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

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# icdar23-entrydetector_plaintext

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: 0.0337
- Ebegin: {'precision': 0.9737045630317092, 'recall': 0.9469725460699511, 'f1': 0.9601525262154433, 'number': 2659}
- Eend: {'precision': 0.9644312708410523, 'recall': 0.9727204783258595, 'f1': 0.9685581395348838, 'number': 2676}
- Overall Precision: 0.9690
- Overall Recall: 0.9599
- Overall F1: 0.9644
- Overall Accuracy: 0.9931

## 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  | 0.0380          | 0.9713    | 0.9691 | 0.9702 | 0.9942   |
| 0.1537        | 0.14  | 600  | 0.0318          | 0.9933    | 0.9550 | 0.9738 | 0.9947   |
| 0.1537        | 0.21  | 900  | 0.0185          | 0.9842    | 0.9780 | 0.9811 | 0.9962   |
| 0.0262        | 0.29  | 1200 | 0.0176          | 0.9883    | 0.9754 | 0.9818 | 0.9963   |
| 0.0171        | 0.36  | 1500 | 0.0174          | 0.9915    | 0.9650 | 0.9781 | 0.9955   |
| 0.0171        | 0.43  | 1800 | 0.0139          | 0.9869    | 0.9787 | 0.9828 | 0.9965   |
| 0.0151        | 0.5   | 2100 | 0.0142          | 0.9845    | 0.9814 | 0.9830 | 0.9965   |
| 0.0151        | 0.57  | 2400 | 0.0185          | 0.9894    | 0.9713 | 0.9803 | 0.9960   |
| 0.0144        | 0.64  | 2700 | 0.0150          | 0.9864    | 0.9789 | 0.9827 | 0.9965   |
| 0.0134        | 0.72  | 3000 | 0.0197          | 0.9848    | 0.9734 | 0.9791 | 0.9957   |
| 0.0134        | 0.79  | 3300 | 0.0201          | 0.9809    | 0.9804 | 0.9806 | 0.9960   |
| 0.012         | 0.86  | 3600 | 0.0163          | 0.9794    | 0.9832 | 0.9813 | 0.9961   |


### Framework versions

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