{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-evaluate","slug":"pypi-evaluate","name":"evaluate","type":"framework","url":"https://github.com/huggingface/evaluate","page_url":"https://unfragile.ai/pypi-evaluate","categories":["testing-quality"],"tags":["metrics","machine","learning","evaluate","evaluation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-evaluate__cap_0","uri":"capability://tool.use.integration.unified.metric.loading.from.multiple.sources.with.factory.pattern","name":"unified metric loading from multiple sources with factory pattern","description":"Implements a factory-based module loading system that dynamically discovers and imports evaluation metrics from three sources: Hugging Face Hub (as Spaces), local filesystem, or community repositories. Uses a standardized EvaluationModule base class hierarchy with lazy loading to defer instantiation until compute time, enabling version control and caching of metric definitions across distributed environments.","intents":["Load a metric by name without knowing its implementation details or source location","Switch between local and Hub-hosted metric versions without code changes","Discover available metrics and their metadata programmatically","Support custom metrics from local paths or private repositories"],"best_for":["ML engineers building evaluation pipelines across multiple projects","Teams sharing standardized metrics via Hugging Face Hub","Researchers prototyping with community-contributed evaluation modules"],"limitations":["Hub-based metrics require internet connectivity; no offline-first mode for discovery","Module loading adds ~100-500ms latency on first load due to Hub API calls and dynamic imports","No built-in version pinning mechanism — always loads latest unless explicitly specified"],"requires":["Python 3.8+","huggingface_hub library for Hub integration","Internet access for Hub-hosted metrics (optional for local-only usage)"],"input_types":["string (metric name or path)","dict (configuration parameters)"],"output_types":["EvaluationModule instance (Metric, Comparison, or Measurement subclass)"],"categories":["tool-use-integration","module-loading"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_1","uri":"capability://data.processing.analysis.distributed.metric.computation.with.caching.and.batching","name":"distributed metric computation with caching and batching","description":"Provides distributed computation infrastructure for metrics through a caching layer that stores intermediate results and supports batch processing across multiple workers. Integrates with distributed frameworks (e.g., Hugging Face Datasets) to parallelize metric computation, with automatic result aggregation and deduplication to avoid redundant calculations across runs.","intents":["Compute metrics on large datasets without recomputing unchanged portions","Parallelize metric calculation across multiple GPUs or machines","Cache metric results to speed up iterative model evaluation","Aggregate partial results from distributed workers into final scores"],"best_for":["Data scientists evaluating models on datasets with millions of examples","Teams running continuous evaluation pipelines with incremental data updates","Researchers comparing multiple model checkpoints efficiently"],"limitations":["Caching assumes deterministic metrics — non-deterministic metrics may produce stale results","Distributed computation requires explicit batching; no automatic partitioning strategy","Cache invalidation is manual; no automatic detection of metric version changes"],"requires":["Python 3.8+","datasets library for distributed computation support","Local disk space for cache storage (configurable)"],"input_types":["predictions (list, numpy array, or Hugging Face Dataset)","references (same types as predictions)","batch_size (int, optional)"],"output_types":["dict with metric scores and optional confidence intervals","cached results stored in .cache/huggingface/evaluate/"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_10","uri":"capability://automation.workflow.custom.module.creation.and.hub.publishing","name":"custom module creation and hub publishing","description":"Provides a command-line interface (evaluate-cli) and programmatic API for creating custom evaluation modules and publishing them to the Hugging Face Hub as Spaces. Scaffolds module structure with boilerplate code, documentation templates, and test files, then handles Hub authentication and deployment with automatic versioning and widget generation.","intents":["Create custom evaluation metrics tailored to specific tasks or domains","Publish metrics to the Hugging Face Hub for community sharing","Generate interactive widgets for metrics on the Hub","Version and maintain custom metrics with Hub integration"],"best_for":["Researchers publishing novel evaluation metrics","Teams building domain-specific metrics for internal use","Community contributors sharing metrics via the Hub"],"limitations":["Module scaffolding requires manual implementation of compute() method","Hub publishing requires Hugging Face account and authentication","No built-in CI/CD for testing custom modules; users must implement tests manually"],"requires":["Python 3.8+","huggingface_hub library for Hub integration","Hugging Face account with Hub write permissions","Git for version control (optional but recommended)"],"input_types":["module_name (string)","module_type (string: 'metric', 'comparison', 'measurement')","optional: description, citation, license"],"output_types":["Scaffolded module directory with compute.py, README.md, and test files","Hub Space URL after publishing"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_11","uri":"capability://search.retrieval.module.metadata.inspection.and.discovery","name":"module metadata inspection and discovery","description":"Provides inspect() and list_evaluation_modules() functions that query module metadata (description, inputs, outputs, citations) without loading the full module. Enables programmatic discovery of available metrics, comparisons, and measurements with filtering by type, task, or keyword, supporting both Hub and local module discovery.","intents":["Discover available metrics without loading them","Query metric metadata (inputs, outputs, citations) programmatically","Filter metrics by type or task","Generate documentation or metric catalogs automatically"],"best_for":["Developers building evaluation tools or dashboards","Teams documenting available metrics for their organization","Researchers exploring available evaluation approaches"],"limitations":["Metadata is static and may not reflect runtime behavior or performance characteristics","No filtering by metric properties (e.g., 'metrics that support multi-label classification')","Hub metadata requires internet connectivity; no offline discovery"],"requires":["Python 3.8+","huggingface_hub library for Hub metadata queries","Internet access for Hub module discovery (optional for local-only)"],"input_types":["optional: module_type (string: 'metric', 'comparison', 'measurement')","optional: task (string, e.g., 'text-classification')","optional: keyword (string for text search)"],"output_types":["list of dicts with module metadata (name, description, inputs, outputs, citations)","dict with single module's metadata (from inspect())"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_12","uri":"capability://tool.use.integration.integration.with.hugging.face.transformers.and.datasets","name":"integration with hugging face transformers and datasets","description":"Provides seamless integration with Hugging Face Transformers (model evaluation) and Datasets (distributed data loading) through shared APIs and automatic format conversion. Metrics accept Datasets objects directly, enabling zero-copy evaluation on partitioned datasets, and integrate with Transformers' Trainer class for automatic evaluation during training.","intents":["Evaluate Transformers models during training with automatic metric computation","Compute metrics on Hugging Face Datasets without manual data loading","Use metrics in distributed training pipelines with automatic partitioning","Integrate evaluation into Transformers Trainer workflows"],"best_for":["ML practitioners using Hugging Face Transformers for training","Teams working with Hugging Face Datasets for data management","Researchers building end-to-end NLP pipelines with Transformers"],"limitations":["Integration is one-way (evaluate → Transformers/Datasets); no reverse integration","Requires Transformers and Datasets libraries; no standalone usage for these integrations","Automatic format conversion may fail for non-standard data formats"],"requires":["Python 3.8+","transformers library (for Trainer integration)","datasets library (for distributed evaluation)","Models and datasets in Transformers/Datasets format"],"input_types":["Transformers model outputs (logits, predictions)","Hugging Face Dataset objects","Trainer compute_metrics callback"],"output_types":["dict with metric scores (compatible with Trainer)","Distributed evaluation results from partitioned Datasets"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_13","uri":"capability://automation.workflow.evaluation.suite.bundling.and.configuration.management","name":"evaluation suite bundling and configuration management","description":"Provides EvaluationSuite class for bundling multiple metrics, comparisons, and measurements into a single reusable configuration that can be saved, versioned, and shared. Suites are defined declaratively (YAML or Python) and can be instantiated with different datasets or models, enabling reproducible evaluation across projects and teams.","intents":["Define standardized evaluation suites for specific tasks or domains","Share evaluation configurations across teams and projects","Version evaluation suites alongside models and datasets","Reproduce evaluation results by reusing saved suites"],"best_for":["Teams standardizing evaluation across projects","Researchers publishing evaluation protocols with papers","Organizations maintaining evaluation standards"],"limitations":["Suite configuration is static; no dynamic metric selection based on data","No built-in validation of suite compatibility with datasets","Versioning requires manual management; no automatic version tracking"],"requires":["Python 3.8+","YAML or Python for suite definition"],"input_types":["list of metric/comparison/measurement names","optional: configuration parameters for each module","optional: metadata (description, citation, license)"],"output_types":["EvaluationSuite object with bundled modules","YAML or JSON representation of suite configuration"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_2","uri":"capability://planning.reasoning.task.specific.automated.evaluators.with.sensible.defaults","name":"task-specific automated evaluators with sensible defaults","description":"Provides high-level Evaluator classes that automatically select and combine appropriate metrics for specific ML tasks (text classification, question answering, summarization, etc.) without requiring users to manually specify metrics. Each task evaluator inherits from a base Evaluator class and implements task-specific logic for metric selection, input validation, and result aggregation based on model type and dataset characteristics.","intents":["Evaluate a model on a task without knowing which metrics are appropriate","Get a standardized evaluation report with multiple complementary metrics","Validate predictions match expected format for a task before computing metrics","Compare models on the same task using consistent metric sets"],"best_for":["ML practitioners new to a task who need guidance on evaluation","Teams standardizing evaluation across projects (e.g., all text classification uses same metrics)","Automated ML pipelines that need reproducible evaluation without manual configuration"],"limitations":["Metric selection is opinionated and may not match domain-specific requirements","No mechanism to customize metric selection per evaluator instance","Supported tasks are fixed at library release time; new tasks require library updates"],"requires":["Python 3.8+","Task-specific dependencies (e.g., transformers for NLP tasks)","Predictions and references in expected format (validated by evaluator)"],"input_types":["predictions (list of strings, integers, or structured data)","references (same format as predictions)","task_name (string, e.g., 'text-classification')"],"output_types":["dict with multiple metric scores (e.g., {'accuracy': 0.95, 'f1': 0.93, 'precision': 0.94})","optional confidence intervals and per-class breakdowns"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_3","uri":"capability://data.processing.analysis.metric.combination.and.ensemble.evaluation","name":"metric combination and ensemble evaluation","description":"Allows bundling multiple metrics into a single CombinedEvaluations instance that computes all metrics in one pass, reducing redundant data loading and enabling efficient ensemble evaluation. The combine() function accepts multiple EvaluationModule instances and orchestrates their execution with shared input caching, returning aggregated results with optional per-metric metadata.","intents":["Compute multiple metrics on the same predictions without loading data multiple times","Create custom metric suites tailored to specific evaluation needs","Reduce evaluation time by batching metric computations","Generate comprehensive evaluation reports with multiple perspectives on model performance"],"best_for":["Researchers comparing models using multiple complementary metrics","Production evaluation pipelines that need comprehensive reports","Teams defining standardized metric suites for specific domains"],"limitations":["No automatic metric selection or conflict detection (e.g., metrics with incompatible input requirements)","Combined results are returned as flat dict; no hierarchical organization of metrics","Metrics are computed sequentially, not in parallel (no parallelization within combine)"],"requires":["Python 3.8+","Two or more EvaluationModule instances to combine"],"input_types":["list of EvaluationModule instances (Metric, Comparison, or Measurement)","predictions and references (passed to all modules)"],"output_types":["dict with flattened metric results (e.g., {'accuracy': 0.95, 'f1': 0.93, 'bleu': 0.42})","optional metadata dict with per-metric computation time and version info"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_4","uri":"capability://data.processing.analysis.statistical.comparison.of.model.predictions","name":"statistical comparison of model predictions","description":"Provides Comparison modules (e.g., McNemar test, exact match comparison) that perform statistical significance testing between predictions from two or more models on the same dataset. Implements hypothesis testing with configurable significance levels and returns p-values, test statistics, and confidence intervals to determine if performance differences are statistically significant.","intents":["Determine if one model significantly outperforms another beyond random variation","Perform statistical hypothesis testing on model predictions","Generate confidence intervals for performance differences","Compare multiple model variants with rigorous statistical rigor"],"best_for":["Researchers publishing results that require statistical significance testing","Teams deciding whether to deploy a new model version based on rigorous comparison","Practitioners validating that performance improvements are not due to chance"],"limitations":["Limited set of comparison methods (~5 implemented); no custom test support","Assumes independent samples; no support for paired or stratified comparisons","Requires predictions from exactly 2 models for most tests; multi-model comparison not supported"],"requires":["Python 3.8+","scipy library for statistical distributions and hypothesis testing","Predictions from 2+ models on identical test set"],"input_types":["predictions_1 (list or array from model 1)","predictions_2 (list or array from model 2)","references (ground truth labels)","significance_level (float, default 0.05)"],"output_types":["dict with test_statistic, p_value, and confidence_interval","boolean indicating statistical significance at chosen level"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_5","uri":"capability://data.processing.analysis.dataset.and.prediction.property.measurement.without.labels","name":"dataset and prediction property measurement without labels","description":"Provides Measurement modules that analyze properties of datasets or predictions without requiring ground truth labels (e.g., toxicity detection, perplexity, word length distribution). Measurements inherit from EvaluationModule and implement compute() methods that take only predictions as input, enabling analysis of dataset characteristics and model outputs independent of task-specific evaluation.","intents":["Analyze dataset properties (e.g., toxicity, bias indicators) without labels","Measure model output characteristics (e.g., perplexity, length distribution)","Detect data quality issues or anomalies in predictions","Generate dataset statistics for documentation and analysis"],"best_for":["Data scientists auditing datasets for quality and bias","Teams monitoring model outputs for anomalies or distribution shifts","Researchers analyzing model behavior beyond task-specific metrics"],"limitations":["Measurements are task-agnostic and may not correlate with downstream performance","No built-in thresholds or alerts for anomalous values","Some measurements (e.g., toxicity) depend on external models and may have latency overhead"],"requires":["Python 3.8+","Measurement-specific dependencies (e.g., transformers for toxicity detection)","Predictions only (no references required)"],"input_types":["predictions (list of strings, integers, or structured data)","optional: references (ignored by measurement modules)"],"output_types":["dict with measurement results (e.g., {'toxicity': 0.15, 'perplexity': 45.2})","optional: per-sample scores or distribution statistics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_6","uri":"capability://data.processing.analysis.classification.specific.metrics.with.multi.class.and.multi.label.support","name":"classification-specific metrics with multi-class and multi-label support","description":"Implements a suite of classification metrics (accuracy, precision, recall, F1, confusion matrix) with built-in support for binary, multi-class, and multi-label classification scenarios. Each metric is a Metric subclass that handles different label formats (integers, strings, one-hot encodings) and averaging strategies (macro, micro, weighted) automatically based on input shape and configuration.","intents":["Compute standard classification metrics (accuracy, F1, precision, recall) for any classification task","Handle multi-class and multi-label classification without manual metric selection","Generate confusion matrices and per-class breakdowns","Compare classification performance across different averaging strategies"],"best_for":["ML practitioners evaluating classification models","Teams standardizing classification evaluation across projects","Researchers comparing classification approaches with standard metrics"],"limitations":["Assumes predictions and references are in compatible formats; no automatic format conversion","Confusion matrix computation can be memory-intensive for high-cardinality labels (>1000 classes)","No support for hierarchical or structured label spaces"],"requires":["Python 3.8+","numpy and scikit-learn for metric computation","Predictions and references as integers, strings, or one-hot arrays"],"input_types":["predictions (list of integers, strings, or one-hot arrays)","references (same format as predictions)","average (string: 'micro', 'macro', 'weighted', optional)"],"output_types":["dict with metric scores (e.g., {'accuracy': 0.95, 'f1': 0.93, 'precision': 0.94, 'recall': 0.92})","optional: confusion_matrix (2D array), per_class_metrics (dict)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_7","uri":"capability://data.processing.analysis.text.generation.metrics.with.reference.based.and.reference.free.variants","name":"text generation metrics with reference-based and reference-free variants","description":"Provides text generation metrics (BLEU, ROUGE, METEOR, BERTScore, BLEURT) that measure quality of generated text against references or independently. Implements both reference-based metrics (comparing to gold-standard text) and reference-free metrics (evaluating intrinsic properties like fluency) with configurable tokenization, smoothing, and aggregation strategies.","intents":["Evaluate machine translation, summarization, and text generation models","Compare generated text to reference translations or summaries","Measure text generation quality without reference texts","Analyze generation quality at corpus and sentence levels"],"best_for":["NLP researchers evaluating machine translation and summarization","Teams building text generation systems (chatbots, summarizers, translators)","Practitioners comparing generation models with standard metrics"],"limitations":["Reference-based metrics (BLEU, ROUGE) correlate poorly with human judgment for some tasks","Reference-free metrics (BLEURT, BERTScore) require large pre-trained models, adding latency","Metrics are language-specific or require language-agnostic embeddings; limited multilingual support"],"requires":["Python 3.8+","nltk for tokenization (BLEU, ROUGE)","transformers for neural metrics (BERTScore, BLEURT)","Predictions and references as strings or lists of strings"],"input_types":["predictions (list of generated strings)","references (list of reference strings or list of lists for multiple references)","optional: language (string for language-specific tokenization)"],"output_types":["dict with metric scores (e.g., {'bleu': 0.35, 'rouge1': 0.42, 'bertscore': 0.88})","optional: per-sentence scores, corpus-level aggregations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_8","uri":"capability://data.processing.analysis.sequence.labeling.metrics.for.token.level.evaluation","name":"sequence labeling metrics for token-level evaluation","description":"Provides sequence labeling metrics (precision, recall, F1, seqeval) that evaluate token-level predictions for tasks like named entity recognition (NER) and part-of-speech tagging. Implements BIO/BIOES tag scheme handling with automatic tag parsing and entity-level evaluation, distinguishing between token-level and entity-level metrics.","intents":["Evaluate NER and sequence labeling models with entity-level metrics","Handle BIO/BIOES tag schemes automatically without manual parsing","Compute precision, recall, and F1 at both token and entity levels","Compare sequence labeling models with standard metrics"],"best_for":["NLP practitioners evaluating NER and sequence labeling models","Teams building information extraction systems","Researchers comparing sequence labeling approaches"],"limitations":["Assumes BIO/BIOES tag schemes; custom tag schemes require manual conversion","Entity-level metrics require exact span match; no partial credit for overlapping entities","No support for nested or hierarchical entity structures"],"requires":["Python 3.8+","seqeval library for entity-level evaluation","Predictions and references as lists of token-level labels"],"input_types":["predictions (list of lists of token labels)","references (list of lists of token labels)","optional: scheme (string: 'BIO', 'BIOES', default 'BIO')"],"output_types":["dict with token-level and entity-level metrics (e.g., {'token_f1': 0.92, 'entity_f1': 0.85})","optional: per-entity-type breakdown, confusion matrix"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-evaluate__cap_9","uri":"capability://data.processing.analysis.question.answering.metrics.with.span.and.f1.evaluation","name":"question answering metrics with span and f1 evaluation","description":"Provides question answering metrics (exact match, F1, BLEU) that evaluate predicted answers against reference answers using token-level overlap and span matching. Implements SQuAD-style evaluation with automatic answer normalization (lowercasing, punctuation removal) and support for multiple reference answers per question.","intents":["Evaluate reading comprehension and QA models with standard metrics","Handle multiple reference answers per question","Compute exact match and F1 scores with automatic normalization","Compare QA models using SQuAD-style evaluation"],"best_for":["NLP practitioners evaluating reading comprehension models","Teams building QA systems","Researchers comparing QA approaches with standard metrics"],"limitations":["Exact match and F1 are surface-level metrics; no semantic similarity","Normalization rules are fixed (lowercasing, punctuation removal); no customization","No support for multi-span or hierarchical answers"],"requires":["Python 3.8+","Predictions and references as strings or lists of strings"],"input_types":["predictions (list of answer strings)","references (list of answer strings or list of lists for multiple references)","optional: normalize (boolean, default True)"],"output_types":["dict with metrics (e.g., {'exact_match': 0.75, 'f1': 0.82})","optional: per-question scores, distribution of F1 values"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","huggingface_hub library for Hub integration","Internet access for Hub-hosted metrics (optional for local-only usage)","datasets library for distributed computation support","Local disk space for cache storage (configurable)","Hugging Face account with Hub write permissions","Git for version control (optional but recommended)","huggingface_hub library for Hub metadata queries","Internet access for Hub module discovery (optional for local-only)","transformers library (for Trainer integration)"],"failure_modes":["Hub-based metrics require internet connectivity; no offline-first mode for discovery","Module loading adds ~100-500ms latency on first load due to Hub API calls and dynamic imports","No built-in version pinning mechanism — always loads latest unless explicitly specified","Caching assumes deterministic metrics — non-deterministic metrics may produce stale results","Distributed computation requires explicit batching; no automatic partitioning strategy","Cache invalidation is manual; no automatic detection of metric version changes","Module scaffolding requires manual implementation of compute() method","Hub publishing requires Hugging Face account and authentication","No built-in CI/CD for testing custom modules; users must implement tests manually","Metadata is static and may not reflect runtime behavior or performance characteristics","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.45,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"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":"2026-05-03T15:20:15.343Z","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=pypi-evaluate","compare_url":"https://unfragile.ai/compare?artifact=pypi-evaluate"}},"signature":"GkxlE+uD5VkfMv53OfSbMYNjg3adYGxnd4yMSR6iNpQy+p9gxQcuUx0P43UnixK2k+yevQRxoss0KUAvNSJwDQ==","signedAt":"2026-06-15T15:21:33.820Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-evaluate","artifact":"https://unfragile.ai/pypi-evaluate","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-evaluate","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"}}