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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-finetuned-Algo_427Images |
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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-finetuned-Algo_427Images |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0013 |
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- Precision: 0.9937 |
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- Recall: 0.9964 |
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- F1: 0.9950 |
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- Accuracy: 0.9999 |
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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: 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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### 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 | 1.25 | 100 | 0.0082 | 0.9505 | 0.9367 | 0.9435 | 0.9983 | |
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| No log | 2.5 | 200 | 0.0024 | 0.9883 | 0.9901 | 0.9892 | 0.9997 | |
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| No log | 3.75 | 300 | 0.0020 | 0.9883 | 0.9919 | 0.9901 | 0.9997 | |
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| No log | 5.0 | 400 | 0.0016 | 0.9910 | 0.9928 | 0.9919 | 0.9998 | |
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| 0.0301 | 6.25 | 500 | 0.0015 | 0.9910 | 0.9928 | 0.9919 | 0.9998 | |
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| 0.0301 | 7.5 | 600 | 0.0014 | 0.9928 | 0.9946 | 0.9937 | 0.9998 | |
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| 0.0301 | 8.75 | 700 | 0.0013 | 0.9928 | 0.9946 | 0.9937 | 0.9998 | |
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| 0.0301 | 10.0 | 800 | 0.0013 | 0.9937 | 0.9964 | 0.9950 | 0.9999 | |
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| 0.0301 | 11.25 | 900 | 0.0013 | 0.9928 | 0.9946 | 0.9937 | 0.9998 | |
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| 0.002 | 12.5 | 1000 | 0.0013 | 0.9937 | 0.9964 | 0.9950 | 0.9999 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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