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Datasets:
HSH-Intelligence
/
github-code-corpus-sample

Tasks:
Text Generation
Fill-Mask
Feature Extraction
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
code
github
ai-training
llm
fine-tuning
code-generation
Libraries:
Datasets
pandas
Polars
License:
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github-code-corpus-sample / README.md
HSH Intelligence
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metadata 
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
  - 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

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
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
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

Quick Start 

from datasets import load_dataset

# Load sample
ds = load_dataset("HSH-Intelligence/github-code-corpus-sample")

# Inspect
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
)

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()}")

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"

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

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.