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

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# 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: 0.0052
- Ebegin: {'precision': 0.9891263592050994, 'recall': 0.9921022940955246, 'f1': 0.9906120916259857, 'number': 2659}
- Eend: {'precision': 0.9947029890276201, 'recall': 0.9824364723467862, 'f1': 0.9885316788870088, 'number': 2676}
- Overall Precision: 0.9919
- Overall Recall: 0.9873
- Overall F1: 0.9896
- Overall Accuracy: 0.9984

## 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  | 0.0329          | 0.9706    | 0.9804 | 0.9755 | 0.9968   |
| 0.1902        | 0.14  | 600  | 0.0141          | 0.9815    | 0.9919 | 0.9867 | 0.9978   |
| 0.1902        | 0.21  | 900  | 0.0130          | 0.9853    | 0.9866 | 0.9860 | 0.9976   |
| 0.0162        | 0.29  | 1200 | 0.0110          | 0.9835    | 0.9932 | 0.9883 | 0.9981   |
| 0.0102        | 0.36  | 1500 | 0.0086          | 0.9856    | 0.9943 | 0.9899 | 0.9983   |
| 0.0102        | 0.43  | 1800 | 0.0052          | 0.9921    | 0.9909 | 0.9915 | 0.9987   |
| 0.0071        | 0.5   | 2100 | 0.0061          | 0.9915    | 0.9913 | 0.9914 | 0.9986   |
| 0.0071        | 0.57  | 2400 | 0.0053          | 0.9938    | 0.9915 | 0.9927 | 0.9988   |
| 0.0083        | 0.64  | 2700 | 0.0054          | 0.9905    | 0.9902 | 0.9904 | 0.9984   |
| 0.0058        | 0.72  | 3000 | 0.0060          | 0.9843    | 0.9953 | 0.9898 | 0.9983   |
| 0.0058        | 0.79  | 3300 | 0.0050          | 0.9919    | 0.9933 | 0.9926 | 0.9988   |
| 0.0067        | 0.86  | 3600 | 0.0062          | 0.9905    | 0.9935 | 0.9920 | 0.9987   |
| 0.0067        | 0.93  | 3900 | 0.0049          | 0.9883    | 0.9956 | 0.9919 | 0.9986   |


### Framework versions

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