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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext_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_plaintext_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: 0.0052
- Ebegin: {'precision': 0.9894004637297118, 'recall': 0.9950033311125916, 'f1': 0.9921939877096828, 'number': 3002}
- Eend: {'precision': 0.9909879839786382, 'recall': 0.9896666666666667, 'f1': 0.9903268845897265, 'number': 3000}
- Overall Precision: 0.9902
- Overall Recall: 0.9923
- Overall F1: 0.9913
- Overall Accuracy: 0.9986
## 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: 6000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.07 | 300 | 0.0273 | 0.9720 | 0.9930 | 0.9824 | 0.9970 |
| 0.1622 | 0.14 | 600 | 0.0127 | 0.9798 | 0.9947 | 0.9871 | 0.9978 |
| 0.1622 | 0.21 | 900 | 0.0104 | 0.9833 | 0.9911 | 0.9872 | 0.9979 |
| 0.0167 | 0.29 | 1200 | 0.0066 | 0.9829 | 0.9953 | 0.9890 | 0.9982 |
| 0.0098 | 0.36 | 1500 | 0.0071 | 0.9776 | 0.9962 | 0.9868 | 0.9978 |
| 0.0098 | 0.43 | 1800 | 0.0048 | 0.9895 | 0.9944 | 0.9919 | 0.9986 |
| 0.0065 | 0.5 | 2100 | 0.0069 | 0.9892 | 0.9933 | 0.9912 | 0.9985 |
| 0.0065 | 0.57 | 2400 | 0.0050 | 0.9873 | 0.9942 | 0.9907 | 0.9985 |
| 0.0066 | 0.64 | 2700 | 0.0043 | 0.9982 | 0.9846 | 0.9914 | 0.9986 |
| 0.0061 | 0.72 | 3000 | 0.0042 | 0.9906 | 0.9907 | 0.9907 | 0.9985 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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