55 lines
2.4 KiB
Markdown
55 lines
2.4 KiB
Markdown
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---
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thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
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tags:
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- conversational
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license: mit
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---
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## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
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DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
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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.
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The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
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* Multi-turn generation examples from an interactive environment:
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|Role | Response |
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|---------|--------|
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|User | Does money buy happiness? |
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| Bot | Depends how much money you spend on it .|
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|User | What is the best way to buy happiness ? |
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| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
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|User |This is so difficult ! |
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| 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 |
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Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
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ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
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### How to use
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Now we are ready to try out how the model works as a chatting partner!
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
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# Let's chat for 5 lines
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for step in range(5):
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# encode the new user input, add the eos_token and return a tensor in Pytorch
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new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
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# append the new user input tokens to the chat history
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bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
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# generated a response while limiting the total chat history to 1000 tokens,
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chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
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# pretty print last ouput tokens from bot
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print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
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```
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