Datasets:

HSH-Intelligence
/

github-code-corpus-sample

@@ -20,35 +20,70 @@ tags:
 - typescript
 - rust
 - go
-- permissive-license
-- apache-2.0
-- mit
 - commercial-safe
-pretty_name: HSH Intelligence — GitHub Code AI Training Corpus (Sample)
 size_categories:
 - 1K<n<10K
 ---
 # HSH Intelligence — GitHub Code AI Training Corpus
-**A 1,000-record evaluation sample of the HSH Intelligence GitHub Code AI Training Corpus.**
-This is a curated, license-audited sample of source code from top-rated public GitHub repositories — prepared 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** with **0.978 average quality score**.
 ---
-## What's In This Sample
 | Metric | Value |
 |---|---|
-| Records | 1,000 (curated subset) |
 | Languages | 5 (Python, JavaScript, TypeScript, Go, Rust) |
-| Format | Apache Parquet (Snappy compression) + CSV |
-| Schema | 24 fields per record |
-| Quality score (avg) | 0.978 / 1.000 |
-| License coverage | 100% commercial-safe |
 ---
@@ -61,44 +96,12 @@ The full corpus contains **5.6 TB** of source code (**211 million+ files**, **7.
 | Total lines of code | 7.05 billion |
 | Unique repositories | 3,710+ permissive-license repos |
 | Programming languages | 14 production languages |
-| Average quality score | 0.978 / 1.000 |
 | Updates | Daily incremental |
 **Languages covered:** Python, JavaScript, TypeScript, Go, Rust, Java, C++, Ruby, Swift, Kotlin, PHP, C#, Scala, Solidity
 ---
-## Schema (24 Fields)
-| Field | Type | Description |
-|---|---|---|
-| `id` | string | Unique record identifier |
-| `repo_name` | string | GitHub repository slug (owner/repo) |
-| `repo_owner` | string | GitHub username or organization |
-| `repo_url` | string | Full HTTPS URL to repository |
-| `file_path` | string | Relative path within repo |
-| `file_name` | string | Filename with extension |
-| `file_sha` | string | SHA-256 hash for deduplication |
-| `code` | string | Raw source code content |
-| `language` | string | Detected programming language |
-| `language_extension` | string | File extension |
-| `line_count` | integer | Total lines of code |
-| `char_count` | integer | Character count |
-| `token_count` | integer | Estimated tokens (tiktoken cl100k) |
-| `license` | string | SPDX license identifier |
-| `license_source` | string | Where license was detected |
-| `license_confidence` | float | Detection confidence (0.0–1.0) |
-| `commercial_safe` | boolean | Commercial-use safe flag |
-| `repo_stars` | integer | GitHub star count |
-| `repo_forks` | integer | GitHub fork count |
-| `repo_description` | string | Repository description |
-| `repo_primary_language` | string | Repository's dominant language |
-| `repo_created_at` | timestamp | Repository creation date |
-| `repo_updated_at` | timestamp | Last activity timestamp |
-| `data_quality_score` | float | Composite quality score (0.0–1.0) |
----
 ## License Coverage (Commercial-Safe Only)
 | License | Status | Notes |
@@ -113,17 +116,44 @@ The full corpus contains **5.6 TB** of source code (**211 million+ files**, **7.
 | LGPL-2.1 / LGPL-3.0 | EXCLUDED | Copyleft |
 | No license / Proprietary | EXCLUDED | Default copyright |
 ---
 ## Quick Start
 ```python
 from datasets import load_dataset
-# Load sample
 ds = load_dataset("HSH-Intelligence/github-code-corpus-sample")
-# Inspect
 print(ds)
 print(ds["train"][0])
@@ -131,19 +161,21 @@ print(ds["train"][0])
 python_only = ds["train"].filter(
     lambda x: x["language"] == "Python" and x["data_quality_score"] >= 0.95
 )
 ```
 ```python
 import pandas as pd
-# Or load directly with pandas
 df = pd.read_parquet(
-    "hf://datasets/HSH-Intelligence/github-code-corpus-sample/github_code_sample_1000.parquet"
 )
 print(df.head())
 print(f"Total records: {len(df):,}")
 print(f"Languages: {df['language'].value_counts()}")
 ```
 ---
@@ -157,8 +189,8 @@ curl -H "X-API-Key: demo-key-12345" \
   "https://api.hshintelligence.com/api/v1/github-code-corpus?language=Rust&license=MIT&page_size=5"
 ```
-Or run the interactive Google Colab notebook:
-👉 https://links.hshintelligence.com/github-demo
 ---
@@ -169,54 +201,106 @@ Or run the interactive Google Colab notebook:
 - **Code search and retrieval** — embedding training
 - **Code understanding research** — academic benchmarks
 - **Vertical AI** — domain-specific code assistants
 ---
 ## Why This Corpus
-| Vs. Alternative | HSH Edge |
 |---|---|
-| The Stack v2 | License-audited per file with provenance trail |
-| Common Crawl code | Pre-filtered, deduplicated, quality-scored |
 | Custom GitHub scraping | Saves 4+ months of engineering work |
 | Internal datasets | EU AI Act Article 10 compliance ready |
 ---
 ## Compliance & Provenance
 - **EU AI Act Article 10** ready (training data governance)
 - Per-record license audit trail
-- Source attribution retained (repo_name, repo_owner)
 - Quality scoring per record
-- No PII, no API keys, no secrets (sensitive content filtered)
-Full provenance and audit report: 👉 https://links.hshintelligence.com/github-docs
 ---
 ## Full Corpus Access
-This is a **1,000-record evaluation sample**. The full corpus is available via commercial license:
-- **Format options:** Apache Parquet (default) / JSONL on request
-- **Delivery:** Cloud signed URL / AWS S3 cross-cloud / sFTP
-- **Custom subsets:** Filter by language, license, repo, or quality threshold
-- **Updates:** Daily incremental updates included with annual licenses
-- **Licensing:** 1-year non-exclusive commercial license
-**Contact:** sales@healingsunhaven.com
-**Website:** https://www.hshintelligence.com
-**Live API:** https://api.hshintelligence.com
 ---
 ## About HSH Intelligence
-HSH Intelligence is a Data Division of Healing Sun Haven LLC, building world-class AI training datasets and B2B intelligence products.
-We build production-grade datasets across AI training, B2B intelligence, and decision-support — purpose-built for frontier AI labs and enterprise teams.
 ---
-*This dataset is provided for evaluation purposes. The full 5.6 TB corpus is available under commercial license. License audit and provenance documentation included with all enterprise contracts.*

 - 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`
 ---
 | 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 |
 | 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])
 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)}")
 ```
 ---
   "https://api.hshintelligence.com/api/v1/github-code-corpus?language=Rust&license=MIT&page_size=5"
 ```
+Or run the interactive Google Colab notebook:
+https://links.hshintelligence.com/github-demo
 ---
 - **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