| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLMv3_large_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. --> |
|
|
| # LayoutLMv3_large_2 |
|
|
| This model is a fine-tuned version of [BadreddineHug/LayoutLM_5](https://huggingface.co/BadreddineHug/LayoutLM_5) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4678 |
| - Precision: 0.7444 |
| - Recall: 0.8462 |
| - F1: 0.792 |
| - Accuracy: 0.9431 |
|
|
| ## 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: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - training_steps: 2000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 2.44 | 100 | 0.2604 | 0.8049 | 0.8462 | 0.8250 | 0.9487 | |
| | No log | 4.88 | 200 | 0.2887 | 0.6923 | 0.8462 | 0.7615 | 0.9294 | |
| | No log | 7.32 | 300 | 0.3961 | 0.6711 | 0.8547 | 0.7519 | 0.9248 | |
| | No log | 9.76 | 400 | 0.3117 | 0.7778 | 0.8376 | 0.8066 | 0.9465 | |
| | 0.1255 | 12.2 | 500 | 0.3344 | 0.7698 | 0.8291 | 0.7984 | 0.9419 | |
| | 0.1255 | 14.63 | 600 | 0.3892 | 0.7197 | 0.8120 | 0.7631 | 0.9339 | |
| | 0.1255 | 17.07 | 700 | 0.3865 | 0.7143 | 0.8547 | 0.7782 | 0.9419 | |
| | 0.1255 | 19.51 | 800 | 0.4737 | 0.6690 | 0.8291 | 0.7405 | 0.9226 | |
| | 0.1255 | 21.95 | 900 | 0.3876 | 0.7405 | 0.8291 | 0.7823 | 0.9442 | |
| | 0.0206 | 24.39 | 1000 | 0.3845 | 0.7444 | 0.8462 | 0.792 | 0.9465 | |
| | 0.0206 | 26.83 | 1100 | 0.4179 | 0.75 | 0.8205 | 0.7837 | 0.9442 | |
| | 0.0206 | 29.27 | 1200 | 0.3942 | 0.7576 | 0.8547 | 0.8032 | 0.9510 | |
| | 0.0206 | 31.71 | 1300 | 0.4521 | 0.7293 | 0.8291 | 0.776 | 0.9408 | |
| | 0.0206 | 34.15 | 1400 | 0.4725 | 0.7050 | 0.8376 | 0.7656 | 0.9328 | |
| | 0.0051 | 36.59 | 1500 | 0.4874 | 0.6849 | 0.8547 | 0.7605 | 0.9317 | |
| | 0.0051 | 39.02 | 1600 | 0.4366 | 0.7519 | 0.8547 | 0.8 | 0.9453 | |
| | 0.0051 | 41.46 | 1700 | 0.4978 | 0.6897 | 0.8547 | 0.7634 | 0.9317 | |
| | 0.0051 | 43.9 | 1800 | 0.4599 | 0.7444 | 0.8462 | 0.792 | 0.9431 | |
| | 0.0051 | 46.34 | 1900 | 0.4765 | 0.7372 | 0.8632 | 0.7953 | 0.9431 | |
| | 0.002 | 48.78 | 2000 | 0.4678 | 0.7444 | 0.8462 | 0.792 | 0.9431 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.29.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
