| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLM_2 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # LayoutLM_2 |
| |
| This model is a fine-tuned version of [BadreddineHug/LayoutLM_1](https://huggingface.co/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 |
| |
