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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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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-cord |
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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-cord |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1589 |
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- Precision: 0.9433 |
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- Recall: 0.9521 |
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- F1: 0.9477 |
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- Accuracy: 0.9669 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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- training_steps: 1000 |
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- mixed_precision_training: Native AMP |
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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 | 0.5 | 100 | 0.6487 | 0.7825 | 0.8006 | 0.7914 | 0.8330 | |
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| No log | 1.0 | 200 | 0.4266 | 0.8496 | 0.8686 | 0.8590 | 0.8925 | |
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| No log | 1.5 | 300 | 0.2553 | 0.9008 | 0.9057 | 0.9033 | 0.9341 | |
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| No log | 2.0 | 400 | 0.2496 | 0.8960 | 0.9057 | 0.9008 | 0.9295 | |
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| 0.5667 | 2.5 | 500 | 0.2016 | 0.9274 | 0.9374 | 0.9324 | 0.9554 | |
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| 0.5667 | 3.0 | 600 | 0.1806 | 0.9387 | 0.9467 | 0.9427 | 0.9609 | |
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| 0.5667 | 3.5 | 700 | 0.1667 | 0.9424 | 0.9474 | 0.9449 | 0.9630 | |
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| 0.5667 | 4.0 | 800 | 0.1735 | 0.9452 | 0.9467 | 0.9459 | 0.9639 | |
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| 0.5667 | 4.5 | 900 | 0.1657 | 0.9456 | 0.9529 | 0.9492 | 0.9660 | |
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| 0.1025 | 5.0 | 1000 | 0.1589 | 0.9433 | 0.9521 | 0.9477 | 0.9669 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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