led large 16384

Introduction

AllenAI's Longformer Encoder-Decoder (LED) is a model designed for processing long documents, based on the architecture of BART. The LED model was developed to handle up to 16,384 tokens by extending the position embedding matrix of BART. It is particularly suitable for tasks such as long-range summarization and question answering. The model details are discussed in the paper "Longformer: The Long-Document Transformer."

Architecture

The LED model shares its architecture with BART, a well-known transformer model. To accommodate longer input sequences, the position embeddings of BART were repeated 16 times. This allows the LED model to process substantially longer text inputs than standard transformer models.

Training

A demonstration of fine-tuning the LED model for specific downstream tasks is provided in the form of a notebook. This example showcases how to adapt the model for specialized applications, facilitating tasks that require processing extensive text data.

Guide: Running Locally

1. Installation: Ensure you have Python and the necessary libraries installed, including PyTorch or TensorFlow, based on your preference.
2. Model Download: Use the Hugging Face Transformers library to download the LED model.
3. Setup Environment: Create a virtual environment and install dependencies from the requirements.txt file.
4. Data Preparation: Prepare your dataset for the task by tokenizing and formatting it according to the model's requirements.
5. Fine-Tuning: Use the provided notebook as a guide to fine-tune the model on your dataset.
6. Inference: Run inference using the fine-tuned model to generate outputs on new data.

Cloud GPUs such as those offered by AWS, Google Cloud, or Azure are recommended for efficient training and inference, especially for handling long documents.

License

The LED model is distributed under the Apache-2.0 license, allowing for wide use with minimal restrictions.