readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: icdar23-entrydetector_breaks
    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_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: 0.0140
  - Begin: {'precision': 0.9951923076923077, 'recall': 0.975801382778127, 'f1': 0.9854014598540147, 'number': 3182}
  - End: {'precision': 0.997755690926579, 'recall': 0.9801574803149606, 'f1': 0.9888782967905942, 'number': 3175}
  - Overall Precision: 0.9965
  - Overall Recall: 0.9780
  - Overall F1: 0.9871
  - Overall Accuracy: 0.9976
  
  ## 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: 5000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.02  | 100  | 0.3253          | 0.0       | 0.0    | 0.0    | 0.9013   |
  | No log        | 0.05  | 200  | 0.0628          | 0.9679    | 0.9725 | 0.9702 | 0.9941   |
  | No log        | 0.07  | 300  | 0.0388          | 0.9588    | 0.9890 | 0.9737 | 0.9947   |
  | No log        | 0.1   | 400  | 0.0223          | 0.9840    | 0.9887 | 0.9864 | 0.9973   |
  | 0.1777        | 0.12  | 500  | 0.0196          | 0.9813    | 0.9903 | 0.9858 | 0.9972   |
  | 0.1777        | 0.14  | 600  | 0.0166          | 0.9872    | 0.9854 | 0.9863 | 0.9973   |
  | 0.1777        | 0.17  | 700  | 0.0164          | 0.9818    | 0.9916 | 0.9867 | 0.9974   |
  | 0.1777        | 0.19  | 800  | 0.0120          | 0.9917    | 0.9891 | 0.9904 | 0.9981   |
  | 0.1777        | 0.21  | 900  | 0.0110          | 0.9933    | 0.9870 | 0.9902 | 0.9981   |
  | 0.0187        | 0.24  | 1000 | 0.0107          | 0.9941    | 0.9883 | 0.9911 | 0.9983   |
  | 0.0187        | 0.26  | 1100 | 0.0097          | 0.9914    | 0.9909 | 0.9912 | 0.9983   |
  | 0.0187        | 0.29  | 1200 | 0.0100          | 0.9955    | 0.9869 | 0.9912 | 0.9983   |
  | 0.0187        | 0.31  | 1300 | 0.0097          | 0.9932    | 0.9897 | 0.9914 | 0.9983   |
  | 0.0187        | 0.33  | 1400 | 0.0107          | 0.9931    | 0.9863 | 0.9897 | 0.9980   |
  | 0.01          | 0.36  | 1500 | 0.0095          | 0.9910    | 0.9906 | 0.9908 | 0.9982   |
  | 0.01          | 0.38  | 1600 | 0.0100          | 0.9836    | 0.9937 | 0.9886 | 0.9977   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  