update readme

README.md

@@ -1,17 +1,118 @@
 ---
-dataset_info:
-  features:
-  - name: content
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 9265066612
-    num_examples: 1800000
-  download_size: 3616457452
-  dataset_size: 9265066612
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: other
+license_name: source-repo-licenses
+license_link: https://huggingface.co/datasets/codeparrot/codeparrot-clean
+language:
+- code
+task_categories:
+- text-generation
+tags:
+- python
+- code
+- pretraining
+size_categories:
+- 1M<n<10M
 ---
+
+# python-clean-codeparrot
+
+A cleaned, deduplicated, Python-only pretraining corpus derived from
+[`codeparrot/codeparrot-clean`](https://huggingface.co/datasets/codeparrot/codeparrot-clean),
+built as the pretraining data for **PocketCoder**, a 95.87M-parameter decoder-only
+code language model. Total: **~2.96 billion tokens** (DeepSeek-Coder tokenizer,
+vocabulary 32,022).
+
+- **Paper:** *PocketCoder: What Distillation, SFT, and DPO Each Buy You at 100M Parameters*
+- **Model:** [`Ananda100/PocketCoder`](https://huggingface.co/Ananda100/PocketCoder)
+- **Code:** [github.com/AnandaRimal/PocketCoder](https://github.com/AnandaRimal/PocketCoder)
+
+## Cleaning pipeline
+
+Applied on top of `codeparrot-clean` (which is itself deduplicated), streaming
+file-by-file:
+
+1. **Drop autogenerated/vendored files** — files flagged `autogenerated` by the
+   upstream dataset, plus regex matches for SWIG output, protocol-buffer compiler
+   output, Cython output, "do not edit"/"this file was generated" markers, and
+   similar machine-produced code.
+2. **Strip license headers** — leading `#`-comment blocks containing
+   copyright/license keywords (up to the first 80 lines).
+3. **Strip non-Python metadata blobs** — Ansible-style
+   `DOCUMENTATION` / `EXAMPLES` / `RETURN` / `ANSIBLE_METADATA` triple-quoted
+   assignments.
+4. **Strip boilerplate comment lines** — `# Filename:`, `# Author:`,
+   `# Created:`, `# Version:`, `# Date:`, `# Maintainer:` lines. Real docstrings
+   are left untouched.
+5. **Redact email addresses** — all email addresses replaced with `<EMAIL>`
+   (PII mitigation).
+6. **Length band** — files shorter than 100 characters or longer than 20,000
+   characters are dropped whole (never truncated).
+7. **Exact deduplication** — SHA-256 hash of the cleaned content; duplicates
+   dropped at collection time.
+
+The full cleaning script is released in the PocketCoder GitHub repository.
+
+## Measured corpus statistics
+
+Audited on a 5,000-document streamed sample (audit notebook and the resulting
+`corpus_quality_report.json` are released alongside the code):
+
+| Metric | Value |
+|---|---|
+| Natural-language content (chars) | 16.0% (7.8% comments, 8.2% docstrings) |
+| Unique documents (exact hash) | 100.0% |
+| Unique documents (near, comments/whitespace stripped) | 99.9% |
+| Syntactic validity (`ast.parse`, Python 3) | 85.6%* |
+| Docstring coverage (functions) | 26.4% |
+| Mean function complexity (control-flow branches +1) | 2.31 |
+| Document length median / mean / p95 (chars) | 3,569 / 5,170 / 14,977 |
+
+\*Parse failures under Python 3 predominantly reflect legacy Python 2 syntax
+present in the upstream GitHub-derived corpus (e.g. `print` statements), not
+corrupted files; documents are dropped whole rather than truncated, so no file
+is cut mid-statement.
+
+## Format
+
+One field per example:
+
+```python
+{"content": "<cleaned Python source file>"}
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Ananda100/python-clean-codeparrot", split="train", streaming=True)
+for example in ds:
+    print(example["content"][:200])
+    break
+```
+
+## Intended use & limitations
+
+Built for pretraining small code language models. Natural language appears only
+as annotation embedded in code (comments/docstrings), never as standalone
+problem-to-solution instruction — models pretrained on this corpus alone will
+not follow natural-language instructions without subsequent instruction tuning
+(measured in the PocketCoder paper: 0.0% MBPP pass@1 before SFT, 8.6% after).
+
+## Licensing
+
+Derived from `codeparrot/codeparrot-clean`, which aggregates public GitHub
+Python files under their original licenses. Use of this derived corpus is
+subject to the licenses of the underlying source files; see the upstream
+dataset card for details.
+
+## Citation
+
+```bibtex
+@misc{rimal2026pocketcoder,
+  title  = {PocketCoder: What Distillation, SFT, and DPO Each Buy You at 100M Parameters},
+  author = {Rimal, Ananda},
+  year   = {2026},
+  url    = {https://github.com/AnandaRimal/PocketCoder}
+}
+```