readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned
    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. -->
  
  # NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0537
  - Precision: 0.8585
  - Recall: 0.7101
  - F1: 0.7773
  - Accuracy: 0.9893
  
  ## 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
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0693        | 1.0   | 514  | 0.0416          | 0.9485    | 0.6492 | 0.7708 | 0.9884   |
  | 0.0367        | 2.0   | 1028 | 0.0396          | 0.9391    | 0.6710 | 0.7827 | 0.9892   |
  | 0.0283        | 3.0   | 1542 | 0.0385          | 0.9388    | 0.6889 | 0.7947 | 0.9899   |
  | 0.0222        | 4.0   | 2056 | 0.0422          | 0.9456    | 0.6790 | 0.7904 | 0.9898   |
  | 0.0182        | 5.0   | 2570 | 0.0457          | 0.9349    | 0.6925 | 0.7956 | 0.9901   |
  | 0.013         | 6.0   | 3084 | 0.0484          | 0.8947    | 0.7062 | 0.7894 | 0.9899   |
  | 0.0084        | 7.0   | 3598 | 0.0537          | 0.8585    | 0.7101 | 0.7773 | 0.9893   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  