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# icdar23-entrydetector_plaintext_breaks_indents_left_diff

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: 0.0072
- Ebegin: {'precision': 0.9935508345978755, 'recall': 0.9849567506581421, 'f1': 0.9892351274787535, 'number': 2659}
- Eend: {'precision': 0.9980857580398163, 'recall': 0.9742152466367713, 'f1': 0.9860060514372163, 'number': 2676}
- Overall Precision: 0.9958
- Overall Recall: 0.9796
- Overall F1: 0.9876
- Overall Accuracy: 0.9980

## 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  | 0.0309          | 0.9593    | 0.9879 | 0.9734 | 0.9955   |
| 0.161         | 0.14  | 600  | 0.0126          | 0.9890    | 0.9911 | 0.9900 | 0.9982   |
| 0.161         | 0.21  | 900  | 0.0116          | 0.9730    | 0.9894 | 0.9811 | 0.9971   |
| 0.0165        | 0.29  | 1200 | 0.0087          | 0.9938    | 0.9918 | 0.9928 | 0.9987   |
| 0.0119        | 0.36  | 1500 | 0.0093          | 0.9851    | 0.9937 | 0.9894 | 0.9981   |
| 0.0119        | 0.43  | 1800 | 0.0055          | 0.9942    | 0.9913 | 0.9928 | 0.9987   |
| 0.0091        | 0.5   | 2100 | 0.0057          | 0.9951    | 0.9904 | 0.9928 | 0.9987   |
| 0.0091        | 0.57  | 2400 | 0.0058          | 0.9920    | 0.9936 | 0.9928 | 0.9987   |
| 0.0083        | 0.64  | 2700 | 0.0059          | 0.9896    | 0.9918 | 0.9907 | 0.9983   |
| 0.0065        | 0.72  | 3000 | 0.0045          | 0.9968    | 0.9917 | 0.9942 | 0.9990   |
| 0.0065        | 0.79  | 3300 | 0.0047          | 0.9920    | 0.9937 | 0.9929 | 0.9987   |
| 0.0054        | 0.86  | 3600 | 0.0050          | 0.9945    | 0.9909 | 0.9926 | 0.9987   |
| 0.0054        | 0.93  | 3900 | 0.0064          | 0.9838    | 0.9968 | 0.9903 | 0.9983   |
| 0.0056        | 1.0   | 4200 | 0.0046          | 0.9971    | 0.9920 | 0.9946 | 0.9990   |
| 0.0034        | 1.07  | 4500 | 0.0037          | 0.9959    | 0.9936 | 0.9948 | 0.9990   |
| 0.0034        | 1.14  | 4800 | 0.0047          | 0.9983    | 0.9900 | 0.9941 | 0.9989   |
| 0.0035        | 1.22  | 5100 | 0.0043          | 0.9936    | 0.9951 | 0.9944 | 0.9990   |
| 0.0035        | 1.29  | 5400 | 0.0061          | 0.9892    | 0.9957 | 0.9925 | 0.9986   |
| 0.002         | 1.36  | 5700 | 0.0057          | 0.9898    | 0.9947 | 0.9923 | 0.9986   |
| 0.0048        | 1.43  | 6000 | 0.0042          | 0.9954    | 0.9933 | 0.9944 | 0.9990   |


### Framework versions

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

{"tags": ["generated_from_trainer"], "model-index": [{"name": "icdar23-entrydetector_plaintext_breaks_indents_left_diff", "results": []}]}
transformers pytorch camembert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

icdar23-entrydetector\_plaintext\_breaks\_indents\_left\_diff
=============================================================


This model is a fine-tuned version of HueyNemud/das22-10-camembert\_pretrained on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0072
* Ebegin: {'precision': 0.9935508345978755, 'recall': 0.9849567506581421, 'f1': 0.9892351274787535, 'number': 2659}
* Eend: {'precision': 0.9980857580398163, 'recall': 0.9742152466367713, 'f1': 0.9860060514372163, 'number': 2676}
* Overall Precision: 0.9958
* Overall Recall: 0.9796
* Overall F1: 0.9876
* Overall Accuracy: 0.9980


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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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