README.md

---
license: mit
language:
- gl
---

**English text [here](https://huggingface.co/proxectonos/Nos_MT-CT2-en-gl/blob/main/README_english.md)**

**Descrición do Modelo** 

Modelo feito con OpenNMT-py 3.5.2 para o par inglés-galego utilizando unha arquitectura transformer. O modelo foi transformado para o formato da ctranslate2.

**Como traducir con este Modelo**

+ Instalar o [Python](https://www.python.org/downloads/release/python-390/) 
+ Instalar o [ctranslate](https://github.com/OpenNMT/CTranslate2)
+ Traducir un input_text utilizando o modelo co seguinte comando:
```bash
    perl tokenizer.perl < input.txt > input.tok
```
```bash
    subword_nmt.apply_bpe -c ./bpe/en.code < input.tok > input.bpe
```
```bash 
    python3 translate.py model_name input.bpe > output.txt
```
```bash
    sed -i 's/@@ //g' output.txt
```
```bash
    perl detokenizer.perl < output.txt >final_output.txt
```
### Example translate.py file : Running CTranslate2 from Python
<details>
<summary>Show code</summary>

```python
import ctranslate2
import sys

model = sys.argv[1]
file_name = sys.argv[2]

file = open(file_name, 'r')

translator = ctranslate2.Translator(model, device="cuda")

for line in file:  
    line = line.strip()
    r = translator.translate_batch(
        [line.split()], replace_unknowns=True, beam_size=5, batch_type='examples'
    )
    results = ' '.join(r[0].hypotheses[0]) 
    print(results)
```
</details>


**Adestramento**

No adestramento, utilizamos córpora auténticos e sintéticos do [ProxectoNós](https://github.com/proxectonos/corpora). Os primeiros son córpora de traducións feitas directamente por tradutores humanos. É importante salientar que a pesar destes textos seren feitos por humanos, non están libres de erros lingüísticos. Os segundos son córpora de traducións español-portugués, que convertemos en español-galego a través da tradución automática portugués-galego con Opentrad/Apertium e transliteración para palabras fóra de vocabulario.

**Procedemento de adestramento**

+ Tokenización dos datasets feita co tokenizador (tokenizer.pl) de [linguakit](https://github.com/citiususc/Linguakit) que foi modificado para evitar o salto de liña por token do ficheiro orixinal. 

+ O vocabulario BPE para os modelos foi xerado a través do script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT



**Avaliación**

A avaliación BLEU dos modelos é feita cunha mistura de tests desenvolvidos internamente (gold1, gold2, test-suite) con outros datasets disponíbeis en galego (Flores).

**Licenzas do Modelo** 

MIT License

Copyright (c) 2023 Proxecto Nós

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

**Financiamento** 

This model was developed within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA] (https://proyectoilenia.es/) with reference 2022/TL22/00215336. 

**Citar este traballo** 

Se utilizar este modelo no seu traballo, cite por favor así:

Daniel Bardanca Outeirinho, Pablo Gamallo Otero, Iria de-Dios-Flores, and José Ramom Pichel Campos. 2024.
Exploring the effects of vocabulary size in neural machine translation: Galician as a target language.
In Proceedings of the 16th International Conference on Computational Processing of Portuguese, pages 600–604,
Santiago de Compostela, Galiza. Association for Computational Lingustics.