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

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# icdar23-entrydetector_jointlabelledtext_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: 0.2611
- Act: {'precision': 0.7855491329479769, 'recall': 0.8905635648754915, 'f1': 0.8347665847665848, 'number': 1526}
- Cardinal: {'precision': 0.9609375, 'recall': 0.9624413145539906, 'f1': 0.9616888193901486, 'number': 2556}
- Cardinal+i-eend: {'precision': 1.0, 'recall': 0.2631578947368421, 'f1': 0.4166666666666667, 'number': 114}
- Ft: {'precision': 0.3125, 'recall': 0.23809523809523808, 'f1': 0.27027027027027023, 'number': 21}
- Loc: {'precision': 0.9030707610146862, 'recall': 0.9397054737427063, 'f1': 0.9210239651416122, 'number': 3599}
- Loc+i-eend: {'precision': 0.9444444444444444, 'recall': 0.3617021276595745, 'f1': 0.5230769230769231, 'number': 47}
- Per: {'precision': 0.915758896151053, 'recall': 0.9231332357247438, 'f1': 0.919431279620853, 'number': 2732}
- Per+i-ebegin: {'precision': 0.9938223938223938, 'recall': 0.9877206446661551, 'f1': 0.9907621247113164, 'number': 2606}
- Titre: {'precision': 0.6972972972972973, 'recall': 0.86, 'f1': 0.7701492537313434, 'number': 150}
- Overall Precision: 0.9156
- Overall Recall: 0.9346
- Overall F1: 0.9250
- Overall Accuracy: 0.9418

## 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: 15000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.07  | 300  | 0.2539          | 0.8509    | 0.9103 | 0.8796 | 0.9523   |
| 0.5632        | 0.14  | 600  | 0.1632          | 0.9225    | 0.9305 | 0.9265 | 0.9647   |
| 0.5632        | 0.21  | 900  | 0.1571          | 0.9300    | 0.9345 | 0.9323 | 0.9638   |
| 0.204         | 0.29  | 1200 | 0.1415          | 0.9322    | 0.9399 | 0.9361 | 0.9669   |
| 0.1626        | 0.36  | 1500 | 0.1331          | 0.9428    | 0.9477 | 0.9452 | 0.9679   |
| 0.1626        | 0.43  | 1800 | 0.1272          | 0.9384    | 0.9537 | 0.9460 | 0.9679   |
| 0.1305        | 0.5   | 2100 | 0.1334          | 0.9435    | 0.9513 | 0.9474 | 0.9696   |
| 0.1305        | 0.57  | 2400 | 0.1199          | 0.9410    | 0.9496 | 0.9452 | 0.9705   |
| 0.1288        | 0.64  | 2700 | 0.1412          | 0.9401    | 0.9530 | 0.9465 | 0.9685   |
| 0.1345        | 0.72  | 3000 | 0.1177          | 0.9407    | 0.9534 | 0.9470 | 0.9711   |
| 0.1345        | 0.79  | 3300 | 0.1191          | 0.9417    | 0.9599 | 0.9507 | 0.9718   |
| 0.1123        | 0.86  | 3600 | 0.1110          | 0.9472    | 0.9609 | 0.9540 | 0.9746   |
| 0.1123        | 0.93  | 3900 | 0.1229          | 0.9343    | 0.9462 | 0.9402 | 0.9712   |
| 0.1047        | 1.0   | 4200 | 0.1032          | 0.9521    | 0.9622 | 0.9571 | 0.9770   |
| 0.0713        | 1.07  | 4500 | 0.1093          | 0.9343    | 0.9642 | 0.9490 | 0.9746   |
| 0.0713        | 1.14  | 4800 | 0.1045          | 0.9499    | 0.9609 | 0.9554 | 0.9758   |
| 0.0674        | 1.22  | 5100 | 0.1287          | 0.9382    | 0.9704 | 0.9541 | 0.9730   |
| 0.0674        | 1.29  | 5400 | 0.0983          | 0.9520    | 0.9547 | 0.9533 | 0.9743   |
| 0.0682        | 1.36  | 5700 | 0.1153          | 0.9468    | 0.9611 | 0.9539 | 0.9752   |


### Framework versions

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