| --- |
| 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 | |
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| ### Framework versions |
|
|
| - Transformers 4.27.1 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.10.1 |
| - Tokenizers 0.13.2 |
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|
