{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-salesforce--wikitext","slug":"salesforce--wikitext","name":"wikitext","type":"dataset","url":"https://huggingface.co/datasets/Salesforce/wikitext","page_url":"https://unfragile.ai/salesforce--wikitext","categories":["model-training"],"tags":["task_categories:text-generation","task_categories:fill-mask","task_ids:language-modeling","task_ids:masked-language-modeling","annotations_creators:no-annotation","language_creators:crowdsourced","multilinguality:monolingual","source_datasets:original","language:en","license:cc-by-sa-3.0","license:gfdl","size_categories:1M<n<10M","format:parquet","modality:text","library:datasets","library:dask","library:polars","library:mlcroissant","arxiv:1609.07843","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-salesforce--wikitext__cap_0","uri":"capability://data.processing.analysis.large.scale.language.modeling.pretraining.dataset.with.wikipedia.source.material","name":"large-scale language modeling pretraining dataset with wikipedia source material","description":"Provides a curated corpus of 100M+ tokens extracted from Wikipedia articles, preprocessed into train/validation/test splits optimized for causal language modeling and masked language modeling tasks. The dataset is distributed via HuggingFace Datasets library with native support for streaming, lazy loading, and multi-format export (Parquet, Arrow, CSV), enabling efficient batch processing at scale without requiring full dataset materialization in memory.","intents":["Train transformer-based language models from scratch or continue pretraining on domain-specific Wikipedia content","Benchmark masked language modeling architectures like BERT against a standardized, reproducible corpus","Evaluate language model perplexity and downstream task transfer learning on a well-established baseline dataset","Prototype and validate text generation pipelines using a clean, deduplicated Wikipedia-sourced corpus"],"best_for":["NLP researchers validating language model architectures against standardized benchmarks","ML engineers building production language models requiring reproducible, versioned training data","Teams fine-tuning pretrained models on domain-specific tasks with Wikipedia as initialization corpus","Academic institutions with limited compute budgets needing efficient, streaming-compatible datasets"],"limitations":["English-only monolingual dataset — no multilingual coverage or cross-lingual transfer capabilities","Wikipedia bias toward encyclopedic, formal writing style — poor representation of conversational, technical, or domain-specific language patterns","Fixed snapshot from specific Wikipedia dump date — does not reflect real-time Wikipedia updates or evolving language use","No built-in deduplication across Wikipedia versions or article revisions — may contain near-duplicate content","Requires external preprocessing for tokenization, vocabulary building, and sequence packing — dataset provides raw text only"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.7+","Minimum 2GB disk space for full dataset or streaming-capable network connection","PyArrow or Polars for efficient columnar processing (optional but recommended)"],"input_types":["None — dataset is self-contained; users consume it directly"],"output_types":["Text (raw Wikipedia article content)","Structured data (Parquet/Arrow columnar format with split metadata)","Tokenized sequences (after user-applied preprocessing)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-salesforce--wikitext__cap_1","uri":"capability://data.processing.analysis.train.validation.test.split.management.with.stratified.sampling","name":"train-validation-test split management with stratified sampling","description":"Automatically partitions the Wikipedia corpus into three disjoint subsets (train: ~90%, validation: ~5%, test: ~5%) with stratified sampling to ensure consistent article-level distribution across splits. The splits are deterministically generated using seeded random sampling, enabling reproducible train/eval workflows and preventing data leakage between model development and evaluation phases.","intents":["Establish reproducible train/validation/test boundaries for hyperparameter tuning and final model evaluation","Prevent accidental data leakage by ensuring article-level separation across development and test sets","Compare model performance across standardized splits to enable fair benchmarking against published baselines","Validate model generalization by evaluating on held-out test data with identical preprocessing as training"],"best_for":["ML researchers publishing results and requiring reproducible, auditable data splits","Teams implementing rigorous model evaluation protocols with separate hyperparameter tuning and final test phases","Practitioners benchmarking against published results that use the same WikiText splits"],"limitations":["Fixed splits cannot be customized per user — no support for k-fold cross-validation or stratified sampling by article category","Article-level split granularity may not prevent semantic overlap (e.g., related articles on similar topics across splits)","No temporal stratification — does not account for Wikipedia edit history or article age distribution"],"requires":["HuggingFace Datasets library with split configuration metadata","Knowledge of split names ('train', 'validation', 'test') to access via dataset.load_dataset()"],"input_types":["None — splits are predefined"],"output_types":["Partitioned text datasets (train/validation/test subsets)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-salesforce--wikitext__cap_2","uri":"capability://data.processing.analysis.streaming.compatible.lazy.loading.with.memory.efficient.batch.iteration","name":"streaming-compatible lazy loading with memory-efficient batch iteration","description":"Implements HuggingFace Datasets' streaming protocol, enabling on-the-fly data loading without downloading the full corpus. Users iterate over batches via a generator interface that fetches and caches chunks from remote storage (Hugging Face Hub CDN), supporting distributed training on clusters with limited local storage. Integrates with PyArrow and Polars for columnar processing, enabling efficient filtering, grouping, and transformation without materializing the entire dataset in memory.","intents":["Train models on large datasets without requiring local disk space or lengthy download times","Iterate rapidly on preprocessing pipelines by streaming data on-demand rather than pre-materializing","Distribute training across multiple machines with shared remote data source, avoiding redundant downloads","Process dataset subsets via filtering and sampling without loading the full corpus into memory"],"best_for":["Teams with limited local storage or GPU memory training on large-scale datasets","Distributed training setups (multi-GPU, multi-node) where local replication is inefficient","Rapid prototyping workflows requiring quick iteration without full dataset downloads","Cloud-based training environments (AWS SageMaker, GCP Vertex AI) with network-attached storage"],"limitations":["Streaming introduces network latency (~50-200ms per batch fetch) compared to local SSD reads","No random access — must iterate sequentially or use buffering to enable shuffling, adding memory overhead","Requires stable internet connection — network interruptions cause training failures unless checkpointing is implemented","Caching behavior is opaque — repeated iterations may hit network again if cache is evicted"],"requires":["HuggingFace Datasets library with streaming=True parameter support","Network connectivity to Hugging Face Hub CDN","PyArrow or Polars for efficient columnar operations (optional)"],"input_types":["None — dataset is streamed on-demand"],"output_types":["Batched text data (via generator interface)","Columnar data (Parquet/Arrow format for PyArrow/Polars processing)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-salesforce--wikitext__cap_3","uri":"capability://data.processing.analysis.multi.format.export.with.native.parquet.and.arrow.serialization","name":"multi-format export with native parquet and arrow serialization","description":"Exports dataset content to multiple columnar and row-based formats (Parquet, Arrow, CSV) via HuggingFace Datasets' native serialization layer. Parquet export enables efficient compression and columnar storage for analytics workflows, while Arrow enables zero-copy in-memory processing for PyArrow and Polars. Metadata (split information, article IDs, token counts) is preserved across formats, enabling downstream tools to reconstruct dataset provenance.","intents":["Export WikiText to Parquet for efficient storage and querying in data warehouses (Snowflake, BigQuery, Redshift)","Convert to Arrow format for zero-copy processing in PyArrow/Polars analytics pipelines","Generate CSV exports for import into custom training frameworks or non-Python ML tools","Preserve dataset metadata and versioning information across format conversions for reproducibility"],"best_for":["Data engineers integrating WikiText into data warehouses or analytics platforms","Teams using Polars or PyArrow for preprocessing and requiring efficient columnar formats","Practitioners exporting to non-Python frameworks (Java, Scala, Go) via Parquet/Arrow interoperability","Organizations requiring long-term storage with compression and schema validation"],"limitations":["CSV export loses columnar efficiency and compression — suitable only for small subsets or downstream tools requiring CSV","Parquet compression adds CPU overhead (~5-10% slower than raw Arrow) — trade-off between storage and speed","No built-in schema evolution — format exports use fixed schema; adding new columns requires re-export","Export process requires sufficient disk space for intermediate files — streaming export not available"],"requires":["HuggingFace Datasets library with export functionality","PyArrow (for Parquet/Arrow export)","Sufficient disk space for exported files (full dataset ~5-10GB in Parquet)"],"input_types":["None — dataset is exported as-is"],"output_types":["Parquet files (columnar, compressed)","Arrow files (columnar, uncompressed)","CSV files (row-based, text)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-salesforce--wikitext__cap_4","uri":"capability://data.processing.analysis.dataset.versioning.and.reproducibility.tracking.via.huggingface.hub","name":"dataset versioning and reproducibility tracking via huggingface hub","description":"Maintains immutable dataset versions on HuggingFace Hub with Git-based version control, enabling users to pin specific dataset versions in code and reproduce results across time. Each version includes metadata (creation date, preprocessing steps, source Wikipedia dump date) and is accessible via semantic versioning (e.g., 'wikitext-3.1.0'). Dataset cards document preprocessing decisions, licensing, and known limitations, enabling transparent auditing of data provenance.","intents":["Pin dataset versions in training scripts to ensure reproducibility across team members and time","Track dataset evolution and understand how preprocessing changes affect model performance","Audit data provenance for regulatory compliance (GDPR, data lineage requirements)","Compare results across different dataset versions to isolate data-driven vs. model-driven improvements"],"best_for":["Research teams publishing results and requiring auditable, reproducible data versions","Organizations with regulatory requirements for data lineage and provenance tracking","ML teams managing multiple experiments across different dataset versions","Open-source projects requiring transparent, community-auditable data sources"],"limitations":["Version pinning requires explicit specification in code — no automatic version locking like package managers","Dataset card documentation is community-maintained — accuracy and completeness vary","No built-in data validation — users must manually verify dataset integrity after version updates","Version history is immutable — cannot retroactively correct errors in published versions"],"requires":["HuggingFace Datasets library with version specification support","Knowledge of semantic versioning and dataset version names"],"input_types":["None — versioning is metadata-driven"],"output_types":["Versioned dataset snapshots with metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.7+","Minimum 2GB disk space for full dataset or streaming-capable network connection","PyArrow or Polars for efficient columnar processing (optional but recommended)","HuggingFace Datasets library with split configuration metadata","Knowledge of split names ('train', 'validation', 'test') to access via dataset.load_dataset()","HuggingFace Datasets library with streaming=True parameter support","Network connectivity to Hugging Face Hub CDN","PyArrow or Polars for efficient columnar operations (optional)","HuggingFace Datasets library with export functionality"],"failure_modes":["English-only monolingual dataset — no multilingual coverage or cross-lingual transfer capabilities","Wikipedia bias toward encyclopedic, formal writing style — poor representation of conversational, technical, or domain-specific language patterns","Fixed snapshot from specific Wikipedia dump date — does not reflect real-time Wikipedia updates or evolving language use","No built-in deduplication across Wikipedia versions or article revisions — may contain near-duplicate content","Requires external preprocessing for tokenization, vocabulary building, and sequence packing — dataset provides raw text only","Fixed splits cannot be customized per user — no support for k-fold cross-validation or stratified sampling by article category","Article-level split granularity may not prevent semantic overlap (e.g., related articles on similar topics across splits)","No temporal stratification — does not account for Wikipedia edit history or article age distribution","Streaming introduces network latency (~50-200ms per batch fetch) compared to local SSD reads","No random access — must iterate sequentially or use buffering to enable shuffling, adding memory overhead","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"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=salesforce--wikitext","compare_url":"https://unfragile.ai/compare?artifact=salesforce--wikitext"}},"signature":"Uw1/9IOGbEC04oIv2hL1SoIAZ4xQRBKoDOVo8/Nm00UMd8uBiaup7rF+2FD8BPESopzyyTWVbEeAPPW1v1RkDw==","signedAt":"2026-06-22T02:37:55.234Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/salesforce--wikitext","artifact":"https://unfragile.ai/salesforce--wikitext","verify":"https://unfragile.ai/api/v1/verify?slug=salesforce--wikitext","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"}}