Prompt Engineering, Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify
This repository is tested on Python 3.7 , openai 0.25 .
You should install Promptify using Pip command
pip3 install promptifyor
pip3 install git https://github.com/promptslab/Promptify.gitTo immediately use a LLM model for your NLP task, we provide the Pipeline API.
from promptify import Prompter,OpenAI, Pipeline
sentence = """The patient is a 93-year-old female with a medical
history of chronic right hip pain, osteoporosis,
hypertension, depression, and chronic atrial
fibrillation admitted for evaluation and management
of severe nausea and vomiting and urinary tract
infection"""
model = OpenAI(api_key) # or `HubModel()` for Huggingface-based inference or 'Azure' etc
prompter = Prompter('ner.jinja') # select a template or provide custom template
pipe = Pipeline(prompter , model)
result = pipe.fit(sentence, domain="medical", labels=None)
### Output
[
{"E": "93-year-old", "T": "Age"},
{"E": "chronic right hip pain", "T": "Medical Condition"},
{"E": "osteoporosis", "T": "Medical Condition"},
{"E": "hypertension", "T": "Medical Condition"},
{"E": "depression", "T": "Medical Condition"},
{"E": "chronic atrial fibrillation", "T": "Medical Condition"},
{"E": "severe nausea and vomiting", "T": "Symptom"},
{"E": "urinary tract infection", "T": "Medical Condition"},
{"Branch": "Internal Medicine", "Group": "Geriatrics"},
]
- 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
- 🤗 Run inference on any model stored on the Huggingface Hub (see notebook guide).
- Optimized prompts to reduce OpenAI token costs (coming soon)
| 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 | ✅ |
| Relation-Extraction | Relation-Extraction Examples with GPT-3 | ✅ |
| Summarization | Summarization Task Examples with GPT-3 | ✅ |
| Explanation | Explanation Task Examples with GPT-3 | ✅ |
| SQL Writer | SQL Writer Example with GPT-3 | ✅ |
| Tabular Data | ||
| Image Data | ||
| More Prompts |
If you are interested in Prompt-Engineering, LLMs, ChatGPT and other latest research discussions, please consider joining PromptsLab
@misc{Promptify2022,
title = {Promptify: Structured Output from LLMs},
author = {Pal, Ankit},
year = {2022},
howpublished = {\url{https://github.com/promptslab/Promptify}},
note = {Prompt-Engineering components for NLP tasks in Python}
}
We welcome any contributions to our open source project, including new features, improvements to infrastructure, and more comprehensive documentation. Please see the contributing guidelines
