Update README.md

README.md

@@ -22,19 +22,28 @@ pipeline_tag: text-to-image
 ## Description
 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](https://www.sbert.net/) 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-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.
+  vectors _u_ and _v_ generated using the cosine similarity metric (_CosineSimilarityLoss()_) are evaluated and compares with the real
+  similarity value obtained from the training corpus. The performance measurement of the model during training was calculated using Spearman's correlation coefficient
+  between the real similarity vector and the calculated similarity vector.
 
-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. 
+The total training time using the _sentence-transformer_ library in Python was 42 days using all the available GPUs of the server, and with exclusive dedication.
 
-## How to use
+A comparison was made between the Spearman's correlation for 1000 test sentences between the base model and our trained model. 
+As can be seen in the following table, our model obtains better results (correlation closer to 1).
+
+| Models            | Spearman's correlation |
+|    :---:          |     :---: |
+| RoBERTa-base-bne  | 0.827176427 | 
+| RoBERTa-celebA-Sp | 0.999913276 | 
 
-To make use of the model use the following code in Python:
+## How to use
+Downloading the model results in a directory called **roberta-large-bne-celebAEs-UNI** that contains its main files. 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')