| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLM_1 |
| 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_1 |
| |
| This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4204 |
| - Precision: 0.6552 |
| - Recall: 0.7480 |
| - F1: 0.6985 |
| - Accuracy: 0.9071 |
| |
| ## 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 |
| - training_steps: 1000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 3.7 | 100 | 0.6185 | 0.0 | 0.0 | 0.0 | 0.8310 | |
| | No log | 7.41 | 200 | 0.4585 | 0.6146 | 0.4646 | 0.5291 | 0.8839 | |
| | No log | 11.11 | 300 | 0.4020 | 0.5870 | 0.6378 | 0.6113 | 0.8929 | |
| | No log | 14.81 | 400 | 0.3775 | 0.6496 | 0.7008 | 0.6742 | 0.9006 | |
| | 0.4776 | 18.52 | 500 | 0.3826 | 0.6268 | 0.7008 | 0.6617 | 0.9019 | |
| | 0.4776 | 22.22 | 600 | 0.3864 | 0.6224 | 0.7008 | 0.6593 | 0.8981 | |
| | 0.4776 | 25.93 | 700 | 0.4307 | 0.5759 | 0.7165 | 0.6386 | 0.8916 | |
| | 0.4776 | 29.63 | 800 | 0.4205 | 0.6738 | 0.7480 | 0.7090 | 0.9123 | |
| | 0.4776 | 33.33 | 900 | 0.4176 | 0.6552 | 0.7480 | 0.6985 | 0.9084 | |
| | 0.0536 | 37.04 | 1000 | 0.4204 | 0.6552 | 0.7480 | 0.6985 | 0.9071 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.29.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
