Pegasus Multi News NewsSummarization BBC

🚀 pegasus-multi_news-NewsSummarization_BBC

This model is a fine - tuned version of google/pegasus-multi_news, designed for news article summarization.

🚀 Quick Start

This is a text summarization model of news articles. For more information on how it was created, check out the following link: Creation Details

✨ Features

• This model is a fine - tuned version of google/pegasus-multi_news.
• It is intended to demonstrate the ability to solve a complex problem using technology.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: BBC News Summary Dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• num_epochs: 2

Training results

Unfortunately, I did not set the metrics to automatically upload here. They are as follows:

Framework versions

• Transformers 4.21.3
• Pytorch 1.12.1
• Datasets 2.4.0
• Tokenizers 0.12.1

🔧 Technical Details

The model is a fine - tuned version of google/pegasus-multi_news, and the training process involves specific hyperparameters and evaluation metrics.

📄 License

No license information is provided in the original document, so this section is skipped.