Update README.md

README.md

@@ -25,11 +25,7 @@ FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researc
 - max_seq_length = 512
 - num_train_epochs = 5
 
-<div style="clear: both;">
-
-  </div>
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-## Metrics
+### Metrics
 The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
 | relation  | precision        | recall           | f1-score  | support  |
 | ------------- |:-------------:|:-------------:|:-------------:| -----:|
@@ -41,6 +37,7 @@ The evaluation metrics used are: Precision, Recall and F1-score. The following i
 |  |      |  |  |  |
 | macro avg | 0.8352     | 0.8251 | 0.8243 | 11810 |
 | weighted avg | 0.8352     | 0.8251 | 0.8243 | 11810 |
+
  ### How to use 
 Our model can be used thanks to Transformers pipeline for sentiment analysis.
 ```python