Unleash the Potential of Datasets with Intelligent Tasks, Tutorials, and Algorithm Recommendations.
Project description
🧑🎓 ProTaska-GPT
Your AI-powered data companion 🤖
Specify your dataset of choice, and ProTaska-GPT generates a tailored codebase, empowering you to visualize and understand the dataset with tasks, tutorials, and actionable insights. Accelerate your data science journey with ease and efficiency!
🖊️ Key Features:
- Dataset Ingestion: ProTaska-GPT seamlessly integrates with dataset sources like Kaggle and Hugging Face (for now), allowing users to easily import and work with diverse datasets.
- Task Recommendations: Leveraging its GPT-backbone, it generates a customized set of tasks tailored to each dataset, providing users with valuable project ideas and challenges.
- Algorithm Suggestions: Based on the dataset characteristics, it suggests suitable machine learning algorithms, enabling users to make informed decisions during their project journey.
- Conversational Chatbot: Allow user to discuss about different techniques and scrape information from Wikipedia to give relevant responses.
🔎 Objectives:
- Beginner-Friendly Tutorials: ProTaska-GPT aims to offer automated generation of a collection of beginner-friendly tutorials that guide users through common data science workflows, step-by-step, fostering practical learning and skill development.
📈 Installation:
pip install ProTaska-GPT --upgrade
🚀 Usage:
Importing base descriptors and ideation bot:
from protaska.describer import describe_dataset
from protaska.ideate import main as chatbot
Providing meta-data about the dataset to be used:
openai_key = '**open-ai-secret-key**'
importer_type = "HuggingFaceDatasetImporter"
dataset_key = 'mteb/tweet_sentiment_extraction'
destination_path = './downloaded_data/'
Getting automated data descriptions:
description, dataloader_obj = describe_dataset(openai_key, importer_type, destination_path, dataset_key)
description
Running an interactive ChatBot for ideation and base-code building:
chatbot(openai_key, description, dataloader_obj.superficial_meta_data, agent_verbose=False)
💁 Contributing
This is an open-source project and we would be really grateful to any contributions.
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