Update README.md

README.md

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 ### FinancialBERT for Sentiment Analysis
 
-[*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, we hope financial practitioners and researchers can benefit from our model without the necessity of the significant computational resources required to train the model. 
+[*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model. 
 
-Our model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset, experiments show that our model outperforms the general BERT and other financial domain-specific models.
+The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models.
  
 ### Training data
 FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
@@ -37,7 +37,7 @@ The evaluation metrics used are: Precision, Recall and F1-score. The following i
 | weighted avg | 0.98     | 0.98 | 0.98 | 970 |
 
  ### How to use 
-Our model can be used thanks to Transformers pipeline for sentiment analysis.
+The model can be used thanks to Transformers pipeline for sentiment analysis.
 ```python
 >>> from transformers import BertTokenizer, BertForSequenceClassification
 >>> from transformers import pipeline