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---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext_breaks_indents_left_diff
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
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: 0.0072
- Ebegin: {'precision': 0.9935508345978755, 'recall': 0.9849567506581421, 'f1': 0.9892351274787535, 'number': 2659}
- Eend: {'precision': 0.9980857580398163, 'recall': 0.9742152466367713, 'f1': 0.9860060514372163, 'number': 2676}
- Overall Precision: 0.9958
- Overall Recall: 0.9796
- Overall F1: 0.9876
- Overall Accuracy: 0.9980
## 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 | 0.0309 | 0.9593 | 0.9879 | 0.9734 | 0.9955 |
| 0.161 | 0.14 | 600 | 0.0126 | 0.9890 | 0.9911 | 0.9900 | 0.9982 |
| 0.161 | 0.21 | 900 | 0.0116 | 0.9730 | 0.9894 | 0.9811 | 0.9971 |
| 0.0165 | 0.29 | 1200 | 0.0087 | 0.9938 | 0.9918 | 0.9928 | 0.9987 |
| 0.0119 | 0.36 | 1500 | 0.0093 | 0.9851 | 0.9937 | 0.9894 | 0.9981 |
| 0.0119 | 0.43 | 1800 | 0.0055 | 0.9942 | 0.9913 | 0.9928 | 0.9987 |
| 0.0091 | 0.5 | 2100 | 0.0057 | 0.9951 | 0.9904 | 0.9928 | 0.9987 |
| 0.0091 | 0.57 | 2400 | 0.0058 | 0.9920 | 0.9936 | 0.9928 | 0.9987 |
| 0.0083 | 0.64 | 2700 | 0.0059 | 0.9896 | 0.9918 | 0.9907 | 0.9983 |
| 0.0065 | 0.72 | 3000 | 0.0045 | 0.9968 | 0.9917 | 0.9942 | 0.9990 |
| 0.0065 | 0.79 | 3300 | 0.0047 | 0.9920 | 0.9937 | 0.9929 | 0.9987 |
| 0.0054 | 0.86 | 3600 | 0.0050 | 0.9945 | 0.9909 | 0.9926 | 0.9987 |
| 0.0054 | 0.93 | 3900 | 0.0064 | 0.9838 | 0.9968 | 0.9903 | 0.9983 |
| 0.0056 | 1.0 | 4200 | 0.0046 | 0.9971 | 0.9920 | 0.9946 | 0.9990 |
| 0.0034 | 1.07 | 4500 | 0.0037 | 0.9959 | 0.9936 | 0.9948 | 0.9990 |
| 0.0034 | 1.14 | 4800 | 0.0047 | 0.9983 | 0.9900 | 0.9941 | 0.9989 |
| 0.0035 | 1.22 | 5100 | 0.0043 | 0.9936 | 0.9951 | 0.9944 | 0.9990 |
| 0.0035 | 1.29 | 5400 | 0.0061 | 0.9892 | 0.9957 | 0.9925 | 0.9986 |
| 0.002 | 1.36 | 5700 | 0.0057 | 0.9898 | 0.9947 | 0.9923 | 0.9986 |
| 0.0048 | 1.43 | 6000 | 0.0042 | 0.9954 | 0.9933 | 0.9944 | 0.9990 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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