---
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: 0.0079
- Ebegin: {'precision': 0.9973414356247626, 'recall': 0.9875893192929672, 'f1': 0.9924414210128495, 'number': 2659}
- Eend: {'precision': 0.9980966882375333, 'recall': 0.9798206278026906, 'f1': 0.9888742221384123, 'number': 2676}
- Overall Precision: 0.9977
- Overall Recall: 0.9837
- Overall F1: 0.9907
- 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  | 0.0341          | 0.9873    | 0.9698 | 0.9785 | 0.9966   |
| 0.173         | 0.14  | 600  | 0.0140          | 0.9895    | 0.9899 | 0.9897 | 0.9982   |
| 0.173         | 0.21  | 900  | 0.0135          | 0.9796    | 0.9884 | 0.9840 | 0.9973   |
| 0.0216        | 0.29  | 1200 | 0.0087          | 0.9938    | 0.9901 | 0.9920 | 0.9986   |
| 0.0138        | 0.36  | 1500 | 0.0061          | 0.9884    | 0.9938 | 0.9911 | 0.9984   |
| 0.0138        | 0.43  | 1800 | 0.0060          | 0.9938    | 0.9919 | 0.9929 | 0.9987   |
| 0.0083        | 0.5   | 2100 | 0.0058          | 0.9963    | 0.9909 | 0.9935 | 0.9989   |
| 0.0083        | 0.57  | 2400 | 0.0064          | 0.9972    | 0.9913 | 0.9942 | 0.9990   |
| 0.0092        | 0.64  | 2700 | 0.0083          | 0.9881    | 0.9947 | 0.9914 | 0.9985   |
| 0.0087        | 0.72  | 3000 | 0.0057          | 0.9924    | 0.9934 | 0.9929 | 0.9987   |
| 0.0087        | 0.79  | 3300 | 0.0044          | 0.9925    | 0.9927 | 0.9926 | 0.9987   |
| 0.0066        | 0.86  | 3600 | 0.0049          | 0.9948    | 0.9917 | 0.9932 | 0.9988   |
| 0.0066        | 0.93  | 3900 | 0.0082          | 0.9886    | 0.9916 | 0.9901 | 0.9982   |


### Framework versions

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