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satkinson/
DialoGPT-medium-marvin

Name: DialoGPT-medium-marvin
Availability: InStock

text-generation-inference

endpoints_compatible

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DialoGPT model fine-tuned using Amazon's Topical Chat Dataset

This model is fine-tuned from the original DialoGPT-medium.

This model was fine-tuned on a subset of messages from Amazon's Topical Chat dataset (due to processing limitations, I restricted my training data to 50,000 observations.

The dataset spands 8 broad topics and contains conversation partner who do not have defined roles. It was created with the goal of aiding in the effort to build a socialbot that can have deep, engaging open-domain conversations with humans.

The eight broad topics are:

fashion
politics
books
sports
general entertainment
music
science and technology
movies

Sample dialogue:

Role	Response
User:	What kind of movies do you like?
Bot:	I like marvel movies like iron man
User:	Do you like horror films?
Bot:	Yea, I like the horror films that have macabre and supernatural themes the most
User:	Interesting.
Bot:	If you watch a horror movie before you view abstract art, you will enjoy the art more
User:	Speaking from experience?
Bot:	Yes, I've seen many horror movies where the characters were quite evil and supernatural.

Using the model

Example code for trying out the model (taken directly from the DialoGPT-medium model card):

python

from transformers import AutoModelWithLMHead, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("satkinson/DialoGPT-small-marvin")
model = AutoModelWithLMHead.from_pretrained("satkinson/DialoGPT-small-marvin")
# 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)))

satkinson/DialoGPT-medium-marvin

DialoGPT model fine-tuned using Amazon's Topical Chat Dataset

Using the model

satkinson/
DialoGPT-medium-marvin