---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref
  results: []
---

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# icdar23-entrydetector_texttokens_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: 0.0444
- Ebegin: {'precision': 0.9864814119414195, 'recall': 0.9751299183370453, 'f1': 0.9807728206085495, 'number': 2694}
- Eend: {'precision': 0.9851024208566108, 'recall': 0.9789045151739453, 'f1': 0.9819936885093744, 'number': 2702}
- Overall Precision: 0.9858
- Overall Recall: 0.9770
- Overall F1: 0.9814
- Overall Accuracy: 0.9868

## 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  | 0.0977          | 0.9752    | 0.9604 | 0.9677 | 0.9788   |
| 0.31          | 0.14  | 600  | 0.0922          | 0.9910    | 0.9415 | 0.9656 | 0.9779   |
| 0.31          | 0.21  | 900  | 0.0628          | 0.9926    | 0.9534 | 0.9726 | 0.9823   |
| 0.1143        | 0.29  | 1200 | 0.0570          | 0.9715    | 0.9802 | 0.9759 | 0.9838   |
| 0.0878        | 0.36  | 1500 | 0.0393          | 0.9914    | 0.9731 | 0.9822 | 0.9885   |
| 0.0878        | 0.43  | 1800 | 0.0437          | 0.9825    | 0.9819 | 0.9822 | 0.9883   |
| 0.0884        | 0.5   | 2100 | 0.0296          | 0.9908    | 0.9861 | 0.9885 | 0.9924   |
| 0.0884        | 0.57  | 2400 | 0.0340          | 0.9913    | 0.9837 | 0.9875 | 0.9918   |
| 0.0898        | 0.64  | 2700 | 0.0294          | 0.9833    | 0.9932 | 0.9882 | 0.9923   |
| 0.066         | 0.72  | 3000 | 0.0369          | 0.9853    | 0.9849 | 0.9851 | 0.9904   |
| 0.066         | 0.79  | 3300 | 0.0245          | 0.9892    | 0.9889 | 0.9890 | 0.9928   |
| 0.0575        | 0.86  | 3600 | 0.0230          | 0.9879    | 0.9924 | 0.9901 | 0.9936   |
| 0.0575        | 0.93  | 3900 | 0.0282          | 0.9865    | 0.9831 | 0.9848 | 0.9908   |
| 0.064         | 1.0   | 4200 | 0.0244          | 0.9945    | 0.9822 | 0.9883 | 0.9923   |
| 0.0626        | 1.07  | 4500 | 0.0203          | 0.9929    | 0.9880 | 0.9905 | 0.9937   |
| 0.0626        | 1.14  | 4800 | 0.0198          | 0.9920    | 0.9891 | 0.9905 | 0.9937   |
| 0.0419        | 1.22  | 5100 | 0.0219          | 0.9895    | 0.9878 | 0.9886 | 0.9925   |
| 0.0419        | 1.29  | 5400 | 0.0235          | 0.9890    | 0.9876 | 0.9883 | 0.9923   |
| 0.0564        | 1.36  | 5700 | 0.0212          | 0.9935    | 0.9880 | 0.9908 | 0.9939   |
| 0.0427        | 1.43  | 6000 | 0.0238          | 0.9934    | 0.9839 | 0.9886 | 0.9925   |
| 0.0427        | 1.5   | 6300 | 0.0193          | 0.9862    | 0.9920 | 0.9891 | 0.9928   |
| 0.0501        | 1.57  | 6600 | 0.0212          | 0.9919    | 0.9885 | 0.9902 | 0.9935   |
| 0.0501        | 1.65  | 6900 | 0.0225          | 0.9911    | 0.9880 | 0.9896 | 0.9931   |
| 0.0488        | 1.72  | 7200 | 0.0212          | 0.9904    | 0.9892 | 0.9898 | 0.9933   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2