{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-nyu-mll--glue","slug":"nyu-mll--glue","name":"glue","type":"dataset","url":"https://huggingface.co/datasets/nyu-mll/glue","page_url":"https://unfragile.ai/nyu-mll--glue","categories":["model-training"],"tags":["task_categories:text-classification","task_ids:acceptability-classification","task_ids:natural-language-inference","task_ids:semantic-similarity-scoring","task_ids:sentiment-classification","task_ids:text-scoring","annotations_creators:other","language_creators:other","multilinguality:monolingual","source_datasets:original","language:en","license:other","size_categories:1M<n<10M","format:parquet","modality:tabular","modality:text","library:datasets","library:pandas","library:mlcroissant","library:polars"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-nyu-mll--glue__cap_0","uri":"capability://data.processing.analysis.multi.task.nlu.benchmark.dataset.loading.and.evaluation","name":"multi-task nlu benchmark dataset loading and evaluation","description":"Provides a curated collection of 9 diverse NLU tasks (CoLA, SST-2, MRPC, QQP, STS-B, MNLI, QNLI, RTE, WNLI) with standardized train/validation/test splits, enabling researchers to evaluate language models across acceptability classification, semantic similarity, natural language inference, and sentiment analysis in a single unified framework. Integrates with HuggingFace Datasets library for streaming, caching, and batch loading with automatic schema validation and format conversion (parquet, CSV, Arrow).","intents":["Load and benchmark a pre-trained language model against multiple NLU tasks simultaneously","Compare model performance across diverse linguistic phenomena (grammaticality, entailment, paraphrase detection)","Train and validate fine-tuned models on standard splits with reproducible evaluation metrics","Analyze model behavior on specific linguistic phenomena without manual dataset curation"],"best_for":["NLP researchers evaluating language model generalization","Teams fine-tuning BERT/RoBERTa/T5 variants on standard benchmarks","Practitioners building production NLU systems requiring baseline performance validation"],"limitations":["English-only (monolingual) — no cross-lingual or multilingual variants included","Fixed task definitions and splits — cannot customize task formulations or data augmentation within the dataset itself","Some tasks have small test sets (e.g., RTE ~277 examples) limiting statistical significance for low-resource evaluation","No built-in handling of class imbalance (e.g., QQP is heavily skewed toward duplicate pairs)","Requires external metric computation libraries (scikit-learn, scipy) for detailed evaluation beyond accuracy"],"requires":["Python 3.7+","HuggingFace datasets library (>=2.0)","Sufficient disk space (~3-5 GB for full dataset with caching)","Internet connection for initial download from HuggingFace Hub"],"input_types":["text (sentence pairs, single sentences, question-answer pairs)","structured metadata (task IDs, split identifiers)"],"output_types":["PyArrow Table (streaming)","Pandas DataFrame (batch loading)","Parquet files (persistent storage)","Structured dictionaries with keys: text, label, sentence_pair, etc."],"categories":["data-processing-analysis","model-training","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_1","uri":"capability://data.processing.analysis.task.specific.train.validation.test.split.provisioning","name":"task-specific train/validation/test split provisioning","description":"Delivers pre-defined, non-overlapping data splits for each of the 9 GLUE tasks with fixed random seeds ensuring reproducibility across research groups. Splits are accessible via HuggingFace Datasets' split selection API (e.g., dataset['train'], dataset['validation']) and include balanced class distributions where applicable, with metadata tracking original source corpus provenance and annotation guidelines.","intents":["Ensure reproducible model evaluation by using standardized splits across papers and implementations","Avoid data leakage by using pre-validated train/validation/test boundaries","Compare results fairly against published baselines that used identical splits","Quickly prototype models without manual data partitioning logic"],"best_for":["Academic researchers publishing results requiring reproducibility","Benchmark leaderboard submissions (e.g., GLUE leaderboard) requiring exact split compliance","Teams implementing baseline models for comparison studies"],"limitations":["Splits are immutable — cannot customize train/val/test ratios for specific use cases","Test set labels are withheld on official leaderboard (requires submission for evaluation)","No stratified splitting by subgroup (e.g., cannot easily evaluate on demographic subsets)","Some tasks have highly imbalanced test sets (e.g., WNLI has only 34 test examples)"],"requires":["HuggingFace datasets library","Knowledge of task-specific split names (train/validation/test vary by task)"],"input_types":["task identifier (string: 'cola', 'sst2', 'mrpc', etc.)"],"output_types":["Dataset split objects with train/validation/test keys","Metadata dictionaries with split sizes and class distributions"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_2","uri":"capability://data.processing.analysis.heterogeneous.task.schema.mapping.and.normalization","name":"heterogeneous task schema mapping and normalization","description":"Abstracts away task-specific column naming and label encoding schemes (e.g., CoLA uses binary acceptability labels, MRPC uses paraphrase binary labels, STS-B uses continuous 0-5 scores) into a unified interface through HuggingFace Datasets' feature schema system. Automatically handles type conversion (string labels to integers, float scores to normalized ranges) and provides task metadata (number of classes, label names, task type) for downstream model configuration.","intents":["Write single training loop code that works across all 9 tasks without task-specific conditional logic","Automatically configure model output layers (binary vs multi-class vs regression) based on task schema","Normalize labels across tasks for meta-learning or multi-task learning experiments","Inspect task properties programmatically without manual documentation lookup"],"best_for":["Researchers building multi-task learning systems that train on multiple GLUE tasks simultaneously","Framework developers implementing task-agnostic fine-tuning pipelines","Teams automating model configuration from dataset metadata"],"limitations":["Schema normalization is read-only — cannot modify task definitions or add custom features","Some tasks have ambiguous label semantics (e.g., WNLI has only 34 examples, making class balance unclear)","Continuous-valued tasks (STS-B) require separate loss functions (MSE vs cross-entropy) — schema doesn't auto-select","No built-in handling of task-specific preprocessing (e.g., MRPC requires sentence tokenization for some metrics)"],"requires":["HuggingFace datasets library with feature schema support","Understanding of task types (classification vs regression)"],"input_types":["raw task data with task-specific column names and label encodings"],"output_types":["normalized feature dictionaries with standardized keys (text, label, etc.)","task metadata objects with num_labels, label2id mappings, task_type"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_3","uri":"capability://data.processing.analysis.efficient.streaming.and.batch.loading.with.caching","name":"efficient streaming and batch loading with caching","description":"Leverages HuggingFace Datasets' streaming architecture to load GLUE data on-demand without materializing full datasets in memory, using memory-mapped Parquet files and Arrow IPC format for zero-copy access. Implements automatic caching to disk (configurable location) after first download, enabling subsequent loads in <1 second without network I/O. Supports batch iteration with configurable batch sizes and prefetching for GPU-efficient training pipelines.","intents":["Train on GLUE without downloading full 3.9GB dataset upfront (stream-as-you-train)","Reduce training startup time by caching parsed data locally after first run","Iterate over large splits (e.g., QQP with 364K training examples) without OOM errors","Integrate with PyTorch DataLoader for efficient batching and multi-worker data loading"],"best_for":["Researchers with limited disk space or slow internet connections","Teams running distributed training requiring efficient data loading across GPUs","Practitioners iterating rapidly on model architectures without re-downloading data"],"limitations":["Streaming mode has ~50-100ms per-batch overhead vs pre-downloaded data due to network I/O buffering","Cache location is global — cannot easily maintain separate caches for different experiments","No built-in deduplication — if same task loaded multiple times, caches are separate","Streaming requires stable internet connection; offline-first workflows need pre-download","Memory-mapped access is read-only — cannot modify cached data in-place"],"requires":["HuggingFace datasets library >=2.0","Disk space for cache (3-5 GB for full dataset, or ~500MB per task)","Python 3.7+ with PyArrow support"],"input_types":["task identifier and split name"],"output_types":["PyArrow Table (streaming mode)","Pandas DataFrame (batch mode)","PyTorch IterableDataset (for DataLoader integration)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_4","uri":"capability://data.processing.analysis.task.specific.metric.computation.and.leaderboard.submission.support","name":"task-specific metric computation and leaderboard submission support","description":"Provides task-specific evaluation metrics (accuracy for CoLA/SST-2/MRPC/QQP/QNLI/RTE/WNLI, Pearson/Spearman correlation for STS-B, Matthews correlation for MNLI) through integration with HuggingFace Evaluate library. Metrics are pre-configured with task-appropriate aggregation (macro vs micro averaging, handling of missing predictions) and support leaderboard submission format validation (e.g., ensuring predictions match test set size and label space).","intents":["Compute official GLUE metrics matching published leaderboard evaluation","Validate model predictions before submission to official leaderboard","Compare model performance across tasks using task-appropriate metrics","Generate evaluation reports with per-task breakdowns and confidence intervals"],"best_for":["Researchers submitting to official GLUE leaderboard","Teams benchmarking models against published baselines","Practitioners validating model outputs match expected format before deployment"],"limitations":["Metrics are task-specific — cannot apply same metric across different tasks","No built-in confidence interval or significance testing — requires external statistical libraries","Leaderboard submission requires manual upload to official website (no automated submission API)","Test set evaluation requires external submission (labels not provided in dataset)","Some metrics (Matthews correlation) are sensitive to class imbalance — may not reflect real-world performance"],"requires":["HuggingFace evaluate library","scikit-learn for some metrics (Matthews correlation, F1)","scipy for correlation metrics (Pearson/Spearman)"],"input_types":["model predictions (integers for classification, floats for STS-B)","ground truth labels from validation/test splits"],"output_types":["metric dictionaries with task-specific keys (e.g., {'accuracy': 0.92})","formatted leaderboard submission files (TSV with predictions)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_5","uri":"capability://data.processing.analysis.source.corpus.provenance.tracking.and.annotation.metadata","name":"source corpus provenance tracking and annotation metadata","description":"Includes structured metadata for each task documenting original source corpus (e.g., SST-2 from Stanford Sentiment Treebank, MRPC from Microsoft Research Paraphrase Corpus), annotation guidelines, inter-annotator agreement scores, and data collection methodology. Metadata is accessible via dataset.info property and includes links to original papers, enabling researchers to understand data quality and potential biases without external documentation lookup.","intents":["Understand data quality and annotation reliability by inspecting inter-annotator agreement scores","Identify potential dataset biases by reviewing annotation guidelines and source corpus characteristics","Cite original data sources correctly in research papers","Assess task difficulty and annotation complexity before fine-tuning"],"best_for":["Researchers conducting meta-analyses of benchmark performance across tasks","Teams assessing dataset quality and potential biases before deployment","Practitioners understanding task difficulty for model selection"],"limitations":["Metadata is static — does not include recent bias analyses or error annotations","Inter-annotator agreement scores are aggregate-level only (no per-example confidence)","No built-in tools for analyzing annotation patterns or identifying problematic examples","Metadata is English-only and reflects original corpus characteristics, not GLUE-specific modifications"],"requires":["HuggingFace datasets library with metadata support"],"input_types":["task identifier"],"output_types":["metadata dictionaries with keys: source_corpus, annotation_guidelines, inter_annotator_agreement, original_paper_url"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_6","uri":"capability://data.processing.analysis.multi.task.learning.and.transfer.learning.dataset.composition","name":"multi-task learning and transfer learning dataset composition","description":"Enables researchers to combine multiple GLUE tasks into unified training datasets for multi-task learning experiments through HuggingFace Datasets' concatenation and interleaving APIs. Supports task-weighted sampling (e.g., oversample small tasks like RTE to balance training) and task-specific loss weighting for joint optimization. Provides utilities for task-aware batch construction (e.g., grouping examples by task type to minimize padding overhead).","intents":["Train single model on multiple GLUE tasks simultaneously to improve generalization","Oversample small tasks (RTE, WNLI) to prevent underfitting during multi-task training","Implement task-specific loss weighting to balance task contributions to gradient updates","Analyze transfer learning effects by measuring performance on held-out tasks"],"best_for":["Researchers exploring multi-task learning for NLU generalization","Teams building universal language models trained on diverse linguistic phenomena","Practitioners implementing curriculum learning with task-based scheduling"],"limitations":["Task weighting requires manual tuning — no automatic balancing algorithm provided","Concatenated datasets lose task identity unless explicitly tracked (requires custom collate functions)","No built-in support for task-specific regularization or auxiliary losses","Memory overhead of concatenating all tasks (394K+ examples) may exceed single-task training","Interleaving order affects convergence — no principled scheduling algorithm provided"],"requires":["HuggingFace datasets library with concatenate_datasets function","Custom training loop or framework support for task-aware batching"],"input_types":["list of task identifiers to combine"],"output_types":["concatenated Dataset object with task_id column for tracking","interleaved Dataset with configurable task sampling weights"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-nyu-mll--glue__cap_7","uri":"capability://data.processing.analysis.cross.task.linguistic.phenomenon.analysis.and.error.categorization","name":"cross-task linguistic phenomenon analysis and error categorization","description":"Enables systematic analysis of model behavior across tasks by providing consistent text representations and label semantics, allowing researchers to identify which linguistic phenomena (grammaticality, entailment, paraphrase, sentiment) models struggle with. Supports error analysis workflows by enabling filtering and grouping of examples by task type, label, and text properties (length, complexity) without custom parsing logic.","intents":["Identify which linguistic phenomena cause model errors (e.g., does model fail more on negation in entailment vs sentiment?)","Analyze model robustness across tasks with different linguistic properties","Conduct ablation studies by selectively removing task types and measuring impact","Generate error analysis reports showing failure patterns across tasks"],"best_for":["Researchers conducting linguistic analysis of model capabilities and limitations","Teams building interpretability tools for understanding model behavior","Practitioners debugging model failures by analyzing error patterns across tasks"],"limitations":["No built-in linguistic annotation (POS tags, parse trees, semantic roles) — requires external NLP tools","Error analysis requires manual inspection or custom visualization code","No statistical significance testing for cross-task comparisons","Task-specific phenomena (e.g., grammaticality in CoLA) cannot be easily compared across tasks"],"requires":["HuggingFace datasets library","Optional: spaCy, NLTK, or other NLP tools for linguistic annotation"],"input_types":["model predictions and ground truth labels"],"output_types":["filtered/grouped datasets by task, label, or text properties","error analysis reports with per-task failure rates"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","HuggingFace datasets library (>=2.0)","Sufficient disk space (~3-5 GB for full dataset with caching)","Internet connection for initial download from HuggingFace Hub","HuggingFace datasets library","Knowledge of task-specific split names (train/validation/test vary by task)","HuggingFace datasets library with feature schema support","Understanding of task types (classification vs regression)","HuggingFace datasets library >=2.0","Disk space for cache (3-5 GB for full dataset, or ~500MB per task)"],"failure_modes":["English-only (monolingual) — no cross-lingual or multilingual variants included","Fixed task definitions and splits — cannot customize task formulations or data augmentation within the dataset itself","Some tasks have small test sets (e.g., RTE ~277 examples) limiting statistical significance for low-resource evaluation","No built-in handling of class imbalance (e.g., QQP is heavily skewed toward duplicate pairs)","Requires external metric computation libraries (scikit-learn, scipy) for detailed evaluation beyond accuracy","Splits are immutable — cannot customize train/val/test ratios for specific use cases","Test set labels are withheld on official leaderboard (requires submission for evaluation)","No stratified splitting by subgroup (e.g., cannot easily evaluate on demographic subsets)","Some tasks have highly imbalanced test sets (e.g., WNLI has only 34 test examples)","Schema normalization is read-only — cannot modify task definitions or add custom features","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"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=nyu-mll--glue","compare_url":"https://unfragile.ai/compare?artifact=nyu-mll--glue"}},"signature":"ZN6bTAiiC/t/DQnZM7b28HX1vGq7w9mFwoqjemJJ6Gt2WQSWkovLl303HhuOZwX4EAP2Kwl00c1f8kNg/B5hCQ==","signedAt":"2026-06-19T19:10:59.632Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nyu-mll--glue","artifact":"https://unfragile.ai/nyu-mll--glue","verify":"https://unfragile.ai/api/v1/verify?slug=nyu-mll--glue","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"}}