| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLM_4 |
| 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_4 |
| |
| This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6673 |
| - Precision: 0.675 |
| - Recall: 0.3576 |
| - F1: 0.4675 |
| - Accuracy: 0.8559 |
| |
| ## 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: 4 |
| - 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 | 7.14 | 100 | 1.2248 | 0.0 | 0.0 | 0.0 | 0.7818 | |
| | No log | 14.29 | 200 | 0.9800 | 0.0 | 0.0 | 0.0 | 0.7818 | |
| | No log | 21.43 | 300 | 0.8988 | 0.0 | 0.0 | 0.0 | 0.7818 | |
| | No log | 28.57 | 400 | 0.8416 | 0.0 | 0.0 | 0.0 | 0.7818 | |
| | 1.0601 | 35.71 | 500 | 0.8025 | 0.0 | 0.0 | 0.0 | 0.7818 | |
| | 1.0601 | 42.86 | 600 | 0.7719 | 0.0 | 0.0 | 0.0 | 0.7818 | |
| | 1.0601 | 50.0 | 700 | 0.7428 | 0.75 | 0.0397 | 0.0755 | 0.7902 | |
| | 1.0601 | 57.14 | 800 | 0.7225 | 0.5714 | 0.0530 | 0.0970 | 0.7972 | |
| | 1.0601 | 64.29 | 900 | 0.7107 | 0.6923 | 0.1192 | 0.2034 | 0.8140 | |
| | 0.6088 | 71.43 | 1000 | 0.6954 | 0.6444 | 0.1921 | 0.2959 | 0.8308 | |
| | 0.6088 | 78.57 | 1100 | 0.6861 | 0.6727 | 0.2450 | 0.3592 | 0.8392 | |
| | 0.6088 | 85.71 | 1200 | 0.6800 | 0.6719 | 0.2848 | 0.4 | 0.8462 | |
| | 0.6088 | 92.86 | 1300 | 0.6694 | 0.6901 | 0.3245 | 0.4414 | 0.8517 | |
| | 0.6088 | 100.0 | 1400 | 0.6684 | 0.675 | 0.3576 | 0.4675 | 0.8573 | |
| | 0.5237 | 107.14 | 1500 | 0.6673 | 0.675 | 0.3576 | 0.4675 | 0.8559 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.29.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
