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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: LayoutLMv3_large_2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LayoutLMv3_large_2 |
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This model is a fine-tuned version of [BadreddineHug/LayoutLM_5](https://huggingface.co/BadreddineHug/LayoutLM_5) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4678 |
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- Precision: 0.7444 |
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- Recall: 0.8462 |
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- F1: 0.792 |
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- Accuracy: 0.9431 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 2000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 2.44 | 100 | 0.2604 | 0.8049 | 0.8462 | 0.8250 | 0.9487 | |
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| No log | 4.88 | 200 | 0.2887 | 0.6923 | 0.8462 | 0.7615 | 0.9294 | |
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| No log | 7.32 | 300 | 0.3961 | 0.6711 | 0.8547 | 0.7519 | 0.9248 | |
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| No log | 9.76 | 400 | 0.3117 | 0.7778 | 0.8376 | 0.8066 | 0.9465 | |
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| 0.1255 | 12.2 | 500 | 0.3344 | 0.7698 | 0.8291 | 0.7984 | 0.9419 | |
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| 0.1255 | 14.63 | 600 | 0.3892 | 0.7197 | 0.8120 | 0.7631 | 0.9339 | |
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| 0.1255 | 17.07 | 700 | 0.3865 | 0.7143 | 0.8547 | 0.7782 | 0.9419 | |
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| 0.1255 | 19.51 | 800 | 0.4737 | 0.6690 | 0.8291 | 0.7405 | 0.9226 | |
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| 0.1255 | 21.95 | 900 | 0.3876 | 0.7405 | 0.8291 | 0.7823 | 0.9442 | |
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| 0.0206 | 24.39 | 1000 | 0.3845 | 0.7444 | 0.8462 | 0.792 | 0.9465 | |
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| 0.0206 | 26.83 | 1100 | 0.4179 | 0.75 | 0.8205 | 0.7837 | 0.9442 | |
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| 0.0206 | 29.27 | 1200 | 0.3942 | 0.7576 | 0.8547 | 0.8032 | 0.9510 | |
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| 0.0206 | 31.71 | 1300 | 0.4521 | 0.7293 | 0.8291 | 0.776 | 0.9408 | |
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| 0.0206 | 34.15 | 1400 | 0.4725 | 0.7050 | 0.8376 | 0.7656 | 0.9328 | |
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| 0.0051 | 36.59 | 1500 | 0.4874 | 0.6849 | 0.8547 | 0.7605 | 0.9317 | |
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| 0.0051 | 39.02 | 1600 | 0.4366 | 0.7519 | 0.8547 | 0.8 | 0.9453 | |
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| 0.0051 | 41.46 | 1700 | 0.4978 | 0.6897 | 0.8547 | 0.7634 | 0.9317 | |
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| 0.0051 | 43.9 | 1800 | 0.4599 | 0.7444 | 0.8462 | 0.792 | 0.9431 | |
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| 0.0051 | 46.34 | 1900 | 0.4765 | 0.7372 | 0.8632 | 0.7953 | 0.9431 | |
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| 0.002 | 48.78 | 2000 | 0.4678 | 0.7444 | 0.8462 | 0.792 | 0.9431 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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