{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"spacy","slug":"spacy","name":"spaCy","type":"framework","url":"https://spacy.io","page_url":"https://unfragile.ai/spacy","categories":["frameworks-sdks","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"spacy__cap_0","uri":"capability://automation.workflow.declarative.pipeline.composition.for.nlp.workflows","name":"declarative pipeline composition for nlp workflows","description":"Constructs NLP processing pipelines by declaratively composing named components (tagger, parser, NER, textcat, etc.) in a TOML-based `.cfg` configuration file with no hidden defaults. Each component processes Doc objects sequentially, enabling reproducible, version-controlled NLP workflows. Configuration specifies component order, hyperparameters, batch sizes, and GPU allocation, making training runs fully transparent and auditable.","intents":["I want to build a reproducible NLP pipeline that I can version control and share with my team","I need to experiment with different component orderings and settings without rewriting Python code","I want to ensure my production NLP system has no hidden defaults or magic behavior"],"best_for":["teams building production NLP systems requiring reproducibility","researchers experimenting with component combinations","developers migrating from ad-hoc NLP scripts to structured pipelines"],"limitations":["Configuration is spaCy-specific (TOML format); pipelines cannot be easily ported to other frameworks","Custom components must implement spaCy's component interface; tight coupling to Doc/Token/Span object model","No built-in version control for config changes; requires external Git/DVC integration for experiment tracking"],"requires":["Python 3.8+","spaCy 3.0+ (config system introduced in v3.0)","TOML-compatible text editor or IDE"],"input_types":["TOML configuration files","Python component definitions"],"output_types":["trained spaCy pipeline model","serialized .cfg file"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_1","uri":"capability://data.processing.analysis.multi.language.linguistic.analysis.with.pre.trained.pipelines","name":"multi-language linguistic analysis with pre-trained pipelines","description":"Provides 84 pre-trained statistical and transformer-based pipelines across 25 languages, enabling immediate tokenization, POS tagging, dependency parsing, lemmatization, and NER without training. Pipelines are language-specific (e.g., `en_core_web_sm`, `de_core_news_md`) and optimized for speed via Cython-based tokenization and efficient memory management. Supports both CPU-based statistical models and GPU-accelerated transformer models (BERT, etc.) for higher accuracy.","intents":["I need to quickly extract entities and parse syntax from text in multiple languages without training models","I want to tokenize and analyze text in 25+ languages with production-grade accuracy","I need to choose between speed (statistical models) and accuracy (transformer models) for my language"],"best_for":["teams building multilingual information extraction systems","developers needing immediate NLP capabilities without model training","organizations processing text in non-English languages at scale"],"limitations":["Pre-trained models are fixed; custom domain adaptation requires fine-tuning or training from scratch","Transformer models add 2-5x latency vs statistical models; GPU required for acceptable throughput","Language coverage is 25 languages; low-resource languages not supported","Model accuracy varies by language and domain; no domain-specific pre-trained models provided"],"requires":["Python 3.8+","spaCy 3.0+","Pre-trained model download (50MB-500MB per model)","GPU optional but recommended for transformer models"],"input_types":["raw text strings","Unicode text in 25+ languages"],"output_types":["Doc objects with tokenization, POS tags, dependency trees, lemmas, NER spans","structured linguistic annotations"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_10","uri":"capability://data.processing.analysis.span.categorization.for.multi.span.classification","name":"span categorization for multi-span classification","description":"Categorizes arbitrary text spans (not just named entities) into user-defined categories via a trainable span categorization component. Unlike NER which identifies entity boundaries, span categorization assumes span boundaries are known (e.g., from NER or manual annotation) and assigns categories to spans. Supports overlapping spans and multiple categories per span. Enables tasks like aspect-based sentiment analysis, attribute extraction, or fine-grained entity typing.","intents":["I need to classify text spans (e.g., product aspects in reviews) into categories","I want to assign multiple fine-grained categories to entities (e.g., entity type + sentiment)","I need to handle overlapping spans that NER cannot represent"],"best_for":["teams building aspect-based sentiment analysis systems","developers performing fine-grained entity typing or attribute extraction","organizations with overlapping span classification needs"],"limitations":["Requires pre-defined span boundaries (from NER, manual annotation, or heuristics); cannot discover spans","Span boundary errors propagate to categorization; depends on upstream span detection","Training requires annotated spans; more annotation effort than document-level classification","No built-in span boundary detection; must be provided externally"],"requires":["Python 3.8+","spaCy 3.0+","Pre-defined span boundaries (from NER or other source)","Labeled training data for span categories"],"input_types":["Doc objects with pre-defined spans"],"output_types":["span category labels","classification scores per category"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_11","uri":"capability://data.processing.analysis.sentence.segmentation.and.boundary.detection","name":"sentence segmentation and boundary detection","description":"Segments text into sentences by detecting sentence boundaries (periods, question marks, exclamation marks, newlines). Uses rule-based heuristics and optional neural models for ambiguous cases (e.g., abbreviations like 'Dr.' or 'U.S.'). Sentence boundaries are marked in Doc objects, enabling downstream components to process sentences independently. Supports custom sentence segmentation rules via component configuration.","intents":["I need to split text into sentences for sentence-level processing (e.g., sentiment per sentence)","I want to handle edge cases like abbreviations that confuse simple period-based splitting","I need to define custom sentence boundaries for domain-specific text (e.g., legal documents)"],"best_for":["developers building sentence-level NLP pipelines","teams processing text with ambiguous sentence boundaries","organizations with domain-specific sentence segmentation needs"],"limitations":["Rule-based segmentation may fail on unusual punctuation or formatting","Neural models add latency; not suitable for real-time processing","Custom segmentation rules are language-specific; not portable across languages","No built-in handling of multi-line sentences or unusual formatting"],"requires":["Python 3.8+","spaCy 3.0+"],"input_types":["raw text strings","Doc objects"],"output_types":["Doc objects with sentence boundaries marked","iterable of sentences (Span objects)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_12","uri":"capability://automation.workflow.project.templates.and.end.to.end.workflow.scaffolding","name":"project templates and end-to-end workflow scaffolding","description":"Provides pre-built project templates for common NLP tasks (NER, text classification, relation extraction, etc.) that can be cloned and customized. Templates include directory structure, configuration files, training scripts, and evaluation code, enabling developers to start with a working end-to-end workflow rather than building from scratch. Templates are version-controlled and can be extended with custom components or data.","intents":["I want to quickly set up a complete NLP project structure without designing it from scratch","I need a reference implementation of a common NLP task (NER, classification) to learn from","I want to standardize project structure across my team"],"best_for":["teams standardizing NLP project structure","developers new to spaCy wanting reference implementations","organizations building multiple similar NLP systems"],"limitations":["Templates are generic; customization required for domain-specific needs","Limited template variety; only common tasks covered (specific templates unknown from documentation)","Templates may become outdated with spaCy version updates","No built-in template discovery or marketplace; templates must be found via documentation"],"requires":["Python 3.8+","spaCy 3.0+","Git (for cloning templates)"],"input_types":["template selection"],"output_types":["project directory with configuration, scripts, and documentation"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_13","uri":"capability://data.processing.analysis.visualization.of.linguistic.annotations","name":"visualization of linguistic annotations","description":"Provides built-in visualizers for displaying linguistic annotations (dependency trees, named entities, text classifications) in interactive HTML or Jupyter notebooks. Visualizers render Doc objects with color-coded entities, dependency arcs, and annotations, enabling debugging and explanation of model predictions. Supports custom styling and filtering of visualizations.","intents":["I need to visualize extracted entities and dependencies to debug my NLP pipeline","I want to explain model predictions to non-technical stakeholders via visualizations","I need to inspect linguistic annotations in Jupyter notebooks during development"],"best_for":["developers debugging NLP pipelines","teams explaining model predictions to stakeholders","researchers analyzing linguistic patterns"],"limitations":["Visualizations are static or interactive HTML; not suitable for real-time monitoring","Large documents may produce cluttered visualizations; no built-in filtering or summarization","Custom styling requires HTML/CSS knowledge; limited built-in styling options","Visualizations are for inspection only; no built-in annotation editing"],"requires":["Python 3.8+","spaCy 3.0+","Jupyter notebook (optional, for interactive visualization)"],"input_types":["Doc objects with annotations"],"output_types":["interactive HTML visualizations","Jupyter notebook displays"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_14","uri":"capability://automation.workflow.model.packaging.and.deployment","name":"model packaging and deployment","description":"Packages trained spaCy pipelines as distributable Python packages (wheels, tarballs) that can be installed via pip. Enables versioning, dependency management, and easy deployment to production environments. Packaged models include all trained components, configuration, and metadata; can be installed as `pip install spacy-model-name` and loaded via `spacy.load()`. Supports model versioning and compatibility checking.","intents":["I need to package my trained NLP model for distribution to other teams or production","I want to version my models and track which model version is deployed","I need to ensure model dependencies are managed correctly in production"],"best_for":["teams deploying NLP models to production","organizations distributing models across teams","developers managing model versioning and updates"],"limitations":["Packaging is Python-specific; models cannot be deployed to non-Python environments without conversion","Model size can be large (50MB-500MB+); may require optimization for edge deployment","No built-in model compression or quantization; full models must be deployed","Dependency management is Python-only; cannot manage external dependencies (e.g., C libraries)"],"requires":["Python 3.8+","spaCy 3.0+","setuptools (for packaging)"],"input_types":["trained spaCy pipeline"],"output_types":["distributable Python package (wheel, tarball)","pip-installable model"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_15","uri":"capability://tool.use.integration.llm.integration.for.few.shot.and.zero.shot.tasks","name":"llm-integration-for-few-shot-and-zero-shot-tasks","description":"Integrates large language models (via spacy-llm package) for few-shot and zero-shot NLP tasks without requiring training data. LLMs are used as components in the pipeline, enabling tasks like entity extraction, text classification, and relation extraction using natural language prompts instead of labeled training data.","intents":["I need to perform NLP tasks without labeled training data using LLM prompting","I want to extract domain-specific entities or relations using few-shot examples","I need to quickly prototype NLP systems without the overhead of data annotation and model training"],"best_for":["rapid prototyping of NLP systems without labeled data","domain-specific tasks where labeled data is expensive to obtain","teams with LLM API access (OpenAI, Anthropic, etc.) and budget for API calls"],"limitations":["LLM inference is slow (1-5 seconds per document) compared to pretrained models (10-100ms)","LLM API costs scale with document volume — expensive for large-scale processing","LLM outputs are less structured than trained models — require post-processing and validation","LLM behavior is non-deterministic — same input may produce different outputs","spacy-llm package is separate and less mature than core spaCy","Requires API key for LLM provider (OpenAI, Anthropic, etc.)"],"requires":["Python 3.8+","spaCy 3.0+","spacy-llm package","API key for LLM provider (OpenAI, Anthropic, Cohere, etc.)","Internet connection for LLM API calls"],"input_types":["Doc object or raw text"],"output_types":["Doc object with LLM-generated annotations","structured outputs from LLM prompts"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_16","uri":"capability://data.processing.analysis.multilingual.support.across.75.languages","name":"multilingual-support-across-75-languages","description":"Provides pretrained models and language-specific components for 75+ languages, enabling NLP pipelines to process text in diverse languages with language-specific tokenization, POS tagging, parsing, and NER. Language selection is automatic based on model choice or explicit in pipeline configuration.","intents":["I need to process text in multiple languages with language-specific NLP components","I want to build a multilingual information extraction system","I need language-specific tokenization and morphological analysis"],"best_for":["multilingual NLP systems processing text in 75+ languages","international organizations processing text in multiple languages","teams building language-agnostic information extraction pipelines"],"limitations":["Not all languages have equal model quality — some languages have fewer training examples","Language detection is not built-in — requires external language detection library","Some languages lack certain components (e.g., morphological analysis not available for all languages)","Transformer-based models for all languages require significant disk space (multiple GBs)"],"requires":["Python 3.8+","spaCy 3.0+","Language models for target languages (e.g., en_core_web_sm, de_core_news_sm, fr_core_news_sm)"],"input_types":["text in any of 75+ supported languages"],"output_types":["Doc objects with language-specific annotations"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_2","uri":"capability://data.processing.analysis.trainable.named.entity.recognition.with.custom.entity.types","name":"trainable named entity recognition with custom entity types","description":"Implements a trainable NER component that learns to identify and classify custom entity types from annotated text. Uses a neural network architecture (Thinc-based) trained via the configuration system with configurable batch sizes, learning rates, and dropout. Supports both statistical models and transformer-based models; enables users to define arbitrary entity types beyond pre-trained categories (e.g., custom 'PRODUCT', 'COMPETITOR' types). Training requires annotated data in spaCy's JSON format or via the Prodigy annotation tool.","intents":["I need to extract domain-specific entities (e.g., drug names, chemical compounds) not covered by pre-trained models","I want to train a custom NER model on my annotated dataset without writing neural network code","I need to combine pre-trained entity recognition with custom entity types in a single pipeline"],"best_for":["teams with domain-specific entity extraction needs (legal, medical, finance)","developers building information extraction systems with custom entity types","organizations with annotated training data wanting to avoid external ML platforms"],"limitations":["Requires annotated training data (minimum 100-200 examples per entity type for reasonable accuracy)","Training time scales with dataset size; no built-in active learning or data augmentation","Entity boundaries must be exact in training data; fuzzy matching not supported","No built-in cross-lingual transfer; models must be trained per language","Overfitting risk on small datasets; requires careful hyperparameter tuning and validation"],"requires":["Python 3.8+","spaCy 3.0+","Annotated training data in spaCy JSON format or Prodigy tool","GPU recommended for large datasets (100k+ examples)"],"input_types":["annotated text in spaCy JSON format","raw text (for inference only)"],"output_types":["trained NER model component","Doc objects with custom entity spans and labels"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_3","uri":"capability://data.processing.analysis.dependency.parsing.and.syntactic.analysis","name":"dependency parsing and syntactic analysis","description":"Performs dependency parsing to extract grammatical relationships (subject-verb-object, modifiers, etc.) from sentences, producing a directed acyclic graph of syntactic dependencies. Uses a transition-based neural parser trained via the configuration system; outputs include head tokens, dependency labels (nsubj, dobj, etc.), and subtree information. Enables syntactic tree visualization and programmatic access to sentence structure for downstream NLP tasks like relation extraction or semantic analysis.","intents":["I need to extract grammatical relationships (who did what to whom) from sentences for information extraction","I want to analyze sentence structure to identify subjects, objects, and modifiers programmatically","I need to visualize dependency trees for debugging or explaining NLP model behavior"],"best_for":["developers building relation extraction systems","teams analyzing sentence structure for semantic understanding","researchers studying syntactic patterns in text"],"limitations":["Accuracy depends on language and domain; out-of-domain text may have lower parsing accuracy","Projective parsing assumption may not hold for all languages (e.g., some non-European languages)","No built-in handling of ellipsis or complex nested structures","Parsing latency adds ~5-10ms per sentence; not suitable for real-time streaming"],"requires":["Python 3.8+","spaCy 3.0+","Pre-trained parser model (included in standard pipelines)"],"input_types":["tokenized text (Doc objects)","raw text (auto-tokenized)"],"output_types":["Doc objects with head tokens and dependency labels","syntactic tree structures","dependency visualizations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_4","uri":"capability://data.processing.analysis.text.classification.with.multi.label.and.multi.class.support","name":"text classification with multi-label and multi-class support","description":"Provides a trainable text classification component supporting both multi-class (one label per document) and multi-label (multiple labels per document) scenarios. Uses a neural network architecture trained via the configuration system with configurable thresholds, class weights, and loss functions. Enables classification at document or span level; integrates with the pipeline to classify entire documents or specific text spans. Supports both statistical and transformer-based models.","intents":["I need to classify documents into categories (sentiment, topic, intent) without using external ML platforms","I want to assign multiple labels to documents (e.g., tags) in a single model","I need to classify text spans or sentences within documents, not just whole documents"],"best_for":["teams building content moderation or sentiment analysis systems","developers classifying documents by topic or intent","organizations with labeled training data wanting to avoid cloud ML services"],"limitations":["Requires labeled training data; minimum 50-100 examples per class for reasonable accuracy","Class imbalance can degrade performance; requires careful data sampling or loss weighting","No built-in explanation or feature importance; black-box predictions","Multi-label threshold tuning requires manual validation; no automatic threshold optimization"],"requires":["Python 3.8+","spaCy 3.0+","Labeled training data in spaCy JSON format"],"input_types":["raw text strings","Doc objects"],"output_types":["classification scores (0-1 per class)","predicted class labels","trained textcat component"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_5","uri":"capability://tool.use.integration.llm.powered.nlp.task.execution.via.spacy.llm","name":"llm-powered nlp task execution via spacy-llm","description":"Integrates Large Language Models into spaCy pipelines via the `spacy-llm` package, enabling LLM-based task execution (NER, classification, relation extraction) without training data. Uses a modular prompting system to convert unstructured LLM responses into robust spaCy-compatible outputs (Doc objects with entities, classifications, etc.). Supports multiple LLM providers (specific providers UNKNOWN from documentation) and enables few-shot prompting for task adaptation. Eliminates need for annotated training data by leveraging LLM zero-shot or few-shot capabilities.","intents":["I want to use LLMs for NLP tasks (NER, classification) without collecting and annotating training data","I need to quickly prototype NLP systems using LLMs before deciding whether to train custom models","I want to combine LLM-based and trained components in a single pipeline"],"best_for":["teams prototyping NLP systems with limited labeled data","developers wanting to leverage LLM capabilities without fine-tuning","organizations exploring LLM-based NLP before committing to training infrastructure"],"limitations":["LLM provider support is undocumented; specific providers (OpenAI, Anthropic, etc.) unknown","LLM latency (500ms-5s per request) makes real-time processing infeasible","LLM costs scale with usage; expensive for high-volume processing","Output parsing from LLM responses is fragile; malformed responses may cause failures","No built-in caching or batching for LLM requests; requires external optimization","LLM behavior is non-deterministic; results vary across runs"],"requires":["Python 3.8+","spaCy 3.0+","spacy-llm package (separate installation)","API key for LLM provider (OpenAI, Anthropic, etc. — specific providers unknown)","Network connectivity for LLM API calls"],"input_types":["raw text strings","Doc objects"],"output_types":["Doc objects with LLM-extracted entities, classifications, or relations","structured outputs parsed from LLM responses"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_6","uri":"capability://tool.use.integration.custom.component.development.and.pipeline.extension","name":"custom component development and pipeline extension","description":"Enables developers to define custom NLP components that integrate into the spaCy pipeline via a component interface. Custom components receive Doc objects, perform arbitrary processing, and return modified Doc objects; can add custom attributes, annotations, or external API calls. Components are registered by name and configured via the `.cfg` file, enabling non-developers to enable/disable or configure custom components without code changes. Supports integration with external ML frameworks (PyTorch, TensorFlow) and APIs.","intents":["I need to add domain-specific processing (e.g., custom tokenization, external API calls) to my NLP pipeline","I want to integrate my existing ML models or APIs into a spaCy pipeline","I need to extend spaCy with custom attributes or annotations on Doc/Token/Span objects"],"best_for":["developers building specialized NLP systems with custom logic","teams integrating spaCy with external ML models or APIs","organizations with existing NLP code wanting to compose it with spaCy components"],"limitations":["Custom components must implement spaCy's component interface; tight coupling to Doc/Token/Span objects","No standardized component marketplace or discovery; custom components are not portable across projects","Component API is undocumented in provided materials; requires consulting GitHub or full documentation","Custom components add latency; no built-in performance profiling or optimization guidance","Testing custom components requires understanding spaCy's internal object model"],"requires":["Python 3.8+","spaCy 3.0+","Understanding of spaCy's Doc/Token/Span object model","Optional: PyTorch, TensorFlow, or other ML frameworks for model integration"],"input_types":["Doc objects","custom Python code"],"output_types":["modified Doc objects with custom annotations","custom component implementations"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_7","uri":"capability://automation.workflow.batch.processing.with.configurable.batch.sizes.and.gpu.acceleration","name":"batch processing with configurable batch sizes and gpu acceleration","description":"Processes multiple documents in batches via the `nlp.pipe()` method, enabling efficient processing of large document collections. Batch size is configurable in the `.cfg` file (e.g., `batch_size = 1000`); larger batches improve throughput but increase memory usage. Supports GPU acceleration for transformer-based models; automatically distributes computation across available GPUs. Enables streaming processing of large datasets without loading entire corpus into memory.","intents":["I need to process millions of documents efficiently without running out of memory","I want to leverage GPU acceleration for faster processing of large document collections","I need to tune batch size for my hardware to maximize throughput"],"best_for":["teams processing large document collections (100k+ documents)","organizations with GPU infrastructure wanting to accelerate NLP processing","developers building data pipelines for information extraction at scale"],"limitations":["Batch processing is sequential; no built-in distributed processing across multiple machines","GPU memory limits batch size; transformer models may require small batches (8-32) on consumer GPUs","No built-in progress tracking or checkpointing; long-running jobs may lose progress on failure","Batch size tuning is manual; no automatic optimization based on hardware","Statistical models don't benefit from GPU acceleration; GPU only helps transformer models"],"requires":["Python 3.8+","spaCy 3.0+","GPU optional but recommended for transformer models (CUDA 11.0+, cuDNN 8.0+)"],"input_types":["iterable of text strings","iterable of Doc objects"],"output_types":["iterable of processed Doc objects"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_8","uri":"capability://memory.knowledge.entity.linking.to.knowledge.bases","name":"entity linking to knowledge bases","description":"Links named entities (extracted by NER) to entries in external knowledge bases (e.g., Wikipedia, Wikidata, custom databases) via entity disambiguation. Uses a neural entity linker trained on entity mention-to-KB-entry pairs; performs candidate generation (retrieve potential KB entries for an entity mention) and ranking (score candidates to select best match). Enables enriching extracted entities with structured information (Wikipedia URLs, entity IDs, properties) from knowledge bases.","intents":["I need to link extracted entities to Wikipedia or Wikidata for enrichment with structured data","I want to disambiguate entity mentions (e.g., 'Apple' → company vs fruit) using a knowledge base","I need to map entities to a custom knowledge base or database"],"best_for":["teams building knowledge graph construction systems","developers enriching extracted entities with external data","organizations linking text to structured databases"],"limitations":["Requires training data (entity mention-to-KB-entry pairs); no pre-trained entity linker for custom KBs","Accuracy depends on KB coverage; entities not in KB cannot be linked","Candidate generation is expensive for large KBs; may require indexing (e.g., BM25, vector search)","Entity linking is language-specific; separate models needed per language","No built-in handling of ambiguous entities or multiple valid links"],"requires":["Python 3.8+","spaCy 3.0+","Knowledge base (Wikipedia, Wikidata, or custom)","Training data for entity linker (optional; pre-trained linkers may be available)"],"input_types":["Doc objects with extracted entities (from NER)"],"output_types":["Doc objects with entity.kb_id attributes linking to KB entries","structured entity information from KB"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__cap_9","uri":"capability://data.processing.analysis.morphological.analysis.and.lemmatization","name":"morphological analysis and lemmatization","description":"Performs morphological analysis to extract morphological features (part-of-speech, case, tense, number, etc.) and lemmatization to reduce words to their base forms. Uses a trainable lemmatizer component (rule-based or neural) configured via `.cfg` files. Morphological features are language-specific and extracted from pre-trained models or custom training. Enables downstream tasks like information extraction or text normalization that benefit from lemmatized forms.","intents":["I need to normalize text by converting words to their base forms (lemmatization) for better matching","I want to extract morphological features (tense, case, number) for linguistic analysis","I need to handle inflected forms (e.g., 'running' → 'run') in information extraction"],"best_for":["developers building text normalization pipelines","teams analyzing morphologically rich languages (German, Russian, Arabic)","researchers studying morphological patterns in text"],"limitations":["Lemmatization accuracy varies by language; morphologically complex languages may have lower accuracy","Rule-based lemmatizers are language-specific; neural lemmatizers require training data","Out-of-vocabulary words may not lemmatize correctly","Morphological features are language-specific; not all languages have rich morphological annotations"],"requires":["Python 3.8+","spaCy 3.0+","Pre-trained morphological models (included in standard pipelines)"],"input_types":["tokenized text (Doc objects)"],"output_types":["lemmatized tokens","morphological feature annotations (POS, case, tense, number, etc.)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"spacy__headline","uri":"capability://data.processing.analysis.industrial.strength.natural.language.processing.library","name":"industrial-strength natural language processing library","description":"spaCy is an industrial-strength natural language processing library for Python, designed for fast and efficient text processing with support for over 75 languages and advanced features like named entity recognition and dependency parsing.","intents":["best NLP library for Python","NLP framework for text classification","top choice for named entity recognition","fast tokenization tool for Python","best library for building chatbots"],"best_for":["text analysis","information extraction","chatbot development"],"limitations":[],"requires":[],"input_types":["text"],"output_types":["structured data","annotations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","spaCy 3.0+ (config system introduced in v3.0)","TOML-compatible text editor or IDE","spaCy 3.0+","Pre-trained model download (50MB-500MB per model)","GPU optional but recommended for transformer models","Pre-defined span boundaries (from NER or other source)","Labeled training data for span categories","Git (for cloning templates)","Jupyter notebook (optional, for interactive visualization)"],"failure_modes":["Configuration is spaCy-specific (TOML format); 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