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

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# icdar23-entrydetector_plaintext_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: 0.0045
- Ebegin: {'precision': 0.9977203647416414, 'recall': 0.9875893192929672, 'f1': 0.9926289926289926, 'number': 2659}
- Eend: {'precision': 0.9962221382697394, 'recall': 0.9854260089686099, 'f1': 0.9907946646627841, 'number': 2676}
- Overall Precision: 0.9970
- Overall Recall: 0.9865
- Overall F1: 0.9917
- Overall Accuracy: 0.9986

## 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  | 0.0294          | 0.9739    | 0.9925 | 0.9831 | 0.9972   |
| 0.1819        | 0.14  | 600  | 0.0152          | 0.9911    | 0.9804 | 0.9857 | 0.9978   |
| 0.1819        | 0.21  | 900  | 0.0067          | 0.9871    | 0.9959 | 0.9915 | 0.9986   |
| 0.0165        | 0.29  | 1200 | 0.0077          | 0.9871    | 0.9961 | 0.9916 | 0.9986   |
| 0.0103        | 0.36  | 1500 | 0.0065          | 0.9872    | 0.9962 | 0.9917 | 0.9986   |
| 0.0103        | 0.43  | 1800 | 0.0053          | 0.9903    | 0.9952 | 0.9927 | 0.9988   |
| 0.0087        | 0.5   | 2100 | 0.0068          | 0.9974    | 0.9886 | 0.9930 | 0.9988   |
| 0.0087        | 0.57  | 2400 | 0.0073          | 0.9951    | 0.9877 | 0.9914 | 0.9985   |
| 0.0078        | 0.64  | 2700 | 0.0045          | 0.9899    | 0.9946 | 0.9923 | 0.9987   |
| 0.0054        | 0.72  | 3000 | 0.0043          | 0.9978    | 0.9905 | 0.9941 | 0.9990   |
| 0.0054        | 0.79  | 3300 | 0.0042          | 0.9976    | 0.9899 | 0.9938 | 0.9989   |
| 0.0047        | 0.86  | 3600 | 0.0042          | 0.9955    | 0.9925 | 0.9940 | 0.9990   |
| 0.0047        | 0.93  | 3900 | 0.0048          | 0.9865    | 0.9974 | 0.9920 | 0.9986   |
| 0.0044        | 1.0   | 4200 | 0.0034          | 0.9979    | 0.9919 | 0.9949 | 0.9991   |
| 0.0026        | 1.07  | 4500 | 0.0041          | 0.9954    | 0.9944 | 0.9949 | 0.9991   |
| 0.0026        | 1.14  | 4800 | 0.0036          | 0.9979    | 0.9922 | 0.9950 | 0.9992   |
| 0.0029        | 1.22  | 5100 | 0.0037          | 0.9956    | 0.9931 | 0.9944 | 0.9991   |
| 0.0029        | 1.29  | 5400 | 0.0050          | 0.9899    | 0.9956 | 0.9927 | 0.9988   |
| 0.0029        | 1.36  | 5700 | 0.0034          | 0.9975    | 0.9935 | 0.9955 | 0.9993   |
| 0.0028        | 1.43  | 6000 | 0.0036          | 0.9970    | 0.9937 | 0.9954 | 0.9992   |
| 0.0028        | 1.5   | 6300 | 0.0038          | 0.9932    | 0.9951 | 0.9942 | 0.9990   |
| 0.0027        | 1.57  | 6600 | 0.0034          | 0.9969    | 0.9933 | 0.9951 | 0.9992   |
| 0.0027        | 1.65  | 6900 | 0.0034          | 0.9974    | 0.9929 | 0.9952 | 0.9992   |
| 0.0027        | 1.72  | 7200 | 0.0036          | 0.9970    | 0.9934 | 0.9952 | 0.9992   |


### Framework versions

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