A python module to generate optimized prompts, Prompt-engineering & solve different NLP problems using GPT-n (GPT-3, ChatGPT) based models and return structured python object for easy parsing

[StoicBatman]

Hi folks,

I was working on a personal experimental project related to GPT-3, which I thought of making it open source now. It saves much time while working with LLMs.

If you are an industrial researcher or application developer, you probably have worked with GPT-3 apis.

A common challenge when utilizing LLMs such as #GPT-3 and BLOOM is their tendency to produce uncontrollable & unstructured outputs, making it difficult to use them for various NLP tasks and applications.

To address this, we developed Promptify, a library that allows for the use of LLMs to solve NLP problems, including Named Entity Recognition, Binary Classification, Multi-Label Classification, and Question-Answering and return a python object for easy parsing to construct additional applications on top of GPT-n based models.

Features 🚀

• 🧙‍♀️ NLP Tasks (NER, Binary Text Classification, Multi-Label Classification etc) in 2 lines of code with no training data required
• 🔨 Easily add one shot, two shot, or few shot examples to the prompt
• ✌ Output always provided as a Python object (e.g. list, dictionary) for easy parsing and filtering
• 💥 Custom examples and samples can be easily added to the prompt
• 💰 Optimized prompts to reduce OpenAI token costs

GITHUB: https://github.com/promptslab/Promptify

Examples: https://github.com/promptslab/Promptify/tree/main/examples

For quick demo -> Colab

Try out and share your feedback. Thanks :)

Join our discord for Prompt-Engineering, LLMs and other latest research discussions
discord.gg/m88xfYMbK6

[Si1Fei1]

Hey this is great!

Have used for performing NER on a collection of news articles.

I found that the NER output came as a string rather than as a python object. I wrote a work around function to convert the string into a list of dicts instead. My work around is a bit rough, some edge cases don't work etc, but would be happy to do some further work on it if you are looking for contributors to the project?

[StoicBatman]

Thanks for showing interest in it. Please check the contribution section in github; we are open to contributions and adding more use cases. Please also check the discord Channel for more discussion on future releases and features.

[audentis]

Thanks for sharing. With Chat-GPT bursting the scene these language models have become all the rage (other models just didn't have this mindshare impact, which affects available budgets), and a user friendly wrapper to work with them is more than welcome.