transformers/examples/research_projects/layoutlmv3/README.md

Token classification with LayoutLMv3 (PyTorch version)

This directory contains a script, run_funsd_cord.py, that can be used to fine-tune (or evaluate) LayoutLMv3 on form understanding datasets, such as FUNSD and CORD.

The script run_funsd_cord.py leverages the 🤗 Datasets library and the Trainer API. You can easily customize it to your needs.

Fine-tuning on FUNSD

Fine-tuning LayoutLMv3 for token classification on FUNSD can be done as follows:

python run_funsd_cord.py \
  --model_name_or_path microsoft/layoutlmv3-base \
  --dataset_name funsd \
  --output_dir layoutlmv3-test \
  --do_train \
  --do_eval \
  --max_steps 1000 \
  --evaluation_strategy steps \
  --eval_steps 100 \
  --learning_rate 1e-5 \
  --load_best_model_at_end \
  --metric_for_best_model "eval_f1" \
  --push_to_hub \
  --push_to_hub°model_id layoutlmv3-finetuned-funsd

👀 The resulting model can be found here: https://huggingface.co/nielsr/layoutlmv3-finetuned-funsd. By specifying the push_to_hub flag, the model gets uploaded automatically to the hub (regularly), together with a model card, which includes metrics such as precision, recall and F1. Note that you can easily update the model card, as it's just a README file of the respective repo on the hub.

There's also the "Training metrics" tab, which shows Tensorboard logs over the course of training. Pretty neat, huh?

Fine-tuning on CORD

Fine-tuning LayoutLMv3 for token classification on CORD can be done as follows:

python run_funsd_cord.py \
  --model_name_or_path microsoft/layoutlmv3-base \
  --dataset_name cord \
  --output_dir layoutlmv3-test \
  --do_train \
  --do_eval \
  --max_steps 1000 \
  --evaluation_strategy steps \
  --eval_steps 100 \
  --learning_rate 5e-5 \
  --load_best_model_at_end \
  --metric_for_best_model "eval_f1" \
  --push_to_hub \
  --push_to_hub°model_id layoutlmv3-finetuned-cord

👀 The resulting model can be found here: https://huggingface.co/nielsr/layoutlmv3-finetuned-cord. Note that a model card gets generated automatically in case you specify the push_to_hub flag.