led base 16384

Introduction

AllenAI's Longformer Encoder-Decoder (LED) model, known as led-base-16384, is designed for processing long documents. It is based on the architecture of bart-base, with its position embedding matrix extended to handle 16,384 tokens. The model is particularly useful for tasks like long-range summarization and question answering. More details can be found in the paper "Longformer: The Long-Document Transformer."

Architecture

The led-base-16384 model shares its architecture with bart-base, a transformer model. It extends the position embedding matrix of bart-base to accommodate the processing of up to 16,384 tokens, allowing it to manage longer input sequences effectively.

Training

The model can be fine-tuned for specific downstream tasks. A Colab notebook is available as a guide for fine-tuning led-base-16384 on various tasks, demonstrating its versatility and adaptability.

Guide: Running Locally

1. Clone the Repository: Begin by cloning the Hugging Face repository containing the model.
2. Set Up Environment: Install dependencies using pip, including PyTorch or TensorFlow, depending on your preference.
3. Load the Model: Use the Hugging Face Transformers library to load the led-base-16384 model.
4. Fine-tuning: Follow the provided Colab notebook to fine-tune the model for your specific needs.
5. Inference: Test the model with your data to ensure it performs as expected.

For enhanced performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The led-base-16384 model is licensed under the Apache-2.0 License, allowing for both personal and commercial use, with certain conditions.