LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
Paper • 2202.13669 • Published • 3
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Check out the documentation for more information.
lilt-camembertv2-base
This repository contains lilt-camembertv2-base weights exported to safetensors format.
Source
- Layout encoder:
jpwang/lilt-only-base— layout-only pretrained checkpoint - Text encoder:
almanach/camembertv2-base— French language model
Methodology
Produced by weight merging following the official gen_weight_roberta_like.py procedure. No training or fine-tuning was performed.
Intended as a starting point for downstream fine-tuning on French document understanding tasks (NER, token classification, extractive QA…).
Files
| File | Description |
|---|---|
model.safetensors |
Model weights |
pytorch_model.bin |
Model weights (PyTorch format) |
config.json |
Model configuration |
tokenizer_config.json |
Tokenizer configuration |
tokenizer.json |
Tokenizer (fast) |
License
Weights are derived from models released under MIT and Apache-2.0 licenses.
Acknowledgements
- LiLT: A Simple yet Effective Language-Independent Layout Transformer — Wang et al., 2022
Note: This is not an official release from any of the above organizations.
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