jpWang/LiLT
Official PyTorch implementation of LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (ACL 2022)
This project helps document intelligence researchers analyze information from visually-rich documents across many languages. It takes scanned documents, PDFs, or images as input and identifies key entities and their relationships, such as names, addresses, or dates, even if the layout is complex. This tool is ideal for those developing systems to automatically extract structured data from diverse document types.
362 stars. No commits in the last 6 months.
Use this if you need to build robust, multilingual information extraction systems for structured documents where both text and visual layout are crucial.
Not ideal if you are looking for an off-the-shelf, plug-and-play solution without any development or fine-tuning effort.
Stars
362
Forks
41
Language
Python
License
MIT
Category
Last pushed
Oct 31, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/jpWang/LiLT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
xv44586/toolkit4nlp
transformers implement (architecture, task example, serving and more)
luozhouyang/transformers-keras
Transformer-based models implemented in tensorflow 2.x(using keras).
ufal/neuralmonkey
An open-source tool for sequence learning in NLP built on TensorFlow.
graykode/xlnet-Pytorch
Simple XLNet implementation with Pytorch Wrapper
uzaymacar/attention-mechanisms
Implementations for a family of attention mechanisms, suitable for all kinds of natural language...