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

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# icdar23-entrydetector_texttokens_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: 0.0474
- Ebegin: {'precision': 0.9822681935722202, 'recall': 0.9870081662954714, 'f1': 0.9846324754675061, 'number': 2694}
- Eend: {'precision': 0.9790132547864506, 'recall': 0.9840858623242043, 'f1': 0.9815430047988187, 'number': 2702}
- Overall Precision: 0.9806
- Overall Recall: 0.9855
- Overall F1: 0.9831
- Overall Accuracy: 0.9880

## 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  | 0.0916          | 0.9560    | 0.9910 | 0.9731 | 0.9821   |
| 0.32          | 0.14  | 600  | 0.0893          | 0.9919    | 0.9482 | 0.9695 | 0.9803   |
| 0.32          | 0.21  | 900  | 0.0772          | 0.9903    | 0.9419 | 0.9655 | 0.9778   |
| 0.114         | 0.29  | 1200 | 0.0442          | 0.9811    | 0.9798 | 0.9805 | 0.9871   |
| 0.0983        | 0.36  | 1500 | 0.0351          | 0.9907    | 0.9826 | 0.9866 | 0.9912   |
| 0.0983        | 0.43  | 1800 | 0.0325          | 0.9917    | 0.9856 | 0.9887 | 0.9926   |
| 0.0869        | 0.5   | 2100 | 0.0237          | 0.9905    | 0.9938 | 0.9921 | 0.9948   |
| 0.0869        | 0.57  | 2400 | 0.0316          | 0.9890    | 0.9870 | 0.9880 | 0.9921   |
| 0.0844        | 0.64  | 2700 | 0.0271          | 0.9863    | 0.9892 | 0.9877 | 0.9919   |
| 0.0794        | 0.72  | 3000 | 0.0287          | 0.9872    | 0.9901 | 0.9887 | 0.9926   |
| 0.0794        | 0.79  | 3300 | 0.0281          | 0.9862    | 0.9891 | 0.9876 | 0.9918   |
| 0.0561        | 0.86  | 3600 | 0.0259          | 0.9883    | 0.9934 | 0.9908 | 0.9939   |


### Framework versions

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