promptify 0.1.4

Use GPT or other prompt based models to get structured output

Promptify

Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify

Installation

With pip

This repository is tested on Python 3.7+, openai 0.25+.

You should install Promptify using Pip command

pip3 install promptify

Quick tour

To immediately use a LLM model for your NLP task, we provide the Prompter API.

from promptify import OpenAI
from promptify import Prompter


model        = OpenAI(api_key)
nlp_prompter = Prompter(model)


result       = nlp_prompter.fit('ner.jinja',
                          domain      = 'medical',
                          text_input  = sentence)

GPT-3 Example with NER, MultiLabel, Question Generation Task

Features 🎮

• Perform NLP tasks (such as NER and classification) in just 2 lines of code, with no training data required
• Easily add one shot, two shot, or few shot examples to the prompt
• Handling out-of-bounds prediction from LLMS (GPT, t5, etc.)
• Output always provided as a Python object (e.g. list, dictionary) for easy parsing and filtering. This is a major advantage over LLMs generated output, whose unstructured and raw output makes it difficult to use in business or other applications.
• Custom examples and samples can be easily added to the prompt
• Optimized prompts to reduce OpenAI token costs (coming soon)

Available Prompts for different NLP Task :

Task Name	Colab Notebook	Status
Named Entity Recognition	NER Examples with GPT-3	✅
Multi-Label Text Classification	Classification Examples with GPT-3	✅
Multi-Class Text Classification	Classification Examples with GPT-3	✅
Binary Text Classification	Classification Examples with GPT-3	✅
Question-Answering	QA Task Examples with GPT-3	✅
Question-Answer Generation	QA Task Examples with GPT-3	✅
Summarization	Summarization Task Examples with GPT-3	✅
Explanation	Explanation Task Examples with GPT-3	✅
Tabular Data
Image Data
More Prompts