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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.
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  # How to use
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  Our model can be used thanks to Transformers pipeline for sentiment analysis.
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  ```python
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  {'label': 'neutral', 'score': 0.9997822642326355},
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  {'label': 'negative', 'score': 0.9877365231513977}]
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  ```
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- # Training data
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- **FinancialBERT** model was fine-tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
 
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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.
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+ # Training data
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+ **FinancialBERT** model was fine-tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
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+
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  # How to use
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  Our model can be used thanks to Transformers pipeline for sentiment analysis.
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  ```python
 
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  {'label': 'neutral', 'score': 0.9997822642326355},
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  {'label': 'negative', 'score': 0.9877365231513977}]
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  ```