Update README.md

README.md

@@ -23,7 +23,7 @@ pipeline_tag: text-to-image
 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. 
-- Divide the training corpus into two parts, training with 249,999 sentences and validation with 10,000 sentences.
+- Divide the training corpus into two parts, training with 249,000 sentences and validation with 1,000 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.