This model is a fine-tuned version of BERT (bert-base-uncased) aimed at classifying text into positive, neutral, and negative sentiments specifically for financial contexts. The fine-tuning was carried out using the Financial Phrase Bank dataset, making it incredibly relevant for applications in finance and business analytics.
Model Performance Results
The evaluation of the model yields impressive results:
- F1 Score: 0.9468
- Validation Loss: 0.1860
Training Data
The dataset used for training consists of 4,840 sentences from the financial phrase bank. It has been meticulously annotated by 16 individuals with substantial knowledge of financial markets, ensuring high-quality training data.
Training Hyperparameters
The following hyperparameters were employed during the training process:
- Learning Rate: 2e-5
- Train Batch Size: 32
- Eval Batch Size: 32
- Seed: 42
- Optimizer: AdamW
- Number of Epochs: 3
Training Results Overview
| Epoch | Validation Loss | Accuracy |
|---|---|---|
| 01 | 0.1860 | 0.9468 |
| 02 | 0.1756 | 0.9424 |
| 03 | 0.1726 | 0.9432 |
Understanding the Code Through Analogy
Imagine you’re training a puppy to recognize commands. You wouldn’t just throw a bunch of treats without teaching proper commands first. Instead, you might start with basic words to see which the puppy responds to best, adjusting the number of treats (hyperparameters) based on how quickly the puppy learns. In this analogy, the puppy represents our model learning from the finance-specific sentences in the dataset. Just as you’d reward the puppy for recognizing commands correctly, our model is trained (or “rewarded”) based on evaluation scores—like learning to differentiate between positive, neutral, and negative sentiments.
Troubleshooting Steps
If you are experiencing issues with this model or have questions, consider the following troubleshooting ideas:
- Verify that your environment has the necessary libraries installed, such as Hugging Face Transformers.
- Ensure your dataset is correctly formatted and adheres to the expected structure.
- Adjust hyperparameters incrementally and monitor changes in performance.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
