readme: ---
  thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
  tags:
  - conversational
  license: mit
  ---
  
  ## dialogGPT-homer-simpson
  
  This model has been fine tuned with the entire scripts of Homer Simpson from the T.V. show The Simpsons
  It will give some nice answers seemingly from Homers brain in the Simpsons Universe during single turn conversation, letting you chat to Homer Simpson
  
  ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
  
  DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
  The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
  The model is trained on 147M multi-turn dialogue from Reddit discussion thread. 
  
  * Multi-turn generation examples from an interactive environment:
  
  |Role | Response |
  |---------|--------|
  |User | Who are you? |
  | HomerBot |  Homer Simpson .|
  |User | What is your favorite Restaurant ? |
  | HomerBot | Moes Tavern. |
  |User | Have you ever been in a band?! |
  | HomerBot | no. |
  
  Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
  
  ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
  
  ### How to use Multi-Turn
  
  #### NOTE: Multi-Turn seems to be broken, after a few exchanges the output will mostly be exclamation marks.
  
  Now we are ready to try out how the model works as a chatting partner!
  
  ```python
  from transformers import AutoModelForCausalLM, AutoTokenizer
  import torch
  
  
  tokenizer = AutoTokenizer.from_pretrained("shalpin87/dialoGPT-homer-simpson")
  model = AutoModelForCausalLM.from_pretrained("shalpin87/dialoGPT-homer-simpson")
  
  # Let's chat for 5 lines
  for step in range(5):
  	# encode the new user input, add the eos_token and return a tensor in Pytorch
  	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
  
  	# append the new user input tokens to the chat history
  	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
  
  	# generated a response while limiting the total chat history to 1000 tokens, 
  	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
  
  	# pretty print last ouput tokens from bot
  	print("DialoG-PT-HomerBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
  ```
  
  ### How to use Single Turn
  
  ```python
  from transformers import AutoModelForCausalLM, AutoTokenizer
  import torch
  
  
  tokenizer = AutoTokenizer.from_pretrained("shalpin87/dialoGPT-homer-simpson")
  model = AutoModelForCausalLM.from_pretrained("shalpin87/dialoGPT-homer-simpson")
  
  questions = [
      "What is your name?",
      "Who are you?",
      "Where do you work?",
      "Who really killed Mr Burns?",
      "Have you ever stolen from the Kwik-E-Mart?",
      "Did you kill Frank Grimes?",
      "Who was the worst member of the Be Sharps?",
      "Hey where did Barney go?",
      "What is your favorite bar to have a beer?",
      "What is the best beer in Springfield?",
      "Is Bart working for the Mob?",
      "I think there was an incident in sector 7 G",
      "Is Ned Flanders house okay?",
      "Oh my god it's Sideshow Bob",
      "What is a Flaming Moe?",
      "What is happening to Apu?",
      "Who quit the band?"
  ]
  
  # Let's chat
  for step in range(len(questions)):
      print("***************************************")
      print("Q. {}".format(questions[step]))
      new_user_input_ids = tokenizer.encode(questions[step] + tokenizer.eos_token, return_tensors='pt')
      # new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
  
      bot_input_ids = new_user_input_ids
  
      num_return_seqs=1
  
      chat_history_ids = model.generate(
          bot_input_ids, 
          max_length=200,
          pad_token_id=tokenizer.eos_token_id,  
          no_repeat_ngram_size=3,       
          do_sample=True, 
          top_k=200, 
          top_p=0.55,
          temperature = 0.85,
          num_return_sequences=num_return_seqs
      )
      
      botname = "HomerBot"
      for i in range(0,num_return_seqs):
        print("{}:{}: {}".format(i, botname, tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][i], skip_special_tokens=True)))
  
      chat_history_ids = []
  ```
  
  Sample Output
  ```
  ***************************************
  Q. What is your name?
  0:HomerBot: Homer Simpson.
  ***************************************
  Q. Who are you?
  0:HomerBot: Homer Simpson.
  ***************************************
  Q. Where do you work?
  0:HomerBot: I work at a nuclear power plant.
  ***************************************
  Q. Who really killed Mr Burns?
  0:HomerBot: I dunno. Some guy named Ot-to.
  ***************************************
  Q. Have you eve