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ahmedrachid
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FinancialBERT-Sentiment-Analysis

Text Classification
Transformers
PyTorch
English
bert
financial-sentiment-analysis
sentiment-analysis
Inference Endpoints
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@@ -30,12 +30,12 @@ The evaluation metrics used are: Precision, Recall and F1-score. The following i
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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 |
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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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  | sentiment | precision | recall | f1-score | support |
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  | ------------- |:-------------:|:-------------:|:-------------:| -----:|
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+ | negative | 0.96 | 0.97 | 0.97 | 116 |
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+ | neutral | 0.98 | 0.99 | 0.98 | 558 |
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+ | positive | 0.98 | 0.97 | 0.97 | 296 |
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  | | | | | |
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+ | macro avg | 0.97 | 0.98 | 0.98 | 970 |
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+ | weighted avg | 0.98 | 0.98 | 0.98 | 970 |
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  ### How to use
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  Our model can be used thanks to Transformers pipeline for sentiment analysis.