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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_texttokens_breaks_indents_left_diff_right_ref
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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_texttokens_breaks_indents_left_diff_right_ref
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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: 0.0448
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- Ebegin: {'precision': 0.9843225083986562, 'recall': 0.9788418708240535, 'f1': 0.9815745393634842, 'number': 2694}
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- Eend: {'precision': 0.9872036130974784, 'recall': 0.9707623982235382, 'f1': 0.9789139764881508, 'number': 2702}
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- Overall Precision: 0.9858
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- Overall Recall: 0.9748
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- Overall F1: 0.9802
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- Overall Accuracy: 0.9860
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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: 7500
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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 | 0.0868 | 0.9708 | 0.9867 | 0.9787 | 0.9858 |
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| 0.31 | 0.14 | 600 | 0.0805 | 0.9890 | 0.9606 | 0.9746 | 0.9834 |
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| 0.31 | 0.21 | 900 | 0.0758 | 0.9793 | 0.9340 | 0.9561 | 0.9733 |
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| 0.1178 | 0.29 | 1200 | 0.0434 | 0.9845 | 0.9808 | 0.9826 | 0.9885 |
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| 0.1413 | 0.36 | 1500 | 0.0635 | 0.9909 | 0.9687 | 0.9796 | 0.9867 |
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| 0.1413 | 0.43 | 1800 | 0.0355 | 0.9848 | 0.9839 | 0.9844 | 0.9907 |
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| 0.1699 | 0.5 | 2100 | 0.0327 | 0.9914 | 0.9843 | 0.9879 | 0.9920 |
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| 0.1699 | 0.57 | 2400 | 0.0330 | 0.9904 | 0.9832 | 0.9868 | 0.9913 |
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| 0.144 | 0.64 | 2700 | 0.0285 | 0.9840 | 0.9891 | 0.9865 | 0.9911 |
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| 0.0958 | 0.72 | 3000 | 0.0264 | 0.9922 | 0.9836 | 0.9879 | 0.9920 |
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| 0.0958 | 0.79 | 3300 | 0.0312 | 0.9912 | 0.9852 | 0.9882 | 0.9922 |
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| 0.0585 | 0.86 | 3600 | 0.0296 | 0.9893 | 0.9862 | 0.9878 | 0.9919 |
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| 0.0585 | 0.93 | 3900 | 0.0259 | 0.9864 | 0.9899 | 0.9881 | 0.9922 |
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| 0.0478 | 1.0 | 4200 | 0.0314 | 0.9933 | 0.9649 | 0.9789 | 0.9862 |
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| 0.0842 | 1.07 | 4500 | 0.0222 | 0.9887 | 0.9897 | 0.9892 | 0.9928 |
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| 0.0842 | 1.14 | 4800 | 0.0189 | 0.9925 | 0.9883 | 0.9904 | 0.9937 |
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| 0.075 | 1.22 | 5100 | 0.0241 | 0.9890 | 0.9898 | 0.9894 | 0.9930 |
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| 0.075 | 1.29 | 5400 | 0.0242 | 0.9915 | 0.9854 | 0.9884 | 0.9924 |
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| 0.0511 | 1.36 | 5700 | 0.0197 | 0.9929 | 0.9885 | 0.9907 | 0.9939 |
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| 0.042 | 1.43 | 6000 | 0.0223 | 0.9936 | 0.9852 | 0.9894 | 0.9930 |
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| 0.042 | 1.5 | 6300 | 0.0203 | 0.9899 | 0.9905 | 0.9902 | 0.9935 |
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| 0.0596 | 1.57 | 6600 | 0.0215 | 0.9892 | 0.9914 | 0.9903 | 0.9936 |
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| 0.0596 | 1.65 | 6900 | 0.0211 | 0.9922 | 0.9875 | 0.9898 | 0.9933 |
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| 0.0489 | 1.72 | 7200 | 0.0212 | 0.9923 | 0.9869 | 0.9896 | 0.9931 |
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### Framework versions
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- Transformers 4.26.1
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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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