dev-author-em-clf
| Property | Value |
|---|---|
| Parameter Count | 184M |
| Base Model | microsoft/deberta-v3-base |
| License | MIT |
| Tensor Type | F32 |
| Downloads | 33,404 |
What is dev-author-em-clf?
dev-author-em-clf is a fine-tuned text classification model based on the DeBERTa-v3 architecture. Built upon Microsoft's DeBERTa-v3-base, this model has been optimized for specific classification tasks while maintaining the robust features of the base architecture.
Implementation Details
The model utilizes the Transformers library and implements several key technical features:
- Fine-tuned using Adam optimizer with betas=(0.9,0.999) and epsilon=1e-08
- Linear learning rate scheduling with rate of 1e-05
- Training batch size of 8 for both training and evaluation
- Single epoch training with seed 12
- Implemented using PyTorch 2.4.1 and Transformers 4.44.2
Core Capabilities
- Text Classification tasks
- Supports TensorBoard integration
- Compatible with Inference Endpoints
- Utilizes Safetensors for model storage
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful DeBERTa-v3 architecture with specific optimizations for text classification tasks, making it particularly suitable for production deployments with its Inference Endpoints support.
Q: What are the recommended use cases?
The model is best suited for text classification tasks where you need a balance of performance and accuracy. Its F32 tensor type and moderate parameter count make it suitable for both research and production environments.
