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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-large |
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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-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-large-cord |
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This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1616 |
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- Precision: 0.9526 |
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- Recall: 0.9482 |
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- F1: 0.9504 |
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- Accuracy: 0.9677 |
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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: 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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- 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 | 0.25 | 100 | 0.5321 | 0.7584 | 0.7859 | 0.7719 | 0.8224 | |
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| No log | 0.5 | 200 | 0.4949 | 0.8091 | 0.8354 | 0.8221 | 0.8683 | |
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| No log | 0.75 | 300 | 0.3478 | 0.8668 | 0.8648 | 0.8658 | 0.8916 | |
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| No log | 1.0 | 400 | 0.5194 | 0.75 | 0.7117 | 0.7304 | 0.8513 | |
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| 0.6065 | 1.25 | 500 | 0.3052 | 0.9059 | 0.9003 | 0.9031 | 0.9341 | |
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| 0.6065 | 1.5 | 600 | 0.2427 | 0.9245 | 0.9173 | 0.9209 | 0.9443 | |
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| 0.6065 | 1.75 | 700 | 0.2372 | 0.9174 | 0.9181 | 0.9177 | 0.9477 | |
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| 0.6065 | 2.0 | 800 | 0.2044 | 0.9247 | 0.9212 | 0.9230 | 0.9494 | |
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| 0.6065 | 2.25 | 900 | 0.1847 | 0.9442 | 0.9413 | 0.9427 | 0.9613 | |
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| 0.1862 | 2.5 | 1000 | 0.1616 | 0.9526 | 0.9482 | 0.9504 | 0.9677 | |
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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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