| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| - code |
| tags: |
| - devops |
| - docker |
| - ci-cd |
| - github-actions |
| - build-systems |
| - configuration |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Build/CI Configuration Corpus |
|
|
| A curated dataset of build, CI/CD, and project configuration files from top GitHub repositories. |
|
|
| Repositories are sourced from [ronantakizawa/github-top-projects](https://huggingface.co/datasets/ronantakizawa/github-top-projects), which tracks GitHub's top repositories from 2013–2025. |
|
|
| ## Use Cases |
|
|
| - Fine-tuning LLMs for DevOps/infrastructure code generation |
| - Training code completion models for configuration files |
| - Benchmarking LLM performance on build/CI tasks |
|
|
|
|
|  |
|
|
|
|
|
|
| ### Schema |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `content` | string | Full file content | |
| | `file_path` | string | Path within repository | |
| | `file_name` | string | Filename only | |
| | `category` | string | High-level category (see above) | |
| | `config_type` | string | Specific config type (e.g., "docker-compose", "tsconfig") | |
| | `repo_name` | string | Repository (owner/name) | |
| | `repo_stars` | int64 | Star count | |
| | `repo_language` | string | Primary language of repository | |
| | `license` | string | SPDX license identifier | |
| | `quality_score` | float32 | Quality score (0.0–1.0), see below | |
| | `is_generated` | bool | Whether file appears auto-generated (lower signal for training) | |
|
|
| ### Quality Filtering |
|
|
| The dataset undergoes three quality filtering stages: |
|
|
| 1. **Minimum size**: Files with fewer than 5 lines or 50 characters are removed (trivial configs like 2-line `.nvmrc` files add no training signal). |
|
|
| 2. **Near-deduplication**: MinHash LSH (128 permutations, Jaccard threshold 0.85) removes near-duplicate files. Within each duplicate cluster, the version from the highest-starred repository is kept. This eliminates hundreds of copies of common starter templates (e.g., default `tsconfig.json`, boilerplate `Dockerfile`). |
|
|
| 3. **Makefile scoping**: Makefiles are restricted to root-level and 1 directory deep, preventing large C/C++ repos from flooding the dataset with subdirectory Makefiles. |
|
|
| ### Quality Score |
|
|
| Each file receives a quality score (0.0–1.0) based on four equally-weighted factors: |
|
|
| - **Comment density** (0–0.25): Files with comments/annotations teach intent, not just syntax |
| - **Content length** (0–0.25): Longer files are more substantive (log-scaled, capped at 500 lines) |
| - **Repository quality** (0–0.25): Higher-starred repos signal better engineering practices (log-scaled) |
| - **Non-trivial ratio** (0–0.25): Ratio of meaningful lines vs blank/bracket-only lines |
|
|
| Use `quality_score` to filter for higher-quality examples during training: |
| ```python |
| high_quality = ds["train"].filter(lambda x: x["quality_score"] >= 0.5) |
| ``` |
|
|
| ### Splits |
|
|
| - **train** (90%): For training |
| - **test** (10%): For evaluation |
|
|
| Splits are deterministic by repository (all files from a repo go to the same split). |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("ronantakizawa/codeconfig") |
| |
| # Filter by category |
| dockerfiles = ds["train"].filter(lambda x: x["category"] == "dockerfile") |
| github_actions = ds["train"].filter(lambda x: x["category"] == "github_actions") |
| |
| # Filter by specific config type |
| tsconfigs = ds["train"].filter(lambda x: x["config_type"] == "tsconfig") |
| ``` |
|
|
