Update README.md

README.md

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 We fine-tuned our model on Sentiment Analysis task using _FinancialPhraseBank_ dataset, experiments show that our model outperforms the general BERT and other financial domain-specific models.
  
+# Training data
+**FinancialBERT** model was fine-tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
+
  # How to use 
 Our model can be used thanks to Transformers pipeline for sentiment analysis.
 ```python
@@ -36,6 +39,3 @@ Our model can be used thanks to Transformers pipeline for sentiment analysis.
  {'label': 'neutral', 'score': 0.9997822642326355},
  {'label': 'negative', 'score': 0.9877365231513977}]
 ```
-
-# Training data
-**FinancialBERT** model was fine-tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).