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Data Labeling Pipeline
Annotation workflow tools, quality assurance scripts, active learning selectors, and labeling interface templates.
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📁 File Structure 3 files
data-labeling-pipeline/
├── LICENSE
├── README.md
└── config.example.yaml
📖 Documentation Preview README excerpt
Data Labeling Pipeline
Annotation workflow tools, quality assurance scripts, active learning selectors, and labeling interface templates.
Contents
config.example.yaml
Quick Start
1. Extract the ZIP archive
2. Review the README and documentation
3. Customize configuration files for your environment
4. Follow the setup guide for your specific use case
Requirements
- Python 3.10+ (for Python scripts)
- Relevant CLI tools for your platform
- Access to your target environment
License
MIT License — see LICENSE file.
Support
Questions or issues? Email megafolder122122@hotmail.com
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Part of [Ml Engineer](https://inity13.github.io/ml-engineer-toolkit/)
📄 Code Sample .yaml preview
config.example.yaml
# Data Labeling Pipeline — Example Configuration
# Copy to config.yaml and customize for your environment
project_name: "my-project"
environment: "development"
# Add your settings below
settings:
enabled: true
log_level: "INFO"