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# 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: 0.0120
- Ebegin: {'precision': 0.9901997738409348, 'recall': 0.9879654005265137, 'f1': 0.9890813253012049, 'number': 2659}
- Eend: {'precision': 0.9916824196597354, 'recall': 0.9801943198804185, 'f1': 0.9859049050930276, 'number': 2676}
- Overall Precision: 0.9909
- Overall Recall: 0.9841
- Overall F1: 0.9875
- Overall Accuracy: 0.9977

## 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  | 0.0318          | 0.9847    | 0.9761 | 0.9803 | 0.9966   |
| 0.1683        | 0.14  | 600  | 0.0164          | 0.9878    | 0.9890 | 0.9884 | 0.9978   |
| 0.1683        | 0.21  | 900  | 0.0146          | 0.9900    | 0.9853 | 0.9876 | 0.9976   |
| 0.0203        | 0.29  | 1200 | 0.0112          | 0.9862    | 0.9902 | 0.9882 | 0.9978   |
| 0.0123        | 0.36  | 1500 | 0.0089          | 0.9943    | 0.9878 | 0.9910 | 0.9983   |
| 0.0123        | 0.43  | 1800 | 0.0139          | 0.9970    | 0.9814 | 0.9891 | 0.9979   |
| 0.0109        | 0.5   | 2100 | 0.0101          | 0.9937    | 0.9882 | 0.9909 | 0.9982   |
| 0.0109        | 0.57  | 2400 | 0.0087          | 0.9949    | 0.9896 | 0.9922 | 0.9985   |
| 0.0092        | 0.64  | 2700 | 0.0081          | 0.9849    | 0.9919 | 0.9884 | 0.9978   |
| 0.0084        | 0.72  | 3000 | 0.0087          | 0.9937    | 0.9867 | 0.9902 | 0.9981   |
| 0.0084        | 0.79  | 3300 | 0.0089          | 0.9915    | 0.9889 | 0.9902 | 0.9981   |
| 0.0069        | 0.86  | 3600 | 0.0092          | 0.9899    | 0.9901 | 0.9900 | 0.9981   |
| 0.0069        | 0.93  | 3900 | 0.0097          | 0.9845    | 0.9915 | 0.9880 | 0.9977   |


### 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", "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
========================================


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: 0.0120
* Ebegin: {'precision': 0.9901997738409348, 'recall': 0.9879654005265137, 'f1': 0.9890813253012049, 'number': 2659}
* Eend: {'precision': 0.9916824196597354, 'recall': 0.9801943198804185, 'f1': 0.9859049050930276, 'number': 2676}
* Overall Precision: 0.9909
* Overall Recall: 0.9841
* Overall F1: 0.9875
* Overall Accuracy: 0.9977


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