metadata
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-funsd
results: []
layoutlm-funsd
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: 0.0754
- Ignal: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
- Oise: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.9670
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Ignal | Oise | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| 0.7198 | 1.0 | 1 | 0.7152 | {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0052 | 0.0435 | 0.0093 | 0.5024 |
| 0.7121 | 2.0 | 2 | 0.7152 | {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0052 | 0.0435 | 0.0093 | 0.5024 |
| 0.7191 | 3.0 | 3 | 0.4802 | {'precision': 0.045454545454545456, 'recall': 0.09090909090909091, 'f1': 0.060606060606060615, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0222 | 0.0435 | 0.0294 | 0.9245 |
| 0.4799 | 4.0 | 4 | 0.3268 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9646 |
| 0.3263 | 5.0 | 5 | 0.2246 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.2269 | 6.0 | 6 | 0.1598 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.1625 | 7.0 | 7 | 0.1227 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.1246 | 8.0 | 8 | 0.1030 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.1042 | 9.0 | 9 | 0.0937 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.0942 | 10.0 | 10 | 0.0892 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.0888 | 11.0 | 11 | 0.0861 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.0834 | 12.0 | 12 | 0.0832 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.0768 | 13.0 | 13 | 0.0805 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.0745 | 14.0 | 14 | 0.0778 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
| 0.071 | 15.0 | 15 | 0.0754 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0