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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/microsoft/DialoGPT-large/README.md

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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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+
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+ ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
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+
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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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+
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+ * Multi-turn generation examples from an interactive environment:
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+
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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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+
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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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+
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+ ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
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+
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+ ### How to use
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+
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+ Now we are ready to try out how the model works as a chatting partner!
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+
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+ tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
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+ model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
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+
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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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+
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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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+
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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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+
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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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+ ```