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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
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