---
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: 0.0046
- Ebegin: {'precision': 0.9946541931172737, 'recall': 0.991672218520986, 'f1': 0.9931609674728941, 'number': 3002}
- Eend: {'precision': 0.9858412907474481, 'recall': 0.998, 'f1': 0.9918833857876428, 'number': 3000}
- Overall Precision: 0.9902
- Overall Recall: 0.9948
- Overall F1: 0.9925
- Overall Accuracy: 0.9988

## 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: 6000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.07  | 300  | 0.0323          | 0.9553    | 0.9933 | 0.9740 | 0.9955   |
| 0.1598        | 0.14  | 600  | 0.0119          | 0.9817    | 0.9931 | 0.9874 | 0.9979   |
| 0.1598        | 0.21  | 900  | 0.0107          | 0.9817    | 0.9944 | 0.9880 | 0.9980   |
| 0.016         | 0.29  | 1200 | 0.0066          | 0.9889    | 0.9907 | 0.9898 | 0.9984   |
| 0.0098        | 0.36  | 1500 | 0.0058          | 0.9845    | 0.9945 | 0.9894 | 0.9982   |
| 0.0098        | 0.43  | 1800 | 0.0071          | 0.9927    | 0.9862 | 0.9895 | 0.9982   |
| 0.0079        | 0.5   | 2100 | 0.0054          | 0.9884    | 0.9940 | 0.9912 | 0.9985   |
| 0.0079        | 0.57  | 2400 | 0.0049          | 0.9930    | 0.9885 | 0.9908 | 0.9985   |
| 0.0061        | 0.64  | 2700 | 0.0059          | 0.9979    | 0.9781 | 0.9879 | 0.9980   |
| 0.0066        | 0.72  | 3000 | 0.0046          | 0.9882    | 0.9956 | 0.9919 | 0.9986   |
| 0.0066        | 0.79  | 3300 | 0.0043          | 0.9861    | 0.9971 | 0.9916 | 0.9986   |
| 0.0066        | 0.86  | 3600 | 0.0038          | 0.9876    | 0.9968 | 0.9922 | 0.9987   |
| 0.0066        | 0.93  | 3900 | 0.0046          | 0.9888    | 0.9961 | 0.9924 | 0.9987   |
| 0.0044        | 1.0   | 4200 | 0.0042          | 0.9880    | 0.9965 | 0.9922 | 0.9987   |
| 0.0035        | 1.07  | 4500 | 0.0038          | 0.9870    | 0.9975 | 0.9922 | 0.9987   |
| 0.0035        | 1.14  | 4800 | 0.0038          | 0.9902    | 0.9951 | 0.9927 | 0.9988   |
| 0.0035        | 1.22  | 5100 | 0.0037          | 0.9897    | 0.9949 | 0.9923 | 0.9987   |
| 0.0035        | 1.29  | 5400 | 0.0038          | 0.9946    | 0.9901 | 0.9924 | 0.9987   |
| 0.0028        | 1.36  | 5700 | 0.0038          | 0.9888    | 0.9963 | 0.9926 | 0.9988   |
| 0.0024        | 1.43  | 6000 | 0.0038          | 0.9885    | 0.9966 | 0.9926 | 0.9988   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2