Update README.md

README.md

@@ -20,18 +20,29 @@ pipeline_tag: text-to-image
 - **Architecture**: roberta-base
   
 ## Description
-In order to improve the RoBERTa encoder performance, this model has been trained using the generated corpus ([in this respository](https://huggingface.co/oeg/RoBERTa-CelebA-Sp/)) 
+In order to improve the [RoBERTa-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) encoder performance, this model has been trained using the generated corpus ([in this respository](https://huggingface.co/oeg/RoBERTa-CelebA-Sp/)) 
 and following the strategy of using a Siamese network together with the loss function of cosine similarity. The following steps were followed:
-- Define sentence-transformer and torch libraries for the implementation of the encoder. 
+- Define [sentence-transformer](https://www.sbert.net/) and torch libraries for the implementation of the encoder. 
 - Divide the training corpus into two parts, training with 249,999 sentences and validation with 10,000 sentences.
 - Load training / validation data for the model. Two lists are generated for the storage of the information and, in each of them,
   the entries are composed of a pair of descriptive sentences and their similarity value.
-- Implement RoBERTa as a baseline model for transformer training.
+- Implement [RoBERTa-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) as a baseline model for transformer training.
 - Train with a Siamese network in which, for a pair of sentences _A_ and _B_ from the training corpus, the similarities of their embedding
 - vectors _u_ and _v_ generated using the cosine similarity metric (_CosineSimilarityLoss()_) are evaluated.
 
+The total training time using the [sentence-transformer](https://www.sbert.net/) library in Python was 42 days using all the available GPUs of the server, and with exclusive dedication. 
+
 ## How to use
 
+To make use of the model use the following code in Python:
+```python
+from sentence_transformers import SentenceTransformer, InputExample, models, losses, util, evaluation
+model_sbert = SentenceTransformer('roberta-large-bne-celebAEs-UNI')
+caption = ['La mujer tiene pomulos altos. Su cabello es de color negro. Tiene las cejas arqueadas y la boca ligeramente abierta. La joven y atractiva mujer sonriente tiene mucho maquillaje. Lleva aretes, collar y lapiz labial.']
+vectors = model_sbert.encode(captions)
+print(vector)
+```
+To see more detailed information about the implementation visit the [following link](https://github.com/eduar03yauri/DCGAN-text2face-forSpanish/Data/encoder-models/RoBERTa_model_trained.md).
 
 ## Licensing information
 This model is available under the [Apache License 2.0.](https://www.apache.org/licenses/LICENSE-2.0)