The labeling team will annotate a financial conversation dataset for an AI financial advisor. Each sample will include: Intent classification (e.g., spending insight, budgeting, card support) Transaction & Merchant category classification Assistant response labeling (tone, completeness, professionalism) Function-call tagging for backend data queries (get_user_summary, get_transactions, run_custom_sql) SQL validation labeling (mark if query is safe, parameterized, and read-only) Annotators will produce structured JSON records containing the user query, labeled intent, function name (if applicable), arguments, and assistant reply. Required skills: Familiarity with personal finance terminology Experience labeling chatbot or conversational data Understanding of function calling / API-style structured data Basic SQL literacy to identify safe vs unsafe queries
Total Budget
$2,000
Pay per Label
-
Time Requirement
Flexible
Duration
1-3 months
Text-based dataset for training an AI financial advisor that provides users with personalized financial insights, budgeting tips, and investment guidance. Includes labeled conversations, backend function-calling examples, and structured JSON outputs for querying user data (including custom SQL support)
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One-time Project
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