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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: 0.0078
- Ebegin: {'precision': 0.9920303605313093, 'recall': 0.9830763444904099, 'f1': 0.9875330562901399, 'number': 2659}
- Eend: {'precision': 0.9958443520967133, 'recall': 0.9850523168908819, 'f1': 0.9904189366898367, 'number': 2676}
- Overall Precision: 0.9939
- Overall Recall: 0.9841
- Overall F1: 0.9890
- Overall Accuracy: 0.9982
## 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 | 0.0314 | 0.9572 | 0.9870 | 0.9719 | 0.9956 |
| 0.1574 | 0.14 | 600 | 0.0145 | 0.9897 | 0.9834 | 0.9866 | 0.9979 |
| 0.1574 | 0.21 | 900 | 0.0098 | 0.9896 | 0.9917 | 0.9907 | 0.9985 |
| 0.0161 | 0.29 | 1200 | 0.0079 | 0.9919 | 0.9921 | 0.9920 | 0.9987 |
| 0.0107 | 0.36 | 1500 | 0.0072 | 0.9895 | 0.9928 | 0.9911 | 0.9986 |
| 0.0107 | 0.43 | 1800 | 0.0116 | 0.9900 | 0.9877 | 0.9888 | 0.9981 |
| 0.0114 | 0.5 | 2100 | 0.0069 | 0.9965 | 0.9898 | 0.9931 | 0.9988 |
| 0.0114 | 0.57 | 2400 | 0.0055 | 0.9955 | 0.9907 | 0.9931 | 0.9989 |
| 0.0082 | 0.64 | 2700 | 0.0051 | 0.9870 | 0.9956 | 0.9913 | 0.9985 |
| 0.0062 | 0.72 | 3000 | 0.0046 | 0.9903 | 0.9957 | 0.9930 | 0.9988 |
| 0.0062 | 0.79 | 3300 | 0.0038 | 0.9957 | 0.9929 | 0.9943 | 0.9990 |
| 0.0051 | 0.86 | 3600 | 0.0038 | 0.9956 | 0.9943 | 0.9949 | 0.9992 |
| 0.0051 | 0.93 | 3900 | 0.0047 | 0.9902 | 0.9942 | 0.9921 | 0.9987 |
| 0.0041 | 1.0 | 4200 | 0.0035 | 0.9979 | 0.9917 | 0.9948 | 0.9991 |
| 0.0029 | 1.07 | 4500 | 0.0036 | 0.9973 | 0.9926 | 0.9949 | 0.9992 |
| 0.0029 | 1.14 | 4800 | 0.0038 | 0.9969 | 0.9916 | 0.9942 | 0.9990 |
| 0.0034 | 1.22 | 5100 | 0.0036 | 0.9953 | 0.9935 | 0.9944 | 0.9991 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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