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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: 1.7699
  • Answer: {'precision': 0.8906439854191981, 'recall': 0.8971848225214198, 'f1': 0.8939024390243904, 'number': 817}
  • Header: {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119}
  • Question: {'precision': 0.8778359511343804, 'recall': 0.9340761374187558, 'f1': 0.9050832208726944, 'number': 1077}
  • Overall Precision: 0.8706
  • Overall Recall: 0.8957
  • Overall F1: 0.8830
  • Overall Accuracy: 0.7973

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
0.4312 10.53 200 0.9853 {'precision': 0.8581818181818182, 'recall': 0.8665850673194615, 'f1': 0.8623629719853837, 'number': 817} {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119} {'precision': 0.8788706739526412, 'recall': 0.8960074280408542, 'f1': 0.8873563218390804, 'number': 1077} 0.8531 0.8624 0.8577 0.8172
0.0478 21.05 400 1.2825 {'precision': 0.8571428571428571, 'recall': 0.9033047735618115, 'f1': 0.8796185935637664, 'number': 817} {'precision': 0.5136986301369864, 'recall': 0.6302521008403361, 'f1': 0.5660377358490567, 'number': 119} {'precision': 0.8739650413983441, 'recall': 0.8820798514391829, 'f1': 0.878003696857671, 'number': 1077} 0.8419 0.8758 0.8585 0.8026
0.0127 31.58 600 1.4791 {'precision': 0.8568075117370892, 'recall': 0.8935128518971848, 'f1': 0.8747753145596165, 'number': 817} {'precision': 0.5779816513761468, 'recall': 0.5294117647058824, 'f1': 0.5526315789473684, 'number': 119} {'precision': 0.8909426987060998, 'recall': 0.8950789229340761, 'f1': 0.8930060213061601, 'number': 1077} 0.8600 0.8728 0.8664 0.7957
0.0073 42.11 800 1.3846 {'precision': 0.8853046594982079, 'recall': 0.9069767441860465, 'f1': 0.8960096735187424, 'number': 817} {'precision': 0.5333333333333333, 'recall': 0.6050420168067226, 'f1': 0.5669291338582677, 'number': 119} {'precision': 0.8932584269662921, 'recall': 0.8857938718662952, 'f1': 0.8895104895104896, 'number': 1077} 0.8662 0.8778 0.8719 0.8142
0.0023 52.63 1000 1.5955 {'precision': 0.8430034129692833, 'recall': 0.9069767441860465, 'f1': 0.8738207547169811, 'number': 817} {'precision': 0.6190476190476191, 'recall': 0.5462184873949579, 'f1': 0.5803571428571429, 'number': 119} {'precision': 0.8935574229691877, 'recall': 0.8885793871866295, 'f1': 0.8910614525139665, 'number': 1077} 0.8579 0.8758 0.8668 0.7992
0.0023 63.16 1200 1.6214 {'precision': 0.8955773955773956, 'recall': 0.8922888616891065, 'f1': 0.8939301042305334, 'number': 817} {'precision': 0.5882352941176471, 'recall': 0.5882352941176471, 'f1': 0.5882352941176471, 'number': 119} {'precision': 0.8841354723707665, 'recall': 0.9210770659238626, 'f1': 0.9022282855843565, 'number': 1077} 0.8715 0.8897 0.8805 0.8057
0.0016 73.68 1400 1.8002 {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} {'precision': 0.5765765765765766, 'recall': 0.5378151260504201, 'f1': 0.5565217391304348, 'number': 119} {'precision': 0.8892921960072595, 'recall': 0.9099350046425255, 'f1': 0.8994951812758146, 'number': 1077} 0.8659 0.8882 0.8769 0.7860
0.0013 84.21 1600 1.7699 {'precision': 0.8906439854191981, 'recall': 0.8971848225214198, 'f1': 0.8939024390243904, 'number': 817} {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} {'precision': 0.8778359511343804, 'recall': 0.9340761374187558, 'f1': 0.9050832208726944, 'number': 1077} 0.8706 0.8957 0.8830 0.7973
0.0008 94.74 1800 1.7824 {'precision': 0.8733572281959379, 'recall': 0.8947368421052632, 'f1': 0.8839177750906893, 'number': 817} {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119} {'precision': 0.8901996370235935, 'recall': 0.9108635097493036, 'f1': 0.9004130335016063, 'number': 1077} 0.8690 0.8833 0.8761 0.8019
0.0005 105.26 2000 1.7894 {'precision': 0.872791519434629, 'recall': 0.9069767441860465, 'f1': 0.8895558223289316, 'number': 817} {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119} {'precision': 0.8931506849315068, 'recall': 0.9080779944289693, 'f1': 0.9005524861878452, 'number': 1077} 0.8691 0.8872 0.8781 0.7940
0.0002 115.79 2200 1.8409 {'precision': 0.8665893271461717, 'recall': 0.9143206854345165, 'f1': 0.8898153662894581, 'number': 817} {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} {'precision': 0.8978644382544104, 'recall': 0.8978644382544104, 'f1': 0.8978644382544104, 'number': 1077} 0.8705 0.8852 0.8778 0.7982
0.0002 126.32 2400 1.8311 {'precision': 0.8709302325581395, 'recall': 0.9167686658506732, 'f1': 0.8932617769827073, 'number': 817} {'precision': 0.6018518518518519, 'recall': 0.5462184873949579, 'f1': 0.5726872246696034, 'number': 119} {'precision': 0.893953488372093, 'recall': 0.8922934076137419, 'f1': 0.8931226765799257, 'number': 1077} 0.8688 0.8818 0.8752 0.7988

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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