Bart Base Samsum

🚀 bart-base-samsum

This model is trained with Amazon SageMaker and the new Hugging Face Deep Learning container, aiming to provide efficient summarization capabilities.

🚀 Quick Start

This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.

You can find the notebook here and the referring blog post here.

For more information look at:

• 🤗 Transformers Documentation: Amazon SageMaker
• Example Notebooks
• Amazon SageMaker documentation for Hugging Face
• Python SDK SageMaker documentation for Hugging Face
• Deep Learning Container

✨ Features

• Trained using Amazon SageMaker and Hugging Face Deep Learning container.
• Suitable for summarization tasks.

📦 Installation

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

💻 Usage Examples

Basic Usage

from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-base-samsum")

conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
Jeff: ok.
Jeff: and how can I get started? 
Jeff: where can I find documentation? 
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face                                           
'''
nlp(conversation)

Advanced Usage

No advanced usage examples are provided in the original document, so this part is skipped.

📚 Documentation

Hyperparameters

{
    "dataset_name": "samsum",
    "do_eval": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "facebook/bart-base",
    "num_train_epochs": 3,
    "output_dir": "/opt/ml/model",
    "per_device_eval_batch_size": 8,
    "per_device_train_batch_size": 8,
    "seed": 7
}

Train results

Eval results

🔧 Technical Details

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

📄 License

This model is licensed under the Apache-2.0 license.