Schema Driven Dataset Configuration With Multi Question Types

via “schema-driven dataset configuration with multi-question types”

Open-source data curation for LLM fine-tuning and RLHF.

Unique: Implements a declarative schema system where question types (Rating, Span, Text) are first-class entities with independent validation rules, stored in the Questions and Fields data model, enabling schema versioning and reuse across workspaces without code changes

vs others: Unlike Label Studio's form-based UI, Argilla's schema-driven approach enables programmatic dataset creation via Python SDK and supports RLHF-specific question types (ratings, rankings) natively rather than as custom plugins

via “multi-question-type-support”

via “multi-question type support”