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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext
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_plaintext
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: 0.0337
- Ebegin: {'precision': 0.9737045630317092, 'recall': 0.9469725460699511, 'f1': 0.9601525262154433, 'number': 2659}
- Eend: {'precision': 0.9644312708410523, 'recall': 0.9727204783258595, 'f1': 0.9685581395348838, 'number': 2676}
- Overall Precision: 0.9690
- Overall Recall: 0.9599
- Overall F1: 0.9644
- Overall Accuracy: 0.9931
## 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 | 0.0380 | 0.9713 | 0.9691 | 0.9702 | 0.9942 |
| 0.1537 | 0.14 | 600 | 0.0318 | 0.9933 | 0.9550 | 0.9738 | 0.9947 |
| 0.1537 | 0.21 | 900 | 0.0185 | 0.9842 | 0.9780 | 0.9811 | 0.9962 |
| 0.0262 | 0.29 | 1200 | 0.0176 | 0.9883 | 0.9754 | 0.9818 | 0.9963 |
| 0.0171 | 0.36 | 1500 | 0.0174 | 0.9915 | 0.9650 | 0.9781 | 0.9955 |
| 0.0171 | 0.43 | 1800 | 0.0139 | 0.9869 | 0.9787 | 0.9828 | 0.9965 |
| 0.0151 | 0.5 | 2100 | 0.0142 | 0.9845 | 0.9814 | 0.9830 | 0.9965 |
| 0.0151 | 0.57 | 2400 | 0.0185 | 0.9894 | 0.9713 | 0.9803 | 0.9960 |
| 0.0144 | 0.64 | 2700 | 0.0150 | 0.9864 | 0.9789 | 0.9827 | 0.9965 |
| 0.0134 | 0.72 | 3000 | 0.0197 | 0.9848 | 0.9734 | 0.9791 | 0.9957 |
| 0.0134 | 0.79 | 3300 | 0.0201 | 0.9809 | 0.9804 | 0.9806 | 0.9960 |
| 0.012 | 0.86 | 3600 | 0.0163 | 0.9794 | 0.9832 | 0.9813 | 0.9961 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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