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
0.0149	9.0	270	0.0214	{'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': 0.9912280701754386, 'recall': 1.0, 'f1': 0.9955947136563876, 'number': 113}	{'precision': 0.9824561403508771, 'recall': 1.0, 'f1': 0.9911504424778761, 'number': 112}	{'precision': 0.9583333333333334, 'recall': 0.9583333333333334, 'f1': 0.9583333333333334, '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': 0.7804878048780488, 'recall': 0.735632183908046, 'f1': 0.757396449704142, 'number': 87}	{'precision': 0.9473684210526315, 'recall': 0.9113924050632911, 'f1': 0.9290322580645162, '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': 0.8918918918918919, 'recall': 0.8461538461538461, 'f1': 0.868421052631579, 'number': 117}	{'precision': 0.9174311926605505, 'recall': 0.8695652173913043, 'f1': 0.8928571428571428, 'number': 115}	{'precision': 0.9782608695652174, 'recall': 0.967741935483871, 'f1': 0.972972972972973, 'number': 93}	0.9426	0.9284	0.9354	0.9954
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