Distilbart Cnn 12 6 Samsum

🚀 distilbart-cnn-12-6-samsum

This model is trained using 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.

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 on Amazon SageMaker with 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/distilbart-cnn-12-6-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)

📚 Documentation

Hyperparameters

{
    "dataset_name": "samsum",
    "do_eval": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "sshleifer/distilbart-cnn-12-6",
    "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

📄 License

This project is licensed under the Apache 2.0 license.