Update README_English.md

README_English.md

@@ -13,40 +13,43 @@ metrics:
 License: MIT
 ---
 
-**Model Description** 
+**Model description** 
 
-OpenNMT model for English-Galician using a transformer architecture. 
+Model developed with OpenNMT for the Galician-Spanish pair using the transformer architecture.
 
 **How to translate**
 
 + Open bash terminal
-+ Install [Python 3.9](https://www.python.org/downloads/release/python-390/)
++ Install [Python 3.9](https://www.python.org/downloads/release/python-390/) 
 + Install [Open NMT toolkit v.2.2](https://github.com/OpenNMT/OpenNMT-py)
 + Translate an input_text using the NOS-MT-gl-es model with the following command:
 
 ```bash 
-onmt_translate -src input_text -model NOS-MT-gl-es.pt -output ./output_file.txt -replace_unk -gpu 0
+onmt_translate -src input_text -model NOS-MT-es-gl -output ./output_file.txt -replace_unk -phrase_table phrase_table-gl-es.txt -gpu 0
 ```
-+ The result of the translation will be in the PATH indicated by the -output flag.
++ The resulting translation will be in the PATH indicated by the -output flag.
 
 **Training**
 
-In the training we have used authentic and synthetic corpora from [ProxectoNós](https://github.com/proxectonos/corpora). The former are corpora of translations directly produced by human translators. The latter are corpora of English-Portuguese translations, which we have converted into English-Galician by means of Portuguese-Galician translation with Opentrad/Apertium and transliteration for out-of-vocabulary words. 
+To train this model, we have used authentic and synthetic corpora from [ProxectoNós](https://github.com/proxectonos/corpora).
+
+Authentic corpora are corpora produced by human translators. Synthetic corpora are Spanish-Portuguese translations, which have been converted to Spanish-Galician by means of Portuguese-Galician translation with Opentrad/Apertium and transliteration for out-of-vocabulary words. 
+
 
 **Training process**
 
-+ Tokenization of the datasets made with linguakit tokeniser https://github.com/citiususc/Linguakit
-+ The vocabulary for the models was generated through the script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) of OpenNMT
-+ Using .yaml in this repository you can replicate the training process as follows
++ Tokenisation was performed with a modified version of the [linguakit](https://github.com/citiususc/Linguakit) tokeniser (tokenizer.pl) that does not append a new line after each token.
++ All BPE models were generated with the script [learn_bpe.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py)
++ Using the .yaml in this repository it is possible to replicate the original training process. Before training the model, please verify that the path to each target (tgt) and (src) file is correct. Once this is done, proceed as follows:
 
 ```bash 
 onmt_build_vocab -config  bpe-gl-es_emb.yaml -n_sample 100000
 onmt_train -config bpe-gl-es_emb.yaml
 ```
 
-**Hyper-parameters** 
+**Hyperparameters** 
 
-The parameters used for the development of the model can be directly consulted in the same .yaml file bpe-en-gl_emb.yaml 
+You may find the parameters used for this model inside the file  bpe-gl-es_emb.yaml 
 
 **Evaluation**