{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pubmedqa","slug":"pubmedqa","name":"PubMedQA","type":"dataset","url":"https://huggingface.co/datasets/qiaojin/PubMedQA","page_url":"https://unfragile.ai/pubmedqa","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pubmedqa__cap_0","uri":"capability://data.processing.analysis.evidence.grounded.biomedical.question.answering.with.structured.labels","name":"evidence-grounded biomedical question answering with structured labels","description":"Provides 1,000 expert-annotated QA pairs where each question-answer pair is grounded in PubMed abstract text with ternary labels (yes/no/maybe) plus long-form explanations. The dataset uses a structured format linking each answer to specific evidence spans within the source abstract, enabling models to learn evidence-based reasoning rather than pattern matching. Supports training systems that must justify clinical claims with cited research.","intents":["Train models to answer biomedical questions with evidence-based reasoning grounded in research abstracts","Evaluate whether a medical AI system can correctly identify supporting, contradicting, or inconclusive evidence for clinical claims","Build question-answering systems that must cite specific passages from scientific literature to justify answers","Benchmark clinical reasoning capabilities on real research comprehension tasks"],"best_for":["ML researchers developing biomedical QA systems and clinical decision support tools","Teams building medical AI that must demonstrate evidence-based reasoning for regulatory compliance","Academic groups benchmarking language models on scientific literature comprehension","Healthcare AI startups needing labeled training data for claim verification against research"],"limitations":["Expert annotations limited to 1,000 pairs; remaining 211,000 are artificially generated via templates, introducing potential noise and distribution shift","Questions derived only from PubMed abstracts, not full-text papers, limiting depth of evidence available for complex claims","Ternary label scheme (yes/no/maybe) may oversimplify nuanced research findings with conditional or context-dependent conclusions","No temporal metadata on abstracts, making it difficult to evaluate model robustness to evolving medical consensus","Artificial generation process not fully transparent, making it unclear how synthetic pairs differ from expert-annotated distribution"],"requires":["Python 3.7+ with Hugging Face datasets library","Internet connection to download from Hugging Face Hub (dataset ~150MB)","Familiarity with biomedical domain terminology to effectively use annotations","GPU memory for fine-tuning large language models (8GB+ recommended for BERT-scale models)"],"input_types":["text (PubMed abstracts)","text (natural language questions)","structured metadata (PMID, publication year)"],"output_types":["text (long-form explanations)","categorical labels (yes/no/maybe)","structured JSON with evidence spans and source citations"],"categories":["data-processing-analysis","biomedical-qa","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pubmedqa__cap_1","uri":"capability://data.processing.analysis.biomedical.claim.verification.against.research.literature","name":"biomedical claim verification against research literature","description":"Enables training models to assess whether a specific biomedical claim is supported, contradicted, or inconclusive based on evidence from PubMed abstracts. The dataset structures this as a claim-verification task where models must read an abstract and determine if it supports a posed claim, outputting both a categorical judgment and a textual justification. This directly supports fact-checking and claim validation workflows in medical AI systems.","intents":["Build fact-checking systems that verify medical claims against published research","Train models to identify when research evidence supports, refutes, or remains inconclusive on a clinical hypothesis","Create systems that automatically flag unsupported medical claims in clinical notes or patient education materials","Develop tools that help clinicians quickly assess whether a proposed treatment claim is backed by evidence"],"best_for":["Biomedical NLP researchers working on claim verification and fact-checking","Healthcare companies building clinical decision support systems with evidence validation","Medical misinformation detection platforms and health information verification services","Regulatory teams needing to validate marketing claims in pharmaceutical or medical device contexts"],"limitations":["Limited to claims that can be addressed by single PubMed abstracts; complex multi-study claims requiring meta-analysis are not represented","Artificial generation of 211,000 pairs may introduce systematic biases in how claims are constructed vs. real-world medical claims","No handling of temporal aspects — cannot distinguish between outdated claims and current medical consensus","Abstracts alone lack the methodological detail and limitations sections of full papers, potentially leading to overconfident claim assessments"],"requires":["Python 3.7+ with datasets library","Understanding of biomedical terminology and research methodology","Model architecture capable of sequence classification (BERT, RoBERTa, or larger LLMs)","Computational resources for fine-tuning (GPU with 8GB+ VRAM)"],"input_types":["text (biomedical claim or question)","text (PubMed abstract as evidence source)"],"output_types":["categorical label (yes/no/maybe)","text (explanation of how evidence supports or contradicts claim)"],"categories":["data-processing-analysis","safety-moderation","biomedical-verification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pubmedqa__cap_2","uri":"capability://data.processing.analysis.multi.task.learning.dataset.for.biomedical.nlp.with.mixed.annotation.quality","name":"multi-task learning dataset for biomedical nlp with mixed annotation quality","description":"Provides a large-scale dataset (211,000 total pairs) suitable for multi-task learning and transfer learning in biomedical NLP, combining 1,000 expert-validated pairs with 211,000 automatically generated pairs. The mixed quality enables training robust models that can handle both high-confidence expert annotations and noisier synthetic data, simulating real-world scenarios where labeled data is scarce but unlabeled or weakly-labeled data is abundant. Supports curriculum learning strategies where models train on expert data first, then synthetic data.","intents":["Train large biomedical language models on a mix of expert and synthetic data to improve generalization","Develop curriculum learning strategies that start with high-quality expert annotations and gradually introduce synthetic data","Build models robust to label noise and distribution shift between expert and automatically-generated examples","Create transfer learning baselines for biomedical QA that can be fine-tuned on downstream clinical tasks"],"best_for":["ML researchers exploring curriculum learning and noise-robust training in biomedical domains","Teams building foundation models for biomedical NLP with limited expert annotation budgets","Academic groups studying the effects of synthetic data quality on model performance","Organizations training domain-specific language models that must generalize across varying data quality sources"],"limitations":["No explicit quality scores or confidence estimates for synthetic pairs, making it difficult to implement principled curriculum learning","Distribution of synthetic data generation process unknown, potentially introducing systematic biases not present in expert annotations","No metadata indicating which pairs are expert-annotated vs. synthetic, requiring external tracking if selective training is desired","Scale imbalance (1,000 expert vs. 211,000 synthetic) may cause models to overfit to synthetic data patterns if not carefully weighted","No validation set explicitly designated; users must manually split data, risking data leakage if not careful"],"requires":["Python 3.7+ with PyTorch or TensorFlow","Hugging Face transformers library for pre-trained biomedical models (e.g., BioBERT, PubMedBERT)","GPU with 16GB+ VRAM for training large models on full dataset","Familiarity with curriculum learning and noise-robust training techniques"],"input_types":["text (questions and abstracts)","categorical labels (yes/no/maybe)"],"output_types":["trained model weights","evaluation metrics (accuracy, F1, etc.)","embeddings for downstream tasks"],"categories":["data-processing-analysis","memory-knowledge","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pubmedqa__cap_3","uri":"capability://text.generation.language.biomedical.reading.comprehension.with.abstractive.summarization.grounding","name":"biomedical reading comprehension with abstractive summarization grounding","description":"Supports training models to perform reading comprehension over biomedical abstracts where answers are not simple spans but require abstractive reasoning and explanation generation. Each QA pair includes a long-form explanation that synthesizes information from the abstract rather than copying text directly, training models to understand and paraphrase biomedical concepts. This enables systems that can explain research findings in natural language rather than just retrieving evidence.","intents":["Train models to generate natural language explanations of how research evidence supports or refutes medical claims","Build systems that can paraphrase and summarize biomedical research findings for non-expert audiences","Develop models that understand biomedical concepts deeply enough to explain them in different ways","Create systems that can justify clinical decisions with generated explanations grounded in research"],"best_for":["NLP researchers working on abstractive summarization and explanation generation in biomedical domains","Teams building patient education systems that must explain medical research in accessible language","Healthcare AI companies developing clinical decision support with natural language justifications","Academic groups studying how language models understand and paraphrase scientific concepts"],"limitations":["Explanations are limited to what can be derived from single abstracts; complex multi-study explanations are not represented","No explicit metrics for explanation quality (coherence, completeness, accuracy) beyond the binary yes/no/maybe label","Synthetic explanations may not reflect how domain experts would naturally explain findings, introducing distribution shift","No evaluation of whether generated explanations are clinically appropriate or safe for patient-facing applications","Abstractive nature makes automatic evaluation difficult; requires human evaluation for real-world deployment"],"requires":["Python 3.7+ with transformers and datasets libraries","Pre-trained sequence-to-sequence model (BART, T5, or biomedical variants like SciBERT)","GPU with 16GB+ VRAM for fine-tuning generative models","Evaluation framework for abstractive text quality (ROUGE, BERTScore, or human evaluation)"],"input_types":["text (biomedical question)","text (PubMed abstract)"],"output_types":["text (generated explanation)","categorical label (yes/no/maybe)"],"categories":["text-generation-language","data-processing-analysis","biomedical-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pubmedqa__cap_4","uri":"capability://data.processing.analysis.biomedical.domain.specific.benchmark.for.evaluating.language.model.reasoning","name":"biomedical domain-specific benchmark for evaluating language model reasoning","description":"Functions as a standardized benchmark for evaluating how well language models can perform evidence-based reasoning on biomedical research questions. The dataset includes a held-out test set with expert annotations, enabling reproducible evaluation of model performance on a well-defined task. Supports systematic comparison of different model architectures, training approaches, and fine-tuning strategies on a consistent biomedical reasoning task.","intents":["Benchmark language models on biomedical question answering to compare model capabilities","Evaluate whether fine-tuning on biomedical data improves model performance vs. general-purpose pre-training","Measure progress in biomedical AI research by tracking model performance improvements over time","Compare different model architectures (BERT, GPT, T5, etc.) on a standardized biomedical task"],"best_for":["ML researchers developing and comparing biomedical language models","Academic groups publishing biomedical NLP research with standardized evaluation","Healthcare AI companies evaluating models before deployment","Benchmark maintainers tracking progress in biomedical AI capabilities"],"limitations":["Test set size (1,000 expert pairs) is relatively small for robust statistical significance testing; confidence intervals may be wide","Benchmark may become saturated as models improve, limiting ability to differentiate between top-performing systems","Single-task benchmark does not capture full spectrum of biomedical reasoning (e.g., no multi-hop reasoning, temporal reasoning, or numerical reasoning)","Expert annotations may contain biases or errors that are not identified or corrected, potentially leading to unfair model evaluation","No analysis of model failure modes or error types, making it difficult to understand what aspects of biomedical reasoning models struggle with"],"requires":["Python 3.7+ with evaluation libraries (scikit-learn, seqeval)","Pre-trained language model (any BERT-compatible or GPT-compatible model)","Computational resources for inference (GPU optional but recommended)","Familiarity with standard NLP evaluation metrics (accuracy, F1, precision, recall)"],"input_types":["text (biomedical questions and abstracts)"],"output_types":["categorical predictions (yes/no/maybe)","evaluation metrics (accuracy, F1, macro-averaged scores)","error analysis and confusion matrices"],"categories":["data-processing-analysis","planning-reasoning","biomedical-benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pubmedqa__cap_5","uri":"capability://data.processing.analysis.biomedical.domain.adaptation.and.transfer.learning.evaluation","name":"biomedical domain adaptation and transfer learning evaluation","description":"Provides a benchmark for evaluating how well models trained on general-domain language understanding transfer to biomedical reasoning tasks. The dataset enables comparison of pre-trained models (BERT, GPT, etc.) versus domain-specific models (SciBERT, BioBERT) on evidence-based reasoning, measuring the performance gap and identifying which architectural choices or pre-training objectives best suit biomedical question answering.","intents":["Measure transfer learning effectiveness from general to biomedical domain","Compare domain-specific pre-trained models against general-purpose baselines","Identify which pre-training objectives (masked language modeling, citation prediction, etc.) best prepare models for biomedical reasoning"],"best_for":["NLP researchers studying domain adaptation and transfer learning","Teams deciding between general-purpose and domain-specific language models for medical applications","Academic groups developing biomedical-specific pre-trained models"],"limitations":["Evaluation limited to QA task — transfer learning effectiveness may differ for other biomedical tasks (NER, relation extraction)","Expert annotations (1,000 pairs) may be insufficient to detect fine-grained differences between models","Domain shift between pre-training data and PubMedQA may not reflect real-world clinical deployment scenarios"],"requires":["Multiple pre-trained language models (BERT, SciBERT, BioBERT, GPT-2, etc.) for comparison","Fine-tuning framework supporting multiple model architectures","Statistical significance testing to validate performance differences"],"input_types":["Pre-trained model checkpoints (HuggingFace format)","Question-abstract pairs from PubMedQA","Reference labels (yes/no/maybe + explanations)"],"output_types":["Fine-tuned model checkpoints","Performance comparison tables (accuracy, F1 across models)","Transfer learning analysis (performance gap between general and domain-specific models)"],"categories":["data-processing-analysis","biomedical-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pubmedqa__headline","uri":"capability://model.training.biomedical.question.answering.dataset","name":"biomedical question answering dataset","description":"A comprehensive dataset designed for biomedical question answering, featuring expert-annotated and artificially generated QA pairs from PubMed abstracts, ideal for training and evaluating medical AI systems on research comprehension and clinical reasoning tasks.","intents":["best biomedical question answering dataset","biomedical QA dataset for training AI","top datasets for clinical reasoning tasks","PubMed-based QA dataset for research","evaluate medical AI systems with QA datasets"],"best_for":["medical AI training","evidence-based reasoning tasks"],"limitations":["limited to biomedical domain"],"requires":["familiarity with PubMed abstracts"],"input_types":["questions"],"output_types":["yes/no/maybe answers with explanations"],"categories":["model-training","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+ with Hugging Face datasets library","Internet connection to download from Hugging Face Hub (dataset ~150MB)","Familiarity with biomedical domain terminology to effectively use annotations","GPU memory for fine-tuning large language models (8GB+ recommended for BERT-scale models)","Python 3.7+ with datasets library","Understanding of biomedical terminology and research methodology","Model architecture capable of sequence classification (BERT, RoBERTa, or larger LLMs)","Computational resources for fine-tuning (GPU with 8GB+ VRAM)","Python 3.7+ with PyTorch or TensorFlow","Hugging Face transformers library for pre-trained biomedical models (e.g., BioBERT, PubMedBERT)"],"failure_modes":["Expert annotations limited to 1,000 pairs; remaining 211,000 are artificially generated via templates, introducing potential noise and distribution shift","Questions derived only from PubMed abstracts, not full-text papers, limiting depth of evidence available for complex claims","Ternary label scheme (yes/no/maybe) may oversimplify nuanced research findings with conditional or context-dependent conclusions","No temporal metadata on abstracts, making it difficult to evaluate model robustness to evolving medical consensus","Artificial generation process not fully transparent, making it unclear how synthetic pairs differ from expert-annotated distribution","Limited to claims that can be addressed by single PubMed abstracts; complex multi-study claims requiring meta-analysis are not represented","Artificial generation of 211,000 pairs may introduce systematic biases in how claims are constructed vs. real-world medical claims","No handling of temporal aspects — cannot distinguish between outdated claims and current medical consensus","Abstracts alone lack the methodological detail and limitations sections of full papers, potentially leading to overconfident claim assessments","No explicit quality scores or confidence estimates for synthetic pairs, making it difficult to implement principled curriculum learning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":null,"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=pubmedqa","compare_url":"https://unfragile.ai/compare?artifact=pubmedqa"}},"signature":"2Tgi4GsPebmdkN1R8B1Xhc+6tQDVT/8qOPBjv5M2iCWXPXBboZ6AhZ0pGzGYLBg0lOm9kb/gYslRuH04W3NsDQ==","signedAt":"2026-06-22T11:15:50.692Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pubmedqa","artifact":"https://unfragile.ai/pubmedqa","verify":"https://unfragile.ai/api/v1/verify?slug=pubmedqa","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"}}