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LayoutLM_2
This model is a fine-tuned version of BadreddineHug/LayoutLM_1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4785
- Precision: 0.6599
- Recall: 0.7638
- F1: 0.7080
- Accuracy: 0.9097
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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 3.7 | 100 | 0.4266 | 0.6597 | 0.7480 | 0.7011 | 0.9110 |
| No log | 7.41 | 200 | 0.4415 | 0.6575 | 0.7559 | 0.7033 | 0.9084 |
| No log | 11.11 | 300 | 0.4478 | 0.6575 | 0.7559 | 0.7033 | 0.9084 |
| No log | 14.81 | 400 | 0.4481 | 0.6690 | 0.7638 | 0.7132 | 0.9123 |
| 0.0237 | 18.52 | 500 | 0.4551 | 0.6644 | 0.7638 | 0.7106 | 0.9097 |
| 0.0237 | 22.22 | 600 | 0.4542 | 0.6736 | 0.7638 | 0.7159 | 0.9097 |
| 0.0237 | 25.93 | 700 | 0.4536 | 0.6783 | 0.7638 | 0.7185 | 0.9123 |
| 0.0237 | 29.63 | 800 | 0.4662 | 0.6644 | 0.7638 | 0.7106 | 0.9097 |
| 0.0237 | 33.33 | 900 | 0.4716 | 0.6486 | 0.7559 | 0.6982 | 0.9071 |
| 0.0146 | 37.04 | 1000 | 0.4644 | 0.6577 | 0.7717 | 0.7101 | 0.9097 |
| 0.0146 | 40.74 | 1100 | 0.4732 | 0.6599 | 0.7638 | 0.7080 | 0.9097 |
| 0.0146 | 44.44 | 1200 | 0.4727 | 0.6667 | 0.7717 | 0.7153 | 0.9110 |
| 0.0146 | 48.15 | 1300 | 0.4774 | 0.6531 | 0.7559 | 0.7007 | 0.9097 |
| 0.0146 | 51.85 | 1400 | 0.4780 | 0.6599 | 0.7638 | 0.7080 | 0.9097 |
| 0.0128 | 55.56 | 1500 | 0.4785 | 0.6599 | 0.7638 | 0.7080 | 0.9097 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
