layoutlmv3-testCUSTOMds20_02
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README.md
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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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