readme: ClinicBERT has the same architecture of RoBERTa model. It has been trained on clinical text and can be used for feature extraction from textual data. 
  
  ## How to use
  
  ### Feature Extraction
  ```
  from transformers import RobertaModel, RobertaTokenizer
  model = RobertaModel.from_pretrained("tdobrxl/ClinicBERT")
  tokenizer = RobertaTokenizer.from_pretrained("tdobrxl/ClinicBERT")
  
  text = "Randomized Study of Shark Cartilage in Patients With Breast Cancer."
  last_hidden_state, pooler_output = model(tokenizer.encode(text, return_tensors="pt")).last_hidden_state, model(tokenizer.encode(text, return_tensors="pt")).pooler_output
  ```
  
  ### Masked Word Prediction
  ```
  from transformers import pipeline
  fill_mask = pipeline("fill-mask", model="tdobrxl/ClinicBERT", tokenizer="tdobrxl/ClinicBERT")
  text = "this is the start of a beautiful <mask>."
  fill_mask(text)
  ```
  
  ```[{'score': 0.26558592915534973, 'token': 363, 'token_str': ' study', 'sequence': 'this is the start of a beautiful study.'}, {'score': 0.06330082565546036, 'token': 2010, 'token_str': ' procedure', 'sequence': 'this is the start of a beautiful procedure.'}, {'score': 0.04393036663532257, 'token': 661, 'token_str': ' trial', 'sequence': 'this is the start of a beautiful trial.'}, {'score': 0.0363750196993351, 'token': 839, 'token_str': ' period', 'sequence': 'this is the start of a beautiful period.'}, {'score': 0.027248281985521317, 'token': 436, 'token_str': ' treatment', 'sequence': 'this is the start of a beautiful treatment.'}```