DeepFabric – Generate High-Quality Synthetic Datasets at Scale
7 hours ago
- #synthetic-data
- #language-models
- #topic-modeling
- DeepFabric transforms synthetic dataset creation for language model training, evaluation, and research.
- It uses topic-driven data generation with hierarchical topic trees and graph-based topic modeling.
- Target users include researchers, engineers, and practitioners needing high-quality synthetic data.
- Core capabilities involve a three-stage pipeline: topic generation, dataset generation, and packaging.
- Topic modeling ensures broader coverage and consistent quality across datasets.
- Topic trees are hierarchical, while topic graphs allow cross-connections for complex domains.
- Topic trees suit clear hierarchical relationships; graphs excel in interconnected domains.
- Getting started involves installation, configuration, and generation with practical examples.
- DeepFabric supports YAML for configuration-driven workflows and Python API for programmatic access.
- Integrates with OpenAI, Anthropic, Ollama, and exports datasets to Hugging Face Hub.
- Modular CLI supports commands like validate, visualize, and upload for complex workflows.
- Next steps include installation, first dataset tutorial, configuration guide, and API reference.