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FinancialBERT-Sentiment-Analysis

Text Classification
Transformers
PyTorch
English
bert
financial-sentiment-analysis
sentiment-analysis
Inference Endpoints
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@@ -25,7 +25,11 @@ FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researc
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  - max_seq_length = 512
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  - num_train_epochs = 5
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- ### Metrics
 
 
 
 
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  The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
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  | relation | precision | recall | f1-score | support |
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  | ------------- |:-------------:|:-------------:|:-------------:| -----:|
@@ -37,7 +41,6 @@ The evaluation metrics used are: Precision, Recall and F1-score. The following i
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  | | | | | |
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  | macro avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
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  | weighted avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
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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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  - max_seq_length = 512
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  - num_train_epochs = 5
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+ <div style="clear: both;">
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+ </div>
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+ ## Metrics
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  The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
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  | relation | precision | recall | f1-score | support |
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  | ------------- |:-------------:|:-------------:|:-------------:| -----:|
 
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  | | | | | |
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  | macro avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
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  | weighted avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
 
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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