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microsoft
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microsoft
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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.
| 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
ArXiv paper: https://arxiv.org/abs/1911.00536
Now we are ready to try out how the model works as a chatting partner!
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchtokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")# Let's chat for 5 linesfor step in range(5):# encode the new user input, add the eos_token and return a tensor in Pytorchnew_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')# append the new user input tokens to the chat historybot_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 botprint("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.02 |
| ARC (25-shot) | 25.77 |
| HellaSwag (10-shot) | 25.79 |
| MMLU (5-shot) | 25.81 |
| TruthfulQA (0-shot) | 47.49 |
| Winogrande (5-shot) | 50.28 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 0.0 |