BERT Fine-tuned - Financial Sentiment Analysis Model
This model is a Fine-Tuned version of BERT (bert-base-uncased) It is designed to classify text into positive, neutral, and negative sentiments. The fine-tuning was performed using the Financial Phrase Bank dataset.
Results
It achieves the following results on the evaluation set:
- F1 Score: 0.9468
- Validation loss: 0.1860
Training Data
The dataset consists of 4840 sentences of the financial phrase bank. The dataset was annotated by 16 people with adequate background knowledge of financial markets.
Training hyperparameters
The following hyperparameters were used during training:
- learning rate : 2e-5
- train_batch_size : 32
- eval_batch_size: 32
- seed: 42
- Optimizer : AdamW
- num_epochs: 3
Training Results
| Epoch | Validation Loss | Accuracy |
|---|---|---|
| 01 | 0.1860 | 0.9468 |
| 02 | 0.1756 | 0.9424 |
| 03 | 0.1726 | 0.9432 |
This model is a part of my thesis: "A Proposal of a Sentiment Analysis Model for Business Intelligence"
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