readme: ---
  language: 
  - en
  thumbnail:
  tags:
  - conversational
  metrics:
  - perplexity
  ---
  
  ## DialoGPT model fine-tuned using Amazon's Topical Chat Dataset
  
  This model is fine-tuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium). 
  
  This model was fine-tuned on a subset of messages from [Amazon's Topical Chat dataset](https://www.kaggle.com/arnavsharmaas/chatbot-dataset-topical-chat) (due to processing limitations, I restricted my training data to 50,000 observations. 
  
  The dataset spands 8 broad topics and contains conversation partner who do not have defined roles. It was created with the goal of [aiding in the effort to build a socialbot that can have deep, engaging open-domain conversations with humans](https://m.media-amazon.com/images/G/01/amazon.jobs/3079_Paper._CB1565131710_.pdf).
  
  The eight broad topics are:
  - fashion
  - politics
  - books
  - sports
  - general entertainment
  - music
  - science and technology
  - movies
  <br><br>
  
  <b>Sample dialogue:</b> 
  
  |Role | Response |
  |---------|--------|
  |User: | What kind of movies do you like? |
  | Bot: | I like marvel movies like iron man |
  |User: | Do you like horror films? |
  | Bot: | Yea, I like the horror films that have macabre and supernatural themes the most |
  |User: | Interesting. |
  | Bot: | If you watch a horror movie before you view abstract art, you will enjoy the art more |
  |User: | Speaking from experience? |
  | Bot: | Yes, I've seen many horror movies where the characters were quite evil and supernatural. |
  
  
  ## Using the model
  
  Example code for trying out the model (taken directly from the [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model card): 
  ```python
  from transformers import AutoModelWithLMHead, AutoTokenizer
  import torch
  tokenizer = AutoTokenizer.from_pretrained("satkinson/DialoGPT-small-marvin")
  model = AutoModelWithLMHead.from_pretrained("satkinson/DialoGPT-small-marvin")
  # 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("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
  ```