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 production standards.
Full audit certificate: 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
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
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):
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 — 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
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