Update README.md

README.md

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 ## Model description
-The **roberta-base-bne-capitel-ner-plus** is Named Entity Recognition (NER) model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. This model is a more robust version of the [roberta-base-bne-capitel-ner](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner) model that recognizes better lowercased Named Entities (NE).
+The **roberta-base-bne-capitel-ner-plus** is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. This model is a more robust version of the [roberta-base-bne-capitel-ner](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner) model that recognizes better lowercased Named Entities (NE).
 
 ## Intended uses and limitations
 
@@ -120,10 +120,10 @@ The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5
 This model was finetuned maximizing F1 score.
 
 ## Evaluation results
-We evaluated the *roberta-base-bne-capitel-ner-plus** on the CAPITEL-NERC test set against standard multilingual and monolingual baselines:
+We evaluated the **roberta-base-bne-capitel-ner-plus** on the CAPITEL-NERC test set against standard multilingual and monolingual baselines:
 
 
-| Model        | XNLI (Accuracy) | 
+| Model        | CAPITEL-NERC (F1) | 
 | ------------|:----|
 | roberta-large-bne-capitel-ner | **90.51** |
 | roberta-base-bne-capitel-ner | 89.60|