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layoutlm
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metadata
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: ocr
    results: []

ocr

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: 0.0206
  • Axyear: {'precision': 0.9908256880733946, 'recall': 1.0, 'f1': 0.9953917050691244, 'number': 108}
  • Inemployeridentificationnumber: {'precision': 0.9819819819819819, 'recall': 0.9819819819819819, 'f1': 0.9819819819819819, 'number': 111}
  • Mployeename: {'precision': 0.9838709677419355, 'recall': 0.991869918699187, 'f1': 0.9878542510121457, 'number': 123}
  • Mployeraddresscity: {'precision': 0.9826086956521739, 'recall': 1.0, 'f1': 0.9912280701754386, 'number': 113}
  • Mployeraddressstate: {'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112}
  • Mployeraddressstreet Name: {'precision': 0.9426229508196722, 'recall': 0.9583333333333334, 'f1': 0.950413223140496, 'number': 120}
  • Mployeraddresszip: {'precision': 0.9912280701754386, 'recall': 1.0, 'f1': 0.9955947136563876, 'number': 113}
  • Mployername: {'precision': 0.9655172413793104, 'recall': 0.9824561403508771, 'f1': 0.9739130434782608, 'number': 114}
  • Ox16statewagestips: {'precision': 0.8192771084337349, 'recall': 0.7816091954022989, 'f1': 0.8, 'number': 87}
  • Ox17stateincometax: {'precision': 0.9342105263157895, 'recall': 0.8987341772151899, 'f1': 0.9161290322580645, 'number': 79}
  • Ox1wagestipsandothercompensations: {'precision': 0.8823529411764706, 'recall': 0.813953488372093, 'f1': 0.846774193548387, 'number': 129}
  • Ox2federalincometaxwithheld: {'precision': 0.8828125, 'recall': 0.8828125, 'f1': 0.8828125, 'number': 128}
  • Ox3socialsecuritywages: {'precision': 0.8928571428571429, 'recall': 0.8547008547008547, 'f1': 0.8733624454148471, 'number': 117}
  • Ox4socialsecuritytaxwithheld: {'precision': 0.9351851851851852, 'recall': 0.8782608695652174, 'f1': 0.905829596412556, 'number': 115}
  • Snofemployee: {'precision': 0.9891304347826086, 'recall': 0.978494623655914, 'f1': 0.9837837837837837, 'number': 93}
  • Overall Precision: 0.9452
  • Overall Recall: 0.9344
  • Overall F1: 0.9398
  • Overall Accuracy: 0.9958

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Axyear Inemployeridentificationnumber Mployeename Mployeraddresscity Mployeraddressstate Mployeraddressstreet Name Mployeraddresszip Mployername Ox16statewagestips Ox17stateincometax Ox1wagestipsandothercompensations Ox2federalincometaxwithheld Ox3socialsecuritywages Ox4socialsecuritytaxwithheld Snofemployee Overall Precision Overall Recall Overall F1 Overall Accuracy
0.9955 1.0 30 0.3203 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 108} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 111} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 123} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 113} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 112} {'precision': 0.20855614973262032, 'recall': 0.325, 'f1': 0.254071661237785, 'number': 120} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 113} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 114} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 87} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 79} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 129} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 128} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 117} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 115} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 93} 0.1423 0.0235 0.0403 0.9154
0.2276 2.0 60 0.1192 {'precision': 0.9797979797979798, 'recall': 0.8981481481481481, 'f1': 0.9371980676328502, 'number': 108} {'precision': 0.832, 'recall': 0.9369369369369369, 'f1': 0.8813559322033897, 'number': 111} {'precision': 0.9444444444444444, 'recall': 0.967479674796748, 'f1': 0.9558232931726908, 'number': 123} {'precision': 0.9224137931034483, 'recall': 0.9469026548672567, 'f1': 0.9344978165938865, 'number': 113} {'precision': 0.9823008849557522, 'recall': 0.9910714285714286, 'f1': 0.9866666666666667, 'number': 112} {'precision': 0.9032258064516129, 'recall': 0.9333333333333333, 'f1': 0.9180327868852459, 'number': 120} {'precision': 0.9333333333333333, 'recall': 0.9911504424778761, 'f1': 0.9613733905579399, 'number': 113} {'precision': 0.9487179487179487, 'recall': 0.9736842105263158, 'f1': 0.9610389610389611, 'number': 114} {'precision': 0.25, 'recall': 0.011494252873563218, 'f1': 0.02197802197802198, 'number': 87} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 79} {'precision': 0.3597122302158273, 'recall': 0.3875968992248062, 'f1': 0.3731343283582089, 'number': 129} {'precision': 0.30808080808080807, 'recall': 0.4765625, 'f1': 0.3742331288343558, 'number': 128} {'precision': 0.2785714285714286, 'recall': 0.3333333333333333, 'f1': 0.3035019455252918, 'number': 117} {'precision': 0.2903225806451613, 'recall': 0.0782608695652174, 'f1': 0.12328767123287673, 'number': 115} {'precision': 1.0, 'recall': 0.44086021505376344, 'f1': 0.6119402985074627, 'number': 93} 0.7194 0.6462 0.6808 0.9716
0.0999 3.0 90 0.0550 {'precision': 0.9722222222222222, 'recall': 0.9722222222222222, 'f1': 0.9722222222222222, 'number': 108} {'precision': 0.9469026548672567, 'recall': 0.963963963963964, 'f1': 0.9553571428571428, 'number': 111} {'precision': 0.967741935483871, 'recall': 0.975609756097561, 'f1': 0.97165991902834, 'number': 123} {'precision': 0.9576271186440678, 'recall': 1.0, 'f1': 0.9783549783549783, 'number': 113} {'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112} {'precision': 0.9421487603305785, 'recall': 0.95, 'f1': 0.946058091286307, 'number': 120} {'precision': 0.9912280701754386, 'recall': 1.0, 'f1': 0.9955947136563876, 'number': 113} {'precision': 0.9655172413793104, 'recall': 0.9824561403508771, 'f1': 0.9739130434782608, 'number': 114} {'precision': 0.43243243243243246, 'recall': 0.367816091954023, 'f1': 0.39751552795031053, 'number': 87} {'precision': 0.5125, 'recall': 0.5189873417721519, 'f1': 0.5157232704402516, 'number': 79} {'precision': 0.7121212121212122, 'recall': 0.7286821705426356, 'f1': 0.7203065134099617, 'number': 129} {'precision': 0.6466165413533834, 'recall': 0.671875, 'f1': 0.6590038314176245, 'number': 128} {'precision': 0.8095238095238095, 'recall': 0.7264957264957265, 'f1': 0.7657657657657657, 'number': 117} {'precision': 0.7373737373737373, 'recall': 0.6347826086956522, 'f1': 0.6822429906542056, 'number': 115} {'precision': 0.9662921348314607, 'recall': 0.9247311827956989, 'f1': 0.945054945054945, 'number': 93} 0.8494 0.8381 0.8437 0.9890
0.0508 4.0 120 0.0354 {'precision': 0.963302752293578, 'recall': 0.9722222222222222, 'f1': 0.9677419354838711, 'number': 108} {'precision': 0.972972972972973, 'recall': 0.972972972972973, 'f1': 0.972972972972973, 'number': 111} {'precision': 0.983739837398374, 'recall': 0.983739837398374, 'f1': 0.983739837398374, 'number': 123} {'precision': 0.9741379310344828, 'recall': 1.0, 'f1': 0.9868995633187774, 'number': 113} {'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112} {'precision': 0.9504132231404959, 'recall': 0.9583333333333334, 'f1': 0.9543568464730291, 'number': 120} {'precision': 0.9826086956521739, 'recall': 1.0, 'f1': 0.9912280701754386, 'number': 113} {'precision': 0.9572649572649573, 'recall': 0.9824561403508771, 'f1': 0.9696969696969696, 'number': 114} {'precision': 0.6527777777777778, 'recall': 0.5402298850574713, 'f1': 0.5911949685534591, 'number': 87} {'precision': 0.5795454545454546, 'recall': 0.6455696202531646, 'f1': 0.6107784431137726, 'number': 79} {'precision': 0.8623853211009175, 'recall': 0.7286821705426356, 'f1': 0.7899159663865546, 'number': 129} {'precision': 0.7804878048780488, 'recall': 0.75, 'f1': 0.7649402390438248, 'number': 128} {'precision': 0.7815126050420168, 'recall': 0.7948717948717948, 'f1': 0.788135593220339, 'number': 117} {'precision': 0.8240740740740741, 'recall': 0.7739130434782608, 'f1': 0.7982062780269059, 'number': 115} {'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 93} 0.8907 0.8779 0.8842 0.9918
0.0333 5.0 150 0.0310 {'precision': 0.954954954954955, 'recall': 0.9814814814814815, 'f1': 0.9680365296803655, 'number': 108} {'precision': 0.9557522123893806, 'recall': 0.972972972972973, 'f1': 0.9642857142857144, 'number': 111} {'precision': 0.983739837398374, 'recall': 0.983739837398374, 'f1': 0.983739837398374, 'number': 123} {'precision': 0.9741379310344828, 'recall': 1.0, 'f1': 0.9868995633187774, 'number': 113} {'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112} {'precision': 0.9426229508196722, 'recall': 0.9583333333333334, 'f1': 0.950413223140496, 'number': 120} {'precision': 0.9416666666666667, 'recall': 1.0, 'f1': 0.9699570815450643, 'number': 113} {'precision': 0.9739130434782609, 'recall': 0.9824561403508771, 'f1': 0.9781659388646288, 'number': 114} {'precision': 0.625, 'recall': 0.6896551724137931, 'f1': 0.6557377049180328, 'number': 87} {'precision': 0.7391304347826086, 'recall': 0.8607594936708861, 'f1': 0.7953216374269007, 'number': 79} {'precision': 0.8521739130434782, 'recall': 0.7596899224806202, 'f1': 0.8032786885245902, 'number': 129} {'precision': 0.8412698412698413, 'recall': 0.828125, 'f1': 0.8346456692913385, 'number': 128} {'precision': 0.8083333333333333, 'recall': 0.8290598290598291, 'f1': 0.818565400843882, 'number': 117} {'precision': 0.8727272727272727, 'recall': 0.8347826086956521, 'f1': 0.8533333333333333, 'number': 115} {'precision': 0.9565217391304348, 'recall': 0.946236559139785, 'f1': 0.9513513513513514, 'number': 93} 0.8979 0.9103 0.9041 0.9923
0.0244 6.0 180 0.0233 {'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 108} {'precision': 0.9557522123893806, 'recall': 0.972972972972973, 'f1': 0.9642857142857144, 'number': 111} {'precision': 0.9838709677419355, 'recall': 0.991869918699187, 'f1': 0.9878542510121457, 'number': 123} {'precision': 0.9826086956521739, 'recall': 1.0, 'f1': 0.9912280701754386, 'number': 113} {'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112} {'precision': 0.9426229508196722, 'recall': 0.9583333333333334, 'f1': 0.950413223140496, 'number': 120} {'precision': 0.9741379310344828, 'recall': 1.0, 'f1': 0.9868995633187774, 'number': 113} {'precision': 0.9739130434782609, 'recall': 0.9824561403508771, 'f1': 0.9781659388646288, 'number': 114} {'precision': 0.7625, 'recall': 0.7011494252873564, 'f1': 0.7305389221556886, 'number': 87} {'precision': 0.9210526315789473, 'recall': 0.8860759493670886, 'f1': 0.9032258064516129, 'number': 79} {'precision': 0.8771929824561403, 'recall': 0.7751937984496124, 'f1': 0.8230452674897119, 'number': 129} {'precision': 0.8582677165354331, 'recall': 0.8515625, 'f1': 0.8549019607843137, 'number': 128} {'precision': 0.8596491228070176, 'recall': 0.8376068376068376, 'f1': 0.8484848484848484, 'number': 117} {'precision': 0.8879310344827587, 'recall': 0.8956521739130435, 'f1': 0.8917748917748919, 'number': 115} {'precision': 0.9888888888888889, 'recall': 0.956989247311828, 'f1': 0.9726775956284154, 'number': 93} 0.9313 0.9212 0.9262 0.9949
0.02 7.0 210 0.0224 {'precision': 0.9906542056074766, 'recall': 0.9814814814814815, 'f1': 0.986046511627907, 'number': 108} {'precision': 0.9732142857142857, 'recall': 0.9819819819819819, 'f1': 0.9775784753363228, 'number': 111} {'precision': 0.983739837398374, 'recall': 0.983739837398374, 'f1': 0.983739837398374, 'number': 123} {'precision': 0.9912280701754386, 'recall': 1.0, 'f1': 0.9955947136563876, 'number': 113} {'precision': 0.9911504424778761, 'recall': 1.0, 'f1': 0.9955555555555555, 'number': 112} {'precision': 0.9583333333333334, 'recall': 0.9583333333333334, 'f1': 0.9583333333333334, 'number': 120} {'precision': 1.0, 'recall': 0.9911504424778761, 'f1': 0.9955555555555555, 'number': 113} {'precision': 0.9739130434782609, 'recall': 0.9824561403508771, 'f1': 0.9781659388646288, 'number': 114} {'precision': 0.8051948051948052, 'recall': 0.7126436781609196, 'f1': 0.7560975609756099, 'number': 87} {'precision': 0.9855072463768116, 'recall': 0.8607594936708861, 'f1': 0.918918918918919, 'number': 79} {'precision': 0.8487394957983193, 'recall': 0.7829457364341085, 'f1': 0.814516129032258, 'number': 129} {'precision': 0.8582677165354331, 'recall': 0.8515625, 'f1': 0.8549019607843137, 'number': 128} {'precision': 0.8928571428571429, 'recall': 0.8547008547008547, 'f1': 0.8733624454148471, 'number': 117} {'precision': 0.9245283018867925, 'recall': 0.8521739130434782, 'f1': 0.8868778280542987, 'number': 115} {'precision': 0.978021978021978, 'recall': 0.956989247311828, 'f1': 0.967391304347826, 'number': 93} 0.9443 0.9188 0.9314 0.9952
0.0174 8.0 240 0.0219 {'precision': 0.9907407407407407, 'recall': 0.9907407407407407, 'f1': 0.9907407407407407, 'number': 108} {'precision': 0.9819819819819819, 'recall': 0.9819819819819819, 'f1': 0.9819819819819819, 'number': 111} {'precision': 0.976, 'recall': 0.991869918699187, 'f1': 0.9838709677419355, 'number': 123} {'precision': 0.9741379310344828, 'recall': 1.0, 'f1': 0.9868995633187774, 'number': 113} {'precision': 0.9739130434782609, 'recall': 1.0, 'f1': 0.986784140969163, 'number': 112} {'precision': 0.9426229508196722, 'recall': 0.9583333333333334, 'f1': 0.950413223140496, 'number': 120} {'precision': 0.9912280701754386, 'recall': 1.0, 'f1': 0.9955947136563876, 'number': 113} {'precision': 0.9739130434782609, 'recall': 0.9824561403508771, 'f1': 0.9781659388646288, 'number': 114} {'precision': 0.7654320987654321, 'recall': 0.7126436781609196, 'f1': 0.738095238095238, 'number': 87} {'precision': 0.9459459459459459, 'recall': 0.8860759493670886, 'f1': 0.9150326797385621, 'number': 79} {'precision': 0.8793103448275862, 'recall': 0.7906976744186046, 'f1': 0.8326530612244898, 'number': 129} {'precision': 0.8790322580645161, 'recall': 0.8515625, 'f1': 0.8650793650793651, 'number': 128} {'precision': 0.8849557522123894, 'recall': 0.8547008547008547, 'f1': 0.8695652173913043, 'number': 117} {'precision': 0.9017857142857143, 'recall': 0.8782608695652174, 'f1': 0.8898678414096917, 'number': 115} {'precision': 0.9782608695652174, 'recall': 0.967741935483871, 'f1': 0.972972972972973, 'number': 93} 0.9383 0.9248 0.9315 0.9952
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0.0135 10.0 300 0.0208 {'precision': 0.9907407407407407, 'recall': 0.9907407407407407, 'f1': 0.9907407407407407, 'number': 108} {'precision': 0.9732142857142857, 'recall': 0.9819819819819819, 'f1': 0.9775784753363228, 'number': 111} {'precision': 0.9838709677419355, 'recall': 0.991869918699187, 'f1': 0.9878542510121457, 'number': 123} {'precision': 0.9826086956521739, 'recall': 1.0, 'f1': 0.9912280701754386, 'number': 113} {'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112} {'precision': 0.9426229508196722, 'recall': 0.9583333333333334, 'f1': 0.950413223140496, 'number': 120} {'precision': 0.9912280701754386, 'recall': 1.0, 'f1': 0.9955947136563876, 'number': 113} {'precision': 0.9739130434782609, 'recall': 0.9824561403508771, 'f1': 0.9781659388646288, 'number': 114} {'precision': 0.8, 'recall': 0.735632183908046, 'f1': 0.7664670658682634, 'number': 87} {'precision': 0.9583333333333334, 'recall': 0.8734177215189873, 'f1': 0.913907284768212, 'number': 79} {'precision': 0.8803418803418803, 'recall': 0.7984496124031008, 'f1': 0.8373983739837397, 'number': 129} {'precision': 0.8682170542635659, 'recall': 0.875, 'f1': 0.8715953307392996, 'number': 128} {'precision': 0.9090909090909091, 'recall': 0.8547008547008547, 'f1': 0.8810572687224669, 'number': 117} {'precision': 0.9528301886792453, 'recall': 0.8782608695652174, 'f1': 0.9140271493212669, 'number': 115} {'precision': 0.9782608695652174, 'recall': 0.967741935483871, 'f1': 0.972972972972973, 'number': 93} 0.9460 0.9278 0.9368 0.9958
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Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1