---
license: mit
---

**Descrición do Modelo / Model description** 

Modelo feito con OpenNMT para o par español-galego utilizando unha arquitectura transformer / Model developed with OpenNMT for the Spanish-Galician pair using a transformer architecture.

**Como utilizar**

+ Abrir terminal bash / Open bash terminal
+ Instalar / Installing [Python 3.9](https://www.python.org/downloads/release/python-390/) 
+ Instalar / Installing [Open NMT toolkit v.2.2](https://github.com/OpenNMT/OpenNMT-py)
+ Traducir un input_text utilizando o modelo NOS-MT-es-gl co seguinte comando / Translating an input_text using the NOS-MT-en-gl model with the following command:

```bash 
onmt_translate -src input_text -model NOS-MT-es-gl -output ./output_file.txt -replace_unk -phrase_table phrase_table-es-gl.txt -gpu 0
```
+ O resultado da tradución estará no PATH indicado no flag -output / The result of the translation will be in the PATH indicated by the -output flag.

**Adestramento / Training**

No adestramento, utilizamos corpora auténticos e sintéticos. Os primeiros son corpora de traducións feitas directamente por tradutores humanos. Os segundos son corpora de traducións español-portugués e inglés-portugués, que convertemos en español-galego e inglés-galego a través da tradución automática portugués-galego con Opentrad/Apertium e transliteración para palabras fóra de vocabulário / In the training we have used authentic and synthetic corpora. The former are corpora of translations directly produced by human translators. The latter are corpora of Spanish-Portuguese and English-Portuguese translations, which we have converted into Spanish-Galician and English-Galician by means of Portuguese-Galician translation with Opentrad/Apertium and transliteration for out-of-vocabulary words. 


**Procedemento de adestramento**

+ Tokenization dos datasets feita co tokenizador de linguakit https://github.com/citiususc/Linguakit

+ O vocabulario 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 open NMT

+ Usando o .yaml neste repositorio pode replicar o proceso de adestramento do seguinte xeito

```bash 
onmt_build_vocab -config  bpe-es-gl_emb.yaml -n_sample 100000
onmt_train -config bpe-es-gl_emb.yaml
```

**Hiperparámetros** 

Os parámetros usados para o desenvolvimento do modelo poden ser consultados directamente no mesmo ficheiro .yaml  bpe-es-gl_emb.yaml 


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

| GOLD 1        | GOLD 2        | FLORES  | TEST-SUITE|
| ------------- |:-------------:| -------:|----------:| 
| 79.6          | 43.3          | 21.8    | 74.3      |



**Información adicional** 

Licensing information 

Apache License, Version 2.0 

**Financiamento** 

 This research was funded by the project "Nós: Galician in the society and economy of artificial intelligence", agreement between Xunta de Galicia and University of Santiago de Compostela, and grant ED431G2019/04 by the Galician Ministry of Education, University and Professional Training, and the European Regional Development Fund (ERDF/FEDER program), and Groups of Reference: ED431C 2020/21.
 
**Citation Information** 

@article{garriga2022catalan, 
  title={}, 
  author={}, 
  year={2023}, 
  url={} 
}