metadata
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: 0.0043
- Ame: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
- Andom number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
- Ather Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
- Lace Of Birth: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
- Other Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
- 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: 16
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Ame | Andom number | Ather Name | Lace Of Birth | Other Name | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.9017 | 1.0 | 6 | 1.1501 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | 0.0 | 0.0 | 0.0 | 0.7967 |
| 0.8813 | 2.0 | 12 | 0.5397 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} | 0.0 | 0.0 | 0.0 | 0.7967 |
| 0.4889 | 3.0 | 18 | 0.3035 | {'precision': 0.5862068965517241, 'recall': 0.8947368421052632, 'f1': 0.7083333333333333, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 0.75, 'recall': 0.15789473684210525, 'f1': 0.2608695652173913, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.56, 'recall': 0.7368421052631579, 'f1': 0.6363636363636364, 'number': 19} | 0.6883 | 0.6543 | 0.6709 | 0.9431 |
| 0.2784 | 4.0 | 24 | 0.1590 | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 0.8260869565217391, 'recall': 1.0, 'f1': 0.9047619047619047, 'number': 19} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.9333333333333333, 'recall': 0.7368421052631579, 'f1': 0.8235294117647058, 'number': 19} | 0.9221 | 0.8765 | 0.8987 | 0.9797 |
| 0.1669 | 5.0 | 30 | 0.0903 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 0.9444444444444444, 'recall': 0.8947368421052632, 'f1': 0.918918918918919, 'number': 19} | {'precision': 1.0, 'recall': 0.4, 'f1': 0.5714285714285715, 'number': 5} | {'precision': 0.8260869565217391, 'recall': 1.0, 'f1': 0.9047619047619047, 'number': 19} | 0.9383 | 0.9383 | 0.9383 | 0.9898 |
| 0.1034 | 6.0 | 36 | 0.0486 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 5} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | 0.9877 | 0.9877 | 0.9877 | 0.9980 |
| 0.0637 | 7.0 | 42 | 0.0232 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0403 | 8.0 | 48 | 0.0125 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0259 | 9.0 | 54 | 0.0087 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.02 | 10.0 | 60 | 0.0068 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0166 | 11.0 | 66 | 0.0058 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0148 | 12.0 | 72 | 0.0053 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0126 | 13.0 | 78 | 0.0047 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0122 | 14.0 | 84 | 0.0044 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.014 | 15.0 | 90 | 0.0043 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1