| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LayoutLMv3_5_entities_filtred_13 |
| 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_5_entities_filtred_13 |
|
|
| This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5851 |
| - Precision: 0.875 |
| - Recall: 0.7778 |
| - F1: 0.8235 |
| - Accuracy: 0.9540 |
|
|
| ## 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: 2000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 20.0 | 100 | 0.3668 | 0.7917 | 0.7037 | 0.7451 | 0.9425 | |
| | No log | 40.0 | 200 | 0.5200 | 0.8182 | 0.6667 | 0.7347 | 0.9368 | |
| | No log | 60.0 | 300 | 0.5244 | 0.8333 | 0.7407 | 0.7843 | 0.9483 | |
| | No log | 80.0 | 400 | 0.5471 | 0.8261 | 0.7037 | 0.76 | 0.9425 | |
| | 0.0818 | 100.0 | 500 | 0.5854 | 0.8261 | 0.7037 | 0.76 | 0.9425 | |
| | 0.0818 | 120.0 | 600 | 0.5497 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0818 | 140.0 | 700 | 0.5480 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0818 | 160.0 | 800 | 0.5709 | 0.8696 | 0.7407 | 0.8000 | 0.9483 | |
| | 0.0818 | 180.0 | 900 | 0.5587 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0007 | 200.0 | 1000 | 0.5676 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0007 | 220.0 | 1100 | 0.5674 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0007 | 240.0 | 1200 | 0.5688 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0007 | 260.0 | 1300 | 0.5733 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0007 | 280.0 | 1400 | 0.5786 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0003 | 300.0 | 1500 | 0.5767 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0003 | 320.0 | 1600 | 0.5766 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0003 | 340.0 | 1700 | 0.5813 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0003 | 360.0 | 1800 | 0.5831 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0003 | 380.0 | 1900 | 0.5851 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
| | 0.0003 | 400.0 | 2000 | 0.5851 | 0.875 | 0.7778 | 0.8235 | 0.9540 | |
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| ### Framework versions |
|
|
| - Transformers 4.29.2 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.14.6 |
| - Tokenizers 0.13.3 |
|
|
