---
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