layoutlm-FUNSD-only-5fold

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0018
  • Eader: {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}
  • Nswer: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}
  • Uestion: {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}
  • Overall Precision: 0.9955
  • Overall Recall: 0.9978
  • Overall F1: 0.9967
  • Overall Accuracy: 0.9998

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Eader Nswer Uestion Overall Precision Overall Recall Overall F1 Overall Accuracy
0.0276 1.0 8 0.0029 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 0.9943181818181818, 'recall': 0.9943181818181818, 'f1': 0.9943181818181818, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9933 0.9955 0.9944 0.9995
0.0298 2.0 16 0.0053 {'precision': 0.9558823529411765, 'recall': 0.9701492537313433, 'f1': 0.962962962962963, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9806763285024155, 'recall': 0.9902439024390244, 'f1': 0.9854368932038836, 'number': 205} 0.9845 0.9911 0.9878 0.9993
0.0256 3.0 24 0.0027 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0177 4.0 32 0.0024 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0129 5.0 40 0.0022 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.012 6.0 48 0.0021 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0092 7.0 56 0.0019 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0079 8.0 64 0.0018 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0075 9.0 72 0.0018 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0078 10.0 80 0.0019 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0073 11.0 88 0.0020 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0063 12.0 96 0.0021 {'precision': 0.9705882352941176, 'recall': 0.9850746268656716, 'f1': 0.9777777777777777, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9855072463768116, 'recall': 0.9951219512195122, 'f1': 0.9902912621359223, 'number': 205} 0.9889 0.9955 0.9922 0.9995
0.0054 13.0 104 0.0018 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0046 14.0 112 0.0018 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998
0.0052 15.0 120 0.0018 {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176} {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205} 0.9955 0.9978 0.9967 0.9998

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0
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