Update README.md

README.md

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 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-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)))
+```