---
license: other
license_name: permissive-mixed
license_link: LICENSE
task_categories:
- text-generation
- fill-mask
- feature-extraction
language:
- en
tags:
- code
- github
- ai-training
- llm
- fine-tuning
- code-generation
- python
- javascript
- typescript
- rust
- go
- bigcode-standard
- stack-v2-methodology
- commercial-safe
- pii-scrubbed
- license-audited
pretty_name: HSH Intelligence — GitHub Code AI Training Corpus (5K Sample)
size_categories:
- 1K<n<10K
---

# HSH Intelligence — GitHub Code AI Training Corpus

**5,000-record sample of the HSH Intelligence GitHub Code AI Training Corpus.**

A curated, production-grade sample of source code from top-tier public GitHub repositories — engineered for large language model training, fine-tuning, and code understanding research.

The full corpus contains **5.6 TB** of source code (**211 million+ files**, **7.05 billion lines**) across **14 production languages**.

---

## 10/10 Quality Checks 

This sample passes all 10 industry-standard quality checks following **BigCode / The Stack v2** production methodology.

| # | Check | Tool | Result |
|---|---|---|---|
| 1 | License compliance | scancode-toolkit 32.5.0 | 0% copyleft |
| 2 | Secret detection | gitleaks 8.18.4 | 0 leaks |
| 3 | Near-duplicate removal | MinHash LSH (256-perm, 5-gram, 0.9 threshold) | 0% duplicates |
| 4 | Code complexity | radon 6.0.1 | 3.92 avg cyclomatic |
| 5 | Token diversity | tiktoken cl100k_base (GPT-4) | 63,712 unique tokens |
| 6 | Statistical balance | Custom audit | 1K per language |
| 7 | Benchmark contamination | vs HumanEval (164) + MBPP (500) | 0 matches |
| 8 | PII beyond secrets | Custom regex + Luhn validation | 0 real PII |
| 9 | Syntax validation | Babel parser, syn 2.0, tsc, ast, gofmt | 98.0% parseable |
| 10 | Repo legitimacy | GitHub REST API verification | 100% verified |

**Reference:** Methodology follows [BigCode / The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2) production standards.

Full audit certificate: [`QUALITY_CERTIFICATE.json`](./QUALITY_CERTIFICATE.json)

---

## Sample Specifications

| Metric | Value |
|---|---|
| Records | 5,000 (curated subset) |
| Languages | 5 (Python, JavaScript, TypeScript, Go, Rust) |
| Records per language | 1,000 (perfectly balanced) |
| Unique repositories | 1,499 verified active on GitHub |
| Format | Apache Parquet (zstd compression) + CSV |
| Schema | 19 fields per record |
| Size | 13.4 MB (Parquet) / 49.9 MB (CSV) |
| License coverage | 100% commercial-safe (MIT, Apache-2.0, BSD, ISC) |
| PII status | Fully scrubbed (zero secrets, emails, IPs, SSNs) |
| Syntax validation | 98.0% parseable (industry standard greater than or equal to 95%) |

### Repository Quality

- 56.1% from repos with 10,000+ GitHub stars
- 6.1% archived repos (still valid, just not actively maintained)
- 0.0% deleted repos
- Top repos include: `facebook/react`, `ollama/ollama`, `django/django`, `AUTOMATIC1111/stable-diffusion-webui`

---

## Full Corpus Specifications

| Metric | Value |
|---|---|
| Total dataset size | 5.6 TB (raw) / 391 GB (Parquet, compressed) |
| Total records | 211 million+ code files |
| Total lines of code | 7.05 billion |
| Unique repositories | 3,710+ permissive-license repos |
| Programming languages | 14 production languages |
| Updates | Daily incremental |

**Languages covered:** Python, JavaScript, TypeScript, Go, Rust, Java, C++, Ruby, Swift, Kotlin, PHP, C#, Scala, Solidity

---

## License Coverage (Commercial-Safe Only)

| License | Status | Notes |
|---|---|---|
| MIT | INCLUDED | Most permissive |
| Apache-2.0 | INCLUDED | Permissive with patent grant |
| BSD-2-Clause | INCLUDED | Permissive |
| BSD-3-Clause | INCLUDED | Permissive |
| ISC | INCLUDED | Permissive |
| GPL-2.0 / GPL-3.0 | EXCLUDED | Copyleft |
| AGPL-3.0 | EXCLUDED | Strong copyleft |
| LGPL-2.1 / LGPL-3.0 | EXCLUDED | Copyleft |
| No license / Proprietary | EXCLUDED | Default copyright |

License detection performed using **scancode-toolkit 32.5.0** with per-file SPDX classification.

---

## Schema (19 Fields)

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique record identifier (sha256-prefixed) |
| `language` | string | Detected programming language |
| `repo_owner` | string | GitHub username or organization |
| `repo_name` | string | Repository name |
| `repo_stars` | integer | GitHub star count |
| `repo_forks` | integer | GitHub fork count |
| `repo_description` | string | Repository description |
| `repo_topics` | list[string] | GitHub repo topics |
| `license` | string | SPDX license identifier |
| `file_path` | string | Relative path within repo |
| `file_name` | string | Filename with extension |
| `file_size` | integer | File size in bytes |
| `code` | string | Raw source code content (PII-scrubbed) |
| `word_count` | integer | Total word count |
| `char_count` | integer | Character count |
| `line_count` | integer | Total lines of code |
| `data_quality_score` | float | Composite quality score (0.0–1.0) |
| `timestamp` | timestamp | Record creation timestamp |
| `scrubbed` | boolean | PII scrubbing flag (always `True`) |

---

## Quick Start

### Load with Hugging Face Datasets

```python
from datasets import load_dataset

ds = load_dataset("HSH-Intelligence/github-code-corpus-sample")
print(ds)
print(ds["train"][0])

# Filter to high-quality Python only
python_only = ds["train"].filter(
    lambda x: x["language"] == "Python" and x["data_quality_score"] >= 0.95
)
print(f"High-quality Python records: {len(python_only)}")
```

### Load directly with pandas

```python
import pandas as pd

df = pd.read_parquet(
    "hf://datasets/HSH-Intelligence/github-code-corpus-sample/github_code_sample_5000.parquet"
)
print(df.head())
print(f"Total records: {len(df):,}")
print(f"Languages: {df['language'].value_counts()}")
print(f"Top repos: {df['repo_name'].value_counts().head(10)}")
```

---

## Live API Demo

Try the full corpus via the live API sandbox (no signup required):

```bash
curl -H "X-API-Key: demo-key-12345" \
  "https://api.hshintelligence.com/api/v1/github-code-corpus?language=Rust&license=MIT&page_size=5"
```
Returns real Parquet records with full metadata: code, license, repo stars, 
quality score, commit history. Free tier limited to 2 files (~18 records). 
Full corpus delivered via Backblaze B2 download link after purchase.

**API documentation:** 
Or use the [interactive docs](https://api.hshintelligence.com/docs) — click 
any endpoint, click "Try it out", paste the demo key, and run live queries.

**Live endpoint:** https://api.hshintelligence.com/api/v1/github-code-corpus

Or run the interactive Google Colab notebook:  
https://links.hshintelligence.com/github-demo

---

## Use Cases

- **LLM pre-training** — multi-language code corpus for foundation models
- **Code completion fine-tuning** — Copilot-style models
- **Code search and retrieval** — embedding training
- **Code understanding research** — academic benchmarks
- **Vertical AI** — domain-specific code assistants
- **Benchmark-safe evaluation** — zero contamination vs HumanEval/MBPP

---

## Why This Corpus

| vs. Alternative | HSH Intelligence Edge |
|---|---|
| The Stack v2 | Per-file license audit + provenance trail + 10-check quality verification |
| Common Crawl code | Pre-filtered, deduplicated, syntax-validated, PII-scrubbed |
| Custom GitHub scraping | Saves 4+ months of engineering work |
| Internal datasets | EU AI Act Article 10 compliance ready |
| Generic samples | Industry-standard 10/10 quality checks documented |

---

## Compliance & Provenance

- **EU AI Act Article 10** ready (training data governance)
- **GDPR** safe (zero PII verified)
- **CCPA** safe (no California resident data)
- **HIPAA** considerations addressed (no medical data)
- Per-record license audit trail
- Source attribution retained (`repo_owner`, `repo_name`)
- Quality scoring per record
- Zero PII (emails, phones, IPs, SSNs, credit cards verified)
- Zero secrets (API keys, tokens, credentials verified via gitleaks)
- Zero benchmark contamination (HumanEval, MBPP verified)

---

## Methodology

This dataset follows **BigCode / The Stack v2** production methodology with additional quality gates.

### Tools Used

| Category | Tools |
|---|---|
| License detection | scancode-toolkit |
| Secret scanning | gitleaks |
| Deduplication | datasketch MinHash LSH |
| Complexity analysis | radon |
| Tokenization | tiktoken (cl100k_base) |
| Syntax validation | Babel parser, syn 2.0, tsc, Python ast, gofmt |
| Repo verification | GitHub REST API v3 |

### Quality Thresholds

- License compliance: less than 0.1% copyleft (achieved: 0%)
- Secret leaks: 0 tolerance (achieved: 0)
- Near-duplicates: less than 5% (achieved: 0%)
- PII: 0 tolerance (achieved: 0)
- Syntax validation: greater than or equal to 95% parseable (achieved: 98%)
- Repo legitimacy: less than 1% deleted (achieved: 0%)

Full quality certificate: [`QUALITY_CERTIFICATE.json`](./QUALITY_CERTIFICATE.json)

---

## Full Corpus Access

This is a **5,000-record evaluation sample**. The full corpus is available via commercial license:

| Tier | Records | Languages | Format |
|---|---|---|---|
| Sample (this dataset) | 5,000 | 5 | Parquet + CSV |
| Standard | 10M+ | 14 | Parquet |
| Enterprise | 211M+ (full) | 14 | Parquet (+JSONL on request) |

**Delivery options:**
- Cloud signed URL (Backblaze B2, AWS S3)
- Cross-cloud transfer (AWS, GCP, Azure)
- sFTP delivery for on-prem
- Daily incremental updates (Enterprise tier)

**Custom subsets available:** Filter by language, license, repo stars, complexity, or quality threshold.

**Licensing:** 1-year non-exclusive commercial license.

---

## Contact

- **Email:** sales@healingsunhaven.com
- **Website:** https://www.hshintelligence.com
- **Live API:** https://api.hshintelligence.com
- **Documentation:** https://links.hshintelligence.com/github-docs
- **Demo Colab:** https://links.hshintelligence.com/github-demo

---

## About HSH Intelligence

**HSH Intelligence** is the Data Division of **Healing Sun Haven LLC**, building production-grade AI training datasets and B2B intelligence products.

We engineer datasets across AI training, B2B intelligence, and decision-support — purpose-built for frontier AI labs and enterprise teams who demand industry-standard quality verification.

---

*This dataset is provided for evaluation purposes. The full 5.6 TB corpus is available under commercial license. Quality audit certificate, license documentation, and provenance trail included with all enterprise contracts.*

Audit date: 2026-05-07 | Methodology reference: BigCode/Stack v2 | Full quality report: QUALITY_CERTIFICATE.json