readme: # HCAHPS survey comments multilabel classification
  
  This model is a fine-tuned version of [Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on a dataset of HCAHPS survey comments.
  
  It achieves the following results on the evaluation set:
                       
                   precision    recall  f1-score   support
  
      medical          0.87      0.81      0.84        83
      environmental    0.77      0.91      0.84        93
      administration   0.58      0.32      0.41        22
      communication    0.85      0.82      0.84        50
      condition        0.42      0.52      0.46        29
      treatment        0.90      0.78      0.83        68
      food             0.92      0.94      0.93        36
      clean            0.65      0.83      0.73        18
      bathroom         0.64      0.64      0.64        14
      discharge        0.83      0.83      0.83        24
      wait             0.96      1.00      0.98        24
      financial        0.44      1.00      0.62         4
      extra_nice       0.20      0.13      0.16        23
      rude             1.00      0.64      0.78        11
      nurse            0.92      0.98      0.95       110
      doctor           0.96      0.84      0.90        57
  
      micro avg        0.81      0.81      0.81       666
      macro avg        0.75      0.75      0.73       666
      weighted avg     0.82      0.81      0.81       666
      samples avg      0.64      0.64      0.62       666
  
  ## Model description
  
  The model classifies free-text comments into the following labels
  
  * Medical
  * Environmental
  * Administration
  * Communication
  * Condition
  * Treatment
  * Food
  * Clean
  * Bathroom
  * Discharge
  * Wait
  * Financial
  * Extra_nice
  * Rude
  * Nurse
  * Doctor 
  
  ## How to use
  
  You can now use the models directly through the transformers library. Check out the [model's page](https://huggingface.co/joniponi/multilabel_inpatient_comments_16labels) for instructions on how to use the models within the Transformers library.
  
  Load the model via the transformers library:
  ```
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained("joniponi/multilabel_inpatient_comments_16labels")
  model = AutoModel.from_pretrained("joniponi/multilabel_inpatient_comments_16labels")
  ```
  