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 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
Does money buy happiness?
Bot
Depends how much money you spend on it .
User
What is the best way to buy happiness ?
Bot
You just have to be a millionaire by your early 20s, then you can be happy .
User
This is so difficult !
Bot
You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money
Please find the information about preprocessing, training and full details of the DialoGPT in the original DialoGPT repository
Now we are ready to try out how the model works as a chatting partner!
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
# 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)))
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