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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# icdar23-entrydetector_plaintext_breaks

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: 0.0084
- Ebegin: {'precision': 0.9988514548238897, 'recall': 0.9811959383226777, 'f1': 0.9899449819768545, 'number': 2659}
- Eend: {'precision': 0.9984726995036274, 'recall': 0.977204783258595, 'f1': 0.987724268177526, 'number': 2676}
- Overall Precision: 0.9987
- Overall Recall: 0.9792
- Overall F1: 0.9888
- 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  | 0.0367          | 0.9567    | 0.9818 | 0.9691 | 0.9948   |
| 0.1432        | 0.14  | 600  | 0.0181          | 0.9809    | 0.9811 | 0.9810 | 0.9971   |
| 0.1432        | 0.21  | 900  | 0.0111          | 0.9877    | 0.9920 | 0.9899 | 0.9981   |
| 0.0188        | 0.29  | 1200 | 0.0111          | 0.9955    | 0.9869 | 0.9912 | 0.9983   |
| 0.0121        | 0.36  | 1500 | 0.0094          | 0.9902    | 0.9899 | 0.9901 | 0.9981   |
| 0.0121        | 0.43  | 1800 | 0.0083          | 0.9914    | 0.9912 | 0.9913 | 0.9983   |
| 0.0106        | 0.5   | 2100 | 0.0078          | 0.9932    | 0.9902 | 0.9917 | 0.9984   |
| 0.0106        | 0.57  | 2400 | 0.0083          | 0.9906    | 0.9911 | 0.9909 | 0.9982   |
| 0.0105        | 0.64  | 2700 | 0.0083          | 0.9871    | 0.9927 | 0.9899 | 0.9981   |
| 0.0093        | 0.72  | 3000 | 0.0085          | 0.9938    | 0.9851 | 0.9894 | 0.9980   |
| 0.0093        | 0.79  | 3300 | 0.0075          | 0.9962    | 0.9879 | 0.9920 | 0.9985   |
| 0.0073        | 0.86  | 3600 | 0.0081          | 0.9927    | 0.9901 | 0.9914 | 0.9984   |
| 0.0073        | 0.93  | 3900 | 0.0083          | 0.9856    | 0.9923 | 0.9890 | 0.9980   |
| 0.0073        | 1.0   | 4200 | 0.0063          | 0.9936    | 0.9912 | 0.9924 | 0.9985   |
| 0.0041        | 1.07  | 4500 | 0.0063          | 0.9959    | 0.9902 | 0.9931 | 0.9987   |
| 0.0041        | 1.14  | 4800 | 0.0068          | 0.9948    | 0.9907 | 0.9928 | 0.9986   |
| 0.0048        | 1.22  | 5100 | 0.0074          | 0.9937    | 0.9905 | 0.9921 | 0.9985   |
| 0.0048        | 1.29  | 5400 | 0.0074          | 0.9912    | 0.9906 | 0.9909 | 0.9982   |
| 0.0043        | 1.36  | 5700 | 0.0070          | 0.9947    | 0.9907 | 0.9927 | 0.9986   |
| 0.0046        | 1.43  | 6000 | 0.0072          | 0.9948    | 0.9914 | 0.9931 | 0.9987   |
| 0.0046        | 1.5   | 6300 | 0.0080          | 0.9939    | 0.9915 | 0.9927 | 0.9986   |
| 0.0038        | 1.57  | 6600 | 0.0072          | 0.9939    | 0.9921 | 0.9930 | 0.9986   |
| 0.0038        | 1.65  | 6900 | 0.0061          | 0.9952    | 0.9916 | 0.9934 | 0.9987   |
| 0.0051        | 1.72  | 7200 | 0.0060          | 0.9959    | 0.9913 | 0.9936 | 0.9988   |
| 0.005         | 1.79  | 7500 | 0.0060          | 0.9959    | 0.9913 | 0.9936 | 0.9988   |


### Framework versions

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