readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: icdar23-entrydetector_nolayout
    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_nolayout
  
  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: 0.0222
  - Begin: {'precision': 0.984443061605476, 'recall': 0.9690658499234304, 'f1': 0.9766939342491125, 'number': 3265}
  - End: {'precision': 0.9762412427657631, 'recall': 0.9807221542227662, 'f1': 0.9784765684628302, 'number': 3268}
  - Overall Precision: 0.9803
  - Overall Recall: 0.9749
  - Overall F1: 0.9776
  - Overall Accuracy: 0.9957
  
  ## 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  | 0.3488          | 0.0       | 0.0    | 0.0    | 0.8960   |
  | No log        | 0.05  | 200  | 0.0602          | 0.9699    | 0.9807 | 0.9753 | 0.9949   |
  | No log        | 0.07  | 300  | 0.0345          | 0.9948    | 0.9725 | 0.9835 | 0.9966   |
  | No log        | 0.1   | 400  | 0.0285          | 0.9807    | 0.9807 | 0.9807 | 0.9960   |
  | 0.1859        | 0.12  | 500  | 0.0256          | 0.9942    | 0.9686 | 0.9812 | 0.9962   |
  | 0.1859        | 0.14  | 600  | 0.0188          | 0.9877    | 0.9820 | 0.9848 | 0.9969   |
  | 0.1859        | 0.17  | 700  | 0.0177          | 0.9910    | 0.9796 | 0.9853 | 0.9970   |
  | 0.1859        | 0.19  | 800  | 0.0150          | 0.9877    | 0.9823 | 0.9850 | 0.9969   |
  | 0.1859        | 0.21  | 900  | 0.0152          | 0.9927    | 0.9824 | 0.9875 | 0.9975   |
  | 0.0193        | 0.24  | 1000 | 0.0188          | 0.9937    | 0.9769 | 0.9852 | 0.9970   |
  | 0.0193        | 0.26  | 1100 | 0.0149          | 0.9936    | 0.9796 | 0.9866 | 0.9973   |
  | 0.0193        | 0.29  | 1200 | 0.0139          | 0.9876    | 0.9874 | 0.9875 | 0.9974   |
  | 0.0193        | 0.31  | 1300 | 0.0125          | 0.9930    | 0.9820 | 0.9875 | 0.9974   |
  | 0.0193        | 0.33  | 1400 | 0.0153          | 0.9908    | 0.9803 | 0.9855 | 0.9970   |
  | 0.0161        | 0.36  | 1500 | 0.0158          | 0.9922    | 0.9753 | 0.9837 | 0.9967   |
  | 0.0161        | 0.38  | 1600 | 0.0138          | 0.9912    | 0.9809 | 0.9860 | 0.9971   |
  | 0.0161        | 0.41  | 1700 | 0.0152          | 0.9915    | 0.9833 | 0.9874 | 0.9974   |
  | 0.0161        | 0.43  | 1800 | 0.0128          | 0.9914    | 0.9869 | 0.9892 | 0.9978   |
  | 0.0161        | 0.45  | 1900 | 0.0153          | 0.9900    | 0.9840 | 0.9870 | 0.9973   |
  | 0.0126        | 0.48  | 2000 | 0.0129          | 0.9882    | 0.9877 | 0.9880 | 0.9975   |
  | 0.0126        | 0.5   | 2100 | 0.0120          | 0.9938    | 0.9823 | 0.9880 | 0.9976   |
  | 0.0126        | 0.52  | 2200 | 0.0154          | 0.9887    | 0.9811 | 0.9849 | 0.9969   |
  | 0.0126        | 0.55  | 2300 | 0.0141          | 0.9922    | 0.9824 | 0.9873 | 0.9974   |
  | 0.0126        | 0.57  | 2400 | 0.0112          | 0.9919    | 0.9870 | 0.9895 | 0.9978   |
  | 0.0122        | 0.6   | 2500 | 0.0125          | 0.9934    | 0.9846 | 0.9890 | 0.9977   |
  | 0.0122        | 0.62  | 2600 | 0.0105          | 0.9923    | 0.9875 | 0.9899 | 0.9979   |
  | 0.0122        | 0.64  | 2700 | 0.0124          | 0.9900    | 0.9880 | 0.9890 | 0.9977   |
  | 0.0122        | 0.67  | 2800 | 0.0106          | 0.9893    | 0.9895 | 0.9894 | 0.9978   |
  | 0.0122        | 0.69  | 2900 | 0.0120          | 0.9887    | 0.9879 | 0.9883 | 0.9976   |
  | 0.01          | 0.72  | 3000 | 0.0131          | 0.9925    | 0.9840 | 0.9882 | 0.9976   |
  | 0.01          | 0.74  | 3100 | 0.0105          | 0.9932    | 0.9865 | 0.9898 | 0.9979   |
  | 0.01          | 0.76  | 3200 | 0.0127          | 0.9940    | 0.9842 | 0.9891 | 0.9978   |
  | 0.01          | 0.79  | 3300 | 0.0117          | 0.9945    | 0.9843 | 0.9894 | 0.9978   |
  | 0.01          | 0.81  | 3400 | 0.0102          | 0.9929    | 0.9871 | 0.9900 | 0.9979   |
  | 0.0108        | 0.83  | 3500 | 0.0106          | 0.9943    | 0.9868 | 0.9906 | 0.9981   |
  | 0.0108        | 0.86  | 3600 | 0.0107          | 0.9941    | 0.9876 | 0.9909 | 0.9981   |
  | 0.0108        | 0.88  | 3700 | 0.0112          | 0.9936    | 0.9861 | 0.9898 | 0.9979   |
  | 0.0108        | 0.91  | 3800 | 0.