doc2txt/layoutlmv2_cord

overfitting issue

I use this colab: https://colab.research.google.com/drive/1AXh3G3-VmbMWlwbSvesVIurzNlcezTce?usp=sharing

to Fine tuning LayoutLMv2ForTokenClassification on CORD dataset

here is the result: https://huggingface.co/doc2txt/layoutlmv2-finetuned-cord

• F1: 0.9665

and indeed the result are pretty amazing when running on the test set, however when running on any other receipt (printed or pdf) the result are completely off

So from some reason the model is overfitting to the cord dataset, even though I use similar images for testing.

I don't think that there is a Data leakage unless the cord DS is not clean (which I assume it is clean)

What could be the reason for this? Is it some inherent property of LayoutLM? The LayoutLM models are somewhat old, and it seems deserted...

I don't have much experience so I would appreciate any info Thanks

here is an example code of how to run this model on a specific img folder: https://huggingface.co/doc2txt/layoutlmv2-finetuned-cord/blob/main/LayoutLMv2Main_cord2_gOcr_folder.py

layoutlmv2-finetuned-cord

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the cord dataset. It achieves the following results on the evaluation set:

• Loss: 0.2819
• Precision: 0.9653
• Recall: 0.9676
• F1: 0.9665
• Accuracy: 0.9703

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	400	1.2752	0.8527	0.8382	0.8454	0.8481
1.9583	2.0	800	0.6372	0.8799	0.8948	0.8873	0.9021
0.7097	3.0	1200	0.4255	0.9241	0.9264	0.9253	0.9414
0.3845	4.0	1600	0.3021	0.9414	0.9482	0.9448	0.9611
0.2699	5.0	2000	0.2819	0.9653	0.9676	0.9665	0.9703

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1