---
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: 0.0448
- Ebegin: {'precision': 0.9843225083986562, 'recall': 0.9788418708240535, 'f1': 0.9815745393634842, 'number': 2694}
- Eend: {'precision': 0.9872036130974784, 'recall': 0.9707623982235382, 'f1': 0.9789139764881508, 'number': 2702}
- Overall Precision: 0.9858
- Overall Recall: 0.9748
- Overall F1: 0.9802
- Overall Accuracy: 0.9860

## 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  | 0.0868          | 0.9708    | 0.9867 | 0.9787 | 0.9858   |
| 0.31          | 0.14  | 600  | 0.0805          | 0.9890    | 0.9606 | 0.9746 | 0.9834   |
| 0.31          | 0.21  | 900  | 0.0758          | 0.9793    | 0.9340 | 0.9561 | 0.9733   |
| 0.1178        | 0.29  | 1200 | 0.0434          | 0.9845    | 0.9808 | 0.9826 | 0.9885   |
| 0.1413        | 0.36  | 1500 | 0.0635          | 0.9909    | 0.9687 | 0.9796 | 0.9867   |
| 0.1413        | 0.43  | 1800 | 0.0355          | 0.9848    | 0.9839 | 0.9844 | 0.9907   |
| 0.1699        | 0.5   | 2100 | 0.0327          | 0.9914    | 0.9843 | 0.9879 | 0.9920   |
| 0.1699        | 0.57  | 2400 | 0.0330          | 0.9904    | 0.9832 | 0.9868 | 0.9913   |
| 0.144         | 0.64  | 2700 | 0.0285          | 0.9840    | 0.9891 | 0.9865 | 0.9911   |
| 0.0958        | 0.72  | 3000 | 0.0264          | 0.9922    | 0.9836 | 0.9879 | 0.9920   |
| 0.0958        | 0.79  | 3300 | 0.0312          | 0.9912    | 0.9852 | 0.9882 | 0.9922   |
| 0.0585        | 0.86  | 3600 | 0.0296          | 0.9893    | 0.9862 | 0.9878 | 0.9919   |
| 0.0585        | 0.93  | 3900 | 0.0259          | 0.9864    | 0.9899 | 0.9881 | 0.9922   |
| 0.0478        | 1.0   | 4200 | 0.0314          | 0.9933    | 0.9649 | 0.9789 | 0.9862   |
| 0.0842        | 1.07  | 4500 | 0.0222          | 0.9887    | 0.9897 | 0.9892 | 0.9928   |
| 0.0842        | 1.14  | 4800 | 0.0189          | 0.9925    | 0.9883 | 0.9904 | 0.9937   |
| 0.075         | 1.22  | 5100 | 0.0241          | 0.9890    | 0.9898 | 0.9894 | 0.9930   |
| 0.075         | 1.29  | 5400 | 0.0242          | 0.9915    | 0.9854 | 0.9884 | 0.9924   |
| 0.0511        | 1.36  | 5700 | 0.0197          | 0.9929    | 0.9885 | 0.9907 | 0.9939   |
| 0.042         | 1.43  | 6000 | 0.0223          | 0.9936    | 0.9852 | 0.9894 | 0.9930   |
| 0.042         | 1.5   | 6300 | 0.0203          | 0.9899    | 0.9905 | 0.9902 | 0.9935   |
| 0.0596        | 1.57  | 6600 | 0.0215          | 0.9892    | 0.9914 | 0.9903 | 0.9936   |
| 0.0596        | 1.65  | 6900 | 0.0211          | 0.9922    | 0.9875 | 0.9898 | 0.9933   |
| 0.0489        | 1.72  | 7200 | 0.0212          | 0.9923    | 0.9869 | 0.9896 | 0.9931   |


### Framework versions

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