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Document Visual Question Answering

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anisha2102/docvqa

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This repo hosts the basic functional code for our approach entitledHyperDQA in theDocument Visual Question Answering competition hosted as a part ofWorkshop on Text and Documents in Deep Learning Era atCVPR2020. Our approach stands at position 4 on theLeaderboard.

Read more about our approach in thisblogpost!

Installation

Virtual Environment Python 3 (Recommended)

  1. Clone the repository
git clone https://github.com/anisha2102/docvqa.git
  1. Install libraries
pip install -r requirements.txt

Downloads

  1. Download the datasetThe dataset for Task 1 can be downloaded from the CompetitionWebsite from the Downloads Section.The dataset consists of document images and their corresponding OCR transcriptions.

  2. Download the pretrained modelDownload the pretrained model for LayoutLM-Base, Uncased fromhere

Prepare dataset

python create_dataset.py \         <data-ocr-folder> \         <data-documents-folder> \         <path-to-train_v1.0.json> \         <train-output-json-path> \         <validation-output-json-path>

Train the model

CUDA_VISIBLE_DEVICES=0 python run_docvqa.py \    --data_dir <data-folder> \    --model_type layoutlm \    --model_name_or_path <pretrained-model-path> \ #example ./models/layoutlm-base-uncased    --do_lower_case \    --max_seq_length 512 \    --do_train \    --num_train_epochs 15 \    --logging_steps 500 \    --evaluate_during_training \    --save_steps 500 \    --do_eval \    --output_dir  <data-folder>/<exp-folder> \    --per_gpu_train_batch_size 8 \    --overwrite_output_dir \    --cache_dir <data-folder>/models \    --skip_match_answers \    --val_json <train-output-json-path> \    --train_json <train-output-json-path> \

Model Checkpoints

Download the pytorch_model.bin file from the link below and copy it to the models folder.Google Drive Link

Demo

Try out the demo on a sample datapoint with demo.ipynb

Acknowledgements

The code and pretrained models are based onLayoutLM andHuggingFace Transformers. Many thanks for their amazing open source contributions.

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