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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
| 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
