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

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# icdar23-entrydetector_plaintext_breaks_indents_left_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: 0.0062
- Ebegin: {'precision': 0.997709049255441, 'recall': 0.9827002632568634, 'f1': 0.9901477832512315, 'number': 2659}
- Eend: {'precision': 0.9973363774733638, 'recall': 0.9794469357249627, 'f1': 0.9883107088989442, 'number': 2676}
- Overall Precision: 0.9975
- Overall Recall: 0.9811
- Overall F1: 0.9892
- Overall Accuracy: 0.9982

## 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  | 0.0389          | 0.9556    | 0.9663 | 0.9609 | 0.9949   |
| 0.1775        | 0.14  | 600  | 0.0162          | 0.9854    | 0.9893 | 0.9873 | 0.9977   |
| 0.1775        | 0.21  | 900  | 0.0114          | 0.9928    | 0.9889 | 0.9909 | 0.9984   |
| 0.0229        | 0.29  | 1200 | 0.0172          | 0.9793    | 0.9851 | 0.9822 | 0.9975   |
| 0.016         | 0.36  | 1500 | 0.0087          | 0.9906    | 0.9907 | 0.9907 | 0.9984   |
| 0.016         | 0.43  | 1800 | 0.0079          | 0.9955    | 0.9879 | 0.9917 | 0.9985   |
| 0.0115        | 0.5   | 2100 | 0.0093          | 0.9910    | 0.9912 | 0.9911 | 0.9984   |
| 0.0115        | 0.57  | 2400 | 0.0102          | 0.9816    | 0.9942 | 0.9878 | 0.9978   |
| 0.0109        | 0.64  | 2700 | 0.0072          | 0.9895    | 0.9939 | 0.9917 | 0.9985   |
| 0.0075        | 0.72  | 3000 | 0.0055          | 0.9919    | 0.9917 | 0.9918 | 0.9985   |
| 0.0075        | 0.79  | 3300 | 0.0078          | 0.9948    | 0.9910 | 0.9929 | 0.9987   |
| 0.007         | 0.86  | 3600 | 0.0057          | 0.9937    | 0.9933 | 0.9935 | 0.9989   |
| 0.007         | 0.93  | 3900 | 0.0059          | 0.9830    | 0.9957 | 0.9893 | 0.9981   |
| 0.0055        | 1.0   | 4200 | 0.0049          | 0.9972    | 0.9899 | 0.9935 | 0.9988   |
| 0.0029        | 1.07  | 4500 | 0.0064          | 0.9944    | 0.9926 | 0.9935 | 0.9989   |
| 0.0029        | 1.14  | 4800 | 0.0057          | 0.9927    | 0.9919 | 0.9923 | 0.9987   |
| 0.0043        | 1.22  | 5100 | 0.0064          | 0.9890    | 0.9945 | 0.9917 | 0.9986   |
| 0.0043        | 1.29  | 5400 | 0.0058          | 0.9857    | 0.9957 | 0.9907 | 0.9983   |
| 0.0028        | 1.36  | 5700 | 0.0049          | 0.9961    | 0.9922 | 0.9941 | 0.9990   |
| 0.0034        | 1.43  | 6000 | 0.0048          | 0.9952    | 0.9937 | 0.9945 | 0.9990   |
| 0.0034        | 1.5   | 6300 | 0.0050          | 0.9936    | 0.9937 | 0.9937 | 0.9989   |
| 0.0022        | 1.57  | 6600 | 0.0046          | 0.9937    | 0.9934 | 0.9936 | 0.9989   |
| 0.0022        | 1.65  | 6900 | 0.0042          | 0.9954    | 0.9929 | 0.9941 | 0.9990   |
| 0.0039        | 1.72  | 7200 | 0.0042          | 0.9959    | 0.9931 | 0.9945 | 0.9990   |
| 0.003         | 1.79  | 7500 | 0.0039          | 0.9968    | 0.9927 | 0.9947 | 0.9991   |


### Framework versions

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