Update README.md

Add an updated version of the README file

README.md

@@ -36,19 +36,19 @@ widget:
 - text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
 ---
 
-## Model `bertin_base_sentiment_analysis_es`
+# Model bertin_base_sentiment_analysis_es
 
-### **A finetuned model for Sentiment analysis in Spanish**
+## **A finetuned model for Sentiment analysis in Spanish**
 
 This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
 The base model is **Bertin base** which is a RoBERTa-base model pre-trained  on the Spanish portion of mC4 using Flax.
 It was trained by the Bertin Project.[Link to base model](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) 
 
 Article: BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling
-Author = Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury,
-journal = Procesamiento del Lenguaje Natural,
-volume = 68, number = 0, year = 2022
-url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403},
+- Author = Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury,
+- journal = Procesamiento del Lenguaje Natural,
+- volume = 68, number = 0, year = 2022
+- url = http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403
 
 ## Dataset
 The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages. 
@@ -75,10 +75,14 @@ This model is intented for Sentiment Analysis for spanish corpus and finetuned s
     }
 
 ## Evaluation results
-Accuracy = 0.8989333333333334
-F1 Score = 0.8989063750333421
-Precision = 0.877147319104633
-Recall = 0.9217724288840262
+
+- Accuracy = 0.8989333333333334
+
+- F1 Score = 0.8989063750333421
+
+- Precision = 0.877147319104633
+
+- Recall = 0.9217724288840262
 
 ## Test results