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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-testCUSTOMds20_02 |
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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-testCUSTOMds20_02 |
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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: 0.1298 |
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- Precision: 0.8546 |
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- Recall: 0.8362 |
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- F1: 0.8453 |
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- Accuracy: 0.9807 |
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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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- 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 | 0.0624 | 0.8578 | 0.8578 | 0.8578 | 0.9837 | |
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| No log | 2.5 | 200 | 0.0858 | 0.8603 | 0.8491 | 0.8547 | 0.9814 | |
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| No log | 3.75 | 300 | 0.0826 | 0.9062 | 0.875 | 0.8904 | 0.9859 | |
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| No log | 5.0 | 400 | 0.0940 | 0.9018 | 0.8707 | 0.8860 | 0.9851 | |
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| 0.0658 | 6.25 | 500 | 0.1237 | 0.8502 | 0.8319 | 0.8410 | 0.9807 | |
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| 0.0658 | 7.5 | 600 | 0.1125 | 0.9045 | 0.8578 | 0.8805 | 0.9844 | |
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| 0.0658 | 8.75 | 700 | 0.1252 | 0.8448 | 0.8448 | 0.8448 | 0.9799 | |
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| 0.0658 | 10.0 | 800 | 0.1156 | 0.8678 | 0.8491 | 0.8584 | 0.9829 | |
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| 0.0658 | 11.25 | 900 | 0.1238 | 0.8559 | 0.8448 | 0.8503 | 0.9822 | |
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| 0.0036 | 12.5 | 1000 | 0.1298 | 0.8546 | 0.8362 | 0.8453 | 0.9807 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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