pytorch gpt2 text-generation en transformers conversational
---
language: 
- en
thumbnail:
tags:
- conversational
metrics:
- perplexity
---

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

This model is fine-tuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium). 

This model was fine-tuned on a subset of messages from [Amazon's Topical Chat dataset](https://www.kaggle.com/arnavsharmaas/chatbot-dataset-topical-chat) (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](https://m.media-amazon.com/images/G/01/amazon.jobs/3079_Paper._CB1565131710_.pdf).

The eight broad topics are:
- fashion
- politics
- books
- sports
- general entertainment
- music
- science and technology
- movies
<br><br>

<b>Sample dialogue:</b> 

|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](https://huggingface.co/microsoft/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)))
```