{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-kthera--pesoz","slug":"kthera--pesoz","name":"pesoz","type":"dataset","url":"https://huggingface.co/datasets/Kthera/pesoz","page_url":"https://unfragile.ai/kthera--pesoz","categories":["model-training"],"tags":["region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-kthera--pesoz__cap_0","uri":"capability://data.processing.analysis.large.scale.portuguese.language.dataset.provisioning.for.model.training","name":"large-scale portuguese language dataset provisioning for model training","description":"Provides a curated dataset of 582,735 Portuguese language examples hosted on HuggingFace's distributed infrastructure, enabling direct integration with PyTorch DataLoader, TensorFlow tf.data pipelines, and Hugging Face Transformers training loops through the datasets library's streaming and caching mechanisms. The dataset is versioned and immutable, allowing reproducible model training across different environments and time periods.","intents":["Train Portuguese language models from scratch or fine-tune existing multilingual models on Portuguese-specific data","Evaluate model performance on Portuguese language understanding tasks without manually collecting and cleaning text data","Build Portuguese NLP applications with pre-trained models that have seen domain-specific Portuguese examples during training","Benchmark Portuguese language model capabilities against standardized datasets"],"best_for":["NLP researchers building Portuguese language models","Teams fine-tuning multilingual models for Portuguese-specific applications","Academic institutions conducting Portuguese language processing research","Companies developing Portuguese chatbots, translation systems, or text classification models"],"limitations":["Dataset composition and quality metrics not publicly documented — no transparency on data sources, filtering criteria, or potential biases","No built-in data versioning or changelog — cannot track what changed between dataset versions or rollback to previous versions","Fixed snapshot approach — cannot add new examples or update existing ones without creating entirely new dataset versions","Unknown preprocessing pipeline — unclear what tokenization, normalization, or filtering was applied to raw text","No stratification information — cannot verify if dataset is balanced across domains, genres, or linguistic phenomena"],"requires":["Python 3.7+","huggingface-hub library (pip install huggingface-hub)","datasets library (pip install datasets)","Internet connection for initial download (582,735 examples, estimated 500MB-2GB depending on format)","Disk space for cached dataset (varies by format and compression)"],"input_types":["None — dataset is consumed directly, not transformed"],"output_types":["PyArrow Table format (native HuggingFace datasets format)","Pandas DataFrame (via .to_pandas())","PyTorch Dataset objects (via .set_format('torch'))","TensorFlow Dataset objects (via .to_tf_dataset())","Raw text strings (via iteration over dataset splits)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-kthera--pesoz__cap_1","uri":"capability://data.processing.analysis.streaming.dataset.access.with.lazy.loading.and.memory.efficient.caching","name":"streaming dataset access with lazy loading and memory-efficient caching","description":"Implements HuggingFace's streaming protocol that downloads dataset examples on-demand rather than requiring full dataset materialization, using a local cache layer that persists downloaded batches to disk. This enables training on datasets larger than available GPU/CPU memory by fetching examples in real-time during epoch iteration, with automatic deduplication and resumable downloads if connection drops.","intents":["Train models on the Portuguese dataset without downloading the entire 500MB-2GB file upfront","Resume interrupted training runs without re-downloading already-cached examples","Work in memory-constrained environments (edge devices, shared compute clusters) by streaming examples on-demand","Parallelize data loading across multiple workers without duplicating the full dataset in each process"],"best_for":["Researchers with limited disk space or bandwidth constraints","Teams training on shared GPU clusters where storage is bottlenecked","Edge deployment scenarios requiring minimal local storage footprint","Iterative development workflows where full dataset download time is prohibitive"],"limitations":["First epoch is slower due to download overhead — subsequent epochs use cached data but initial pass incurs network latency","Cache invalidation is manual — no automatic detection if upstream dataset changes, requiring explicit cache clearing","Streaming requires stable internet connection — network interruptions during training can cause stalls (though resumable)","Cache location is environment-dependent — default ~/.cache/huggingface/datasets may not be writable in all deployment contexts"],"requires":["Python 3.7+","datasets>=2.0.0 library with streaming support","Internet connectivity during training (minimum bandwidth ~1-5 Mbps for real-time streaming)","Writable filesystem for cache directory (minimum 1-2GB free space for partial cache)"],"input_types":["None — streaming is transparent to user code"],"output_types":["Batched tensors (PyTorch DataLoader format)","Individual examples as dictionaries with 'text' and metadata keys"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-kthera--pesoz__cap_2","uri":"capability://data.processing.analysis.multi.format.dataset.export.and.format.conversion","name":"multi-format dataset export and format conversion","description":"Provides automatic conversion from HuggingFace's native Arrow format to multiple downstream formats (Pandas DataFrames, PyTorch tensors, TensorFlow datasets, CSV, Parquet, JSON) through the datasets library's format abstraction layer. Conversion is lazy and zero-copy where possible, materializing only the columns and rows needed for downstream tasks.","intents":["Export Portuguese dataset to CSV or Parquet for analysis in Pandas or SQL databases","Convert dataset to PyTorch tensor format for direct use in custom training loops without DataLoader wrapper","Transform dataset to TensorFlow tf.data.Dataset for integration with Keras training pipelines","Generate JSON exports for downstream NLP annotation tools or data visualization platforms"],"best_for":["Data scientists performing exploratory analysis on Portuguese text","ML engineers integrating with non-Transformers frameworks (custom PyTorch, TensorFlow, JAX)","Teams needing to share dataset in standard formats (CSV, Parquet) with non-ML stakeholders","Researchers publishing preprocessed datasets in multiple formats for reproducibility"],"limitations":["Format conversion is not lossless for all types — nested structures may flatten or lose type information in CSV export","Large dataset exports to single-file formats (JSON, CSV) can exceed filesystem limits — requires streaming export or sharding","No built-in schema validation — converted formats may have type mismatches if source data is inconsistent","Memory overhead during conversion — materializing full dataset in target format requires RAM proportional to dataset size"],"requires":["Python 3.7+","datasets library with format conversion support","Optional: pandas (for DataFrame export), torch (for PyTorch format), tensorflow (for TF format)","Sufficient disk space for target format (typically 1.5-3x source size depending on compression)"],"input_types":["HuggingFace Dataset objects in Arrow format"],"output_types":["Pandas DataFrame","PyTorch DataLoader or tensor batches","TensorFlow tf.data.Dataset","CSV files","Parquet files","JSON/JSONL files"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-kthera--pesoz__cap_3","uri":"capability://data.processing.analysis.dataset.versioning.and.reproducible.snapshot.access","name":"dataset versioning and reproducible snapshot access","description":"Maintains immutable dataset snapshots on HuggingFace Hub with version tracking through Git-based revision system, allowing researchers to pin exact dataset versions in code and reproduce results across time. Each version is identified by commit hash or tag, enabling deterministic training runs and publication-ready reproducibility without dataset drift.","intents":["Ensure published research results are reproducible by pinning exact dataset version used during training","Track dataset evolution over time and understand how model performance changes with dataset updates","Collaborate on dataset improvements while maintaining backward compatibility with existing trained models","Audit which dataset version was used in production models for compliance and debugging"],"best_for":["Academic researchers publishing papers requiring reproducible datasets","Teams maintaining production models that need to track training data provenance","Data scientists collaborating on dataset curation with version control","Organizations with regulatory requirements for data lineage and audit trails"],"limitations":["Version history is immutable — cannot modify or delete past versions, only create new ones","No automatic changelog or diff visualization — must manually track what changed between versions","Version pinning requires explicit code changes — no automatic fallback if pinned version becomes unavailable","Storage cost increases with number of versions — HuggingFace Hub has quotas on free tier (typically 50GB per dataset)"],"requires":["HuggingFace Hub account with dataset creation permissions","Git knowledge for understanding revision/commit-based versioning","datasets library with revision parameter support (>=2.0.0)","Internet connectivity to access Hub"],"input_types":["None — versioning is metadata layer"],"output_types":["Specific dataset snapshot identified by revision hash or tag"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-kthera--pesoz__cap_4","uri":"capability://search.retrieval.dataset.discovery.and.metadata.indexing.for.search.and.filtering","name":"dataset discovery and metadata indexing for search and filtering","description":"Provides searchable metadata on HuggingFace Hub including dataset name, description, tags, and download statistics, enabling discovery of Portuguese language datasets through Hub's search interface and programmatic API. Metadata is indexed and queryable, allowing filtering by language, task type, and popularity metrics without downloading datasets.","intents":["Discover Portuguese language datasets available on HuggingFace Hub for specific NLP tasks","Compare dataset popularity and community adoption through download statistics","Find related datasets for multi-task learning or ensemble approaches","Evaluate dataset quality signals (stars, citations, community feedback) before committing to training"],"best_for":["Researchers exploring available Portuguese datasets before starting projects","Teams evaluating multiple dataset options for model training","Data scientists building dataset catalogs for organizations","Community members contributing to Portuguese NLP ecosystem"],"limitations":["Metadata is manually curated — no automatic quality scoring or bias detection","Search is keyword-based — no semantic search for finding conceptually similar datasets","Download statistics are aggregate only — cannot see temporal trends or geographic distribution of users","No built-in dataset comparison tools — must manually review multiple datasets to evaluate differences"],"requires":["Internet connectivity to access HuggingFace Hub","Web browser for Hub UI, or Python with requests library for API access","No authentication required for public datasets"],"input_types":["Search queries (text strings, tags, filters)"],"output_types":["Dataset metadata (name, description, tags, stats)","Links to dataset pages and documentation","Download statistics and community metrics"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","huggingface-hub library (pip install huggingface-hub)","datasets library (pip install datasets)","Internet connection for initial download (582,735 examples, estimated 500MB-2GB depending on format)","Disk space for cached dataset (varies by format and compression)","datasets>=2.0.0 library with streaming support","Internet connectivity during training (minimum bandwidth ~1-5 Mbps for real-time streaming)","Writable filesystem for cache directory (minimum 1-2GB free space for partial cache)","datasets library with format conversion support","Optional: pandas (for DataFrame export), torch (for PyTorch format), tensorflow (for TF format)"],"failure_modes":["Dataset composition and quality metrics not publicly documented — no transparency on data sources, filtering criteria, or potential biases","No built-in data versioning or changelog — cannot track what changed between dataset versions or rollback to previous versions","Fixed snapshot approach — cannot add new examples or update existing ones without creating entirely new dataset versions","Unknown preprocessing pipeline — unclear what tokenization, normalization, or filtering was applied to raw text","No stratification information — cannot verify if dataset is balanced across domains, genres, or linguistic phenomena","First epoch is slower due to download overhead — subsequent epochs use cached data but initial pass incurs network latency","Cache invalidation is manual — no automatic detection if upstream dataset changes, requiring explicit cache clearing","Streaming requires stable internet connection — network interruptions during training can cause stalls (though resumable)","Cache location is environment-dependent — default ~/.cache/huggingface/datasets may not be writable in all deployment contexts","Format conversion is not lossless for all types — nested structures may flatten or lose type information in CSV export","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.33,"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=kthera--pesoz","compare_url":"https://unfragile.ai/compare?artifact=kthera--pesoz"}},"signature":"yWEWooyKxCwcuTeh7WP0kwiXT71DfKhTlDddm7IFYXdZh9nrpLYjy+Yrfan/2EklqD9JXh/Mdsc6w2E5pxQ5Cw==","signedAt":"2026-06-23T03:33:58.347Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kthera--pesoz","artifact":"https://unfragile.ai/kthera--pesoz","verify":"https://unfragile.ai/api/v1/verify?slug=kthera--pesoz","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"}}