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---
library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-mcocr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-mcocr
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0293
- Ddress: {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}
- Eller: {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}
- Imestamp: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}
- Otal Cost: {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}
- Overall Precision: 0.9909
- Overall Recall: 0.992
- Overall F1: 0.9914
- Overall Accuracy: 0.9960
## 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 | Ddress | Eller | Imestamp | Otal Cost | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3063 | 1.0 | 55 | 0.0304 | {'precision': 0.9585253456221198, 'recall': 0.9585253456221198, 'f1': 0.9585253456221198, 'number': 217} | {'precision': 0.991304347826087, 'recall': 0.9743589743589743, 'f1': 0.9827586206896551, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.986046511627907, 'recall': 0.9953051643192489, 'f1': 0.9906542056074766, 'number': 213} | 0.9828 | 0.9806 | 0.9817 | 0.9912 |
| 0.0332 | 2.0 | 110 | 0.0303 | {'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 217} | {'precision': 0.991304347826087, 'recall': 0.9743589743589743, 'f1': 0.9827586206896551, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9906976744186047, 'recall': 1.0, 'f1': 0.9953271028037384, 'number': 213} | 0.9874 | 0.9851 | 0.9863 | 0.9928 |
| 0.0174 | 3.0 | 165 | 0.0252 | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863 | 0.9886 | 0.9874 | 0.9944 |
| 0.0145 | 4.0 | 220 | 0.0271 | {'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 217} | {'precision': 0.9913793103448276, 'recall': 0.9829059829059829, 'f1': 0.9871244635193134, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863 | 0.9851 | 0.9857 | 0.9936 |
| 0.0114 | 5.0 | 275 | 0.0254 | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9886 | 0.9897 | 0.9891 | 0.9952 |
| 0.0079 | 6.0 | 330 | 0.0273 | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863 | 0.9886 | 0.9874 | 0.9944 |
| 0.0053 | 7.0 | 385 | 0.0259 | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213} | 0.9909 | 0.992 | 0.9914 | 0.9960 |
| 0.005 | 8.0 | 440 | 0.0255 | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9863 | 0.9886 | 0.9874 | 0.9944 |
| 0.0034 | 9.0 | 495 | 0.0281 | {'precision': 0.9768518518518519, 'recall': 0.9723502304147466, 'f1': 0.97459584295612, 'number': 217} | {'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213} | 0.9897 | 0.9909 | 0.9903 | 0.9952 |
| 0.0032 | 10.0 | 550 | 0.0290 | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9914163090128756, 'recall': 0.9871794871794872, 'f1': 0.9892933618843683, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213} | 0.9874 | 0.9874 | 0.9874 | 0.9944 |
| 0.0032 | 11.0 | 605 | 0.0306 | {'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217} | {'precision': 0.9913793103448276, 'recall': 0.9829059829059829, 'f1': 0.9871244635193134, 'number': 234} | {'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211} | {'precision': 0.986046511627907, 'recall': 0.9953051643192489, 'f1': 0.9906542056074766, 'number': 213} | 0.9863 | 0.9863 | 0.9863 | 0.9936 |
| 0.0018 | 12.0 | 660 | 0.0273 | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213} | 0.9909 | 0.992 | 0.9914 | 0.9960 |
| 0.0007 | 13.0 | 715 | 0.0266 | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213} | 0.9909 | 0.992 | 0.9914 | 0.9960 |
| 0.0006 | 14.0 | 770 | 0.0292 | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213} | 0.9909 | 0.992 | 0.9914 | 0.9960 |
| 0.0006 | 15.0 | 825 | 0.0293 | {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217} | {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211} | {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213} | 0.9909 | 0.992 | 0.9914 | 0.9960 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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