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my-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: 1.7942
  • Answer: {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817}
  • Header: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
  • Question: {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077}
  • Overall Precision: 0.8740
  • Overall Recall: 0.8927
  • Overall F1: 0.8833
  • Overall Accuracy: 0.8042

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.1935 26.32 500 1.2125 {'precision': 0.8702830188679245, 'recall': 0.9033047735618115, 'f1': 0.8864864864864864, 'number': 817} {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} {'precision': 0.8748921484037964, 'recall': 0.9415041782729805, 'f1': 0.9069767441860466, 'number': 1077} 0.8605 0.9041 0.8818 0.8024
0.0063 52.63 1000 1.4406 {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} {'precision': 0.632183908045977, 'recall': 0.46218487394957986, 'f1': 0.5339805825242718, 'number': 119} {'precision': 0.8827708703374778, 'recall': 0.9229340761374187, 'f1': 0.902405810258738, 'number': 1077} 0.8683 0.8907 0.8794 0.8175
0.002 78.95 1500 1.6624 {'precision': 0.861904761904762, 'recall': 0.8861689106487148, 'f1': 0.8738684369342186, 'number': 817} {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} {'precision': 0.8920863309352518, 'recall': 0.9210770659238626, 'f1': 0.9063499314755596, 'number': 1077} 0.8674 0.8838 0.8755 0.7998
0.0006 105.26 2000 1.7942 {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817} {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119} {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077} 0.8740 0.8927 0.8833 0.8042
0.0002 131.58 2500 1.8161 {'precision': 0.8591385331781141, 'recall': 0.9033047735618115, 'f1': 0.8806682577565632, 'number': 817} {'precision': 0.6346153846153846, 'recall': 0.5546218487394958, 'f1': 0.5919282511210763, 'number': 119} {'precision': 0.9047619047619048, 'recall': 0.9173630454967502, 'f1': 0.9110189027201475, 'number': 1077} 0.8720 0.8902 0.8810 0.8021

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2
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