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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: 1.1164
  • Answer: {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817}
  • Header: {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119}
  • Question: {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077}
  • Overall Precision: 0.8800
  • Overall Recall: 0.8892
  • Overall F1: 0.8846
  • Overall Accuracy: 0.8211

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 1500

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.0418 1.34 200 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211
0.0473 2.68 400 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211
0.0444 4.03 600 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211
0.0532 5.37 800 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211
0.0405 6.71 1000 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211
0.0383 8.05 1200 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211
0.0494 9.4 1400 1.1164 {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} 0.8800 0.8892 0.8846 0.8211

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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