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FinancialBERT-Sentiment-Analysis

Text Classification
Transformers
PyTorch
English
bert
financial-sentiment-analysis
sentiment-analysis
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xet
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Update README.md

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@@ -28,13 +28,11 @@ FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researc
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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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- | has | 0.7416 | 0.9674 | 0.8396 | 2362 |
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- | is in | 0.7813 | 0.7925 | 0.7869 | 2362 |
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- | is | 0.8650 | 0.6863 | 0.7653 | 2362 |
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- | are | 0.8365 | 0.8493 | 0.8429 | 2362 |
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- | x | 0.9515 | 0.8302 | 0.8867 | 2362 |
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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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  ### 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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+ | sentiment | precision | recall | f1-score | support |
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  | ------------- |:-------------:|:-------------:|:-------------:| -----:|
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+ | negative | 0.7416 | 0.9674 | 0.8396 | 2362 |
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+ | neutral | 0.7813 | 0.7925 | 0.7869 | 2362 |
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+ | positive | 0.8650 | 0.6863 | 0.7653 | 2362 |
 
 
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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 |