readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  model-index:
  - name: camembert-base-cae-territoire
    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-cae-territoire
  
  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: 1.1611
  - Precision: 0.7543
  - Recall: 0.7387
  - F1: 0.7429
  
  ## 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: 8
  - eval_batch_size: 8
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
  | 1.2546        | 1.0   | 56   | 1.0262          | 0.3120    | 0.5586 | 0.4004 |
  | 0.9866        | 2.0   | 112  | 0.7729          | 0.7243    | 0.7207 | 0.7068 |
  | 0.6312        | 3.0   | 168  | 0.6801          | 0.7566    | 0.7387 | 0.7439 |
  | 0.3597        | 4.0   | 224  | 0.8519          | 0.7896    | 0.6577 | 0.6598 |
  | 0.1825        | 5.0   | 280  | 0.8771          | 0.7397    | 0.7387 | 0.7380 |
  | 0.0893        | 6.0   | 336  | 1.0148          | 0.7418    | 0.7297 | 0.7289 |
  | 0.0533        | 7.0   | 392  | 1.0817          | 0.7525    | 0.7387 | 0.7421 |
  | 0.0398        | 8.0   | 448  | 1.1348          | 0.7527    | 0.7387 | 0.7423 |
  | 0.0412        | 9.0   | 504  | 1.1646          | 0.7434    | 0.7297 | 0.7331 |
  | 0.0349        | 10.0  | 560  | 1.1611          | 0.7543    | 0.7387 | 0.7429 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.0
  - Tokenizers 0.13.1
  