Update README.md

README.md

@@ -39,24 +39,24 @@ The evaluation metrics used are: Precision, Recall and F1-score. The following i
  ### How to use 
 The 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
 
->>> model = 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=model, 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)
 
 [{'label': 'positive', 'score': 0.9998133778572083},
  {'label': 'neutral', 'score': 0.9997822642326355},
  {'label': 'negative', 'score': 0.9877365231513977}]
 ```
 
->Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)
+> Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)