Updated Dataset card/Readme

README.md

@@ -18,4 +18,59 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+pretty_name: Reddit RPG Rules Question Classification
+size_categories:
+- 1K<n<10K
 ---
+
+# 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` *(string)*: `Question` for rules questions, `Other` for everything else.
+
+## Labels
+The dataset uses the folowing allowed values for the 'label'  column:
+
+- 'Question' - The value of the 'content' field represnts a rules question about a tabletop roleplaying game posted to reddit.
+- `Other' - The value of the 'content' field DOES NOT represnt 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)
+```