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---
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:

```text
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.