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

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# icdar23-entrydetector_plaintext_breaks_indents_left_diff

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: 0.0091
- Ebegin: {'precision': 0.9909729187562688, 'recall': 0.9873417721518988, 'f1': 0.9891540130151844, 'number': 3002}
- Eend: {'precision': 0.986648865153538, 'recall': 0.9853333333333333, 'f1': 0.9859906604402935, 'number': 3000}
- Overall Precision: 0.9888
- Overall Recall: 0.9863
- Overall F1: 0.9876
- Overall Accuracy: 0.9979

## 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  | 0.0315          | 0.9611    | 0.9872 | 0.9740 | 0.9956   |
| 0.1635        | 0.14  | 600  | 0.0130          | 0.9850    | 0.9908 | 0.9879 | 0.9979   |
| 0.1635        | 0.21  | 900  | 0.0096          | 0.9818    | 0.9951 | 0.9884 | 0.9979   |
| 0.0194        | 0.29  | 1200 | 0.0074          | 0.9888    | 0.9908 | 0.9898 | 0.9982   |
| 0.0107        | 0.36  | 1500 | 0.0062          | 0.9885    | 0.9943 | 0.9914 | 0.9984   |
| 0.0107        | 0.43  | 1800 | 0.0082          | 0.9928    | 0.9870 | 0.9899 | 0.9982   |
| 0.0078        | 0.5   | 2100 | 0.0060          | 0.9860    | 0.9948 | 0.9904 | 0.9983   |
| 0.0078        | 0.57  | 2400 | 0.0064          | 0.9865    | 0.9941 | 0.9903 | 0.9983   |
| 0.0061        | 0.64  | 2700 | 0.0055          | 0.9938    | 0.9876 | 0.9907 | 0.9983   |


### Framework versions

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