Finetuned Multi News Bart Text Summarisation

🚀 finetuned_multi_news_bart_text_summarisation

This model is a fine - tuned version of slauw87/bart_summarisation on the multi_news dataset, aiming to provide high - quality text summarization.

🚀 Quick Start

This model is a fine - tuned version of slauw87/bart_summarisation on the multi_news dataset. It achieves the following results on the evaluation set:

• Loss: 2.8952
• Rouge1: 0.4038
• Rouge2: 0.1389
• Rougel: 0.2155
• Rougelsum: 0.2147
• Gen Len: 138.7667

📦 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

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3

📄 License

This model is licensed under the Apache - 2.0 license.