mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 14:20:04 +06:00
4d367a3c81
* First draft * Fix more things * Improve more things * Remove some head models * Fix more things * Add missing layers * Remove tokenizer * Fix more things * Fix copied from statements * Make all tests pass * Remove print statements * Remove files * Fix README and docs * Add integration test and fix organization * Add tips * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Make tests faster, improve docs * Fix doc tests * Add model to toctree * Add docs * Add note about creating new checkpoint * Remove is_decoder * Make tests smaller, add docs Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
74 lines
3.9 KiB
Plaintext
74 lines
3.9 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
-->
|
|
|
|
# LiLT
|
|
|
|
## Overview
|
|
|
|
The LiLT model was proposed in [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
|
|
LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enable [LayoutLM](layoutlm)-like document understanding for many
|
|
languages.
|
|
|
|
The abstract from the paper is the following:
|
|
|
|
*Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.*
|
|
|
|
Tips:
|
|
|
|
- To combine the Language-Independent Layout Transformer with a new RoBERTa checkpoint from the [hub](https://huggingface.co/models?search=roberta), refer to [this guide](https://github.com/jpWang/LiLT#or-generate-your-own-checkpoint-optional).
|
|
The script will result in `config.json` and `pytorch_model.bin` files being stored locally. After doing this, one can do the following (assuming you're logged in with your HuggingFace account):
|
|
|
|
```
|
|
from transformers import LiltModel
|
|
|
|
model = LiltModel.from_pretrained("path_to_your_files")
|
|
model.push_to_hub("name_of_repo_on_the_hub")
|
|
```
|
|
|
|
- When preparing data for the model, make sure to use the token vocabulary that corresponds to the RoBERTa checkpoint you combined with the Layout Transformer.
|
|
- As (lilt-roberta-en-base)[https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base] uses the same vocabulary as [LayoutLMv3](layoutlmv3), one can use [`LayoutLMv3TokenizerFast`] to prepare data for the model.
|
|
The same is true for (lilt-roberta-en-base)[https://huggingface.co/SCUT-DLVCLab/lilt-infoxlm-base]: one can use [`LayoutXLMTokenizerFast`] for that model.
|
|
- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
|
|
|
|
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg"
|
|
alt="drawing" width="600"/>
|
|
|
|
<small> LiLT architecture. Taken from the <a href="https://arxiv.org/abs/2202.13669">original paper</a>. </small>
|
|
|
|
This model was contributed by [nielsr](https://huggingface.co/nielsr).
|
|
The original code can be found [here](https://github.com/jpwang/lilt).
|
|
|
|
|
|
## LiltConfig
|
|
|
|
[[autodoc]] LiltConfig
|
|
|
|
## LiltModel
|
|
|
|
[[autodoc]] LiltModel
|
|
- forward
|
|
|
|
## LiltForSequenceClassification
|
|
|
|
[[autodoc]] LiltForSequenceClassification
|
|
- forward
|
|
|
|
## LiltForTokenClassification
|
|
|
|
[[autodoc]] LiltForTokenClassification
|
|
- forward
|
|
|
|
## LiltForQuestionAnswering
|
|
|
|
[[autodoc]] LiltForQuestionAnswering
|
|
- forward
|