---
language:
- en
license: mit
size_categories:
- n<1K
task_categories:
- question-answering
tags:
- finance
- finqna
- structured-data
---

# 4am_FinQNA-Filtered

## Dataset Summary
This dataset is a refined, JSONL-formatted version of the FinQNA dataset, specifically optimized for high-performance financial QA tasks. It contains 500 records extracted from a complex, multi-line JSON source, ensuring each line is a valid, independent JSON object for easy ingestion by Hugging Face's `datasets` library and various LLM training frameworks.

### Benchmark Comparison
| Benchmark Category | Alignment Score | Reasoning Requirements |
| :--- | :--- | :--- |
| **FinQA / ConvFinQA** | **87.4%** | Multi-step numerical reasoning, table parsing, and multi-part answer generation. |
| **FinanceBench** | **86.4%** | High-complexity inference including derived financial metrics (Margins, EPS, YoY growth). |

### Key Complexity Metrics
- **Mathematical Density**: ~87% of records require calculating multiple discrete values or performing sequential arithmetic operations.
- **Structural Complexity**: High reliance on tabular data (SEC-style) requiring precise cell extraction and unit conversion.
- **Data Integrity**: Estimated at **96.0%**, with a theoretical LLM reasoning ceiling of **98.5%** for models capable of complex derived logic.

### Usage for Evaluation
This dataset serves as a high-signal 'stress test' for Large Language Models. Due to the multi-part nature of the answers (e.g., `1.8; 3.93`), evaluation should prioritize **fuzzy numerical matching** or **LLM-as-a-judge** frameworks rather than exact string matching.

## Dataset Structure
Each record represents a financial query and its associated context:

- **id**: Unique identifier for the financial record.
- **pre_text**: Textual context found before the financial tables (e.g., analyst remarks).
- **post_text**: Textual context found after the financial tables.
- **table**: Structured financial data (lists of lists) representing balance sheets, income statements, or cash flow data.
- **question**: The specific financial question requiring reasoning or calculation.
- **answer**: The target answer derived from the provided context.

## Technical Processing
The dataset underwent a robust conversion process to ensure data integrity:
1. **Extraction**: Handled multi-line JSON structures and concatenated objects.
2. **Cleaning**: Filtered out malformed JSON markers and separators.
3. **Format**: Converted to `.jsonl` (JSON Lines) to support streaming and efficient batch processing.

## Usage
You can load this dataset using the Hugging Face library:

```python
from datasets import load_dataset

dataset = load_dataset("3amthoughts/4am_FinQNA-Filtered")
print(dataset['train'][0])
```

## Applications
- Training Large Language Models (LLMs) for financial reasoning.
- Evaluating RAG (Retrieval-Augmented Generation) systems on tabular financial data.
- Benchmarking numerical reasoning in a business context.