---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref
  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_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: 0.0063
- Ebegin: {'precision': 0.9877239548772395, 'recall': 0.991672218520986, 'f1': 0.9896941489361701, 'number': 3002}
- Eend: {'precision': 0.9952893674293405, 'recall': 0.986, 'f1': 0.9906229068988612, 'number': 3000}
- Overall Precision: 0.9915
- Overall Recall: 0.9888
- Overall F1: 0.9902
- Overall Accuracy: 0.9984

## 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  | 0.0267          | 0.9713    | 0.9924 | 0.9818 | 0.9969   |
| 0.1477        | 0.14  | 600  | 0.0149          | 0.9818    | 0.9879 | 0.9848 | 0.9974   |
| 0.1477        | 0.21  | 900  | 0.0159          | 0.9625    | 0.9913 | 0.9767 | 0.9960   |
| 0.0165        | 0.29  | 1200 | 0.0062          | 0.9872    | 0.9923 | 0.9897 | 0.9983   |
| 0.0083        | 0.36  | 1500 | 0.0075          | 0.9772    | 0.9962 | 0.9866 | 0.9977   |
| 0.0083        | 0.43  | 1800 | 0.0058          | 0.9940    | 0.9852 | 0.9896 | 0.9983   |
| 0.0068        | 0.5   | 2100 | 0.0062          | 0.9895    | 0.9911 | 0.9903 | 0.9984   |
| 0.0068        | 0.57  | 2400 | 0.0054          | 0.9930    | 0.9867 | 0.9898 | 0.9983   |
| 0.0054        | 0.64  | 2700 | 0.0058          | 0.9985    | 0.9815 | 0.9899 | 0.9983   |
| 0.0061        | 0.72  | 3000 | 0.0053          | 0.9798    | 0.9961 | 0.9879 | 0.9980   |


### Framework versions

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