ERNIE-Layout-Pytorch
| Property | Value |
|---|---|
| License | MIT |
| Original Source | PaddlePaddle/ernie-layoutx-base-uncased |
| Paper | Research Paper |
| Framework | PyTorch |
What is ERNIE-Layout-Pytorch?
ERNIE-Layout-Pytorch is an unofficial PyTorch implementation of the ERNIE-Layout model, originally developed by PaddleNLP. This model is specifically designed for document understanding tasks, combining visual and textual information with layout awareness. It's converted from the original PaddlePaddle implementation to make it accessible for PyTorch users.
Implementation Details
The model implements a sophisticated architecture that processes both textual and visual information from documents. It utilizes the ErnieLayoutForQuestionAnswering architecture and includes specialized components like ErnieLayoutProcessor and ErnieLayoutTokenizerFast for handling document processing tasks.
- Supports visual question answering on documents
- Processes both text and layout information
- Includes image processing capabilities through LayoutLMv3ImageProcessor
- Features custom tokenization for document understanding
Core Capabilities
- Document visual question answering
- Layout-aware text processing
- Integrated image and text analysis
- Support for OCR box coordinates
- Flexible token classification and sequence handling
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to process both textual content and spatial layout information in documents, making it particularly effective for tasks requiring understanding of document structure and content relationships.
Q: What are the recommended use cases?
The model is ideal for document understanding tasks, particularly visual question answering on documents, form understanding, and any scenario where both text content and spatial layout information need to be processed together.
