readme: 
  # NepaliBERT(Phase 1) 
  NEPALIBERT is a state-of-the-art language model for Nepali based on the BERT model. The model is trained using a masked language modeling (MLM). 
  
  # Loading the model and tokenizer 
  1. clone the model repo 
  ```
  git lfs install
  git clone https://huggingface.co/Rajan/NepaliBERT
  ```
  2. Loading the Tokenizer 
  ```
  from transformers import BertTokenizer
  vocab_file_dir = './NepaliBERT/' 
  tokenizer = BertTokenizer.from_pretrained(vocab_file_dir,
                                          strip_accents=False,
                                           clean_text=False )
  ```
  3. Loading the model:
  ```
  from transformers import BertForMaskedLM
  model = BertForMaskedLM.from_pretrained('./NepaliBERT')
  ```
  
  The easiest way to check whether our language model is learning anything interesting is via the ```FillMaskPipeline```.
  
  Pipelines are simple wrappers around tokenizers and models, and the 'fill-mask' one will let you input a sequence containing a masked token (here, [mask]) and return a list of the most probable filled sequences, with their probabilities.
  
  ```
  from transformers import pipeline
  
  fill_mask = pipeline(
      "fill-mask",
      model=model,
      tokenizer=tokenizer
  )
  ```
  For more info visit the [GITHUB🤗](https://github.com/R4j4n/NepaliBERT)