readme: This is a masked language model that was trained on IMDB dataset using a finetuned DistilBERT model. 
  ### Note: 
  I published a tutorial explaining how transformers work and how to train a masked language model using transformer. https://olafenwaayoola.medium.com/the-concept-of-transformers-and-training-a-transformers-model-45a09ae7fb50
  
  # Rest API Code for Testing the Masked Language Model
  
  Inference API python code for testing the masked language model.
  ``` python
  import requests
  
  API_URL = "https://api-inference.huggingface.co/models/ayoolaolafenwa/Masked-Language-Model"
  headers = {"Authorization": "Bearer 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"}
  
  def query(payload):
  	response = requests.post(API_URL, headers=headers, json=payload)
  	return response.json()
  	
  output = query({
  	"inputs": "Washington DC is the [MASK] of USA.",
  })
  print(output[0]["sequence"])
  ```
  
  Output
  ```
  washington dc is the capital of usa.
  ```
  It produces the correct output, *washington dc is the capital of usa.*
  
  ## Load the Masked Language Model with Transformers
  You can easily load the Language model with transformers using this code.
  ``` python
  from transformers import AutoTokenizer, AutoModelForMaskedLM
  import torch
  
  tokenizer = AutoTokenizer.from_pretrained("ayoolaolafenwa/Masked-Language-Model")
  
  model = AutoModelForMaskedLM.from_pretrained("ayoolaolafenwa/Masked-Language-Model") 
  
  inputs = tokenizer("The internet [MASK] amazing.", return_tensors="pt")
  
  
  with torch.no_grad():
      logits = model(**inputs).logits
  
  # retrieve index of [MASK]
  mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
  
  predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
  output = tokenizer.decode(predicted_token_id)
  print(output)
  ```
  Output
  ```
  is 
  ```
  
  It prints out the predicted masked word *is*.