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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: 1.4588
  • Answer: {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}
  • Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
  • Question: {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}
  • Overall Precision: 0.8705
  • Overall Recall: 0.8917
  • Overall F1: 0.8810
  • Overall Accuracy: 0.8222

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
0.4177 10.53 200 0.9741 {'precision': 0.834106728538283, 'recall': 0.8800489596083231, 'f1': 0.8564621798689696, 'number': 817} {'precision': 0.6363636363636364, 'recall': 0.4117647058823529, 'f1': 0.5, 'number': 119} {'precision': 0.8831985624438454, 'recall': 0.9127205199628597, 'f1': 0.897716894977169, 'number': 1077} 0.8533 0.8698 0.8615 0.8139
0.0528 21.05 400 1.2793 {'precision': 0.8391845979614949, 'recall': 0.9069767441860465, 'f1': 0.871764705882353, 'number': 817} {'precision': 0.5480769230769231, 'recall': 0.4789915966386555, 'f1': 0.5112107623318385, 'number': 119} {'precision': 0.878645343367827, 'recall': 0.8672237697307336, 'f1': 0.8728971962616822, 'number': 1077} 0.8449 0.8604 0.8526 0.8057
0.0154 31.58 600 1.3635 {'precision': 0.8705463182897862, 'recall': 0.8971848225214198, 'f1': 0.8836648583484027, 'number': 817} {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} {'precision': 0.8776041666666666, 'recall': 0.9387186629526463, 'f1': 0.9071332436069988, 'number': 1077} 0.8638 0.8977 0.8804 0.8164
0.0082 42.11 800 1.4185 {'precision': 0.8700361010830325, 'recall': 0.8849449204406364, 'f1': 0.8774271844660194, 'number': 817} {'precision': 0.6428571428571429, 'recall': 0.6050420168067226, 'f1': 0.6233766233766234, 'number': 119} {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} 0.8695 0.8838 0.8766 0.8212
0.0038 52.63 1000 1.4588 {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817} {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119} {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077} 0.8705 0.8917 0.8810 0.8222
0.0026 63.16 1200 1.5730 {'precision': 0.8666666666666667, 'recall': 0.8910648714810282, 'f1': 0.8786964393482196, 'number': 817} {'precision': 0.7073170731707317, 'recall': 0.48739495798319327, 'f1': 0.5771144278606964, 'number': 119} {'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077} 0.8723 0.8788 0.8755 0.8139
0.0015 73.68 1400 1.6294 {'precision': 0.837471783295711, 'recall': 0.9082007343941249, 'f1': 0.8714034057545508, 'number': 817} {'precision': 0.6530612244897959, 'recall': 0.5378151260504201, 'f1': 0.5898617511520737, 'number': 119} {'precision': 0.9039179104477612, 'recall': 0.8997214484679665, 'f1': 0.9018147975802697, 'number': 1077} 0.8633 0.8818 0.8725 0.8173
0.001 84.21 1600 1.6406 {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817} {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119} {'precision': 0.9001865671641791, 'recall': 0.8960074280408542, 'f1': 0.8980921358771522, 'number': 1077} 0.8607 0.8872 0.8738 0.8140
0.0006 94.74 1800 1.6743 {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119} {'precision': 0.8982584784601283, 'recall': 0.9099350046425255, 'f1': 0.904059040590406, 'number': 1077} 0.8687 0.8872 0.8779 0.8082
0.0003 105.26 2000 1.7003 {'precision': 0.8696682464454977, 'recall': 0.8984088127294981, 'f1': 0.8838049367850691, 'number': 817} {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} {'precision': 0.8927272727272727, 'recall': 0.9117920148560817, 'f1': 0.9021589343132751, 'number': 1077} 0.8721 0.8808 0.8764 0.8110
0.0002 115.79 2200 1.7767 {'precision': 0.8564867967853043, 'recall': 0.9130966952264382, 'f1': 0.8838862559241707, 'number': 817} {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119} {'precision': 0.9077212806026366, 'recall': 0.8950789229340761, 'f1': 0.9013557737260401, 'number': 1077} 0.8726 0.8813 0.8769 0.8004
0.0002 126.32 2400 1.7093 {'precision': 0.8546910755148741, 'recall': 0.9143206854345165, 'f1': 0.8835008870490834, 'number': 817} {'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119} {'precision': 0.8956602031394275, 'recall': 0.9006499535747446, 'f1': 0.8981481481481481, 'number': 1077} 0.8668 0.8823 0.8744 0.8027

Framework versions

  • Transformers 4.28.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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