|
--- |
|
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 traducir / How to translate** |
|
|
|
+ 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 / Training process** |
|
|
|
+ Tokenization dos datasets feita co tokenizador de linguakit / Tokenization of the datasets made with linguakit tokeniser https://github.com/citiususc/Linguakit |
|
|
|
+ O vocabulario para os modelos foi xerado a través do script / Vocabulary for the models was created by the 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 / Using the .yaml in this repository you can replicate the training process as follows |
|
|
|
```bash |
|
onmt_build_vocab -config bpe-es-gl_emb.yaml -n_sample 100000 |
|
onmt_train -config bpe-es-gl_emb.yaml |
|
``` |
|
|
|
**Hiperparámetros / Hyper-parameters** |
|
|
|
Os parámetros usados para o desenvolvimento do modelo poden ser consultados directamente no mesmo ficheiro .yaml bpe-es-gl_emb.yaml |
|
|
|
The parameters used for the development of the model can be directly viewed in the same .yaml file bpe-es-gl_emb.yaml |
|
|
|
**Avaliación / Evaluation** |
|
|
|
A avalación dos modelos é feita cunha mistura de tests desenvolvidos internamente (gold1, gold2, test-suite) con outros datasets disponíbeis en galego (Flores). |
|
|
|
The evaluation of the models is done by mixing internally developed tests (gold1, gold2, test-suite) with other datasets available in Galician (Flores). |
|
|
|
| GOLD 1 | GOLD 2 | FLORES | TEST-SUITE| |
|
| ------------- |:-------------:| -------:|----------:| |
|
| 79.6 | 43.3 | 21.8 | 74.3 | |
|
|
|
**Licenzas do Modelo / Licensing information** |
|
|
|
MIT |
|
|
|
**Financiamento / Funding** |
|
|
|
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={} |
|
} |
