readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - recall
  model-index:
  - name: camembert-base-finetuned-ICDCode_5
    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-ICDCode_5
  
  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predict these ICDCode, I shave trained this model for 8 epochs on 400 000 death causes.  Pre-processing of noisy data points was mandatory before tokenization. It allows us to get this accuracy. 
  It achieves the following results on the evaluation set:
  - Loss: 0.6574
  - Accuracy: 0.8964
  - F1: 0.8750
  - Recall: 0.8964
  
  ## 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: 50
  - eval_batch_size: 50
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|
  | 3.7466        | 1.0   | 4411  | 1.9448          | 0.7201   | 0.6541 | 0.7201 |
  | 1.5264        | 2.0   | 8822  | 1.2045          | 0.8134   | 0.7691 | 0.8134 |
  | 1.0481        | 3.0   | 13233 | 0.9473          | 0.8513   | 0.8149 | 0.8513 |
  | 0.8304        | 4.0   | 17644 | 0.8098          | 0.8718   | 0.8427 | 0.8718 |
  | 0.7067        | 5.0   | 22055 | 0.7352          | 0.8834   | 0.8574 | 0.8834 |
  | 0.6285        | 6.0   | 26466 | 0.6911          | 0.8898   | 0.8659 | 0.8898 |
  | 0.5779        | 7.0   | 30877 | 0.6641          | 0.8958   | 0.8741 | 0.8958 |
  | 0.549         | 8.0   | 35288 | 0.6574          | 0.8964   | 0.8750 | 0.8964 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  