Mbart Finetune En Cnn

🚀 mbert-finetune-en-cnn

This model is a fine - tuned version of facebook/mbart-large-50 on the cnn_dailymail dataset. It can achieve good performance in text summarization tasks, providing reliable results for English text summarization.

🚀 Quick Start

This section provides basic information about the model and its performance. For more detailed usage, please refer to the following sections.

✨ Features

• Fine - tuned on cnn_dailymail: Trained on the cnn_dailymail dataset, it is well - adapted to news text summarization.
• Multiple evaluation metrics: Evaluated using multiple metrics such as Loss, Rouge - 1, Rouge - 2, Rouge - l, Gen Len, and Bertscore, demonstrating its effectiveness.

📦 Installation

No specific 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

Model Performance

This model is a fine - tuned version of facebook/mbart-large-50 on the cnn_dailymail dataset. It achieves the following results on the evaluation set:

• Loss: 3.5577
• Rouge - 1: 37.69
• Rouge - 2: 16.47
• Rouge - l: 35.53
• Gen Len: 79.93
• Bertscore: 74.92

Training Hyperparameters

The following hyperparameters were used during training:

Framework Versions

• Transformers 4.20.0
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1

🔧 Technical Details

No specific technical details are provided in the original document, so this section is skipped.

📄 License

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