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Fine-tuning examples

FUNSD EE - BIO (BIES) tagger

Prepare data

We conducted the FUNSD EE experiment based on the FUNSD data preprocessed in LayoutLM. Original code can be found in this link. To run it, please follow the steps below:

  1. move to preprocess/funsd/.
  2. run bash preprocess.sh.
  3. run preprocess_2nd.py. This scripts converts the preprocessed data in LayoutLM to fit this repo.

Data will be created in datasets/funsd/.

Perform fine-tuning
  1. Run the command below:
CUDA_VISIBLE_DEVICES=0 python train.py --config=configs/finetune_funsd_ee_bies.yaml
  1. Evaluate the model
CUDA_VISIBLE_DEVICES=0 python evaluate.py --config=configs/finetune_funsd_ee_bies.yaml --pretrained_model_file=finetune_funsd_ee_bies__bros-base-uncased/checkpoints/epoch=99-last.pt

FUNSD EE - SPADE decoder

Prepare data
  1. move to preprocess/funsd_spade/.
  2. run preprocess.py.

Data will be created in datasets/funsd_spade.

Perform fine-tuning
  1. Run the command below:
CUDA_VISIBLE_DEVICES=0 python train.py --config=configs/finetune_funsd_ee_spade.yaml
  1. Evaluate the model
CUDA_VISIBLE_DEVICES=0 python evaluate.py --config=configs/finetune_funsd_ee_spade.yaml --pretrained_model_file=finetune_funsd_ee_spade__bros-base-uncased/checkpoints/epoch=99-last.pt

FUNSD EL - SPADE decoder

Prepare data

Same as above.

  1. move to preprocess/funsd_spade/.
  2. run preprocess.py.

Data will be created in datasets/funsd_spade.

Perform fine-tuning
  1. Run the command below:
CUDA_VISIBLE_DEVICES=0 python train.py --config=configs/finetune_funsd_el_spade.yaml
  1. Evaluate the model
CUDA_VISIBLE_DEVICES=0 python evaluate.py --config=configs/finetune_funsd_el_spade.yaml --pretrained_model_file=finetune_funsd_el_spade__bros-base-uncased/checkpoints/epoch=99-last.pt

SROIE* EE - BIO tagger

In the original SROIE task, semantic contents (Company, Date, Address, and Total price) are generated without explicit connection to the text blocks. To convert SROIE into a EE task, we developed SROIE* by matching ground truth contents with text blocks. We also split the original training set into 526 training and 100 testing examples because the ground truths are not given in the original test set (now it is opened).

Preprae data

  1. Download SROIE* (sroie.tar.gz)
  2. Extract sroie.tar.gz to datasets/. For the image files, you need to download from the official website.
Perform fine-tuning
  1. Run the command below:
CUDA_VISIBLE_DEVICES=0 python train.py --config=configs/finetune_sroie_ee_bio.yaml
  1. Evaluate the model
CUDA_VISIBLE_DEVICES=0 python evaluate.py --config=configs/finetune_sroie_ee_bio.yaml --pretrained_model_file=finetune_sroie_ee_bio__bros-base-uncased/checkpoints/epoch=29-last.pt