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 promptifyTo immediately use a LLM model for your NLP task, we provide the Prompter API.
from promptify import OpenAI
from promptify import Prompter
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)
nlp_prompter = Prompter(model)
result = nlp_prompter.fit('ner.jinja',
domain = 'medical',
text_input = sentence)
### 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
- 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 | ✅ |
| Summarization | Summarization Task Examples with GPT-3 | ✅ |
| Explanation | Explanation Task Examples with GPT-3 | ✅ |
| Tabular Data | ||
| Image Data | ||
| More Prompts |
We welcome any contributions to our open source project, including new features, improvements to infrastructure, and more comprehensive documentation. Please see the contributing guidelines
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent