readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  model-index:
  - name: distilcamembert-base-cae
    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. -->
  
  # distilcamembert-base-cae
  
  This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6618
  - Precision: 0.8838
  - Recall: 0.8835
  - F1: 0.8833
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
  | 1.4308        | 1.0   | 292  | 0.5898          | 0.8153    | 0.8286 | 0.8134 |
  | 0.4963        | 2.0   | 584  | 0.4794          | 0.8339    | 0.8518 | 0.8416 |
  | 0.2939        | 3.0   | 876  | 0.5137          | 0.8718    | 0.8629 | 0.8574 |
  | 0.1786        | 4.0   | 1168 | 0.5439          | 0.8777    | 0.8698 | 0.8659 |
  | 0.1277        | 5.0   | 1460 | 0.5820          | 0.8701    | 0.8715 | 0.8701 |
  | 0.0817        | 6.0   | 1752 | 0.5889          | 0.8803    | 0.8800 | 0.8800 |
  | 0.0659        | 7.0   | 2044 | 0.6385          | 0.8734    | 0.8757 | 0.8741 |
  | 0.0497        | 8.0   | 2336 | 0.6521          | 0.8806    | 0.8783 | 0.8789 |
  | 0.0432        | 9.0   | 2628 | 0.6618          | 0.8838    | 0.8835 | 0.8833 |
  | 0.039         | 10.0  | 2920 | 0.6583          | 0.8810    | 0.8809 | 0.8807 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  