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# layoutlm-synthchecking-padding

This model is a fine-tuned version of [microsoft/layoutlm-large-uncased](https://huggingface.co/microsoft/layoutlm-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
- Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
- Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
- Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
- Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
- Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Ank Address                                                                                               | Ank Name                                                                                   | Ayee Address                                                                                                | Ayee Name                                                                                               | Icr                                                        | Mount                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.3656        | 1.0   | 30   | 0.8294          | {'precision': 0.17721518987341772, 'recall': 0.4666666666666667, 'f1': 0.25688073394495414, 'number': 30} | {'precision': 0.23076923076923078, 'recall': 0.1, 'f1': 0.13953488372093023, 'number': 30} | {'precision': 0.011235955056179775, 'recall': 0.03333333333333333, 'f1': 0.01680672268907563, 'number': 30} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 0.2989            | 0.4333         | 0.3537     | 0.7804           |
| 0.418         | 2.0   | 60   | 0.0552          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 0.9666666666666667, 'recall': 0.9666666666666667, 'f1': 0.9666666666666667, 'number': 30}     | {'precision': 0.9666666666666667, 'recall': 0.9666666666666667, 'f1': 0.9666666666666667, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 0.9889            | 0.9889         | 0.9889     | 0.9984           |
| 0.033         | 3.0   | 90   | 0.0022          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0056        | 4.0   | 120  | 0.0010          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0032        | 5.0   | 150  | 0.0007          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0025        | 6.0   | 180  | 0.0006          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0028        | 7.0   | 210  | 0.0005          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0022        | 8.0   | 240  | 0.0005          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                                  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30} | 1.0               | 1.0            | 1.0        | 1.0              |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlm-synthchecking-padding", "results": []}]}
transformers pytorch tensorboard layoutlm token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

layoutlm-synthchecking-padding
==============================


This model is a fine-tuned version of microsoft/layoutlm-large-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0005
* Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
* Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
* Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
* Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
* Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
* Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 30}
* Overall Precision: 1.0
* Overall Recall: 1.0
* Overall F1: 1.0
* Overall Accuracy: 1.0


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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlm #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2