| --- |
| license: apache-2.0 |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| tags: |
| - finance |
| - sentiment-analysis |
| language: |
| - en |
| base_model: |
| - google-bert/bert-base-uncased |
| --- |
| |
| # BERT Fine-tuned - Financial Sentiment Analysis Model |
|
|
| <div style="text-align:center;"> |
| <img src="https://huggingface.co/Shaivn/Financial-Sentiment-Analysis/resolve/main/financial-sentiment-analysis-logo.png" alt="logo" style="width:250px;height:250px;"> |
| </div> |
| |
|
|
|
|
| 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" |
