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README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: icdar23-entrydetector_plaintext
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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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# icdar23-entrydetector_plaintext
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This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0172
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- Ebegin: {'precision': 0.9918256130790191, 'recall': 0.9700199866755497, 'f1': 0.9808016167059616, 'number': 3002}
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- Eend: {'precision': 0.9931506849315068, 'recall': 0.9666666666666667, 'f1': 0.9797297297297298, 'number': 3000}
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- Overall Precision: 0.9925
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- Overall Recall: 0.9683
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- Overall F1: 0.9803
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- Overall Accuracy: 0.9962
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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: 0.0001
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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: 6000
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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 | 0.07 | 300 | 0.0335 | 0.9711 | 0.9635 | 0.9673 | 0.9946 |
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| 0.1318 | 0.14 | 600 | 0.0220 | 0.9847 | 0.9755 | 0.9801 | 0.9961 |
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| 0.1318 | 0.21 | 900 | 0.0154 | 0.9956 | 0.9703 | 0.9828 | 0.9965 |
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| 0.0219 | 0.29 | 1200 | 0.0185 | 0.9865 | 0.9761 | 0.9813 | 0.9962 |
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| 0.017 | 0.36 | 1500 | 0.0202 | 0.9830 | 0.9721 | 0.9775 | 0.9954 |
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| 0.017 | 0.43 | 1800 | 0.0169 | 0.9918 | 0.9707 | 0.9811 | 0.9962 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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