Update README.md

README.md

@@ -52,7 +52,11 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The model described here is a fine-tuned version of the BETO (BERT-based Spanish language model) for Named Entity Recognition (NER) tasks, 
+trained on the CoNLL-2002 dataset. BETO is a pre-trained language model specifically designed for the Spanish language, based on the BERT architecture.
+By fine-tuning BETO on the CoNLL-2002 dataset, the model has been adapted to recognize and classify named entities such as persons, organizations, 
+locations, and other miscellaneous entities within Spanish text. The fine-tuning process involves adjusting the pre-trained model weights to better 
+fit the specific task of NER, thereby improving its performance and accuracy on Spanish text.
 
 ## Intended uses & limitations
 
@@ -60,7 +64,8 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+The training was performed using a GPU with 22.5 GB of RAM, 53 GB of system RAM, and 200 GB of disk space. 
+This setup ensured efficient handling of the large dataset and the computational demands of fine-tuning the model.
 
 ## Training procedure