Dialogpt Large

🚀 A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)

DialoGPT is a state-of-the-art large-scale pretrained model designed for generating dialogue responses in multi-turn conversations. The human evaluation results show that in a single-turn conversation Turing test, the responses generated by DialoGPT are comparable to those of humans in quality. This model is trained on 147M multi-turn dialogues from Reddit discussion threads.

🚀 Quick Start

Let's quickly get started with DialoGPT and see how it performs as a chatting partner!

✨ Features

• High-quality Responses: In single-turn conversation Turing tests, its generated responses can rival human responses in quality.
• Trained on Massive Data: Trained on 147M multi-turn dialogues from Reddit discussion threads, enabling it to handle diverse conversation scenarios.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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)))

Example of Multi-turn Generation

📚 Documentation

For detailed information about preprocessing, training, and other aspects of DialoGPT, please refer to the original DialoGPT repository.

ArXiv paper: https://arxiv.org/abs/1911.00536

📄 License

This project is licensed under the MIT license.