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layoutlm-synth2
This model is a fine-tuned version of 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
