lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6139
• Answer: {'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817}
• Header: {'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119}
• Question: {'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077}
• Overall Precision: 0.7659
• Overall Recall: 0.8450
• Overall F1: 0.8035
• Overall Accuracy: 0.8009

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: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 260

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.7536	2.67	200	0.6139	{'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817}	{'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119}	{'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077}	0.7659	0.8450	0.8035	0.8009

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2