my_awesome_model
| Property | Value |
|---|---|
| Parameter Count | 67M |
| License | Apache-2.0 |
| Framework | PyTorch/TensorFlow |
| Base Model | DistilBERT |
| Training Accuracy | 92.95% |
What is my_awesome_model?
my_awesome_model is a fine-tuned version of DistilBERT-base-uncased, specifically optimized for text classification tasks. The model demonstrates impressive performance metrics, achieving a training accuracy of 92.95% and a final training loss of 0.0632 after 2 epochs of training.
Implementation Details
The model utilizes the Adam optimizer with a polynomial decay learning rate strategy, starting at 2e-05. It's implemented using both PyTorch and TensorFlow frameworks, with float32 precision for training.
- Architecture: DistilBERT base architecture with 67M parameters
- Training Framework: Transformers 4.22.2, TensorFlow 2.8.2
- Optimization: Adam optimizer with polynomial decay
- Training Precision: Float32
Core Capabilities
- Text Classification with high accuracy (92.95% on training set)
- Efficient inference with reduced parameter count compared to BERT
- Compatible with TensorBoard for visualization
- Supports Inference Endpoints for deployment
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of DistilBERT with custom fine-tuning, achieving high accuracy while maintaining a relatively small parameter count of 67M. The polynomial decay learning rate strategy helps in optimal convergence.
Q: What are the recommended use cases?
The model is best suited for text classification tasks where efficiency and accuracy are crucial. With its balance of performance and size, it's particularly well-suited for production deployments requiring reliable inference.
