Gender-Classification
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Downloads | 1,324,536 |
| Base Model | DistilBERT-base-uncased |
| Training Accuracy | 100% |
What is Gender-Classification?
Gender-Classification is a fine-tuned version of DistilBERT specifically optimized for gender classification tasks. Built on the efficient DistilBERT architecture, this model has achieved remarkable performance with perfect accuracy on its evaluation set, making it particularly reliable for gender-related text classification tasks.
Implementation Details
The model utilizes the PyTorch framework and Transformers library, incorporating TensorBoard for visualization. Training was conducted over 5 epochs using the Adam optimizer with a learning rate of 2e-05 and achieved perfect convergence with zero loss.
- Base Architecture: DistilBERT-base-uncased
- Training Batch Size: 16
- Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-08)
- Learning Rate Schedule: Linear
- Training Duration: 5 epochs
Core Capabilities
- Text-based gender classification
- High-accuracy predictions
- Efficient inference with DistilBERT architecture
- Support for deployment via Inference Endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its perfect accuracy score and significant adoption with over 1.3 million downloads. It leverages the efficiency of DistilBERT while maintaining high performance for gender classification tasks.
Q: What are the recommended use cases?
The model is specifically designed for gender classification in text data. It's suitable for applications in content analysis, user profiling, and demographic studies where gender identification from text is required.
