---
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: 0.0424
- Ebegin: {'precision': 0.9725125822686799, 'recall': 0.9447160586686725, 'f1': 0.9584128195345288, 'number': 2659}
- Eend: {'precision': 0.9570211189329382, 'recall': 0.9652466367713004, 'f1': 0.9611162790697675, 'number': 2676}
- Overall Precision: 0.9646
- Overall Recall: 0.9550
- Overall F1: 0.9598
- Overall Accuracy: 0.9923

## 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  | 0.0487          | 0.9874    | 0.9565 | 0.9717 | 0.9943   |
| 0.1698        | 0.14  | 600  | 0.0310          | 0.9891    | 0.9709 | 0.9799 | 0.9959   |
| 0.1698        | 0.21  | 900  | 0.0267          | 0.9746    | 0.9764 | 0.9755 | 0.9953   |
| 0.0346        | 0.29  | 1200 | 0.0217          | 0.9885    | 0.9685 | 0.9784 | 0.9956   |
| 0.0237        | 0.36  | 1500 | 0.0201          | 0.9866    | 0.9742 | 0.9804 | 0.9960   |
| 0.0237        | 0.43  | 1800 | 0.0268          | 0.9883    | 0.9561 | 0.9719 | 0.9944   |
| 0.0205        | 0.5   | 2100 | 0.0216          | 0.9823    | 0.9779 | 0.9801 | 0.9959   |
| 0.0205        | 0.57  | 2400 | 0.0236          | 0.9874    | 0.9700 | 0.9787 | 0.9957   |
| 0.0196        | 0.64  | 2700 | 0.0246          | 0.9877    | 0.9668 | 0.9772 | 0.9954   |
| 0.0195        | 0.72  | 3000 | 0.0254          | 0.9789    | 0.9682 | 0.9735 | 0.9950   |


### Framework versions

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