license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- tool_calling
- Qwen3_4B
size_categories:
- 1K<n<10K
Financial Fundamentals Tool-Calling Dataset
This dataset contains synthetic examples for training and evaluating financial tool-calling models. Each example pairs a natural-language user request about company fundamentals with a structured JSON function call.
The dataset is designed for models that should translate financial requests into tool calls instead of answering directly.
Task
Given a user query such as:
Retrieve net income and diluted shares for Broadcom from 2006 to 2018.
The model should produce a JSON call like:
{
"action": "call",
"function": "get_fundamentals",
"arguments": {
"queries": [
{
"symbols": ["Broadcom"],
"metrics": ["Net Income", "Diluted Shares"],
"start_year": 2006,
"end_year": 2018
}
]
}
}
Dataset Fields
The dataset includes fields such as:
query: The natural-language financial request.completion: The target JSON tool call.text: Chat-formatted training text.messages: Chat-style system, user, and assistant messages.metadata: Generation metadata, including companies, metrics, years, and grouping information.source_index: Index of the original source example.
Use Cases
This dataset can be used to train or evaluate models for:
- Financial function calling
- Structured JSON generation
- Tool routing for agentic finance systems
- Fundamentals retrieval request parsing
- Company-metric-year extraction
Limitations
The dataset is synthetic and focused on financial fundamentals requests. It does not contain live financial values, investment advice, or real-time market data. Models trained on this dataset should have their JSON outputs validated before any downstream tool execution.