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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext_breaks_indents_left_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_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: 0.0062
- Ebegin: {'precision': 0.997709049255441, 'recall': 0.9827002632568634, 'f1': 0.9901477832512315, 'number': 2659}
- Eend: {'precision': 0.9973363774733638, 'recall': 0.9794469357249627, 'f1': 0.9883107088989442, 'number': 2676}
- Overall Precision: 0.9975
- Overall Recall: 0.9811
- Overall F1: 0.9892
- 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 | 0.0389 | 0.9556 | 0.9663 | 0.9609 | 0.9949 |
| 0.1775 | 0.14 | 600 | 0.0162 | 0.9854 | 0.9893 | 0.9873 | 0.9977 |
| 0.1775 | 0.21 | 900 | 0.0114 | 0.9928 | 0.9889 | 0.9909 | 0.9984 |
| 0.0229 | 0.29 | 1200 | 0.0172 | 0.9793 | 0.9851 | 0.9822 | 0.9975 |
| 0.016 | 0.36 | 1500 | 0.0087 | 0.9906 | 0.9907 | 0.9907 | 0.9984 |
| 0.016 | 0.43 | 1800 | 0.0079 | 0.9955 | 0.9879 | 0.9917 | 0.9985 |
| 0.0115 | 0.5 | 2100 | 0.0093 | 0.9910 | 0.9912 | 0.9911 | 0.9984 |
| 0.0115 | 0.57 | 2400 | 0.0102 | 0.9816 | 0.9942 | 0.9878 | 0.9978 |
| 0.0109 | 0.64 | 2700 | 0.0072 | 0.9895 | 0.9939 | 0.9917 | 0.9985 |
| 0.0075 | 0.72 | 3000 | 0.0055 | 0.9919 | 0.9917 | 0.9918 | 0.9985 |
| 0.0075 | 0.79 | 3300 | 0.0078 | 0.9948 | 0.9910 | 0.9929 | 0.9987 |
| 0.007 | 0.86 | 3600 | 0.0057 | 0.9937 | 0.9933 | 0.9935 | 0.9989 |
| 0.007 | 0.93 | 3900 | 0.0059 | 0.9830 | 0.9957 | 0.9893 | 0.9981 |
| 0.0055 | 1.0 | 4200 | 0.0049 | 0.9972 | 0.9899 | 0.9935 | 0.9988 |
| 0.0029 | 1.07 | 4500 | 0.0064 | 0.9944 | 0.9926 | 0.9935 | 0.9989 |
| 0.0029 | 1.14 | 4800 | 0.0057 | 0.9927 | 0.9919 | 0.9923 | 0.9987 |
| 0.0043 | 1.22 | 5100 | 0.0064 | 0.9890 | 0.9945 | 0.9917 | 0.9986 |
| 0.0043 | 1.29 | 5400 | 0.0058 | 0.9857 | 0.9957 | 0.9907 | 0.9983 |
| 0.0028 | 1.36 | 5700 | 0.0049 | 0.9961 | 0.9922 | 0.9941 | 0.9990 |
| 0.0034 | 1.43 | 6000 | 0.0048 | 0.9952 | 0.9937 | 0.9945 | 0.9990 |
| 0.0034 | 1.5 | 6300 | 0.0050 | 0.9936 | 0.9937 | 0.9937 | 0.9989 |
| 0.0022 | 1.57 | 6600 | 0.0046 | 0.9937 | 0.9934 | 0.9936 | 0.9989 |
| 0.0022 | 1.65 | 6900 | 0.0042 | 0.9954 | 0.9929 | 0.9941 | 0.9990 |
| 0.0039 | 1.72 | 7200 | 0.0042 | 0.9959 | 0.9931 | 0.9945 | 0.9990 |
| 0.003 | 1.79 | 7500 | 0.0039 | 0.9968 | 0.9927 | 0.9947 | 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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