readme: This can be used to paraphrase. I recommend using the code I have attached below. You can generate it without using LogProbs, but you are likely to be best served by manually examining the most likely outputs.
  
  If this interests you, check out https://huggingface.co/BigSalmon/MrLincoln12 or my other MrLincoln repos.
  
  ```
  from transformers import AutoTokenizer, AutoModelWithLMHead
  tokenizer = AutoTokenizer.from_pretrained("gpt2")
  model = AutoModelWithLMHead.from_pretrained("BigSalmon/Parentheses")
  ```
  
  Example Prompt:
  ```
  ***
  The Milwaukee Bucks are for sure in title contention.
  The Milwaukee Bucks are ( virtually assured / all but certain to / on the cusp of / well positioned for ) title contention.
  ***
  Discord is an up-and-coming platform, attracting people from all walks of life.
  Discord is ( an / a ) ( up-and-coming platform / platform in the ascendant / medium on the rise ), ( drawing in / wooing / winning over ) ( people / individuals / consumers / audiences ) from all ( walks of life / corners of the universe / horizons )...
  ***
  HuggingFace is an amazing company.
  HuggingFace is an (
  ```
  
  ```
  import torch
  prompt = "Insert Your Prompt Here. It is Best To Have a Few Examples Before Like The Example Prompt Shows."
  text = tokenizer.encode(prompt)
  myinput, past_key_values = torch.tensor([text]), None
  myinput = myinput
  myinput= myinput.to(device)
  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
  logits = logits[0,-1]
  probabilities = torch.nn.functional.softmax(logits)
  best_logits, best_indices = logits.topk(500)
  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
  text.append(best_indices[0].item())
  best_probabilities = probabilities[best_indices].tolist()
  words = []
  for i in range(500):
      m = ([best_words[i]])
      m = str(m)
      m = m.replace("[' ", "").replace("']", "")
      print(m)
  ```