---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: chapter
    dtype: string
  - name: section
    dtype: string
  - name: title
    dtype: string
  - name: source_file
    dtype: string
  - name: question_markdown
    dtype: string
  - name: answer_markdown
    dtype: string
  - name: code_blocks
    list:
    - name: lang
      dtype: string
    - name: code
      dtype: string
  - name: has_images
    dtype: bool
  - name: image_refs
    list: string
  splits:
  - name: train
    num_bytes: 1282175
    num_examples: 1016
  download_size: 609478
  dataset_size: 1282175
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# CLRS Solutions QA 

**Short description.**  
A compact Q&A dataset distilled from the community-maintained **CLRS solutions** project. Each row contains:
- the exercise **question** (markdown),
- the **answer** (markdown),
- book **chapter/section** metadata,
- optional **code blocks** (language-tagged),
- optional **image references** (relative paths from the source repo).

This set is useful for building retrieval, RAG, tutoring, and evaluation pipelines for classic algorithms & data structures topics.

> ⚠️ **Attribution:** This dataset is **derived** from the open-source repository **[walkccc/CLRS](https://github.com/walkccc/CLRS)** (MIT license). Credit belongs to **@walkccc** and all contributors. This packaging only restructures their content into a machine-friendly format.

---

## Contents & Stats

- **Split(s):** `train`
- **Rows:** ~1,016
- **Source:** Parsed from markdown files in `walkccc/CLRS` (third-edition exercises/solutions)

> Note: A small number of rows reference images present in the original repo (`docs/img/...`). This dataset includes the image *references* (paths) as metadata; actual image files are not bundled here.

---

**Also available (human-readable copies):**

```python
# JSONL
ds_json = load_dataset(
    "json",
    data_files="hf://datasets/Siddharth899/clrs-qa/data/train.jsonl.gz",
    token=True,  # needed if the repo is private
)

# CSV
ds_csv = load_dataset(
    "csv",
    data_files="hf://datasets/Siddharth899/clrs-qa/data/train.csv.gz",
    token=True,
)
```
## Data Fields

| Field               | Type                             | Description                                                           |
| ------------------- | -------------------------------- | --------------------------------------------------------------------- |
| `id`                | `string`                         | Stable row id composed from chapter/section/title (e.g., `02-2.3-5`). |
| `chapter`           | `string`                         | Chapter number as a zero-padded string (e.g., `"02"`).                |
| `section`           | `string`                         | Section identifier as in the source (e.g., `"2.3"` or `"2-1"`).       |
| `title`             | `string`                         | Exercise/problem label (e.g., `"2.3-5"` or `"2-1"`).                  |
| `source_file`       | `string`                         | Original markdown relative path in the source repo.                   |
| `question_markdown` | `string`                         | Exercise prompt in markdown.                                          |
| `answer_markdown`   | `string`                         | Solution/answer in markdown (often includes LaTeX).                   |
| `code_blocks`       | `list` of objects `{lang, code}` | Zero or more language-tagged code snippets extracted from the answer. |
| `has_images`        | `bool`                           | Whether this item references images.                                  |
| `image_refs`        | `list[string]`                   | Relative paths to referenced images in the original repo.             |

Example `code_blocks` entry:

```json
[
  {"lang": "cpp", "code": "INSERTION-SORT(A)\n  ..."},
  {"lang": "python", "code": "def merge(...):\n  ..."}
]
```

---

## Data Construction

* **Source:** [`walkccc/CLRS`](https://github.com/walkccc/CLRS)
* **License upstream:** MIT
* **Method:** A small script parses chapter/section markdown files, extracts headings, prompts, answers, fenced code blocks, and image references, and emits JSONL → uploaded to the Hub (Parquet auto-materialized).
* **Known quirks:**

  * Some answers are brief/telegraphic (mirroring the original).
  * Image references point to paths in the upstream repo; not all images are bundled here.
  * Math is plain markdown with LaTeX snippets (`$...$`, `$$...$$`); rendering depends on your viewer.

---

## License

* **This dataset (packaging)**: MIT
* **Upstream content**: MIT (from `walkccc/CLRS`)

You must preserve the original MIT license notice and attribute **@walkccc** and contributors when using this dataset.

```
MIT License

Copyright (c) walkccc
... (see upstream repository for the full license text)
```

Additionally, include attribution similar to:

> “Portions of the content are derived from walkccc/CLRS (MIT). © The respective contributors.”

---

## Citation

If you use this dataset, please cite both the dataset and the upstream project:

**Dataset (this repo):**

```
@misc{clrs_qa_dataset_2025,
  title  = {CLRS Solutions QA (walkccc-derived)},
  author = {Siddharth899},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/Siddharth899/clrs-qa}},
  note   = {Derived from walkccc/CLRS (MIT)}
}
```

**Upstream CLRS solutions:**

```
@misc{walkccc_clrs,
  title         = {Solutions to Introduction to Algorithms (Third Edition)},
  author        = {walkccc and contributors},
  howpublished  = {\url{https://github.com/walkccc/CLRS}},
  license       = {MIT}
}
```

## Contact & Maintenance

* **Maintainer of this dataset packaging:** @Siddharth899
* Issues / requests: open an issue on the HF dataset repo.