readme: ---
  language: en
  ---
  Next word generator trained on questions. Receives partial questions and tries to predict the next word.
  Example use: 
  
  ```python
  from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
  
  model_name = "allenai/t5-small-next-word-generator-qoogle"
  tokenizer = T5Tokenizer.from_pretrained(model_name)
  model = T5ForConditionalGeneration.from_pretrained(model_name)
  
  def run_model(input_string, **generator_args):
      input_ids = tokenizer.encode(input_string, return_tensors="pt")
      res = model.generate(input_ids, **generator_args)
      output = tokenizer.batch_decode(res, skip_special_tokens=True)
      print(output)
      return output
  
  
  run_model("Which")
  run_model("Which two")
  run_model("Which two counties")
  run_model("Which two counties are")
  run_model("Which two counties are the")
  run_model("Which two counties are the biggest")
  run_model("Which two counties are the biggest economic")
  run_model("Which two counties are the biggest economic powers")
  
  ```
  which should result in the following:
  ```
  ['one']
  ['statements']
  ['are']
  ['in']
  ['most']
  ['in']
  ['zones']
  ['of']
  ``` 
  
  