readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: NLP-CIC-WFU_Clinical_Cases_NER_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_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: 0.0501
  - Precision: 0.8961
  - Recall: 0.7009
  - F1: 0.7865
  - Accuracy: 0.9898
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 94   | 0.0484          | 0.9002    | 0.6340 | 0.7440 | 0.9876   |
  | No log        | 2.0   | 188  | 0.0436          | 0.9095    | 0.6599 | 0.7649 | 0.9887   |
  | No log        | 3.0   | 282  | 0.0462          | 0.8545    | 0.7043 | 0.7722 | 0.9883   |
  | No log        | 4.0   | 376  | 0.0456          | 0.9058    | 0.6761 | 0.7743 | 0.9894   |
  | No log        | 5.0   | 470  | 0.0447          | 0.9194    | 0.6836 | 0.7841 | 0.9900   |
  | 0.0426        | 6.0   | 564  | 0.0480          | 0.8917    | 0.7026 | 0.7859 | 0.9897   |
  | 0.0426        | 7.0   | 658  | 0.0501          | 0.8961    | 0.7009 | 0.7865 | 0.9898   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  