{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"squad-2-0","slug":"squad-2-0","name":"SQuAD 2.0","type":"dataset","url":"https://huggingface.co/datasets/rajpurkar/squad_v2","page_url":"https://unfragile.ai/squad-2-0","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"squad-2-0__cap_0","uri":"capability://data.processing.analysis.extractive.question.answering.benchmark.with.adversarial.unanswerable.questions","name":"extractive question-answering benchmark with adversarial unanswerable questions","description":"SQuAD 2.0 provides 150,000 questions on Wikipedia articles paired with extractive answer spans, plus 50,000 adversarially-constructed unanswerable questions that appear answerable but lack supporting evidence in the passage. Models must learn to recognize when a question cannot be answered from the given context by predicting a special null token, forcing systems to develop genuine reading comprehension rather than surface-level pattern matching. The dataset uses crowdsourced question generation followed by adversarial filtering to ensure unanswerable questions are plausible but genuinely unanswerable.","intents":["Train and evaluate extractive QA models that can distinguish answerable from unanswerable questions","Benchmark reading comprehension capabilities of pre-trained language models","Develop models that know when to abstain rather than hallucinate answers","Compare model performance against human baseline (89.5% F1) on a standardized task"],"best_for":["NLP researchers developing reading comprehension models","Teams fine-tuning BERT, RoBERTa, or transformer-based QA systems","Builders evaluating whether pre-trained models can handle open-domain QA","Organizations benchmarking LLM reasoning on factual extraction tasks"],"limitations":["Extractive-only: answers must be exact spans from the passage, cannot handle paraphrased or synthesized answers","English-only dataset; cross-lingual transfer requires separate datasets","Wikipedia-biased: questions reflect Wikipedia article structure and writing style, may not generalize to other domains","Static benchmark: no temporal updates; models trained on SQuAD may overfit to specific question patterns","Unanswerable questions are synthetically adversarial, not naturally occurring; real-world unanswerable questions may differ in distribution"],"requires":["Python 3.6+ with HuggingFace datasets library","Minimum 2GB disk space for full dataset download","PyTorch or TensorFlow for model training","JSON parsing capability for data loading"],"input_types":["Wikipedia article text (context passages)","Natural language questions","Answer span indices (start/end character positions)"],"output_types":["Predicted answer span (start/end token indices)","Confidence scores for answerability","Null token prediction (unanswerable indicator)","Evaluation metrics (F1, EM, answerability accuracy)"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_1","uri":"capability://data.processing.analysis.crowdsourced.question.generation.with.quality.filtering","name":"crowdsourced question generation with quality filtering","description":"SQuAD 2.0 uses a two-stage crowdsourcing pipeline: workers first generate questions about Wikipedia passages, then independent workers verify and filter questions for quality, clarity, and answerability. The dataset includes only questions that passed inter-annotator agreement thresholds, ensuring consistent, high-quality question-answer pairs. This human-in-the-loop approach produces naturally-phrased questions that reflect how humans actually ask about text, rather than template-based or synthetic generation.","intents":["Obtain naturally-phrased questions that reflect real reading comprehension patterns","Ensure dataset quality through multi-stage human validation","Understand human question-asking behavior on factual passages","Create a gold-standard benchmark with high inter-annotator agreement"],"best_for":["Researchers building QA datasets who need quality assurance patterns","Teams validating whether crowdsourced data meets benchmark standards","Builders studying human question formulation for conversational AI"],"limitations":["Crowdsourcing introduces cultural and linguistic biases from annotator pool","Quality filtering is retrospective; some low-quality questions may pass thresholds","Annotator agreement metrics (e.g., Cohen's kappa) not fully published for all subsets","Crowdsourced questions may be simpler than expert-written questions"],"requires":["Access to crowdsourcing platform (Amazon Mechanical Turk used in original)","Quality control infrastructure for multi-stage validation","Inter-annotator agreement measurement (e.g., F1 overlap on answer spans)"],"input_types":["Wikipedia article passages (100-200 tokens)","Crowdworker-generated questions","Validation annotations from independent workers"],"output_types":["Filtered question-answer pairs","Inter-annotator agreement scores","Quality metadata (number of annotators, agreement level)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_2","uri":"capability://data.processing.analysis.adversarial.unanswerable.question.generation.and.validation","name":"adversarial unanswerable question generation and validation","description":"SQuAD 2.0 generates 50,000 unanswerable questions through a specialized crowdsourcing process: workers read a passage and a question, then write a plausible question that CANNOT be answered from that passage. These adversarially-constructed questions are then validated to ensure they are genuinely unanswerable (no answer span exists) while remaining semantically similar to answerable questions. This forces models to learn the boundary between questions that have answers in context vs. those that don't, rather than always predicting an answer span.","intents":["Train models to recognize when a question cannot be answered from given context","Evaluate whether models hallucinate answers or correctly abstain","Measure model calibration: confidence in answerable vs. unanswerable questions","Benchmark reading comprehension beyond simple span extraction"],"best_for":["Teams developing QA systems that must handle real-world unanswerable queries","Researchers studying model hallucination and abstention behavior","Builders creating conversational AI that should say 'I don't know' appropriately"],"limitations":["Adversarial questions are synthetically constructed; real-world unanswerable questions may have different linguistic patterns","Unanswerable questions may be easier to detect than naturally-occurring ones (e.g., they might use different vocabulary)","No guarantee that all unanswerable questions are equally difficult; some may be trivially identifiable","Answerability is binary; no gradation for partially-answerable questions"],"requires":["Crowdsourcing platform with quality control","Validation mechanism to confirm no answer span exists in passage","Semantic similarity measurement to ensure unanswerable questions resemble answerable ones"],"input_types":["Wikipedia passages","Answerable questions (to guide adversarial generation)","Crowdworker-generated unanswerable questions"],"output_types":["Validated unanswerable question-passage pairs","Answerability labels (binary: answerable/unanswerable)","Validation metadata (confirmation that no span matches)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_3","uri":"capability://data.processing.analysis.span.based.answer.annotation.with.character.level.indexing","name":"span-based answer annotation with character-level indexing","description":"SQuAD 2.0 represents answers as exact character-level spans within the passage (start and end character indices), enabling precise evaluation of whether models extract the correct answer substring. This span-based representation is language-agnostic and avoids tokenization ambiguities; answers are defined by their exact position in the raw text. The dataset includes multiple valid answer spans when crowdworkers identified different valid answers (e.g., 'United States' vs. 'US'), allowing flexible evaluation.","intents":["Train models to predict exact answer spans rather than free-form text generation","Evaluate answer extraction with character-level precision","Handle multiple valid answers for the same question","Enable reproducible evaluation metrics (F1, Exact Match) across different tokenizers"],"best_for":["Builders training extractive QA models (BERT-based, RoBERTa-based)","Teams evaluating span-extraction accuracy independent of tokenization","Researchers studying answer span distribution and length patterns"],"limitations":["Extractive-only: cannot handle answers that require paraphrasing or synthesis","Character-level indexing is brittle to whitespace/formatting changes in the passage","Multiple valid answers increase annotation complexity; not all valid answers may be captured","Span-based evaluation penalizes partial matches (e.g., 'United' vs. 'United States') equally"],"requires":["Character-level text indexing capability","Tokenizer-agnostic evaluation (F1/EM computed on character spans, not tokens)","Handling of multiple answer spans per question"],"input_types":["Raw passage text (with character positions preserved)","Question text","Answer span indices (start/end character positions)"],"output_types":["Predicted answer span (start/end character indices)","F1 score (token-level overlap between predicted and gold spans)","Exact Match (EM) score (binary: predicted span matches any gold span exactly)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_4","uri":"capability://data.processing.analysis.human.performance.baseline.and.leaderboard.benchmarking","name":"human performance baseline and leaderboard benchmarking","description":"SQuAD 2.0 includes a human performance baseline (89.5% F1 score) computed by measuring inter-annotator agreement: one annotator's answers are evaluated against another's using the same F1/EM metrics applied to model predictions. This human ceiling enables researchers to measure how close models are to human-level performance. The public leaderboard tracks model submissions, allowing researchers to compare their systems against state-of-the-art and identify performance gaps.","intents":["Establish a human performance ceiling to contextualize model improvements","Compare model performance against human-level reading comprehension","Track progress of QA research over time via public leaderboard","Identify which question types or passages are hardest for models vs. humans"],"best_for":["Researchers publishing QA papers who need a standardized benchmark","Teams evaluating whether their models have reached human-level performance","Builders tracking progress of pre-trained models over time"],"limitations":["Human baseline (89.5% F1) is not 100%; some questions are ambiguous even to humans","Leaderboard may suffer from overfitting: models trained specifically to maximize SQuAD 2.0 metrics may not generalize","Human baseline is computed on a specific annotator pool; different annotators might have different agreement levels","Leaderboard submissions may include ensemble models or test-time augmentation not practical for real-world deployment"],"requires":["Leaderboard submission infrastructure (HuggingFace or official SQuAD website)","Model predictions in SQuAD format (JSON with question IDs and predicted answers)","Evaluation script to compute F1 and EM metrics"],"input_types":["Model predictions (question ID → predicted answer span)","Gold annotations (question ID → list of valid answer spans)"],"output_types":["F1 score (macro-averaged across questions)","Exact Match (EM) score (percentage of questions with perfect span match)","Leaderboard ranking and performance comparison"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_5","uri":"capability://data.processing.analysis.wikipedia.passage.selection.and.preprocessing","name":"wikipedia passage selection and preprocessing","description":"SQuAD 2.0 selects 442 Wikipedia articles across diverse topics (history, science, sports, etc.) and extracts passages of 100-200 tokens from each article. Passages are preprocessed to remove formatting artifacts, preserve sentence boundaries, and ensure sufficient context for question answering. The selection process aims for topical diversity while maintaining passage quality and answerability, creating a representative corpus for reading comprehension evaluation.","intents":["Ensure dataset covers diverse topics and writing styles","Provide sufficient context (100-200 tokens) for meaningful question answering","Create a representative benchmark of encyclopedic text","Enable reproducible evaluation by fixing passage selection"],"best_for":["Researchers studying how models perform across different domains","Teams evaluating domain transfer of QA models trained on SQuAD","Builders understanding Wikipedia-specific biases in their models"],"limitations":["Wikipedia-biased: articles reflect Wikipedia's coverage (overrepresents certain topics, underrepresents others)","Passage length (100-200 tokens) may not reflect real-world document lengths","Wikipedia writing style is formal and encyclopedic; may not generalize to news, social media, or technical documentation","Article selection is static; no temporal updates or coverage of recent events"],"requires":["Wikipedia dump or API access","Text preprocessing pipeline (tokenization, sentence segmentation)","Passage extraction logic (sliding window or sentence-based selection)"],"input_types":["Wikipedia article text","Article metadata (title, topic)"],"output_types":["Preprocessed passages (100-200 tokens each)","Passage-article mappings","Topic/domain labels"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_6","uri":"capability://code.generation.editing.model.training.and.fine.tuning.pipeline.integration","name":"model training and fine-tuning pipeline integration","description":"SQuAD 2.0 is designed as a fine-tuning benchmark for pre-trained language models: the dataset format (passage + question → answer span) directly maps to transformer model architectures (e.g., BERT, RoBERTa) that predict start/end token positions. The dataset includes standard train/dev splits (130K/12K questions) enabling reproducible fine-tuning experiments. Integration with HuggingFace datasets library enables one-line loading and automatic preprocessing (tokenization, padding, batching).","intents":["Fine-tune pre-trained models (BERT, RoBERTa, ELECTRA) on reading comprehension","Evaluate model performance on a standardized task with reproducible splits","Benchmark different pre-training approaches by fine-tuning on SQuAD 2.0","Develop and compare QA model architectures using a fixed dataset"],"best_for":["NLP researchers fine-tuning transformer models","Teams benchmarking different pre-trained models (BERT vs. RoBERTa vs. ELECTRA)","Builders prototyping QA systems with minimal data engineering"],"limitations":["Fine-tuning on SQuAD 2.0 may overfit to the dataset's specific question patterns and Wikipedia domain","Models fine-tuned on SQuAD 2.0 may not generalize to other QA datasets or domains","Standard train/dev split is fixed; no cross-validation or multiple random seeds recommended in original paper","Requires GPU memory for batch training; full fine-tuning of large models (e.g., BERT-large) is computationally expensive"],"requires":["PyTorch or TensorFlow","HuggingFace transformers library (3.0+)","GPU with 12GB+ VRAM for fine-tuning large models","Python 3.6+"],"input_types":["Pre-trained model checkpoint (BERT, RoBERTa, etc.)","SQuAD 2.0 dataset (loaded via HuggingFace datasets)","Hyperparameters (learning rate, batch size, epochs)"],"output_types":["Fine-tuned model checkpoint","Evaluation metrics (F1, EM on dev set)","Training logs (loss, learning rate schedule)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__cap_7","uri":"capability://planning.reasoning.cross.lingual.and.domain.transfer.evaluation","name":"cross-lingual and domain transfer evaluation","description":"While SQuAD 2.0 itself is English-only and Wikipedia-focused, it serves as a reference benchmark for evaluating transfer learning: researchers use SQuAD 2.0 performance as a baseline to measure how well models transfer to other languages (via XQuAD, MLQA) or domains (via NewsQA, NaturalQuestions). The standardized metrics (F1, EM) and fixed splits enable reproducible transfer evaluation, allowing researchers to quantify domain shift and cross-lingual degradation.","intents":["Measure how well models trained on SQuAD 2.0 transfer to other languages","Evaluate domain shift when applying SQuAD-trained models to news or web text","Benchmark multilingual and cross-domain QA models","Identify which question types or passages are hardest to transfer"],"best_for":["Researchers studying cross-lingual NLP and domain adaptation","Teams developing multilingual QA systems","Builders evaluating whether SQuAD-trained models work in production domains"],"limitations":["SQuAD 2.0 is English-only; cross-lingual evaluation requires separate datasets (XQuAD, MLQA)","Wikipedia domain is not representative of all target domains; transfer to news/web may be poor","No built-in domain adaptation techniques; researchers must implement their own","Transfer performance depends heavily on model architecture and pre-training; no universal transfer guarantees"],"requires":["Models fine-tuned on SQuAD 2.0","Cross-lingual or domain-specific QA datasets (XQuAD, NewsQA, etc.)","Evaluation script to compute F1/EM on target datasets"],"input_types":["SQuAD 2.0-trained model","Target domain/language QA dataset"],"output_types":["F1 and EM scores on target dataset","Performance degradation vs. SQuAD 2.0 baseline","Analysis of which question types transfer well"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"squad-2-0__headline","uri":"capability://model.training.benchmark.dataset.for.extractive.question.answering","name":"benchmark dataset for extractive question answering","description":"SQuAD 2.0 is a comprehensive benchmark dataset for evaluating extractive question answering models, featuring both answerable and unanswerable questions to test model robustness and comprehension skills.","intents":["best dataset for question answering","benchmark for extractive QA models","SQuAD 2.0 for model training","datasets for evaluating reading comprehension","top datasets for NLP tasks"],"best_for":["NLP model training","evaluating QA systems"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+ with HuggingFace datasets library","Minimum 2GB disk space for full dataset download","PyTorch or TensorFlow for model training","JSON parsing capability for data loading","Access to crowdsourcing platform (Amazon Mechanical Turk used in original)","Quality control infrastructure for multi-stage validation","Inter-annotator agreement measurement (e.g., F1 overlap on answer spans)","Crowdsourcing platform with quality control","Validation mechanism to confirm no answer span exists in passage","Semantic similarity measurement to ensure unanswerable questions resemble answerable ones"],"failure_modes":["Extractive-only: answers must be exact spans from the passage, cannot handle paraphrased or synthesized answers","English-only dataset; cross-lingual transfer requires separate datasets","Wikipedia-biased: questions reflect Wikipedia article structure and writing style, may not generalize to other domains","Static benchmark: no temporal updates; models trained on SQuAD may overfit to specific question patterns","Unanswerable questions are synthetically adversarial, not naturally occurring; real-world unanswerable questions may differ in distribution","Crowdsourcing introduces cultural and linguistic biases from annotator pool","Quality filtering is retrospective; some low-quality questions may pass thresholds","Annotator agreement metrics (e.g., Cohen's kappa) not fully published for all subsets","Crowdsourced questions may be simpler than expert-written questions","Adversarial questions are synthetically constructed; real-world unanswerable questions may have different linguistic patterns","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":"2026-05-24T12:16:28.695Z","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=squad-2-0","compare_url":"https://unfragile.ai/compare?artifact=squad-2-0"}},"signature":"/GBmWf/riaHORTIfWpY5e79RyxJsTueDMpRTpeh6P01v7amzFr+KP/efp0/bxV6GzdHLYIpRIz9JnTnlZ97YDA==","signedAt":"2026-06-21T07:16:00.851Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/squad-2-0","artifact":"https://unfragile.ai/squad-2-0","verify":"https://unfragile.ai/api/v1/verify?slug=squad-2-0","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"}}