readme: ---
  
  tags:
  - conversational
  license: mit
  ---
  ## I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript)
  
  
  
  
  ```python
  from transformers import AutoModelForCausalLM, AutoTokenizer
  import torch
  tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
  model = AutoModelForCausalLM.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
  # 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=200,
          pad_token_id=tokenizer.eos_token_id,  
          no_repeat_ngram_size=3,       
          do_sample=True, 
          top_k=100, 
          top_p=0.7,
          temperature = 0.8
      )
      	# pretty print last ouput tokens from bot
      print("TBBT Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
  ```