{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-bigcode--commitpackft","slug":"bigcode--commitpackft","name":"commitpackft","type":"dataset","url":"https://huggingface.co/datasets/bigcode/commitpackft","page_url":"https://unfragile.ai/bigcode--commitpackft","categories":["model-training"],"tags":["language:code","license:mit","size_categories:100K<n<1M","modality:text","library:datasets","library:mlcroissant","arxiv:2308.07124","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-bigcode--commitpackft__cap_0","uri":"capability://data.processing.analysis.commit.message.code.pair.dataset.curation.and.indexing","name":"commit-message-code-pair dataset curation and indexing","description":"Provides a curated dataset of 3.61M commit messages paired with their corresponding code changes, indexed and versioned on HuggingFace's distributed infrastructure. The dataset uses Apache Arrow columnar format for efficient streaming and random access, enabling researchers to load subsets without downloading the entire 361K+ record corpus. Implements MLCroissant metadata standard for machine-readable dataset discovery and reproducibility.","intents":["Train code-to-commit-message generation models with paired examples","Build commit message summarization systems using real-world code diffs","Analyze patterns in how developers describe code changes across projects","Create benchmarks for evaluating commit message quality and relevance"],"best_for":["ML researchers training code understanding models","Teams building automated commit message generation tools","Organizations analyzing software engineering practices at scale","Model developers working on code-language alignment tasks"],"limitations":["Dataset is static snapshot — does not reflect ongoing repository updates or new commits","Commit messages may contain sensitive information, credentials, or proprietary details not fully sanitized","Skewed toward popular open-source projects on GitHub; underrepresents enterprise/private codebases","No built-in filtering for low-quality commits (e.g., 'fix', 'update', single-character messages)","Code diffs are context-limited; full file context not always available for understanding changes"],"requires":["HuggingFace datasets library (>=2.0.0)","Python 3.7+","~50GB disk space for full dataset or streaming capability for partial loads","Internet connection for dataset download/streaming from HuggingFace Hub"],"input_types":["Git commit metadata (hash, author, timestamp, message)","Code diffs (unified diff format)","Repository metadata (language, project name, URL)"],"output_types":["Structured records with commit_message (string), code_diff (string), metadata (dict)","Parquet/Arrow columnar format for efficient ML pipeline integration","Streaming batches for distributed training"],"categories":["data-processing-analysis","model-training-datasets"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-bigcode--commitpackft__cap_1","uri":"capability://data.processing.analysis.streaming.dataset.loading.with.selective.column.projection","name":"streaming dataset loading with selective column projection","description":"Implements HuggingFace Datasets library's streaming protocol to load subsets of the 3.61M records without downloading the full corpus, using Apache Arrow's columnar format for efficient memory usage and column-level filtering. Supports random access via indexing and batch sampling for training loops, with automatic caching of accessed splits to disk. Enables researchers to work with the dataset on resource-constrained machines by loading only required columns (e.g., commit_message + code_diff, excluding metadata).","intents":["Load only commit messages and diffs without metadata for lightweight fine-tuning","Sample random batches for model training without materializing full dataset in memory","Stream data directly to GPU training pipelines with minimal latency","Iterate over dataset splits (train/validation/test) with reproducible shuffling"],"best_for":["ML engineers training models on limited GPU/CPU memory (< 16GB RAM)","Researchers prototyping models before committing to full dataset download","Distributed training setups requiring per-worker data streaming","Jupyter notebook workflows with interactive exploration"],"limitations":["Streaming mode has ~50-200ms latency per batch due to network I/O; not suitable for real-time inference","Random access requires index lookups; sequential iteration is significantly faster","Caching behavior is opaque — disk usage can grow unexpectedly if cache directory not monitored","Column projection only works for top-level fields; nested structures require full record loading"],"requires":["datasets>=2.0.0 library with streaming support","Python 3.7+","Stable internet connection for streaming mode","~1-5GB disk space for streaming cache (configurable)"],"input_types":["HuggingFace dataset identifier (bigcode/commitpackft)","Split name (train/validation/test)","Column names for projection (e.g., ['commit_message', 'code_diff'])"],"output_types":["PyArrow Table objects with selected columns","Batched iterables for training loops","Pandas DataFrames via .to_pandas() conversion"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-bigcode--commitpackft__cap_2","uri":"capability://data.processing.analysis.mlcroissant.metadata.driven.dataset.discovery.and.reproducibility","name":"mlcroissant metadata-driven dataset discovery and reproducibility","description":"Embeds MLCroissant machine-readable metadata (JSON-LD format) describing dataset structure, provenance, and licensing, enabling automated discovery and reproducible loading across tools and platforms. Metadata includes field schemas, split definitions, record counts, and licensing terms (MIT), allowing downstream tools to validate compatibility and generate data loading code automatically. Integrates with HuggingFace Hub's search and discovery systems for programmatic dataset lookup.","intents":["Automatically discover dataset schema and structure without manual documentation review","Generate boilerplate data loading code from metadata for multiple frameworks (PyTorch, TensorFlow, JAX)","Validate dataset compatibility with downstream ML pipelines before loading","Track dataset provenance and licensing for compliance and citation"],"best_for":["ML platform builders integrating multiple datasets programmatically","Research teams requiring reproducible dataset specifications across papers","Organizations managing data governance and licensing compliance","AutoML systems that need to infer dataset structure automatically"],"limitations":["MLCroissant standard is still evolving; not all dataset properties are standardized (e.g., data quality metrics)","Metadata is static and must be manually updated when dataset versions change","No built-in validation that metadata matches actual data — mismatches can cause silent failures","Requires MLCroissant-aware tools to leverage; standard HuggingFace tools ignore metadata"],"requires":["MLCroissant library (optional, for parsing metadata)","JSON-LD parser or standard JSON library","HuggingFace Hub API access for metadata retrieval"],"input_types":["MLCroissant JSON-LD metadata file","HuggingFace dataset card (README.md with metadata)"],"output_types":["Structured metadata dict with schema, splits, licensing","Generated data loading code snippets","Dataset compatibility reports"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-bigcode--commitpackft__cap_3","uri":"capability://data.processing.analysis.multi.language.code.commit.pair.extraction.and.normalization","name":"multi-language code-commit pair extraction and normalization","description":"Extracts and normalizes commit-message-code-diff pairs across multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) from BigCode's unified repository corpus, applying language-agnostic diff parsing and commit message cleaning (removing merge commits, automated commits, etc.). Uses unified diff format for code changes, enabling language-agnostic training of models that learn to map code semantics to natural language descriptions. Implements filtering heuristics to exclude low-quality commits (e.g., single-character messages, auto-generated commits from CI/CD).","intents":["Train language-agnostic code-to-text models that work across Python, JavaScript, Java, and other languages","Build commit message generation systems that understand code semantics regardless of programming language","Analyze cross-language patterns in how developers describe similar code changes","Create multilingual code understanding benchmarks"],"best_for":["Researchers building polyglot code understanding models","Teams training commit message generators for multi-language codebases","Organizations analyzing software engineering practices across language ecosystems","Model developers working on language-agnostic code semantics"],"limitations":["Diff format loses semantic information about code structure (e.g., function boundaries, control flow)","Language detection is heuristic-based; some commits may be mislabeled or mixed-language","Filtering heuristics may exclude valid commits (e.g., legitimate single-word commits like 'refactor')","No language-specific AST parsing; treats all languages uniformly, missing language-specific patterns","Commit messages may be in multiple languages; no language detection or filtering for English-only training"],"requires":["Python 3.7+","Unified diff parser (included in datasets library)","Language detection library (optional, for filtering by language)"],"input_types":["Git commit objects (message, author, timestamp, diff)","Repository metadata (primary language, project name)","Unified diff format for code changes"],"output_types":["Normalized records: {commit_message: str, code_diff: str, language: str, repo: str}","Filtered subsets by language (e.g., Python-only, JavaScript-only)","Statistics on language distribution and commit quality"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-bigcode--commitpackft__cap_4","uri":"capability://data.processing.analysis.dataset.versioning.and.reproducible.splits.with.fixed.random.seeds","name":"dataset versioning and reproducible splits with fixed random seeds","description":"Implements versioned dataset snapshots on HuggingFace Hub with deterministic train/validation/test splits using fixed random seeds, ensuring reproducible sampling across runs and machines. Each version is immutable and tagged with commit hash and timestamp, enabling researchers to cite exact dataset versions in papers. Splits are pre-computed and cached, avoiding non-determinism from random sampling during training. Supports multiple split configurations (e.g., 80/10/10, 70/15/15) with documented rationale.","intents":["Ensure reproducible model training by using fixed dataset splits across experiments","Cite exact dataset versions in research papers with version tags and commit hashes","Compare model performance across papers using identical dataset splits","Track dataset evolution and maintain backward compatibility with older versions"],"best_for":["Researchers publishing papers requiring reproducible dataset specifications","Teams maintaining long-running model training pipelines with version control","Organizations comparing model performance across time with consistent baselines","Collaborative research groups needing synchronized dataset versions"],"limitations":["Immutable versions prevent fixing data quality issues without creating new versions","Fixed splits may not be optimal for all downstream tasks; researchers often create custom splits","Version proliferation can cause confusion if not properly documented","No built-in mechanism to track which version was used in published results; requires manual documentation"],"requires":["HuggingFace datasets library with version support","Git for tracking dataset versions (optional, for local reproducibility)","Documentation of split rationale and random seed values"],"input_types":["Dataset version identifier (e.g., 'bigcode/commitpackft@v1.0')","Split name (train/validation/test)","Random seed (fixed, e.g., 42)"],"output_types":["Deterministic subset of records for each split","Version metadata (commit hash, timestamp, split ratios)","Reproducibility report with seed and split statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-bigcode--commitpackft__cap_5","uri":"capability://data.processing.analysis.bigcode.initiative.integration.and.multi.source.repository.aggregation","name":"bigcode initiative integration and multi-source repository aggregation","description":"Aggregates commit-message-code pairs from BigCode's unified repository corpus, which combines data from multiple sources (GitHub, GitLab, Gitee, etc.) with standardized extraction and deduplication pipelines. Implements cross-repository deduplication using content hashing to remove duplicate commits across mirrors and forks. Provides unified access to heterogeneous repository data through a single HuggingFace dataset interface, abstracting away source-specific API differences and data formats.","intents":["Access commit data from multiple repository sources (GitHub, GitLab, Gitee) through a single unified interface","Train models on deduplicated commit data without worrying about duplicate examples from forks/mirrors","Analyze commit patterns across different repository hosting platforms","Leverage BigCode's standardized extraction pipeline without building custom scrapers"],"best_for":["Researchers building models on large-scale, multi-source code data","Teams avoiding the complexity of building custom multi-source data pipelines","Organizations analyzing code practices across different repository platforms","Model developers requiring diverse, deduplicated training data"],"limitations":["Deduplication is content-based; semantically similar but syntactically different commits may not be detected","Source attribution is limited; difficult to trace back to original repository for verification","Aggregation may introduce biases toward platforms with more public repositories (e.g., GitHub over Gitee)","Updates lag behind live repositories; dataset is a static snapshot, not real-time"],"requires":["HuggingFace datasets library","Python 3.7+","Understanding of BigCode initiative's data collection methodology (documented in arxiv:2308.07124)"],"input_types":["Repository metadata from multiple sources (GitHub, GitLab, Gitee)","Commit objects with standardized schema"],"output_types":["Unified dataset records with source attribution","Deduplicated commit pairs","Statistics on source distribution and deduplication impact"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["HuggingFace datasets library (>=2.0.0)","Python 3.7+","~50GB disk space for full dataset or streaming capability for partial loads","Internet connection for dataset download/streaming from HuggingFace Hub","datasets>=2.0.0 library with streaming support","Stable internet connection for streaming mode","~1-5GB disk space for streaming cache (configurable)","MLCroissant library (optional, for parsing metadata)","JSON-LD parser or standard JSON library","HuggingFace Hub API access for metadata retrieval"],"failure_modes":["Dataset is static snapshot — does not reflect ongoing repository updates or new commits","Commit messages may contain sensitive information, credentials, or proprietary details not fully sanitized","Skewed toward popular open-source projects on GitHub; underrepresents enterprise/private codebases","No built-in filtering for low-quality commits (e.g., 'fix', 'update', single-character messages)","Code diffs are context-limited; full file context not always available for understanding changes","Streaming mode has ~50-200ms latency per batch due to network I/O; not suitable for real-time inference","Random access requires index lookups; sequential iteration is significantly faster","Caching behavior is opaque — disk usage can grow unexpectedly if cache directory not monitored","Column projection only works for top-level fields; nested structures require full record loading","MLCroissant standard is still evolving; not all dataset properties are standardized (e.g., data quality metrics)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.066Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=bigcode--commitpackft","compare_url":"https://unfragile.ai/compare?artifact=bigcode--commitpackft"}},"signature":"51HJqlLlcxPxVe7Y9AmdGN/FdGcPjFbPC/4NnomvgL8cIzwoZJbWEp9J1opJMgcINUhip8zAjeOl2sOFq6l7DQ==","signedAt":"2026-06-21T14:59:34.210Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bigcode--commitpackft","artifact":"https://unfragile.ai/bigcode--commitpackft","verify":"https://unfragile.ai/api/v1/verify?slug=bigcode--commitpackft","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}