Pytorch Implementation of Value Retrieval with Arbitrary Queries for Form-like Documents.
CUDA="11.0"
CUDNN="8"
UBUNTU="18.04"
bash install.sh
git clone https://github.com/NVIDIA/apex && cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
pip install .
# under our project root folder
pip install .Our model is pre-trained on IIT-CDIP dataset, fine-tuned on FUNSD train set and evaluated on FUNSD test set and INV-CDIP test set.
-
Download our processed OCR results of IIT-CDIP with hocr_list_addr.txt and put under PRETRAIN_DATA_FOLDER/.
-
Download our processed FUNSD and INV-CDIP datasets and put under DATA_DIR/.
-
Download our model fine-tuned on FUNSD here.
-
Do inference following
# $MODEL_PATH here is where you save the fine-tuned model.
# DATASET_NAME is FUNSD or INV-CDIP.
bash reproduce_results.sh $MODEL_PATH $DATA_DIR/DATASET_NAME
- You should get the following results.
| Datasets | Precision | Recall | F1 |
|---|---|---|---|
| FUNSD | 60.4 | 60.9 | 60.7 |
| INV-CDIP | 50.5 | 47.6 | 49.0 |
-
You can skip the following steps by downloading our pre-trained SimpleDLM model here.
-
Or download layoutlm-base-uncased.
-
Do pre-training following
# $NUM_GPUS is the number of gpus you want to do the pretraining on. To reproduce the paper's results we recommend to use 8 gpus.
# $MODEL_PATH here is where you save the LayoutLM model.
# $PRETRAIN_DATA_FOLDER is the folder of IIT-CDIP hocr files.
python -m torch.distributed.launch --nproc_per_node=$NUM_GPUS pretraining.py \
--model_name_or_path $MODEL_PATH --data_dir $PRETRAIN_DATA_FOLDER \
--output_dir $OUTPUT_DIR
- Do fine-tuning following
# $MODEL_PATH is where you save the pre-trained simpleDLM model.
CUDA_VISIBLE_DEVICES=0 python run_query_value_retrieval.py --model_type simpledlm --model_name_or_path $MODEL_PATH \
--data_dir $DATA_DIR/FUNSD/ --output_dir $OUTPUT_DIR --do_train --evaluate_during_training
If you find this codebase useful, please cite our paper:
@article{gao2021value,
title={Value Retrieval with Arbitrary Queries for Form-like Documents},
author={Gao, Mingfei and Xue, Le and Ramaiah, Chetan and Xing, Chen and Xu, Ran and Xiong, Caiming},
journal={arXiv preprint arXiv:2112.07820},
year={2021}
}Please send an email to [email protected] or [email protected] if you have questions.
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent