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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# icdar23-entrydetector_plaintext_breaks

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: 0.0171
- Ebegin: {'precision': 0.9908381404818459, 'recall': 0.9726848767488341, 'f1': 0.9816775928727518, 'number': 3002}
- Eend: {'precision': 0.9856379425517702, 'recall': 0.9836666666666667, 'f1': 0.9846513179846514, 'number': 3000}
- Overall Precision: 0.9882
- Overall Recall: 0.9782
- Overall F1: 0.9832
- Overall Accuracy: 0.9971

## 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  | 0.0317          | 0.9815    | 0.9778 | 0.9796 | 0.9965   |
| 0.1664        | 0.14  | 600  | 0.0166          | 0.9892    | 0.9815 | 0.9853 | 0.9972   |
| 0.1664        | 0.21  | 900  | 0.0158          | 0.9773    | 0.9861 | 0.9817 | 0.9965   |
| 0.0198        | 0.29  | 1200 | 0.0127          | 0.9834    | 0.9864 | 0.9849 | 0.9971   |
| 0.0152        | 0.36  | 1500 | 0.0126          | 0.9860    | 0.9842 | 0.9851 | 0.9971   |
| 0.0152        | 0.43  | 1800 | 0.0118          | 0.9961    | 0.9733 | 0.9846 | 0.9971   |


### Framework versions

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