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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_breaks
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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_breaks
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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: 0.0119
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- Ebegin: {'precision': 0.9996581196581197, 'recall': 0.9740173217854764, 'f1': 0.9866711658511895, 'number': 3002}
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- Eend: {'precision': 0.9866799866799867, 'recall': 0.9876666666666667, 'f1': 0.9871730801266034, 'number': 3000}
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- Overall Precision: 0.9931
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- Overall Recall: 0.9808
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- Overall F1: 0.9869
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- Overall Accuracy: 0.9976
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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 | 0.0362 | 0.9796 | 0.9789 | 0.9793 | 0.9961 |
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| 0.1771 | 0.14 | 600 | 0.0178 | 0.9918 | 0.9784 | 0.9851 | 0.9971 |
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| 0.1771 | 0.21 | 900 | 0.0181 | 0.9915 | 0.9760 | 0.9837 | 0.9969 |
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| 0.0232 | 0.29 | 1200 | 0.0146 | 0.9878 | 0.9842 | 0.9860 | 0.9973 |
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| 0.016 | 0.36 | 1500 | 0.0122 | 0.9797 | 0.9872 | 0.9835 | 0.9968 |
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| 0.016 | 0.43 | 1800 | 0.0119 | 0.9927 | 0.9807 | 0.9866 | 0.9974 |
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| 0.0117 | 0.5 | 2100 | 0.0114 | 0.9897 | 0.9824 | 0.9860 | 0.9973 |
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| 0.0117 | 0.57 | 2400 | 0.0109 | 0.9913 | 0.9849 | 0.9881 | 0.9977 |
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| 0.0105 | 0.64 | 2700 | 0.0111 | 0.9971 | 0.9746 | 0.9857 | 0.9973 |
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| 0.0109 | 0.72 | 3000 | 0.0101 | 0.9887 | 0.9859 | 0.9873 | 0.9976 |
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| 0.0109 | 0.79 | 3300 | 0.0115 | 0.9857 | 0.9865 | 0.9861 | 0.9973 |
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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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