|
--- |
|
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png |
|
tags: |
|
- conversational |
|
license: mit |
|
--- |
|
|
|
## 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](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. |
|
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](https://github.com/microsoft/DialoGPT) |
|
|
|
ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) |
|
|
|
### 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("microsoft/DialoGPT-large") |
|
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") |
|
|
|
# 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))) |
|
``` |
|
|
|
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
|
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__DialoGPT-large) |
|
|
|
| Metric | Value | |
|
|-----------------------|---------------------------| |
|
| Avg. | 25.17 | |
|
| ARC (25-shot) | 23.38 | |
|
| HellaSwag (10-shot) | 25.77 | |
|
| MMLU (5-shot) | 23.81 | |
|
| TruthfulQA (0-shot) | 50.27 | |
|
| Winogrande (5-shot) | 52.41 | |
|
| GSM8K (5-shot) | 0.0 | |
|
| DROP (3-shot) | 0.58 | |
|
|
