{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-rowan--hellaswag","slug":"rowan--hellaswag","name":"hellaswag","type":"dataset","url":"https://huggingface.co/datasets/Rowan/hellaswag","page_url":"https://unfragile.ai/rowan--hellaswag","categories":["model-training"],"tags":["language:en","size_categories:10K<n<100K","format:parquet","modality:text","library:datasets","library:pandas","library:mlcroissant","library:polars","arxiv:1905.07830","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-rowan--hellaswag__cap_0","uri":"capability://data.processing.analysis.commonsense.reasoning.benchmark.dataset.loading","name":"commonsense-reasoning-benchmark-dataset-loading","description":"Loads a curated dataset of 302,975 multiple-choice video-grounded commonsense reasoning examples from HuggingFace's datasets library, with built-in support for streaming, caching, and format conversion (parquet, arrow, CSV). The dataset is structured as context-question-answer tuples derived from ActivityNet Captions video descriptions, enabling models to predict plausible next events in video scenarios. Integrates directly with HuggingFace's `datasets` library for lazy loading, train/validation/test splits, and automatic schema validation.","intents":["Load and benchmark language models on commonsense reasoning tasks without manual data curation","Train or fine-tune models on video-grounded event prediction with standardized evaluation splits","Evaluate model performance on out-of-distribution commonsense reasoning compared to human baselines","Integrate commonsense reasoning evaluation into model training pipelines and CI/CD workflows"],"best_for":["ML researchers evaluating language model reasoning capabilities","Teams building commonsense reasoning benchmarks for model evaluation","Developers training instruction-tuned or RLHF models requiring diverse reasoning tasks"],"limitations":["Dataset is English-only; no multilingual variants for cross-lingual evaluation","Video descriptions are text-only; original video frames not included, limiting multimodal reasoning evaluation","Fixed train/validation/test splits cannot be customized; no built-in stratification by difficulty or category","Parquet format requires pandas/polars for efficient filtering; no native SQL query support","No temporal metadata for video sequences; treats each example as independent without temporal context"],"requires":["Python 3.7+","HuggingFace datasets library (pip install datasets)","pandas or polars for data manipulation (optional but recommended)","~2GB disk space for full dataset download and caching","Internet connection for initial dataset download from HuggingFace Hub"],"input_types":["dataset identifier string (e.g., 'Rowan/hellaswag')","split parameter (train/validation/test)","optional filtering/sampling parameters"],"output_types":["Apache Arrow tables (in-memory columnar format)","pandas DataFrames","polars DataFrames","streaming iterators for memory-efficient processing","parquet files for disk storage"],"categories":["data-processing-analysis","model-evaluation","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_1","uri":"capability://data.processing.analysis.multi.format.dataset.export.and.serialization","name":"multi-format-dataset-export-and-serialization","description":"Exports the hellaswag dataset to multiple serialization formats (parquet, arrow, CSV, JSON) via HuggingFace's datasets library, with automatic schema inference, compression options, and batch processing support. Handles columnar storage (parquet/arrow) for efficient analytics and row-oriented formats (CSV/JSON) for downstream consumption. Supports streaming export for datasets larger than available RAM, with configurable batch sizes and partitioning strategies.","intents":["Export dataset to parquet for efficient storage and columnar analytics in data warehouses","Convert dataset to CSV/JSON for integration with non-Python ML frameworks or web applications","Partition large datasets across multiple files for distributed processing on Spark or Dask","Create dataset snapshots in different formats for reproducibility and version control"],"best_for":["Data engineers building ETL pipelines that consume multiple format types","Teams migrating between ML frameworks (PyTorch, TensorFlow, JAX) with different data loaders","Researchers sharing datasets in format-agnostic ways across institutions"],"limitations":["Parquet export requires pyarrow library; CSV export loses nested column structure if present","No built-in compression for JSON export; files can be 3-5x larger than parquet equivalents","Streaming export requires manual batch size tuning; no automatic optimization for network bandwidth","Schema inference is automatic but may infer incorrect types for ambiguous columns (e.g., numeric strings)"],"requires":["Python 3.7+","HuggingFace datasets library","pyarrow (for parquet/arrow export)","pandas (for CSV export)","Sufficient disk space for target format (~2-5GB depending on compression)"],"input_types":["HuggingFace Dataset object","format string (parquet/arrow/csv/json)","optional compression codec (gzip, snappy, brotli)"],"output_types":["parquet files (columnar, compressed)","arrow files (columnar, uncompressed)","CSV files (row-oriented, text)","JSON files (row-oriented, text)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_2","uri":"capability://data.processing.analysis.train.validation.test.split.management","name":"train-validation-test-split-management","description":"Provides pre-defined train/validation/test splits for the hellaswag dataset via HuggingFace's split parameter, with deterministic sampling and no data leakage between splits. Splits are computed once during dataset creation and cached locally, enabling reproducible train/eval workflows. The dataset uses stratified sampling to ensure balanced distribution of difficulty levels and answer patterns across splits.","intents":["Load train/validation/test splits without manual data partitioning or risk of data leakage","Reproduce published benchmark results by using identical splits across experiments","Evaluate models on held-out test set with confidence that no training data was seen","Compare model performance across multiple runs with consistent evaluation methodology"],"best_for":["Researchers publishing benchmark results requiring reproducible evaluation","Teams training models with strict train/test separation requirements","Practitioners comparing against published baselines on standardized splits"],"limitations":["Splits are fixed and cannot be customized (e.g., 80/10/10 vs 70/15/15 ratios)","No cross-validation support; only single train/val/test split available","Stratification strategy is not documented; cannot verify balance across difficulty levels","No temporal or domain-based stratification; splits may not be representative of real-world distribution shifts"],"requires":["Python 3.7+","HuggingFace datasets library","Sufficient RAM to load at least one split (~500MB for train split)"],"input_types":["split parameter: 'train', 'validation', or 'test'"],"output_types":["HuggingFace Dataset object containing examples for specified split","metadata: split size, number of examples, schema"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_3","uri":"capability://data.processing.analysis.streaming.dataset.iteration.for.memory.constrained.environments","name":"streaming-dataset-iteration-for-memory-constrained-environments","description":"Enables streaming iteration over the hellaswag dataset without loading the entire 302K examples into memory, using HuggingFace's streaming API to fetch batches on-demand from the Hub. Each batch is fetched, processed, and discarded, keeping memory footprint constant regardless of dataset size. Supports configurable batch sizes, prefetching, and parallel workers for efficient I/O.","intents":["Train models on large datasets on machines with limited RAM (e.g., edge devices, shared clusters)","Process dataset examples one-at-a-time for online learning or continual evaluation","Reduce initial download time by streaming only required splits instead of full dataset","Implement memory-efficient data augmentation or preprocessing pipelines"],"best_for":["Researchers training on resource-constrained hardware (mobile, edge, shared clusters)","Teams implementing online learning or continual evaluation workflows","Practitioners prototyping models without committing to full dataset downloads"],"limitations":["Streaming adds network latency (~50-200ms per batch) compared to local disk access","No random access; must iterate sequentially, making stratified sampling difficult","Requires stable internet connection; network interruptions require restart from last checkpoint","Prefetching and caching strategies are not configurable; default behavior may not match workload patterns","Batch ordering is deterministic but not shuffled; requires manual shuffling for training stability"],"requires":["Python 3.7+","HuggingFace datasets library with streaming support","Stable internet connection with sufficient bandwidth (>1 Mbps recommended)","Minimal RAM (streaming uses <100MB regardless of dataset size)"],"input_types":["streaming=True parameter","batch_size parameter (default 1000)","num_workers parameter for parallel fetching"],"output_types":["Iterator yielding batches of examples","Each batch: dict with keys 'ctx', 'question', 'endings', 'label'"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_4","uri":"capability://data.processing.analysis.schema.aware.data.validation.and.type.inference","name":"schema-aware-data-validation-and-type-inference","description":"Automatically infers and validates the schema of hellaswag examples (context string, question string, multiple-choice endings list, label integer) using HuggingFace's schema inference engine. Validates that each example conforms to expected types and structure, catching malformed or missing fields before model training. Schema is cached and reused across loads, enabling fast validation without re-scanning the dataset.","intents":["Catch data quality issues (missing fields, type mismatches) early in the pipeline before training","Understand dataset structure programmatically for dynamic model input layer configuration","Generate type hints for downstream processing code (e.g., PyTorch DataLoader)","Validate custom dataset modifications without manual schema specification"],"best_for":["ML engineers building robust data pipelines with automatic quality checks","Teams integrating hellaswag with type-checked frameworks (Pydantic, dataclasses)","Researchers extending the dataset with custom fields requiring schema validation"],"limitations":["Schema inference is automatic but may infer incorrect types for edge cases (e.g., numeric strings as integers)","No custom validation rules; only type checking, not semantic validation (e.g., label must be in [0, 3])","Schema is read-only; cannot modify inferred types without reloading dataset","No nested schema support; complex structures are flattened to top-level columns"],"requires":["Python 3.7+","HuggingFace datasets library","pyarrow for schema representation"],"input_types":["HuggingFace Dataset object"],"output_types":["pyarrow.Schema object with column names and types","validation errors (if any) with field names and expected vs actual types"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_5","uri":"capability://data.processing.analysis.cross.framework.dataset.compatibility.and.adapter.generation","name":"cross-framework-dataset-compatibility-and-adapter-generation","description":"Provides adapters to convert hellaswag into framework-specific formats (PyTorch DataLoader, TensorFlow Dataset, JAX numpy arrays) via HuggingFace's ecosystem integrations. Each adapter handles batching, padding, tokenization, and type conversion automatically. Supports lazy evaluation (streaming) and eager loading (in-memory) modes depending on framework requirements.","intents":["Load hellaswag directly into PyTorch training loops without custom data loading code","Use hellaswag with TensorFlow/Keras models via tf.data.Dataset API","Convert hellaswag to numpy arrays for JAX or scikit-learn workflows","Switch between frameworks without re-implementing data loading logic"],"best_for":["ML practitioners using multiple frameworks (PyTorch, TensorFlow, JAX) in same project","Teams standardizing on HuggingFace ecosystem for framework-agnostic data handling","Researchers comparing models across frameworks with identical data pipelines"],"limitations":["Adapters require framework-specific libraries (torch, tensorflow, jax) to be installed","Tokenization and padding are not automatic; require separate preprocessing step","No built-in batching for variable-length sequences; requires custom collate functions","Type conversion may introduce precision loss (e.g., float64 to float32)","Framework-specific optimizations (e.g., GPU pinning in PyTorch) require manual configuration"],"requires":["Python 3.7+","HuggingFace datasets library","Target framework: torch, tensorflow, jax, or numpy","Optional: transformers library for tokenization"],"input_types":["HuggingFace Dataset object","target framework string (pytorch/tensorflow/jax/numpy)"],"output_types":["PyTorch DataLoader with batched tensors","TensorFlow Dataset with batched tensors","JAX pytree with numpy arrays","numpy arrays for direct consumption"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_6","uri":"capability://data.processing.analysis.dataset.filtering.and.subset.selection.by.metadata","name":"dataset-filtering-and-subset-selection-by-metadata","description":"Filters hellaswag examples by metadata attributes (e.g., activity category, difficulty level, answer distribution) using HuggingFace's filter API with predicate functions. Supports efficient filtering via columnar operations (parquet/arrow) without loading full dataset into memory. Filtered subsets are cached for reuse across experiments.","intents":["Create domain-specific subsets (e.g., only cooking or sports activities) for targeted evaluation","Filter by difficulty level to evaluate model performance on easy vs hard examples","Remove examples with specific characteristics (e.g., ambiguous answers) for cleaner evaluation","Create balanced subsets with equal representation of answer patterns for fairness evaluation"],"best_for":["Researchers analyzing model performance on domain-specific or difficulty-stratified subsets","Teams evaluating fairness and bias across different example categories","Practitioners debugging model failures on specific example types"],"limitations":["Filtering requires loading dataset into memory or using columnar operations (slower than indexed queries)","No built-in metadata for difficulty or category; requires manual annotation or external mapping","Filter predicates are Python functions; cannot be optimized by query planner","Filtered subsets are not automatically cached; require manual caching logic","No support for complex queries (e.g., 'difficulty > 0.5 AND category == cooking')"],"requires":["Python 3.7+","HuggingFace datasets library","Optional: pandas for complex filtering logic"],"input_types":["HuggingFace Dataset object","filter predicate function (takes example dict, returns bool)","optional batch_size for efficient filtering"],"output_types":["Filtered HuggingFace Dataset object","metadata: original size, filtered size, filter selectivity"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-rowan--hellaswag__cap_7","uri":"capability://automation.workflow.dataset.versioning.and.reproducible.snapshot.management","name":"dataset-versioning-and-reproducible-snapshot-management","description":"Manages dataset versions and snapshots via HuggingFace's Hub versioning system, enabling reproducible access to specific dataset versions (e.g., 'revision=main' or 'revision=v1.0'). Each version is immutable and cached locally, preventing silent data changes between experiments. Supports rollback to previous versions and tracking of version history via Git-like semantics.","intents":["Ensure reproducibility by pinning dataset version in experiment configs","Track dataset evolution and compare model performance across versions","Rollback to previous dataset version if issues are discovered in current version","Share exact dataset snapshot with collaborators for reproducible research"],"best_for":["Researchers publishing results requiring exact dataset reproducibility","Teams managing long-running experiments with potential dataset updates","Practitioners collaborating across institutions with version-controlled datasets"],"limitations":["Versioning is managed by HuggingFace Hub; no local version control without Git integration","Version history is not queryable; requires manual tracking of version changes","No automatic version detection; must explicitly specify revision parameter","Rollback requires re-downloading dataset version; no efficient delta updates","Version metadata (author, timestamp, changelog) is not standardized"],"requires":["Python 3.7+","HuggingFace datasets library","HuggingFace Hub account (for pushing new versions)","Git (optional, for local version tracking)"],"input_types":["revision parameter: branch name, tag, or commit hash","optional: version metadata (author, changelog)"],"output_types":["HuggingFace Dataset object for specified version","version metadata: revision, timestamp, author"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","HuggingFace datasets library (pip install datasets)","pandas or polars for data manipulation (optional but recommended)","~2GB disk space for full dataset download and caching","Internet connection for initial dataset download from HuggingFace Hub","HuggingFace datasets library","pyarrow (for parquet/arrow export)","pandas (for CSV export)","Sufficient disk space for target format (~2-5GB depending on compression)","Sufficient RAM to load at least one split (~500MB for train split)"],"failure_modes":["Dataset is English-only; no multilingual variants for cross-lingual evaluation","Video descriptions are text-only; original video frames not included, limiting multimodal reasoning evaluation","Fixed train/validation/test splits cannot be customized; no built-in stratification by difficulty or category","Parquet format requires pandas/polars for efficient filtering; no native SQL query support","No temporal metadata for video sequences; treats each example as independent without temporal context","Parquet export requires pyarrow library; CSV export loses nested column structure if present","No built-in compression for JSON export; files can be 3-5x larger than parquet equivalents","Streaming export requires manual batch size tuning; no automatic optimization for network bandwidth","Schema inference is automatic but may infer incorrect types for ambiguous columns (e.g., numeric strings)","Splits are fixed and cannot be customized (e.g., 80/10/10 vs 70/15/15 ratios)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"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":"2026-05-24T12:16:22.764Z","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=rowan--hellaswag","compare_url":"https://unfragile.ai/compare?artifact=rowan--hellaswag"}},"signature":"LNeUBqANZEGTwBepllGDssi4grBaZaa0PZSuC7ijGc9/ddydLQzLawujMcZlt+FwHmElJ/rzte2BnNSMsa8/AA==","signedAt":"2026-06-21T19:39:41.391Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rowan--hellaswag","artifact":"https://unfragile.ai/rowan--hellaswag","verify":"https://unfragile.ai/api/v1/verify?slug=rowan--hellaswag","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"}}