readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: Bio_ClinicalBERT-finetuned-medicalcondition
    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. -->
  
  # Bio_ClinicalBERT-finetuned-medicalcondition
  
  This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7201
  - F1 Score: 0.8254
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | F1 Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.8002        | 1.0   | 1772  | 0.6327          | 0.7759   |
  | 0.5933        | 2.0   | 3544  | 0.5906          | 0.7934   |
  | 0.5015        | 3.0   | 5316  | 0.5768          | 0.8033   |
  | 0.4265        | 4.0   | 7088  | 0.5792          | 0.8099   |
  | 0.3698        | 5.0   | 8860  | 0.6030          | 0.8109   |
  | 0.3229        | 6.0   | 10632 | 0.6366          | 0.8167   |
  | 0.2907        | 7.0   | 12404 | 0.6671          | 0.8198   |
  | 0.2649        | 8.0   | 14176 | 0.6850          | 0.8237   |
  | 0.2477        | 9.0   | 15948 | 0.7072          | 0.8247   |
  | 0.2348        | 10.0  | 17720 | 0.7201          | 0.8254   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  