bert_roberta_summarization_cnn_dailymail
| Property | Value |
|---|---|
| Author | Ayham |
| Framework | Transformers 4.12.0.dev0, PyTorch 1.10.0 |
| Dataset | CNN/DailyMail |
| Model URL | Hugging Face Hub |
What is bert_roberta_summarization_cnn_dailymail?
This is a specialized text summarization model that combines BERT and RoBERTa architectures, fine-tuned specifically on the CNN/DailyMail dataset. The model leverages advanced transformer-based architectures to generate concise and accurate summaries of news articles.
Implementation Details
The model was trained using careful hyperparameter optimization, including:
- Learning rate: 5e-05 with Adam optimizer
- Batch size: 8 for both training and evaluation
- Training duration: 3 epochs
- Warmup steps: 2000
- Mixed precision training using Native AMP
Core Capabilities
- Text summarization optimized for news articles
- Efficient processing with mixed precision training
- Balanced performance with moderate batch sizes
- Optimized learning schedule with warmup period
Frequently Asked Questions
Q: What makes this model unique?
This model combines the strengths of both BERT and RoBERTa architectures while being specifically optimized for news summarization tasks through the CNN/DailyMail dataset fine-tuning.
Q: What are the recommended use cases?
The model is best suited for summarizing news articles and similar long-form content, particularly those similar in style to CNN and DailyMail articles.
