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

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# icdar23-entrydetector_plaintext_breaks_indents_left_ref_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: 0.0084
- Ebegin: {'precision': 0.9916415914409896, 'recall': 0.9880079946702198, 'f1': 0.9898214583680961, 'number': 3002}
- Eend: {'precision': 0.9879919946631087, 'recall': 0.9873333333333333, 'f1': 0.9876625541847281, 'number': 3000}
- Overall Precision: 0.9898
- Overall Recall: 0.9877
- Overall F1: 0.9887
- Overall Accuracy: 0.9982

## 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  | 0.0364          | 0.9697    | 0.9694 | 0.9695 | 0.9949   |
| 0.1737        | 0.14  | 600  | 0.0146          | 0.9849    | 0.9880 | 0.9865 | 0.9977   |
| 0.1737        | 0.21  | 900  | 0.0092          | 0.9835    | 0.9929 | 0.9881 | 0.9980   |
| 0.0158        | 0.29  | 1200 | 0.0074          | 0.9904    | 0.9912 | 0.9908 | 0.9984   |
| 0.0098        | 0.36  | 1500 | 0.0058          | 0.9866    | 0.9943 | 0.9904 | 0.9984   |
| 0.0098        | 0.43  | 1800 | 0.0075          | 0.9883    | 0.9898 | 0.9890 | 0.9982   |
| 0.0073        | 0.5   | 2100 | 0.0068          | 0.9962    | 0.9815 | 0.9888 | 0.9981   |
| 0.0073        | 0.57  | 2400 | 0.0065          | 0.9899    | 0.9900 | 0.9899 | 0.9983   |


### Framework versions

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