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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: 0.0046
- Ebegin: {'precision': 0.9946541931172737, 'recall': 0.991672218520986, 'f1': 0.9931609674728941, 'number': 3002}
- Eend: {'precision': 0.9858412907474481, 'recall': 0.998, 'f1': 0.9918833857876428, 'number': 3000}
- Overall Precision: 0.9902
- Overall Recall: 0.9948
- Overall F1: 0.9925
- Overall Accuracy: 0.9988
## 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 | 0.0323 | 0.9553 | 0.9933 | 0.9740 | 0.9955 |
| 0.1598 | 0.14 | 600 | 0.0119 | 0.9817 | 0.9931 | 0.9874 | 0.9979 |
| 0.1598 | 0.21 | 900 | 0.0107 | 0.9817 | 0.9944 | 0.9880 | 0.9980 |
| 0.016 | 0.29 | 1200 | 0.0066 | 0.9889 | 0.9907 | 0.9898 | 0.9984 |
| 0.0098 | 0.36 | 1500 | 0.0058 | 0.9845 | 0.9945 | 0.9894 | 0.9982 |
| 0.0098 | 0.43 | 1800 | 0.0071 | 0.9927 | 0.9862 | 0.9895 | 0.9982 |
| 0.0079 | 0.5 | 2100 | 0.0054 | 0.9884 | 0.9940 | 0.9912 | 0.9985 |
| 0.0079 | 0.57 | 2400 | 0.0049 | 0.9930 | 0.9885 | 0.9908 | 0.9985 |
| 0.0061 | 0.64 | 2700 | 0.0059 | 0.9979 | 0.9781 | 0.9879 | 0.9980 |
| 0.0066 | 0.72 | 3000 | 0.0046 | 0.9882 | 0.9956 | 0.9919 | 0.9986 |
| 0.0066 | 0.79 | 3300 | 0.0043 | 0.9861 | 0.9971 | 0.9916 | 0.9986 |
| 0.0066 | 0.86 | 3600 | 0.0038 | 0.9876 | 0.9968 | 0.9922 | 0.9987 |
| 0.0066 | 0.93 | 3900 | 0.0046 | 0.9888 | 0.9961 | 0.9924 | 0.9987 |
| 0.0044 | 1.0 | 4200 | 0.0042 | 0.9880 | 0.9965 | 0.9922 | 0.9987 |
| 0.0035 | 1.07 | 4500 | 0.0038 | 0.9870 | 0.9975 | 0.9922 | 0.9987 |
| 0.0035 | 1.14 | 4800 | 0.0038 | 0.9902 | 0.9951 | 0.9927 | 0.9988 |
| 0.0035 | 1.22 | 5100 | 0.0037 | 0.9897 | 0.9949 | 0.9923 | 0.9987 |
| 0.0035 | 1.29 | 5400 | 0.0038 | 0.9946 | 0.9901 | 0.9924 | 0.9987 |
| 0.0028 | 1.36 | 5700 | 0.0038 | 0.9888 | 0.9963 | 0.9926 | 0.9988 |
| 0.0024 | 1.43 | 6000 | 0.0038 | 0.9885 | 0.9966 | 0.9926 | 0.9988 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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