---
tags:
- generated_from_trainer
model-index:
- name: layoutlm-synth3
  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-synth3

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: 0.0021
- Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.9365        | 1.0   | 20   | 0.1057          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 0.9829            | 0.9829         | 0.9829     | 0.9976           |
| 0.0449        | 2.0   | 40   | 0.0058          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0075        | 3.0   | 60   | 0.0028          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.005         | 4.0   | 80   | 0.0022          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0               | 1.0            | 1.0        | 1.0              |
| 0.0042        | 5.0   | 100  | 0.0021          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 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