The challenge
Maya is building a legal copilot fine-tuned for European jurisdictions. She needs multilingual legal QA data, jurisdiction-specific case law citations, and edge-case adversarial examples. None of it exists as a single curated dataset. Buying generic legal corpora wastes weeks of cleaning, and hiring annotators herself means writing guidelines, running QA, and chasing contractors across timezones, let alone the time to invest in setting up the right labeling infrastructure and tooling before any real work begins.
How Maya uses Dataset Finder
She searches the workspace catalog for legal QA datasets to anchor the model, orders multilingual case-law citation pairs as a custom dataset with credits, and references a community recipe for adapter-based legal fine-tuning. Datasets, the custom-data brief, recipe configs, and project notes all live in one workspace.
The outcome
Maya goes from blank notebook to first eval run in 6 days. The custom dataset arrives in 3 weeks. Her team can focus on the model rather than on managing labelers.