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The dataset captures real-world search intents across diverse domains with explicit document-level provenance, enabling training of retrieval-augmented generation (RAG) systems and search-grounded reasoning models. Each record includes query text, ranked document results with relevance scores, and verified answer spans with source attribution.","intents":["Train retrieval-augmented generation models that can ground answers in web search results","Benchmark search ranking and relevance prediction systems against human-annotated ground truth","Develop question-answering systems that require multi-document evidence synthesis","Evaluate how well language models can leverage search results to answer factual queries","Build datasets for training dense retrieval models with explicit relevance judgments"],"best_for":["ML researchers developing retrieval-augmented generation (RAG) architectures","Teams building production search and QA systems requiring benchmark evaluation","Academic groups studying information retrieval and answer grounding","Organizations training domain-specific search ranking models"],"limitations":["Dataset is static snapshot of web search results at annotation time; URLs and content may become stale or unavailable","Annotation quality depends on human raters; potential for subjective answer verification across edge cases","Biased toward English-language queries and Western web sources; limited multilingual coverage","Document relevance judgments are binary or limited-scale (not fine-grained relevance gradations)","No explicit handling of temporal queries or time-sensitive information freshness"],"requires":["HuggingFace Datasets library (transformers>=4.0)","Python 3.7+","Sufficient disk space for 2.99M+ records (estimated 5-15GB depending on document text inclusion)","Internet connection for initial dataset download from HuggingFace Hub"],"input_types":["Query strings (natural language search intents)","Document URLs and snippets (web search results)","Answer text spans (ground-truth reference answers)"],"output_types":["Structured records with query-document-answer triples","Relevance labels (binary or graded)","Document ranking lists with scores","Answer span annotations with source attribution"],"categories":["data-processing-analysis","search-retrieval","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-siril-spcc--gaia__cap_1","uri":"capability://data.processing.analysis.multi.domain.search.intent.distribution.sampling","name":"multi-domain search intent distribution sampling","description":"GAIA dataset includes queries sampled across diverse domains and intent types (navigational, informational, transactional), allowing models trained on it to generalize across different search behaviors. The dataset construction process explicitly stratified sampling to ensure representation of long-tail queries and niche domains, not just high-frequency search patterns. This enables evaluation of model robustness across heterogeneous query distributions.","intents":["Evaluate whether search ranking models generalize across different query domains and intent types","Train models that handle both common and long-tail search queries effectively","Assess model performance on diverse information needs beyond mainstream topics","Build search systems that maintain quality across niche and specialized domains"],"best_for":["Researchers studying domain generalization in information retrieval","Teams building search systems for specialized verticals (medical, legal, technical)","Organizations evaluating cross-domain robustness of ranking models"],"limitations":["Domain distribution may not reflect actual search engine traffic patterns (skewed toward research-relevant domains)","Long-tail query representation is limited by annotation budget; extremely rare queries may be underrepresented","No explicit query intent labels (navigational vs informational vs transactional) in dataset structure","Domain boundaries are implicit; no explicit taxonomy of domain categories provided"],"requires":["Python 3.7+","HuggingFace Datasets library","Domain classification logic if stratified evaluation is needed"],"input_types":["Query strings across multiple domains"],"output_types":["Query-document-answer records stratified by domain","Implicit domain distribution statistics"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-siril-spcc--gaia__cap_2","uri":"capability://data.processing.analysis.human.verified.answer.grounding.with.document.attribution","name":"human-verified answer grounding with document attribution","description":"GAIA includes human-annotated ground-truth answers with explicit attribution to source documents, enabling training of models that learn to cite and ground their responses. The annotation pipeline involves multiple verification stages to ensure answer correctness and document relevance, creating a high-quality benchmark for evaluating answer grounding and hallucination reduction. Each answer is linked to specific document spans, allowing models to learn the relationship between evidence and conclusions.","intents":["Train language models to generate answers grounded in retrieved documents with explicit citations","Evaluate whether models can correctly attribute answers to source documents","Benchmark hallucination rates in retrieval-augmented generation systems","Develop metrics for measuring answer grounding quality and citation accuracy"],"best_for":["Teams building production RAG systems that require answer attribution and citation","Researchers studying hallucination reduction through grounding","Organizations evaluating trustworthiness and explainability of QA systems"],"limitations":["Answer annotations are subjective; multiple valid answers may exist but only one is annotated","Document attribution is limited to provided search results; answers requiring synthesis across multiple documents may have ambiguous grounding","No explicit confidence scores or uncertainty estimates for answer correctness","Verification process may miss subtle factual errors or outdated information in source documents"],"requires":["Python 3.7+","HuggingFace Datasets library","Ability to parse and match answer spans to document text"],"input_types":["Query strings","Document text snippets","Answer text spans"],"output_types":["Answer annotations with source document attribution","Document relevance labels","Answer span positions within documents"],"categories":["data-processing-analysis","search-retrieval","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-siril-spcc--gaia__cap_3","uri":"capability://data.processing.analysis.benchmark.evaluation.dataset.for.retrieval.augmented.generation.systems","name":"benchmark evaluation dataset for retrieval-augmented generation systems","description":"GAIA functions as a standardized benchmark for evaluating end-to-end RAG system performance, with metrics covering retrieval quality (document ranking), answer generation accuracy, and grounding correctness. The dataset enables reproducible evaluation of different retrieval strategies, ranking models, and generation approaches through a consistent evaluation framework. Researchers can measure performance across query types, document difficulty levels, and answer complexity.","intents":["Benchmark retrieval quality of different dense and sparse retrieval methods","Evaluate end-to-end RAG system performance with consistent metrics","Compare answer generation quality across different LLM backbones and prompting strategies","Measure grounding accuracy and citation correctness in generated answers","Track improvements in RAG systems over time with a fixed evaluation set"],"best_for":["ML researchers publishing RAG system improvements with standardized benchmarks","Teams evaluating commercial vs open-source retrieval and generation models","Organizations tracking RAG system performance improvements across iterations"],"limitations":["Benchmark is static; does not capture performance on emerging query types or new domains","Evaluation metrics are limited to provided annotations; no automatic metrics for answer quality","No explicit difficulty stratification; some queries may be trivial while others require complex reasoning","Benchmark results may not transfer to production systems with different document collections or retrieval infrastructure"],"requires":["Python 3.7+","HuggingFace Datasets library","Evaluation scripts or custom metric implementations","Retrieval system and LLM for end-to-end evaluation"],"input_types":["Query strings","Retrieved document rankings","Generated answers"],"output_types":["Retrieval metrics (MRR, NDCG, recall@k)","Answer accuracy metrics (EM, F1)","Grounding accuracy (citation correctness)","Comparative performance reports"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-siril-spcc--gaia__cap_4","uri":"capability://data.processing.analysis.training.data.for.dense.retrieval.and.embedding.models","name":"training data for dense retrieval and embedding models","description":"GAIA provides query-document pairs with relevance judgments suitable for training dense retrieval models (e.g., DPR, ColBERT, E5) through contrastive learning objectives. The dataset includes both positive (relevant) and negative (irrelevant) document examples for each query, enabling training of embedding models that learn to map queries and documents into a shared semantic space. The scale (2.99M records) and diversity enable training of robust, generalizable retrieval models.","intents":["Train dense retrieval models using contrastive learning with query-document pairs","Fine-tune embedding models on domain-specific search relevance patterns","Create query and document embeddings that capture semantic relevance","Develop retrieval models that generalize across diverse query types and domains"],"best_for":["ML engineers training custom dense retrieval models for production systems","Researchers developing new embedding architectures for information retrieval","Teams fine-tuning pre-trained retrieval models on domain-specific data"],"limitations":["Relevance judgments are binary or limited-scale; no fine-grained relevance gradations for training ranking losses","No explicit negative sampling strategy provided; requires custom implementation for hard negative mining","Document text may be truncated or summarized; full document context may not be available","Training on this dataset may not transfer well to retrieval tasks with different document collections or query distributions"],"requires":["Python 3.7+","PyTorch or TensorFlow for model training","HuggingFace Transformers library for pre-trained embedding models","GPU for efficient training of dense retrieval models"],"input_types":["Query strings","Document text","Relevance labels (binary or graded)"],"output_types":["Trained embedding models","Query and document embeddings","Retrieval rankings based on embedding similarity"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["HuggingFace Datasets library (transformers>=4.0)","Python 3.7+","Sufficient disk space for 2.99M+ records (estimated 5-15GB depending on document text inclusion)","Internet connection for initial dataset download from HuggingFace Hub","HuggingFace Datasets library","Domain classification logic if stratified evaluation is needed","Ability to parse and match answer spans to document text","Evaluation scripts or custom metric implementations","Retrieval system and LLM for end-to-end evaluation","PyTorch or TensorFlow for model training"],"failure_modes":["Dataset is static snapshot of web search results at annotation time; URLs and content may become stale or unavailable","Annotation quality depends on human raters; potential for subjective answer verification across edge cases","Biased toward English-language queries and Western web sources; limited multilingual coverage","Document relevance judgments are binary or limited-scale (not fine-grained relevance gradations)","No explicit handling of temporal queries or time-sensitive information freshness","Domain distribution may not reflect actual search engine traffic patterns (skewed toward research-relevant domains)","Long-tail query representation is limited by annotation budget; extremely rare queries may be underrepresented","No explicit query intent labels (navigational vs informational vs transactional) in dataset structure","Domain boundaries are implicit; no explicit taxonomy of domain categories provided","Answer annotations are subjective; multiple valid answers may exist but only one is annotated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.36,"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=siril-spcc--gaia","compare_url":"https://unfragile.ai/compare?artifact=siril-spcc--gaia"}},"signature":"oOKJBAr+mhSohsZKFNUXco9kw6hGdr6XppGz6q7HN6iG9UVOM0WTY+8DKPZbs2po+oMwhfpvo8MvAVPd8nPuDw==","signedAt":"2026-06-21T14:49:36.028Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/siril-spcc--gaia","artifact":"https://unfragile.ai/siril-spcc--gaia","verify":"https://unfragile.ai/api/v1/verify?slug=siril-spcc--gaia","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"}}