Conversational

Use Cases

Chatbot 💬

Chatbots are used to have conversations instead of providing direct contact with a live human. They are used to provide customer service, sales, and can even be used to play games (see ELIZA from 1966 for one of the earliest examples).

Voice Assistants 🎙️

Conversational response models are used as part of voice assistants to provide appropriate responses to voice based queries.

Inference

You can infer with Conversational models with the 🤗 Transformers library using the conversational pipeline. This pipeline takes a conversation prompt or a list of conversations and generates responses for each prompt. The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task (see https://Model Database.co/models?filter=conversational for a list of updated Conversational models).

from transformers import pipeline, Conversation
converse = pipeline("conversational")

conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
converse([conversation_1, conversation_2])

## Output:
## Conversation 1
## user >> Going to the movies tonight - any suggestions? 
## bot >> The Big Lebowski ,
## Conversation 2
## user >> What's the last book you have read? 
## bot >> The Last Question

You can use Model Database.js to infer with conversational models on Model Database Hub.

import { HfInference } from "@Model Database/inference";

const inference = new HfInference(HF_ACCESS_TOKEN);
await inference.conversational({
  model: 'facebook/blenderbot-400M-distill',
  inputs: "Going to the movies tonight - any suggestions?"
})

Useful Resources

• Learn how ChatGPT and InstructGPT work in this blog: Illustrating Reinforcement Learning from Human Feedback (RLHF)
• Reinforcement Learning from Human Feedback From Zero to ChatGPT
• A guide on Dialog Agents

This page was made possible thanks to the efforts of Viraat Aryabumi.