---
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: 0.0191
- Ebegin: {'precision': 0.993127147766323, 'recall': 0.9626915389740173, 'f1': 0.9776725304465493, 'number': 3002}
- Eend: {'precision': 0.9910313901345291, 'recall': 0.9576666666666667, 'f1': 0.9740634005763689, 'number': 3000}
- Overall Precision: 0.9921
- Overall Recall: 0.9602
- Overall F1: 0.9759
- Overall Accuracy: 0.9954

## 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  | 0.0312          | 0.9804    | 0.9791 | 0.9797 | 0.9960   |
| 0.1423        | 0.14  | 600  | 0.0232          | 0.9920    | 0.9670 | 0.9793 | 0.9958   |
| 0.1423        | 0.21  | 900  | 0.0164          | 0.9959    | 0.9696 | 0.9825 | 0.9965   |
| 0.0242        | 0.29  | 1200 | 0.0174          | 0.9850    | 0.9744 | 0.9797 | 0.9959   |
| 0.0169        | 0.36  | 1500 | 0.0165          | 0.9913    | 0.9696 | 0.9803 | 0.9960   |
| 0.0169        | 0.43  | 1800 | 0.0168          | 0.9908    | 0.9715 | 0.9810 | 0.9962   |
| 0.0153        | 0.5   | 2100 | 0.0164          | 0.9884    | 0.9702 | 0.9793 | 0.9960   |


### Framework versions

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