metadata
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- layoutlmv3
model-index:
- name: LayoutLM_Invoice6
results: []
LayoutLM_Invoice6
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0219
- Ax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
- Endor Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
- Nvoice Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
- Otal Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
- Ustomer Address: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}
- Ustomer Name: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}
- Overall Precision: 0.9846
- Overall Recall: 0.9697
- Overall F1: 0.9771
- Overall Accuracy: 0.9939
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: 6
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 300
Training results
| Training Loss | Epoch | Step | Validation Loss | Ax Amount | Endor Name | Nvoice Number | Otal Amount | Ustomer Address | Ustomer Name | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8763 | 6.25 | 50 | 0.2290 | {'precision': 1.0, 'recall': 0.5454545454545454, 'f1': 0.7058823529411764, 'number': 11} | {'precision': 0.8181818181818182, 'recall': 0.8181818181818182, 'f1': 0.8181818181818182, 'number': 11} | {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11} | {'precision': 0.5454545454545454, 'recall': 0.5454545454545454, 'f1': 0.5454545454545454, 'number': 11} | {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11} | {'precision': 0.75, 'recall': 0.8181818181818182, 'f1': 0.7826086956521738, 'number': 11} | 0.7903 | 0.7424 | 0.7656 | 0.9666 |
| 0.1315 | 12.5 | 100 | 0.0312 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | 0.9701 | 0.9848 | 0.9774 | 0.9970 |
| 0.0239 | 18.75 | 150 | 0.0371 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 |
| 0.0098 | 25.0 | 200 | 0.0450 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 |
| 0.0085 | 31.25 | 250 | 0.0360 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 |
| 0.0065 | 37.5 | 300 | 0.0219 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.2.0+cpu
- Datasets 2.12.0
- Tokenizers 0.13.2