|
--- |
|
language: |
|
- en |
|
- es |
|
tags: |
|
- translation |
|
- opus-mt-tc |
|
license: cc-by-4.0 |
|
model-index: |
|
- name: opus-mt-tc-big-en-es |
|
results: |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: eng spa devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 28.5 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: news-test2008 |
|
type: news-test2008 |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 30.1 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 57.2 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: tico19-test |
|
type: tico19-test |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 53.0 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: newstest2009 |
|
type: wmt-2009-news |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 30.2 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: newstest2010 |
|
type: wmt-2010-news |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 37.6 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: newstest2011 |
|
type: wmt-2011-news |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 38.9 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: newstest2012 |
|
type: wmt-2012-news |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 39.5 |
|
- task: |
|
name: Translation eng-spa |
|
type: translation |
|
args: eng-spa |
|
dataset: |
|
name: newstest2013 |
|
type: wmt-2013-news |
|
args: eng-spa |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 35.9 |
|
--- |
|
# opus-mt-tc-big-en-es |
|
|
|
Neural machine translation model for translating from English (en) to Spanish (es). |
|
|
|
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, |
|
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: 2022-03-13 |
|
* source language(s): eng |
|
* target language(s): spa |
|
* model: transformer-big |
|
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
|
* tokenization: SentencePiece (spm32k,spm32k) |
|
* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.zip) |
|
* more information released models: [OPUS-MT eng-spa README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md) |
|
|
|
## Usage |
|
|
|
A short example code: |
|
|
|
```python |
|
from transformers import MarianMTModel, MarianTokenizer |
|
|
|
src_text = [ |
|
"A wasp stung him and he had an allergic reaction.", |
|
"I love nature." |
|
] |
|
|
|
model_name = "pytorch-models/opus-mt-tc-big-en-es" |
|
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: |
|
# Una avispa lo picó y tuvo una reacción alérgica. |
|
# Me encanta la naturaleza. |
|
``` |
|
|
|
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-big-en-es") |
|
print(pipe("A wasp stung him and he had an allergic reaction.")) |
|
|
|
# expected output: Una avispa lo picó y tuvo una reacción alérgica. |
|
``` |
|
|
|
## Benchmarks |
|
|
|
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) |
|
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.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 | |
|
|----------|---------|-------|-------|-------|--------| |
|
| eng-spa | tatoeba-test-v2021-08-07 | 0.73863 | 57.2 | 16583 | 134710 | |
|
| eng-spa | flores101-devtest | 0.56440 | 28.5 | 1012 | 29199 | |
|
| eng-spa | newssyscomb2009 | 0.58415 | 31.5 | 502 | 12503 | |
|
| eng-spa | news-test2008 | 0.56707 | 30.1 | 2051 | 52586 | |
|
| eng-spa | newstest2009 | 0.57836 | 30.2 | 2525 | 68111 | |
|
| eng-spa | newstest2010 | 0.62357 | 37.6 | 2489 | 65480 | |
|
| eng-spa | newstest2011 | 0.62415 | 38.9 | 3003 | 79476 | |
|
| eng-spa | newstest2012 | 0.63031 | 39.5 | 3003 | 79006 | |
|
| eng-spa | newstest2013 | 0.60354 | 35.9 | 3000 | 70528 | |
|
| eng-spa | tico19-test | 0.73554 | 53.0 | 2100 | 66563 | |
|
|
|
## Acknowledgements |
|
|
|
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. |
|
|
|
## Model conversion info |
|
|
|
* transformers version: 4.16.2 |
|
* OPUS-MT git hash: 3405783 |
|
* port time: Wed Apr 13 18:03:53 EEST 2022 |
|
* port machine: LM0-400-22516.local |
|
|
