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# Dataset: `eriksalt/reddit-rpg-rules-question-classification`

## What it is
A small, curated **binary text-classification** dataset intended to train a model to decide whether a Reddit post from tabletop-RPG communities is a **rules question** or **not a rules question**.

Hugging Face Hub page: https://huggingface.co/datasets/eriksalt/reddit-rpg-rules-question-classification


## Row schema
Each example is a single Reddit post (as plain text) with three fields:

- `id` *(string)*: A stable identifier that also encodes the source file and line number (e.g. `blades_posts.txt:755`).
- `content` *(string)*: The post text used for classification (typically includes the post title plus body/description where present).
- `label` *(ClassLabel)*: `Question` (0) for rules questions, `Other` (1) for everything else.

## Labels
The `label` column is a `ClassLabel` feature with the following integer-to-name mapping:

| id | name | meaning |
|----|------|---------|
| 0 | `Question` | The `content` field represents a rules question about a tabletop roleplaying game posted to Reddit. |
| 1 | `Other` | The `content` field does **not** represent a rules question about a tabletop roleplaying game posted to Reddit. |


## Splits and size
The dataset is published in Parquet format with one config (`default`) and one split:

- `train`: **1,949** rows

Total: **1,949** rows.

## Notable characteristics
- **Source hinting via `id`:** IDs commonly look like `blades_posts.txt:<n>` or `mothership_posts.txt:<n>`, which makes it easy to trace examples back to the original extraction batch.
- **Wide length range:** `content` ranges from very short titles to multi-paragraph posts (the dataset viewer shows examples up to ~16k characters).

## Intended use
- Fine-tuning / instruction-tuning a classifier (e.g., Qwen2.5-14B-Instruct) to output one of two labels.
- Training/evaluating a cheaper routing model (e.g., fast filter → expensive model only when likely rules-related).
- Building a rules-QA pipeline where only "Rules Question" posts get routed into downstream answer extraction.

## Loading example
```python
from datasets import load_dataset

ds = load_dataset("eriksalt/reddit-rpg-rules-question-classification")
print(ds)
print(ds["train"].features)
```