readme: ---
  thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
  language:
  - en
  license: cc-by-4.0
  tags:
  - conversational
  - transformers
  datasets:
  - multi_woz_v22 and PIE-English idioms corpus
  metrics:
  - perplexity
  widget:
  - text: "Does that mean Jane is off the hook?"
  ---
  
  ## DialoGPT_MWOZ_Idioms
  
  This is a fine-tuned model of DialoGPT (medium)-MultiWOZ on the PIE-English idioms corpus. It is intended to be used as an idiom-aware conversational system.
  The dataset it's trained on is limited in scope, as it covers only 10 classes of idioms ( metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal).
  
  The perplexity achieved on the test and validation sets are 160.9 and 160.46, respectively. More information about the original pre-trained model can be found [here](https://huggingface.co/tosin/dialogpt_mwoz)
  
  * Generation example from an interactive environment:
  
  |Role | Response |
  |---------|------------|
  |User | remind her to take it off the hook. |
  |Bot | the bookie took it on the nod |
  |User | A lengthy break is in order ; so make sure you keep your nose clean |
  |Bot | they are under a cloud because of fears about US President's wife Hillary Clinton |
  |User | Some families breed like rabbits and send us all their male heirs |
  |Bot | he was out for the count |
  
  Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
  
  ### How to use
  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("tosin/dialogpt_mwoz_idioms")
  model = AutoModelForCausalLM.from_pretrained("tosin/dialogpt_mwoz_idioms")
  # 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_MWOZ_Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
  
  