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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- finance |
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- sentiment-analysis |
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--- |
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# BERT Fine-tuned - Financial Sentiment Analysis Model |
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<div style="text-align:center;"> |
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<img src="https://huggingface.co/Shaivn/Financial-Sentiment-Analysis/resolve/main/financial-sentiment-analysis-logo.png" alt="logo" style="width:250px;height:250px;"> |
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</div> |
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This model is a Fine-Tuned version of BERT (bert-base-uncased) |
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It is designed to classify text into positive, neutral, and negative sentiments. The fine-tuning was performed using the Financial Phrase Bank dataset. |
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## Results |
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It achieves the following results on the evaluation set: |
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* F1 Score: 0.9468 |
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* Validation loss: 0.1860 |
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## Training Data |
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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. |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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* learning rate : 2e-5 |
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* train_batch_size : 32 |
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* eval_batch_size: 32 |
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* seed: 42 |
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* Optimizer : AdamW |
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* num_epochs: 3 |
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## Training Results |
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| **Epoch** | **Validation Loss** | **Accuracy** | |
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|:----------:|:---------------------:|:-------------:| |
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|01 | 0.1860 | 0.9468 | |
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|02 | 0.1756 | 0.9424 | |
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|03 | 0.1726 | 0.9432 | |
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This model is a part of my thesis: "A Proposal of a Sentiment Analysis Model for Business Intelligence" |
