layoutlm-FUNSDxSynthetic-5fold

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0137
  • Eader: {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}
  • Nswer: {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256}
  • Uestion: {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273}
  • Overall Precision: 0.9850
  • Overall Recall: 0.9867
  • Overall F1: 0.9858
  • Overall Accuracy: 0.9965

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
0.0504 1.0 11 0.0183 {'precision': 0.9722222222222222, 'recall': 0.9859154929577465, 'f1': 0.979020979020979, 'number': 71} {'precision': 0.984375, 'recall': 0.984375, 'f1': 0.984375, 'number': 256} {'precision': 0.9782608695652174, 'recall': 0.989010989010989, 'f1': 0.9836065573770493, 'number': 273} 0.9801 0.9867 0.9834 0.9956
0.0459 2.0 22 0.0175 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.98828125, 'recall': 0.98828125, 'f1': 0.98828125, 'number': 256} {'precision': 0.9747292418772563, 'recall': 0.989010989010989, 'f1': 0.9818181818181818, 'number': 273} 0.9801 0.9867 0.9834 0.9953
0.0289 3.0 33 0.0146 {'precision': 0.9859154929577465, 'recall': 0.9859154929577465, 'f1': 0.9859154929577465, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9890909090909091, 'recall': 0.9963369963369964, 'f1': 0.9927007299270072, 'number': 273} 0.9900 0.9917 0.9908 0.9965
0.0243 4.0 44 0.0157 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9782608695652174, 'recall': 0.989010989010989, 'f1': 0.9836065573770493, 'number': 273} 0.9834 0.9867 0.9850 0.9958
0.0215 5.0 55 0.0129 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921875, 'recall': 0.9921875, 'f1': 0.9921875, 'number': 256} {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273} 0.9850 0.9883 0.9867 0.9975
0.0176 6.0 66 0.0151 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921875, 'recall': 0.9921875, 'f1': 0.9921875, 'number': 256} {'precision': 0.9782608695652174, 'recall': 0.989010989010989, 'f1': 0.9836065573770493, 'number': 273} 0.9834 0.9883 0.9859 0.9963
0.0151 7.0 77 0.0149 {'precision': 0.9583333333333334, 'recall': 0.971830985915493, 'f1': 0.965034965034965, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9817518248175182, 'recall': 0.9853479853479854, 'f1': 0.9835466179159049, 'number': 273} 0.9834 0.985 0.9842 0.9968
0.0136 8.0 88 0.0142 {'precision': 0.9583333333333334, 'recall': 0.971830985915493, 'f1': 0.965034965034965, 'number': 71} {'precision': 0.98828125, 'recall': 0.98828125, 'f1': 0.98828125, 'number': 256} {'precision': 0.9781818181818182, 'recall': 0.9853479853479854, 'f1': 0.9817518248175183, 'number': 273} 0.9801 0.985 0.9825 0.9965
0.0136 9.0 99 0.0148 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273} 0.9850 0.9867 0.9858 0.9963
0.0103 10.0 110 0.0138 {'precision': 0.9859154929577465, 'recall': 0.9859154929577465, 'f1': 0.9859154929577465, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9854545454545455, 'recall': 0.9926739926739927, 'f1': 0.989051094890511, 'number': 273} 0.9884 0.99 0.9892 0.9965
0.0091 11.0 121 0.0136 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9854014598540146, 'recall': 0.989010989010989, 'f1': 0.9872029250457038, 'number': 273} 0.9867 0.9867 0.9867 0.9968
0.0081 12.0 132 0.0131 {'precision': 0.9859154929577465, 'recall': 0.9859154929577465, 'f1': 0.9859154929577465, 'number': 71} {'precision': 0.98828125, 'recall': 0.98828125, 'f1': 0.98828125, 'number': 256} {'precision': 0.9890510948905109, 'recall': 0.9926739926739927, 'f1': 0.9908592321755026, 'number': 273} 0.9884 0.99 0.9892 0.9973
0.0098 13.0 143 0.0136 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9854014598540146, 'recall': 0.989010989010989, 'f1': 0.9872029250457038, 'number': 273} 0.9867 0.9867 0.9867 0.9968
0.0066 14.0 154 0.0139 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273} 0.9850 0.9867 0.9858 0.9965
0.007 15.0 165 0.0137 {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71} {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273} 0.9850 0.9867 0.9858 0.9965

Framework versions

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