readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: camembert-base-finetuned-mixte-symbole-dd
    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. -->
  
  # camembert-base-finetuned-mixte-symbole-dd
  
  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3576
  - Precision: 0.8788
  - Recall: 0.8986
  - F1: 0.8886
  - Accuracy: 0.9230
  
  ## 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: 2e-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
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1977        | 1.0   | 4322  | 0.2256          | 0.8388    | 0.8980 | 0.8674 | 0.9240   |
  | 0.1498        | 2.0   | 8644  | 0.2765          | 0.8666    | 0.8916 | 0.8789 | 0.9235   |
  | 0.1085        | 3.0   | 12966 | 0.2945          | 0.8666    | 0.9028 | 0.8844 | 0.9211   |
  | 0.0775        | 4.0   | 17288 | 0.3181          | 0.8735    | 0.9015 | 0.8873 | 0.9226   |
  | 0.0605        | 5.0   | 21610 | 0.3576          | 0.8788    | 0.8986 | 0.8886 | 0.9230   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  