readme: 
  
  ## 🧐 About <a name = "about"></a>
  
  tunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.
  
  The model was trained for over 600 000 phrases written in the tunisian dialect. 
  
  ## 🏁 Getting Started <a name = "getting_started"></a>
  
  Load <strong>tunbert_zied</strong> and its sub-word tokenizer
  
  Don'use the <em>AutoTokenizer.from_pretrained(...)</em> method to load the tokenizer, instead use <em>BertTokeinzer.from_pretrained(...)</em> method. (this is because I haven't use the bultin tokenizer of roberta model which is the GPT tokenizer, instead i have used BertTokenizer)
  
  ### Example
  
  
  
  ```
  import transformers as tr
  
  tokenizer = tr.BertTokenizer.from_pretrained("ziedsb19/tunbert_zied")
  
  model = tr.AutoModelForMaskedLM.from_pretrained("ziedsb19/tunbert_zied")
  
  pipeline = tr.pipeline("fill-mask", model= model, tokenizer=tokenizer)
  
  #test the model by masking a word in a phrase with [MASK]
  
  pipeline("Ahla winek [MASK] lioum ?")
  
  #results 
  """
  [{'sequence': 'ahla winek cv lioum?',
    'score': 0.07968682795763016,
    'token': 869,
    'token_str': 'c v'},
   {'sequence': 'ahla winek enty lioum?',
    'score': 0.06116843968629837,
    'token': 448,
    'token_str': 'e n t y'},
   {'sequence': 'ahla winek ch3amla lioum?',
    'score': 0.057379286736249924,
    'token': 7342,
    'token_str': 'c h 3 a m l a'},
   {'sequence': 'ahla winek cha3malt lioum?',
    'score': 0.028112901374697685,
    'token': 4663,
    'token_str': 'c h a 3 m a l t'},
   {'sequence': 'ahla winek enti lioum?',
    'score': 0.025781650096178055,
    'token': 436,
    'token_str': 'e n t i'}]
  """
  ```
  
  ## ✍️ Authors <a name = "authors"></a>
  
  - [zied sbabti](https://www.linkedin.com/in/zied-sbabti-a58a56139) - Idea & Initial work
  
  