Update README.md

README.md

@@ -17,18 +17,18 @@ We fine-tuned our model on Sentiment Analysis task using _FinancialPhraseBank_ d
  # How to use 
 Our model can be used thanks to Transformers pipeline for sentiment analysis.
 ```python
-from transformers import BertTokenizer, BertForSequenceClassification
-from transformers import pipeline
+>>> from transformers import BertTokenizer, BertForSequenceClassification
+>>> from transformers import pipeline
 
-finbert = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3)
-tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis")
+>>> model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3)
+>>> tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis")
 
-nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
+>>> nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
-sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.",  
+>>> sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.",  
              "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", 
              "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", 
              ]
-results = nlp(sentences)
-print(results)
+>>> results = nlp(sentences)
+>>> print(results)
 ```