
- type: bleu |
|
value: 22.0 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: multi30k_test_2018_flickr |
|
type: multi30k-2018_flickr |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 30.0 |
|
name: BLEU |
|
- type: bleu |
|
value: 25.3 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: news-test2008 |
|
type: news-test2008 |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 23.8 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation afr-deu |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: afr-deu |
|
metrics: |
|
- type: bleu |
|
value: 48.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 58.8 |
|
name: BLEU |
|
- type: bleu |
|
value: 54.5 |
|
name: BLEU |
|
- type: bleu |
|
value: 52.4 |
|
name: BLEU |
|
- type: bleu |
|
value: 42.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 48.7 |
|
name: BLEU |
|
- type: bleu |
|
value: 56.5 |
|
name: BLEU |
|
- type: bleu |
|
value: 35.9 |
|
name: BLEU |
|
- type: bleu |
|
value: 48.3 |
|
name: BLEU |
|
- type: bleu |
|
value: 32.5 |
|
name: BLEU |
|
- type: bleu |
|
value: 43.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 24.7 |
|
name: BLEU |
|
- type: bleu |
|
value: 20.4 |
|
name: BLEU |
|
- type: bleu |
|
value: 37.2 |
|
name: BLEU |
|
- type: bleu |
|
value: 32.4 |
|
name: BLEU |
|
- type: bleu |
|
value: 39.3 |
|
name: BLEU |
|
- type: bleu |
|
value: 34.5 |
|
name: BLEU |
|
- type: bleu |
|
value: 29.9 |
|
name: BLEU |
|
- type: bleu |
|
value: 42.3 |
|
name: BLEU |
|
- type: bleu |
|
value: 58.8 |
|
name: BLEU |
|
- type: bleu |
|
value: 50.4 |
|
name: BLEU |
|
- type: bleu |
|
value: 53.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 25.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 21.4 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2009 |
|
type: wmt-2009-news |
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args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 23.4 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2010 |
|
type: wmt-2010-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 25.8 |
|
name: BLEU |
|
- type: bleu |
|
value: 20.7 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2011 |
|
type: wmt-2011-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 23.7 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2012 |
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type: wmt-2012-news |
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args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 24.8 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2013 |
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type: wmt-2013-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 27.7 |
|
name: BLEU |
|
- type: bleu |
|
value: 22.5 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2014-deen |
|
type: wmt-2014-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 27.3 |
|
name: BLEU |
|
- type: bleu |
|
value: 22.0 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2015-deen |
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type: wmt-2015-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 28.6 |
|
name: BLEU |
|
- type: bleu |
|
value: 25.7 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2016-deen |
|
type: wmt-2016-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 33.3 |
|
name: BLEU |
|
- type: bleu |
|
value: 30.0 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2017-deen |
|
type: wmt-2017-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 29.5 |
|
name: BLEU |
|
- type: bleu |
|
value: 24.1 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2018-deen |
|
type: wmt-2018-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 36.1 |
|
name: BLEU |
|
- type: bleu |
|
value: 35.4 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2019-deen |
|
type: wmt-2019-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 32.3 |
|
name: BLEU |
|
- type: bleu |
|
value: 31.2 |
|
name: BLEU |
|
- task: |
|
type: translation |
|
name: Translation deu-eng |
|
dataset: |
|
name: newstest2020-deen |
|
type: wmt-2020-news |
|
args: deu-eng |
|
metrics: |
|
- type: bleu |
|
value: 32.0 |
|
name: BLEU |
|
- type: bleu |
|
value: 23.9 |
|
name: BLEU |
|
--- |
|
# opus-mt-tc-base-gmw-gmw |
|
|
|
Neural machine translation model for translating from West Germanic languages (gmw) to West Germanic languages (gmw). |
|
|
|
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). |
|
|
|
* 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.) |
|
|
|
``` |
|
@inproceedings{tiedemann-thottingal-2020-opus, |
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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: 2021-02-23 |
|
* source language(s): afr deu eng fry gos hrx ltz nds nld pdc yid |
|
* target language(s): afr deu eng fry nds nld |
|
* valid target language labels: >>afr<< >>ang_Latn<< >>deu<< >>eng<< >>fry<< >>ltz<< >>nds<< >>nld<< >>sco<< >>yid<< |
|
* model: transformer (base) |
|
* data: opus ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
|
* tokenization: SentencePiece (spm32k,spm32k) |
|
* original model: [opus-2021-02-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.zip) |
|
* more information released models: [OPUS-MT gmw-gmw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmw-gmw/README.md) |
|
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) |
|
|
|
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>afr<<` |
|
|
|
## Usage |
|
|
|
A short example code: |
|
|
|
```python |
|
from transformers import MarianMTModel, MarianTokenizer |
|
|
|
src_text = [ |
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">>nld<< You need help.", |
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">>afr<< I love your son." |
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] |
|
|
|
model_name = "pytorch-models/opus-mt-tc-base-gmw-gmw" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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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: |
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# Je hebt hulp nodig. |
|
# Ek is lief vir jou seun. |
|
``` |
|
|
|
You can also use OPUS-MT models with the transformers pipelines, for example: |
|
|
|
```python |
|
from transformers import pipeline |
|
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-gmw-gmw") |
|
print(pipe(>>nld<< You need help.)) |
|
|
|
# expected output: Je hebt hulp nodig. |
|
``` |
|
|
|
## Benchmarks |
|
|
|
* test set translations: [opus-2021-02-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.test.txt) |
|
* test set scores: [opus-2021-02-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmw-gmw/opus-2021-02-23.eval.txt) |
|
* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
|
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
|
|
|
| langpair | testset | chr-F | BLEU | #sent | #words | |
|
|----------|---------|-------|-------|-------|--------| |
|
| afr-deu | tatoeba-test-v2021-08-07 | 0.674 | 48.1 | 1583 | 9105 | |
|
| afr-eng | tatoeba-test-v2021-08-07 | 0.728 | 58.8 | 1374 | 9622 | |
|
| afr-nld | tatoeba-test-v2021-08-07 | 0.711 | 54.5 | 1056 | 6710 | |
|
| deu-afr | tatoeba-test-v2021-08-07 | 0.696 | 52.4 | 1583 | 9507 | |
|
| deu-eng | tatoeba-test-v2021-08-07 | 0.609 | 42.1 | 17565 | 149462 | |
|
| deu-nds | tatoeba-test-v2021-08-07 | 0.442 | 18.6 | 9999 | 76137 | |
|
| deu-nld | tatoeba-test-v2021-08-07 | 0.672 | 48.7 | 10218 | 75235 | |
|
| eng-afr | tatoeba-test-v2021-08-07 | 0.735 | 56.5 | 1374 | 10317 | |
|
| eng-deu | tatoeba-test-v2021-08-07 | 0.580 | 35.9 | 17565 | 151568 | |
|
| eng-nds | tatoeba-test-v2021-08-07 | 0.412 | 16.6 | 2500 | 18264 | |
|
| eng-nld | tatoeba-test-v2021-08-07 | 0.663 | 48.3 | 12696 | 91796 | |
|
| fry-eng | tatoeba-test-v2021-08-07 | 0.500 | 32.5 | 220 | 1573 | |
|
| fry-nld | tatoeba-test-v2021-08-07 | 0.633 | 43.1 | 260 | 1854 | |
|
| gos-nld | tatoeba-test-v2021-08-07 | 0.405 | 15.6 | 1852 | 9903 | |
|
| hrx-deu | tatoeba-test-v2021-08-07 | 0.484 | 24.7 | 471 | 2805 | |
|
| hrx-eng | tatoeba-test-v2021-08-07 | 0.362 | 20.4 | 221 | 1235 | |
|
| ltz-deu | tatoeba-test-v2021-08-07 | 0.556 | 37.2 | 347 | 2208 | |
|
| ltz-eng | tatoeba-test-v2021-08-07 | 0.485 | 32.4 | 293 | 1840 | |
|
| ltz-nld | tatoeba-test-v2021-08-07 | 0.534 | 39.3 | 292 | 1685 | |
|
| nds-deu | tatoeba-test-v2021-08-07 | 0.572 | 34.5 | 9999 | 74564 | |
|
| nds-eng | tatoeba-test-v2021-08-07 | 0.493 | 29.9 | 2500 | 17589 | |
|
| nds-nld | tatoeba-test-v2021-08-07 | 0.621 | 42.3 | 1657 | 11490 | |
|
| nld-afr | tatoeba-test-v2021-08-07 | 0.755 | 58.8 | 1056 | 6823 | |
|
| nld-deu | tatoeba-test-v2021-08-07 | 0.686 | 50.4 | 10218 | 74131 | |
|
| nld-eng | tatoeba-test-v2021-08-07 | 0.690 | 53.1 | 12696 | 89978 | |
|
| nld-fry | tatoeba-test-v2021-08-07 | 0.478 | 25.1 | 260 | 1857 | |
|
| nld-nds | tatoeba-test-v2021-08-07 | 0.462 | 21.4 | 1657 | 11711 | |
|
| afr-deu | flores101-devtest | 0.524 | 21.6 | 1012 | 25094 | |
|
| afr-eng | flores101-devtest | 0.693 | 46.8 | 1012 | 24721 | |
|
| afr-nld | flores101-devtest | 0.509 | 18.4 | 1012 | 25467 | |
|
| deu-afr | flores101-devtest | 0.534 | 21.4 | 1012 | 25740 | |
|
| deu-eng | flores101-devtest | 0.616 | 33.8 | 1012 | 24721 | |
|
| deu-nld | flores101-devtest | 0.516 | 19.2 | 1012 | 25467 | |
|
| eng-afr | flores101-devtest | 0.628 | 33.8 | 1012 | 25740 | |
|
| eng-deu | flores101-devtest | 0.581 | 29.1 | 1012 | 25094 | |
|
| eng-nld | flores101-devtest | 0.533 | 21.0 | 1012 | 25467 | |
|
| ltz-afr | flores101-devtest | 0.430 | 12.9 | 1012 | 25740 | |
|
| ltz-deu | flores101-devtest | 0.482 | 17.1 | 1012 | 25094 | |
|
| ltz-eng | flores101-devtest | 0.468 | 18.8 | 1012 | 24721 | |
|
| ltz-nld | flores101-devtest | 0.409 | 10.7 | 1012 | 25467 | |
|
| nld-afr | flores101-devtest | 0.494 | 16.8 | 1012 | 25740 | |
|
| nld-deu | flores101-devtest | 0.501 | 17.9 | 1012 | 25094 | |
|
| nld-eng | flores101-devtest | 0.551 | 25.6 | 1012 | 24721 | |
|
| deu-eng | multi30k_test_2016_flickr | 0.546 | 32.2 | 1000 | 12955 | |
|
| eng-deu | multi30k_test_2016_flickr | 0.582 | 28.8 | 1000 | 12106 | |
|
| deu-eng | multi30k_test_2017_flickr | 0.561 | 32.7 | 1000 | 11374 | |
|
| eng-deu | multi30k_test_2017_flickr | 0.573 | 27.6 | 1000 | 10755 | |
|
| deu-eng | multi30k_test_2017_mscoco | 0.499 | 25.5 | 461 | 5231 | |
|
| eng-deu | multi30k_test_2017_mscoco | 0.514 | 22.0 | 461 | 5158 | |
|
| deu-eng | multi30k_test_2018_flickr | 0.535 | 30.0 | 1071 | 14689 | |
|
| eng-deu | multi30k_test_2018_flickr | 0.547 | 25.3 | 1071 | 13703 | |
|
| deu-eng | newssyscomb2009 | 0.527 | 25.4 | 502 | 11818 | |
|
| eng-deu | newssyscomb2009 | 0.504 | 19.3 | 502 | 11271 | |
|
| deu-eng | news-test2008 | 0.518 | 23.8 | 2051 | 49380 | |
|
| eng-deu | news-test2008 | 0.492 | 19.3 | 2051 | 47447 | |
|
| deu-eng | newstest2009 | 0.516 | 23.4 | 2525 | 65399 | |
|
| eng-deu | newstest2009 | 0.498 | 18.8 | 2525 | 62816 | |
|
| deu-eng | newstest2010 | 0.546 | 25.8 | 2489 | 61711 | |
|
| eng-deu | newstest2010 | 0.508 | 20.7 | 2489 | 61503 | |
|
| deu-eng | newstest2011 | 0.524 | 23.7 | 3003 | 74681 | |
|
| eng-deu | newstest2011 | 0.493 | 19.2 | 3003 | 72981 | |
|
| deu-eng | newstest2012 | 0.532 | 24.8 | 3003 | 72812 | |
|
| eng-deu | newstest2012 | 0.493 | 19.5 | 3003 | 72886 | |
|
| deu-eng | newstest2013 | 0.548 | 27.7 | 3000 | 64505 | |
|
| eng-deu | newstest2013 | 0.517 | 22.5 | 3000 | 63737 | |
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| deu-eng | newstest2014-deen | 0.548 | 27.3 | 3003 | 67337 | |
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| eng-deu | newstest2014-deen | 0.532 | 22.0 | 3003 | 62688 | |
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| deu-eng | newstest2015-deen | 0.553 | 28.6 | 2169 | 46443 | |
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| eng-deu | newstest2015-ende | 0.544 | 25.7 | 2169 | 44260 | |
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| deu-eng | newstest2016-deen | 0.596 | 33.3 | 2999 | 64119 | |
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| eng-deu | newstest2016-ende | 0.580 | 30.0 | 2999 | 62669 | |
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| deu-eng | newstest2017-deen | 0.561 | 29.5 | 3004 | 64399 | |
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| eng-deu | newstest2017-ende | 0.535 | 24.1 | 3004 | 61287 | |
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| deu-eng | newstest2018-deen | 0.610 | 36.1 | 2998 | 67012 | |
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| eng-deu | newstest2018-ende | 0.613 | 35.4 | 2998 | 64276 | |
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| deu-eng | newstest2019-deen | 0.582 | 32.3 | 2000 | 39227 | |
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| eng-deu | newstest2019-ende | 0.583 | 31.2 | 1997 | 48746 | |
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| deu-eng | newstest2020-deen | 0.604 | 32.0 | 785 | 38220 | |
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| eng-deu | newstest2020-ende | 0.542 | 23.9 | 1418 | 52383 | |
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| deu-eng | newstestB2020-deen | 0.598 | 31.2 | 785 | 37696 | |
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| eng-deu | newstestB2020-ende | 0.532 | 23.3 | 1418 | 53092 | |
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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: 4.12.3 |
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* OPUS-MT git hash: e56a06b |
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* port time: Sun Feb 13 14:42:10 EET 2022 |
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* port machine: LM0-400-22516.local |
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