{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-hugging-face-datasets","slug":"hugging-face-datasets","name":"Hugging face datasets","type":"dataset","url":"https://huggingface.co/camel-ai","page_url":"https://unfragile.ai/hugging-face-datasets","categories":["model-training"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-hugging-face-datasets__cap_0","uri":"capability://data.processing.analysis.distributed.dataset.streaming.and.caching.with.memory.efficient.loading","name":"distributed dataset streaming and caching with memory-efficient loading","description":"Implements a streaming architecture that loads datasets in chunks rather than fully into memory, using Apache Arrow columnar format for efficient serialization and a local caching layer that stores downloaded datasets with automatic deduplication. The system uses memory-mapped files and lazy evaluation to defer data loading until access time, enabling work with datasets larger than available RAM through intelligent prefetching and background downloads.","intents":["Load multi-gigabyte datasets on machines with limited RAM without running out of memory","Cache downloaded datasets locally to avoid repeated network transfers across training runs","Stream data directly from Hugging Face Hub to training loops without intermediate storage","Work with datasets that are too large to fit in memory by accessing them in batches"],"best_for":["ML researchers training on large-scale datasets with resource constraints","Teams building data pipelines that need reproducible, versioned dataset access","Developers prototyping models without managing local dataset infrastructure"],"limitations":["Streaming performance degrades with high-latency network connections (>500ms RTT)","No built-in compression for cached datasets — disk usage mirrors raw dataset size","Arrow format conversion adds 5-15% overhead on first load compared to raw binary formats","Cache invalidation requires manual deletion or version-based key rotation"],"requires":["Python 3.7+","Internet connectivity for initial dataset download from Hugging Face Hub","Disk space equal to dataset size for local caching","PyArrow library (automatically installed as dependency)"],"input_types":["dataset identifiers (string paths like 'wikitext', 'openwebtext')","configuration dicts specifying splits and features to load","local file paths for custom datasets"],"output_types":["Dataset objects with arrow-backed columnar storage","DatasetDict for multi-split datasets (train/validation/test)","Iterable batches for streaming mode"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_1","uri":"capability://data.processing.analysis.dataset.transformation.and.feature.engineering.with.map.filter.select.operations","name":"dataset transformation and feature engineering with map/filter/select operations","description":"Provides a functional programming API for composable data transformations using lazy evaluation — map(), filter(), select(), rename(), and cast() operations are queued and executed only when data is accessed, allowing efficient chaining of multiple transformations without intermediate materialization. Transformations are compiled into optimized execution plans that push column selection and filtering down to the Arrow layer for early pruning.","intents":["Apply preprocessing functions (tokenization, normalization) to raw text datasets without creating intermediate copies","Filter datasets by conditions (e.g., keep only examples with length > 100 tokens) before training","Select and rename columns to match model input schemas without rewriting entire datasets","Chain multiple transformations (clean → tokenize → filter) in a readable, composable way"],"best_for":["Data engineers building reproducible preprocessing pipelines","ML practitioners iterating on feature engineering without storage overhead","Teams needing deterministic, version-controlled data transformations"],"limitations":["Custom map functions must be serializable (pickle-compatible) — lambdas and closures may fail in distributed settings","Transformation execution is single-threaded by default; parallelization requires explicit batching configuration","No automatic type inference for map outputs — output schema must be manually specified for complex transformations","Lazy evaluation means errors in transformations only surface when data is accessed, not at definition time"],"requires":["Python 3.7+","Hugging Face datasets library","For distributed execution: Apache Spark or Ray (optional)"],"input_types":["Dataset objects","Python callables (functions) for map/filter operations","Column names (strings) for select/rename operations","Type specifications (DatasetFeatures) for schema definition"],"output_types":["Transformed Dataset objects with same interface","Materialized datasets (via .save_to_disk()) as Parquet or Arrow files"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_10","uri":"capability://automation.workflow.dataset.documentation.and.metadata.management.with.automatic.card.generation","name":"dataset documentation and metadata management with automatic card generation","description":"Generates and manages dataset documentation (dataset cards) in markdown format with automatic extraction of schema, statistics, and license information. Supports custom metadata fields and integrates with Hugging Face Hub's dataset card system for web-based browsing. Cards include sections for dataset description, intended use, limitations, and citation information. The system validates metadata completeness and provides templates for common dataset types.","intents":["Create comprehensive dataset documentation for reproducibility and sharing","Automatically generate dataset cards from metadata and statistics","Document dataset limitations, biases, and intended use cases","Provide citation information and licensing details for datasets"],"best_for":["Researchers publishing datasets and needing comprehensive documentation","Teams maintaining internal datasets with governance requirements","Open science initiatives requiring transparent dataset documentation"],"limitations":["Automatic card generation produces basic templates — manual editing required for comprehensive documentation","No automatic bias detection or limitation identification — requires manual specification","Metadata validation is basic — no enforcement of documentation completeness","Cards are static markdown — no dynamic content or interactive visualizations"],"requires":["Python 3.7+","Hugging Face datasets library","Optional: markdown editor for manual card editing"],"input_types":["Dataset objects","Metadata dictionary (description, license, citation, etc.)","Custom markdown content"],"output_types":["Markdown dataset cards","Structured metadata JSON","Hub-compatible card files"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_2","uri":"capability://data.processing.analysis.multi.format.dataset.import.and.export.with.automatic.schema.inference","name":"multi-format dataset import and export with automatic schema inference","description":"Supports loading datasets from diverse sources (CSV, JSON, Parquet, Arrow, SQL databases, local files) with automatic schema detection that infers column types and handles missing values. Export functionality writes datasets to multiple formats with configurable compression and partitioning strategies. The system uses format-specific parsers (pyarrow.csv, pandas for JSON) and automatically handles encoding detection and delimiter inference for ambiguous formats.","intents":["Load CSV or JSON files from local disk or cloud storage without manually defining schemas","Convert between dataset formats (CSV → Parquet, JSON → Arrow) for storage optimization","Export training datasets in formats compatible with downstream tools (TensorFlow, PyTorch, SQL databases)","Work with datasets from multiple sources (local files, databases, cloud buckets) through a unified interface"],"best_for":["Data scientists migrating datasets from legacy formats to modern columnar storage","Teams integrating Hugging Face datasets with existing data pipelines using different formats","Researchers sharing datasets in multiple formats for reproducibility"],"limitations":["Schema inference can fail on heterogeneous columns (e.g., mixed int/string in same column) — requires manual type specification","CSV parsing performance degrades on files >10GB without explicit chunking configuration","SQL database support requires additional dependencies (sqlalchemy) and connection management","Automatic encoding detection uses heuristics and may fail on non-UTF8 files with special characters"],"requires":["Python 3.7+","PyArrow for Arrow/Parquet support","pandas for CSV/JSON parsing (automatically installed)","Optional: sqlalchemy for database connections, s3fs for S3 access"],"input_types":["File paths (local or cloud URLs)","File formats: CSV, JSON, Parquet, Arrow, HuggingFace native format","SQL connection strings and queries","Pandas DataFrames"],"output_types":["Dataset objects with inferred schema","Exported files in CSV, JSON, Parquet, Arrow formats","Partitioned dataset directories for large-scale exports"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_3","uri":"capability://automation.workflow.dataset.versioning.and.reproducibility.with.commit.based.tracking","name":"dataset versioning and reproducibility with commit-based tracking","description":"Implements Git-like versioning for datasets using content-addressed storage where each dataset version is identified by a commit hash derived from its contents and metadata. Versions are immutable snapshots stored on the Hugging Face Hub with full lineage tracking — users can revert to previous versions, compare changes, and reproduce exact dataset states from past experiments. The system tracks dataset configuration, transformations applied, and source data fingerprints.","intents":["Reproduce exact dataset state used in a published paper or experiment from months ago","Track changes to datasets across team iterations without maintaining separate copies","Revert to a previous dataset version if a transformation introduced errors","Share datasets with exact version pinning to ensure reproducibility across collaborators"],"best_for":["Research teams publishing papers and needing long-term dataset reproducibility","ML ops teams managing dataset evolution across multiple model versions","Open science initiatives requiring transparent dataset provenance"],"limitations":["Version history is immutable — cannot delete or modify past versions, only add new ones","Hub storage is limited by account quotas; large dataset histories consume significant space","No automatic conflict resolution for concurrent dataset edits — requires manual merge","Commit hash changes if any metadata or transformation parameters change, making incremental updates inefficient"],"requires":["Hugging Face Hub account with dataset creation permissions","Git-like workflow understanding (commits, branches, versioning concepts)","huggingface_hub library for programmatic version management"],"input_types":["Dataset objects with transformations applied","Commit messages describing changes","Version tags (optional, for semantic versioning)"],"output_types":["Versioned dataset identifiers (e.g., 'dataset-name@v1.2.3')","Commit history with diffs showing transformation changes","Dataset cards documenting version-specific metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_4","uri":"capability://automation.workflow.batch.processing.and.distributed.dataset.operations.with.multi.worker.execution","name":"batch processing and distributed dataset operations with multi-worker execution","description":"Enables parallel processing of datasets across multiple CPU cores or distributed workers using a map-reduce pattern where transformations are applied in batches across processes. The system handles work distribution, result aggregation, and failure recovery automatically. Supports both local multiprocessing (using Python's multiprocessing) and distributed execution via Apache Spark or Ray for cluster-scale operations. Batching is configurable to balance memory usage and parallelism.","intents":["Apply expensive transformations (e.g., model inference, complex NLP processing) to large datasets 10-100x faster using multiple cores","Process datasets that don't fit in memory by distributing work across a cluster","Parallelize dataset preprocessing to reduce training pipeline bottlenecks","Scale dataset operations from laptop to cloud infrastructure without code changes"],"best_for":["Teams with large datasets and access to multi-core machines or clusters","ML engineers optimizing data pipeline throughput for training","Researchers processing datasets with expensive per-example operations"],"limitations":["Multiprocessing overhead is significant for lightweight operations (<1ms per example) — may be slower than single-threaded execution","Worker processes must serialize data and code, adding 10-50ms per batch for IPC overhead","Distributed execution (Spark/Ray) requires cluster setup and introduces network latency — best for operations >100ms per example","Determinism requires careful handling of random seeds across workers; default behavior may produce non-deterministic results"],"requires":["Python 3.7+","For multiprocessing: no additional dependencies","For distributed execution: Apache Spark 3.0+ or Ray 1.0+","For cloud execution: cloud credentials (AWS, GCP, Azure)"],"input_types":["Dataset objects","Batch size configuration (number of examples per worker task)","Python callables for map operations","Worker count or cluster configuration"],"output_types":["Transformed Dataset objects with results aggregated from workers","Execution metrics (processing time, throughput)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_5","uri":"capability://data.processing.analysis.dataset.splitting.and.train.validation.test.partitioning.with.stratification","name":"dataset splitting and train/validation/test partitioning with stratification","description":"Provides utilities to split datasets into multiple subsets (train/validation/test) with configurable strategies including random splitting, stratified splitting (preserving label distributions), and temporal splitting (for time-series data). Supports both fixed splits (e.g., 80/10/10) and dynamic splits based on dataset size. Splits are deterministic and reproducible using seed-based randomization, and can be applied to datasets with or without explicit labels.","intents":["Create train/validation/test splits from a raw dataset while preserving label distributions for imbalanced classification","Split time-series datasets chronologically to avoid data leakage","Generate multiple random splits for cross-validation without reloading the dataset","Ensure reproducible splits across team members and experiments using fixed random seeds"],"best_for":["ML practitioners building supervised learning pipelines","Researchers conducting cross-validation studies","Teams needing reproducible dataset splits for model evaluation"],"limitations":["Stratified splitting requires explicit label column — fails silently if labels are missing","Temporal splitting assumes data is pre-sorted by time; no automatic time-based ordering","Large datasets may require multiple passes to compute stratification statistics, adding latency","No built-in support for group-based splitting (e.g., keeping examples from same user together)"],"requires":["Python 3.7+","Hugging Face datasets library","Optional: scikit-learn for advanced stratification strategies"],"input_types":["Dataset objects","Split ratios (e.g., [0.8, 0.1, 0.1] for train/val/test)","Label column name for stratified splitting","Random seed for reproducibility"],"output_types":["DatasetDict with 'train', 'validation', 'test' keys","Individual Dataset objects for each split","Split indices for custom partitioning"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_6","uri":"capability://data.processing.analysis.dataset.metrics.and.statistics.computation.with.built.in.aggregations","name":"dataset metrics and statistics computation with built-in aggregations","description":"Computes dataset-level statistics (row counts, column types, missing value rates, value distributions) and example-level metrics (text length, token counts, label distributions) using efficient aggregation functions. Metrics are computed lazily and cached to avoid recomputation. Supports custom metric functions and integrates with visualization libraries for exploratory data analysis. Uses Arrow's compute kernels for built-in metrics to achieve near-native performance.","intents":["Understand dataset composition (size, feature distributions, missing values) before training","Identify data quality issues (missing values, outliers, class imbalance) automatically","Compute dataset statistics for documentation and reproducibility","Monitor dataset changes across versions to detect unexpected shifts"],"best_for":["Data scientists performing exploratory data analysis","ML engineers monitoring data quality in production pipelines","Teams documenting datasets for reproducibility and sharing"],"limitations":["Computing statistics on very large datasets (>100GB) requires multiple passes and can be slow","Custom metric functions must be serializable and cannot depend on external state","No automatic outlier detection — requires manual threshold specification","Cached statistics become stale if dataset is modified; manual cache invalidation required"],"requires":["Python 3.7+","Hugging Face datasets library","Optional: matplotlib/seaborn for visualization"],"input_types":["Dataset objects","Column names for statistics computation","Custom metric functions (optional)","Visualization preferences"],"output_types":["Dictionary of statistics (counts, distributions, missing rates)","Matplotlib/Plotly visualizations","Pandas DataFrames for tabular statistics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_7","uri":"capability://data.processing.analysis.dataset.interleaving.and.concatenation.with.automatic.schema.alignment","name":"dataset interleaving and concatenation with automatic schema alignment","description":"Combines multiple datasets into a single dataset using interleaving (round-robin mixing) or concatenation (sequential joining) with automatic schema alignment and type coercion. Handles datasets with different column sets by padding missing columns with null values or dropping unmatched columns. Supports weighted interleaving to control the proportion of examples from each source dataset. The system validates schema compatibility and provides detailed error messages for mismatches.","intents":["Combine multiple datasets from different sources (e.g., Wikipedia + Common Crawl) into a single training dataset","Mix datasets with different label distributions to balance class representation","Merge datasets with slightly different schemas by automatically aligning columns","Create multi-source datasets with controlled sampling ratios from each source"],"best_for":["ML practitioners building large-scale training datasets from multiple sources","Teams combining public and proprietary datasets with schema alignment","Researchers studying the effects of data mixture on model performance"],"limitations":["Schema alignment requires compatible types — cannot automatically convert between incompatible types (e.g., string to int)","Interleaving with unequal dataset sizes may cause uneven sampling if not carefully configured","No automatic deduplication across datasets — requires manual filtering to remove duplicates","Concatenation requires all datasets to fit in memory for metadata computation"],"requires":["Python 3.7+","Hugging Face datasets library","Multiple Dataset objects with compatible schemas"],"input_types":["List of Dataset objects","Interleaving probabilities or weights (optional)","Column mapping for schema alignment (optional)"],"output_types":["Combined Dataset object with merged schema","DatasetDict for multi-split combinations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_8","uri":"capability://automation.workflow.dataset.push.and.pull.with.hugging.face.hub.integration.for.sharing","name":"dataset push and pull with hugging face hub integration for sharing","description":"Enables one-command upload of datasets to Hugging Face Hub with automatic versioning, metadata generation, and access control. Pull functionality downloads datasets from Hub with caching and version pinning. Supports both public and private datasets with fine-grained access control. The system generates dataset cards (documentation) automatically and integrates with Hub's web interface for browsing and discovery. Uses Git-based infrastructure under the hood for efficient storage and bandwidth management.","intents":["Share datasets with the research community or team members via Hugging Face Hub","Download and cache datasets from Hub with automatic version management","Publish datasets with documentation and metadata for reproducibility","Control dataset access (public/private) and manage collaborators"],"best_for":["Researchers publishing datasets for reproducibility and community use","Teams sharing datasets across organizations with access control","Open science initiatives requiring transparent dataset sharing"],"limitations":["Hub storage is limited by account quotas — very large datasets (>100GB) may require special approval","Upload bandwidth is limited by network connection; large datasets can take hours to upload","Private datasets are only accessible to authorized users — no fine-grained row-level access control","Dataset cards require manual creation for good documentation; no automatic schema documentation generation"],"requires":["Hugging Face Hub account with dataset creation permissions","huggingface_hub library and authentication token","Internet connectivity for upload/download","Disk space for local caching"],"input_types":["Dataset objects","Dataset name and description","Access level (public/private)","Dataset card content (markdown)"],"output_types":["Hub dataset URL","Dataset identifier for loading (e.g., 'username/dataset-name')","Versioned dataset references with commit hashes"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hugging-face-datasets__cap_9","uri":"capability://data.processing.analysis.dataset.filtering.and.sampling.with.complex.query.expressions","name":"dataset filtering and sampling with complex query expressions","description":"Provides a query language for filtering datasets based on complex conditions (e.g., 'length > 100 AND label == \"positive\"') with support for string matching, numerical comparisons, and logical operators. Sampling utilities enable random sampling, stratified sampling, and deterministic sampling based on hashing. Filters are applied lazily using Arrow's compute kernels for efficient execution without materializing filtered data. Supports both simple column-based filters and custom Python functions.","intents":["Filter datasets to keep only examples matching specific criteria (e.g., minimum text length, specific labels)","Sample a subset of a large dataset for quick experimentation without loading the full dataset","Create balanced subsets by sampling equal numbers from each class","Remove outliers or low-quality examples based on computed metrics"],"best_for":["Data scientists iterating on dataset composition during model development","ML engineers creating balanced subsets for evaluation","Researchers studying the effects of dataset filtering on model performance"],"limitations":["Complex filter expressions may be slow on very large datasets (>100GB) without proper indexing","Custom filter functions cannot depend on external state or random number generators","No automatic index creation for frequently-filtered columns — all filters require full table scans","Sampling without replacement requires materializing the entire dataset to compute probabilities"],"requires":["Python 3.7+","Hugging Face datasets library","Optional: pandas for advanced filtering operations"],"input_types":["Dataset objects","Filter expressions (strings or Python functions)","Sampling ratios or counts","Random seed for reproducibility"],"output_types":["Filtered Dataset objects","Sampled Dataset objects","Indices of matching examples (optional)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","Internet connectivity for initial dataset download from Hugging Face Hub","Disk space equal to dataset size for local caching","PyArrow library (automatically installed as dependency)","Hugging Face datasets library","For distributed execution: Apache Spark or Ray (optional)","Optional: markdown editor for manual card editing","PyArrow for Arrow/Parquet support","pandas for CSV/JSON parsing (automatically installed)","Optional: sqlalchemy for database connections, s3fs for S3 access"],"failure_modes":["Streaming performance degrades with high-latency network connections (>500ms RTT)","No built-in compression for cached datasets — disk usage mirrors raw dataset size","Arrow format conversion adds 5-15% overhead on first load compared to raw binary formats","Cache invalidation requires manual deletion or version-based key rotation","Custom map functions must be serializable (pickle-compatible) — lambdas and closures may fail in distributed settings","Transformation execution is single-threaded by default; parallelization requires explicit batching configuration","No automatic type inference for map outputs — output schema must be manually specified for complex transformations","Lazy evaluation means errors in transformations only surface when data is accessed, not at definition time","Automatic card generation produces basic templates — manual editing required for comprehensive documentation","No automatic bias detection or limitation identification — requires manual specification","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.25,"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":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=hugging-face-datasets","compare_url":"https://unfragile.ai/compare?artifact=hugging-face-datasets"}},"signature":"ote+tHRGqvS+9unN94qvWIF+CxapzPRyU6u/F4YM2emDDPnCUebFcgQNCYiAjMu/ZIReLKkpzJn7zhoXQf9rDw==","signedAt":"2026-06-22T18:15:19.043Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hugging-face-datasets","artifact":"https://unfragile.ai/hugging-face-datasets","verify":"https://unfragile.ai/api/v1/verify?slug=hugging-face-datasets","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"}}