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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref
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.
It achieves the following results on the evaluation set:
- Loss: 0.0474
- Ebegin: {'precision': 0.9822681935722202, 'recall': 0.9870081662954714, 'f1': 0.9846324754675061, 'number': 2694}
- Eend: {'precision': 0.9790132547864506, 'recall': 0.9840858623242043, 'f1': 0.9815430047988187, 'number': 2702}
- Overall Precision: 0.9806
- Overall Recall: 0.9855
- Overall F1: 0.9831
- Overall Accuracy: 0.9880
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.07 | 300 | 0.0916 | 0.9560 | 0.9910 | 0.9731 | 0.9821 |
| 0.32 | 0.14 | 600 | 0.0893 | 0.9919 | 0.9482 | 0.9695 | 0.9803 |
| 0.32 | 0.21 | 900 | 0.0772 | 0.9903 | 0.9419 | 0.9655 | 0.9778 |
| 0.114 | 0.29 | 1200 | 0.0442 | 0.9811 | 0.9798 | 0.9805 | 0.9871 |
| 0.0983 | 0.36 | 1500 | 0.0351 | 0.9907 | 0.9826 | 0.9866 | 0.9912 |
| 0.0983 | 0.43 | 1800 | 0.0325 | 0.9917 | 0.9856 | 0.9887 | 0.9926 |
| 0.0869 | 0.5 | 2100 | 0.0237 | 0.9905 | 0.9938 | 0.9921 | 0.9948 |
| 0.0869 | 0.57 | 2400 | 0.0316 | 0.9890 | 0.9870 | 0.9880 | 0.9921 |
| 0.0844 | 0.64 | 2700 | 0.0271 | 0.9863 | 0.9892 | 0.9877 | 0.9919 |
| 0.0794 | 0.72 | 3000 | 0.0287 | 0.9872 | 0.9901 | 0.9887 | 0.9926 |
| 0.0794 | 0.79 | 3300 | 0.0281 | 0.9862 | 0.9891 | 0.9876 | 0.9918 |
| 0.0561 | 0.86 | 3600 | 0.0259 | 0.9883 | 0.9934 | 0.9908 | 0.9939 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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