2.9 KiB
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.