metadata
tags:
- generated_from_trainer
model-index:
- name: layoutlm-synth3
results: []
layoutlm-synth3
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: 0.0021
- Ank Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Ank Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Ayee Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Ayee Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Icr: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9365 | 1.0 | 20 | 0.1057 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 0.9829 | 0.9829 | 0.9829 | 0.9976 |
| 0.0449 | 2.0 | 40 | 0.0058 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0075 | 3.0 | 60 | 0.0028 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.005 | 4.0 | 80 | 0.0022 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0042 | 5.0 | 100 | 0.0021 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | 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