lilt roberta en base

Introduction

LILT-RoBERTa (Base-Sized Model) is a Language-Independent Layout Transformer that integrates a pre-trained RoBERTa model with a Language-Independent Layout Transformer (LiLT). It is designed for structured document understanding tasks such as document image classification, parsing, and question answering. This model was introduced in the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding" by Wang et al.

Architecture

The architecture of LILT-RoBERTa combines any pre-trained RoBERTa encoder with a lightweight Layout Transformer. This setup allows for creating a LayoutLM-like model adaptable to any language, thus broadening the scope of structured document processing beyond language limitations.

Training

The model is intended to be fine-tuned for specific tasks related to document analysis, such as classification, parsing, and QA. Users are encouraged to explore the model hub for task-specific fine-tuned versions.

Guide: Running Locally

1. Environment Setup: Ensure Python and PyTorch are installed. Use a virtual environment for project dependencies.
2. Install Transformers: Run pip install transformers to get the latest version of the Hugging Face library.
3. Load the Model: Use the Transformers library to load LILT-RoBERTa for inference or fine-tuning.
4. Example Scripts: Refer to the Hugging Face documentation for code examples on using the model.
5. Hardware Recommendation: For optimal performance, consider using cloud GPU services such as AWS, GCP, or Azure.

License

The model is distributed under the MIT License, allowing for open-source usage and modification.