qs-classifier-bert
| Property | Value |
|---|---|
| Base Model | bert-base-uncased |
| Framework | PyTorch 1.12.0+cu113 |
| Transformers Version | 4.21.1 |
| Training Accuracy | 99.94% |
What is qs-classifier-bert?
qs-classifier-bert is a fine-tuned variant of the BERT base uncased model, specifically optimized for classification tasks. The model demonstrates exceptional performance with a validation accuracy of 99.94% and a remarkably low validation loss of 0.0023.
Implementation Details
The model utilizes the BERT architecture with carefully tuned hyperparameters. Training was conducted using the Adam optimizer with customized beta parameters (0.9, 0.999) and epsilon=1e-08. The implementation leverages Native AMP for mixed precision training, optimizing both performance and resource utilization.
- Learning rate: 2e-05 with linear scheduling
- Batch sizes: 16 for both training and evaluation
- Training epochs: 1
- Seed: 42 for reproducibility
Core Capabilities
- High-accuracy sequence classification
- Efficient training with mixed precision support
- Optimized for production deployment with PyTorch
- Consistent performance with controlled randomization
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional accuracy (99.94%) achieved in just one epoch of training, suggesting highly efficient fine-tuning and robust architecture adaptation. The implementation of Native AMP and carefully chosen hyperparameters contributes to its superior performance.
Q: What are the recommended use cases?
While specific use cases aren't detailed in the model card, the architecture is suitable for various sequence classification tasks. The high accuracy suggests it's particularly effective for well-defined classification problems where precise categorization is crucial.
