---
tags:
- generated_from_trainer
model-index:
- name: layoutlm-synth2
  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-synth2

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0270
- Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
- Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
- Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
- Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
- Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
- 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4218        | 1.0   | 10   | 0.9682          | {'precision': 0.03225806451612903, 'recall': 0.1, 'f1': 0.04878048780487805, 'number': 20} | {'precision': 0.3333333333333333, 'recall': 0.05, 'f1': 0.08695652173913045, 'number': 20} | {'precision': 0.03125, 'recall': 0.1, 'f1': 0.047619047619047616, 'number': 20}          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}                | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 20} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20} | 0.2393            | 0.325          | 0.2756     | 0.5811           |
| 0.7362        | 2.0   | 20   | 0.3668          | {'precision': 0.8636363636363636, 'recall': 0.95, 'f1': 0.9047619047619048, 'number': 20}  | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 20}   | {'precision': 0.8571428571428571, 'recall': 0.9, 'f1': 0.8780487804878048, 'number': 20} | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 20} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20} | 0.904             | 0.9417         | 0.9224     | 0.9855           |
| 0.2488        | 3.0   | 30   | 0.0892          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0877        | 4.0   | 40   | 0.0373          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0491        | 5.0   | 50   | 0.0270          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20} | 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