layoutlm-funsd / README.md
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metadata
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
  - generated_from_trainer
datasets:
  - funsd
model-index:
  - name: layoutlm-funsd
    results: []

layoutlm-funsd

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: 1.1017
  • Answer: {'precision': 0.40439158279963405, 'recall': 0.546353522867738, 'f1': 0.46477392218717145, 'number': 809}
  • Header: {'precision': 0.3368421052631579, 'recall': 0.2689075630252101, 'f1': 0.29906542056074764, 'number': 119}
  • Question: {'precision': 0.5619128949615713, 'recall': 0.6178403755868545, 'f1': 0.5885509838998211, 'number': 1065}
  • Overall Precision: 0.4799
  • Overall Recall: 0.5680
  • Overall F1: 0.5202
  • Overall Accuracy: 0.6339

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: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
1.3745 1.0 5 1.1446 {'precision': 0.24631396357328708, 'recall': 0.3510506798516687, 'f1': 0.28950050968399593, 'number': 809} {'precision': 0.20930232558139536, 'recall': 0.226890756302521, 'f1': 0.21774193548387097, 'number': 119} {'precision': 0.4135151890886547, 'recall': 0.6262910798122066, 'f1': 0.4981329350261389, 'number': 1065} 0.3378 0.4907 0.4002 0.5475
1.0195 2.0 10 1.0518 {'precision': 0.29006968641114983, 'recall': 0.411619283065513, 'f1': 0.340316811446091, 'number': 809} {'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119} {'precision': 0.42618741976893454, 'recall': 0.6234741784037559, 'f1': 0.5062905070529927, 'number': 1065} 0.3653 0.5148 0.4273 0.5967
0.8996 3.0 15 1.0952 {'precision': 0.3147887323943662, 'recall': 0.5525339925834364, 'f1': 0.4010767160161508, 'number': 809} {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} {'precision': 0.4714285714285714, 'recall': 0.5267605633802817, 'f1': 0.4975609756097561, 'number': 1065} 0.3821 0.5163 0.4392 0.5831
0.8294 4.0 20 1.0418 {'precision': 0.3429571303587052, 'recall': 0.484548825710754, 'f1': 0.4016393442622951, 'number': 809} {'precision': 0.32, 'recall': 0.20168067226890757, 'f1': 0.24742268041237112, 'number': 119} {'precision': 0.49588815789473684, 'recall': 0.5661971830985916, 'f1': 0.5287154756685665, 'number': 1065} 0.4187 0.5113 0.4604 0.6110
0.773 5.0 25 1.0412 {'precision': 0.34150772025431425, 'recall': 0.4647713226205192, 'f1': 0.393717277486911, 'number': 809} {'precision': 0.2857142857142857, 'recall': 0.20168067226890757, 'f1': 0.23645320197044337, 'number': 119} {'precision': 0.4541223404255319, 'recall': 0.6413145539906103, 'f1': 0.5317244063838069, 'number': 1065} 0.4028 0.5434 0.4626 0.6114
0.731 6.0 30 1.0832 {'precision': 0.352991452991453, 'recall': 0.5105067985166872, 'f1': 0.4173825164224356, 'number': 809} {'precision': 0.2708333333333333, 'recall': 0.2184873949579832, 'f1': 0.24186046511627907, 'number': 119} {'precision': 0.5029686174724343, 'recall': 0.5568075117370892, 'f1': 0.5285204991087344, 'number': 1065} 0.4221 0.5178 0.4651 0.6014
0.6884 7.0 35 1.1304 {'precision': 0.3588709677419355, 'recall': 0.5500618046971569, 'f1': 0.4343582235236701, 'number': 809} {'precision': 0.36619718309859156, 'recall': 0.2184873949579832, 'f1': 0.2736842105263158, 'number': 119} {'precision': 0.5510204081632653, 'recall': 0.5577464788732395, 'f1': 0.5543630424638357, 'number': 1065} 0.4458 0.5344 0.4861 0.6078
0.6731 8.0 40 1.0667 {'precision': 0.3651096282173499, 'recall': 0.47342398022249693, 'f1': 0.41227125941872983, 'number': 809} {'precision': 0.29, 'recall': 0.24369747899159663, 'f1': 0.2648401826484018, 'number': 119} {'precision': 0.49964912280701756, 'recall': 0.6685446009389672, 'f1': 0.5718875502008032, 'number': 1065} 0.4367 0.5640 0.4922 0.6205
0.6441 9.0 45 1.0893 {'precision': 0.3948576675849403, 'recall': 0.5315203955500618, 'f1': 0.45310853530031614, 'number': 809} {'precision': 0.3238095238095238, 'recall': 0.2857142857142857, 'f1': 0.30357142857142855, 'number': 119} {'precision': 0.5439367311072056, 'recall': 0.5812206572769953, 'f1': 0.5619609623241035, 'number': 1065} 0.4644 0.5434 0.5008 0.6241
0.6139 10.0 50 1.0987 {'precision': 0.37037037037037035, 'recall': 0.5562422744128553, 'f1': 0.44466403162055335, 'number': 809} {'precision': 0.313953488372093, 'recall': 0.226890756302521, 'f1': 0.2634146341463415, 'number': 119} {'precision': 0.533678756476684, 'recall': 0.5802816901408451, 'f1': 0.5560053981106613, 'number': 1065} 0.4453 0.5494 0.4919 0.6253
0.6007 11.0 55 1.0803 {'precision': 0.40096618357487923, 'recall': 0.5129789864029666, 'f1': 0.45010845986984815, 'number': 809} {'precision': 0.29591836734693877, 'recall': 0.24369747899159663, 'f1': 0.26728110599078336, 'number': 119} {'precision': 0.5409054805401112, 'recall': 0.6394366197183099, 'f1': 0.5860585197934596, 'number': 1065} 0.4703 0.5645 0.5131 0.6317
0.5985 12.0 60 1.0997 {'precision': 0.4080846968238691, 'recall': 0.5241038318912238, 'f1': 0.45887445887445893, 'number': 809} {'precision': 0.31683168316831684, 'recall': 0.2689075630252101, 'f1': 0.29090909090909095, 'number': 119} {'precision': 0.5536303630363036, 'recall': 0.6300469483568075, 'f1': 0.5893719806763285, 'number': 1065} 0.4792 0.5655 0.5188 0.6323
0.5828 13.0 65 1.0996 {'precision': 0.40275229357798165, 'recall': 0.5426452410383189, 'f1': 0.46234860452869925, 'number': 809} {'precision': 0.33695652173913043, 'recall': 0.2605042016806723, 'f1': 0.29383886255924174, 'number': 119} {'precision': 0.5685936151855048, 'recall': 0.6187793427230047, 'f1': 0.5926258992805755, 'number': 1065} 0.4823 0.5665 0.5210 0.6345
0.5656 14.0 70 1.1065 {'precision': 0.40542986425339367, 'recall': 0.553770086526576, 'f1': 0.46812957157784746, 'number': 809} {'precision': 0.32967032967032966, 'recall': 0.25210084033613445, 'f1': 0.28571428571428575, 'number': 119} {'precision': 0.5730735163861824, 'recall': 0.6075117370892019, 'f1': 0.5897903372835004, 'number': 1065} 0.4839 0.5645 0.5211 0.6338
0.5625 15.0 75 1.1017 {'precision': 0.40439158279963405, 'recall': 0.546353522867738, 'f1': 0.46477392218717145, 'number': 809} {'precision': 0.3368421052631579, 'recall': 0.2689075630252101, 'f1': 0.29906542056074764, 'number': 119} {'precision': 0.5619128949615713, 'recall': 0.6178403755868545, 'f1': 0.5885509838998211, 'number': 1065} 0.4799 0.5680 0.5202 0.6339

Framework versions

  • Transformers 4.33.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3