dingo-data-quality

Name: dingo-data-quality
Availability: InStock
Author: MigoXLab

Evaluate machine learning datasets with rule-based and LLM-based checks.

736|74|Updated Dec 24, 2024

Search Tags:#data quality #machine learning #AI evaluation #LLM validation #RAG evaluation

Ready to run

npx skills add https://github.com/MigoXLab/dingo --skill dingo-data-quality-migoxlab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dingo-data-quality
Source: https://github.com/MigoXLab/dingo/tree/main/clawhub
Command: npx skills add https://github.com/MigoXLab/dingo --skill dingo-data-quality-migoxlab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dingo-python, dingo-python[agent], dingo-python[hhem], and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of ensuring high-quality data in machine learning datasets, providing a comprehensive evaluation platform for data, model, and application quality.

Core Features & Use Cases

Data Quality Assessment: Offers rule-based and LLM-based evaluations for datasets, detecting issues like format errors, completeness, and semantic quality.
Machine Learning Validation: Validates LLM training data and RAG systems to ensure accuracy and reliability.
Use Case: With a large corpus of text data, use this Skill to detect hallucinations and ensure data consistency in your language model.

Quick Start

Run the dingo eval command with a config file to evaluate your dataset's quality.

Frequently Asked Questions about dingo-data-quality

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I evaluate data quality for machine learning datasets?▼

To evaluate data quality for machine learning datasets, you can run the dingo eval command with a config file to assess format errors, completeness, and semantic quality using rule-based and LLM-based evaluations.

What is the best way to validate LLM training data and detect hallucinations?▼

Validating LLM training data to detect hallucinations is achieved through LLM-based evaluations that assess semantic quality and consistency, ensuring accuracy and reliability across large text corpora.

Can I use this approach to evaluate my RAG system?▼

Yes, you can evaluate your RAG system. The platform provides specific validation capabilities to assess retrieval-augmented generation systems, ensuring the accuracy and reliability of the integrated data.

Does data quality assessment work with rule-based and AI-based evaluations?▼

Yes, data quality assessment supports both rule-based and AI-based evaluations. This dual approach allows you to detect format errors and completeness issues alongside deeper semantic quality checks.

Do I need Python to run LLM validation for dataset consistency?▼

Yes, you need Python to run LLM validation. The skill relies on specific Python dependencies, including dingo-python packages with agent and hhem extensions, to execute the evaluation commands.

What limitations should I expect when checking dataset format errors and completeness?▼

When checking dataset format errors and completeness, limitations depend on the configured rules and the LLM's ability to detect semantic issues. You should anticipate constraints based on the selected evaluation method's scope.