{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-stanza","slug":"pypi-stanza","name":"stanza","type":"repo","url":"https://github.com/stanfordnlp/stanza","page_url":"https://unfragile.ai/pypi-stanza","categories":["frameworks-sdks"],"tags":["natural-language-processing","nlp","natural-language-understanding","stanford-nlp","deep-learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-stanza__cap_0","uri":"capability://data.processing.analysis.multi.language.tokenization.and.sentence.segmentation.with.language.specific.rules","name":"multi-language tokenization and sentence segmentation with language-specific rules","description":"Splits raw text into sentences and tokens using language-specific neural models and rule-based segmentation. The tokenizer handles multi-word tokens (MWT) common in languages like Arabic and Czech, expanding them into individual words. It uses a two-stage approach: first identifying sentence boundaries, then tokenizing within sentences using pre-trained neural models that understand language-specific morphology and punctuation conventions.","intents":["I need to break raw text into sentences and tokens for downstream NLP processing","I want to handle multi-word token expansion for morphologically rich languages","I need accurate sentence boundary detection across 60+ languages"],"best_for":["NLP researchers working with multilingual corpora","Teams building production NLP pipelines requiring high-accuracy tokenization","Developers processing morphologically complex languages (Arabic, Czech, Turkish)"],"limitations":["Tokenization quality varies by language; less-resourced languages may have lower accuracy","Requires downloading language-specific models (50-200MB per language)","No real-time streaming tokenization; processes complete documents"],"requires":["Python 3.6+","PyTorch 1.3+","Language-specific pre-trained models downloaded via stanza.download()"],"input_types":["raw text string"],"output_types":["Document object with Sentence and Token hierarchy"],"categories":["data-processing-analysis","nlp-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_1","uri":"capability://data.processing.analysis.part.of.speech.tagging.and.morphological.feature.annotation.with.dependency.parsing","name":"part-of-speech tagging and morphological feature annotation with dependency parsing","description":"Assigns part-of-speech tags and morphological features (case, gender, number, tense, mood, etc.) to tokens using neural sequence models, then constructs syntactic dependency trees showing grammatical relationships between words. The architecture uses a BiLSTM-based tagger followed by a transition-based or graph-based dependency parser that learns to predict head-dependent relationships. Both components are trained jointly on Universal Dependencies treebanks, enabling cross-lingual transfer and consistent annotation schemes.","intents":["I need POS tags and morphological features for linguistic analysis or downstream tasks","I want to extract syntactic dependencies to understand sentence structure","I need consistent grammatical annotations across multiple languages using UD standards"],"best_for":["Linguists analyzing syntactic structure across languages","NLP engineers building semantic role labeling or information extraction systems","Teams requiring Universal Dependencies-compliant annotations for cross-lingual models"],"limitations":["Dependency parsing accuracy degrades on out-of-domain text; typically 90-95% UAS on in-domain test sets","Morphological feature prediction requires sufficient training data; sparse languages have lower accuracy","No support for non-projective parsing in some language models; assumes mostly projective structures"],"requires":["Python 3.6+","PyTorch 1.3+","Pre-trained POS/dependency models for target language"],"input_types":["Document with tokenized sentences"],"output_types":["Words with pos, xpos, feats attributes; Sentence with dependencies"],"categories":["data-processing-analysis","nlp-linguistic-annotation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_10","uri":"capability://tool.use.integration.integration.with.java.stanford.corenlp.for.advanced.features.and.backward.compatibility","name":"integration with java stanford corenlp for advanced features and backward compatibility","description":"Provides Python bindings to the Java Stanford CoreNLP library, enabling access to CoreNLP's advanced features (Semgrex pattern matching, Ssurgeon tree surgery, enhanced dependencies) while maintaining Stanza's Python API. The integration layer converts between Stanza's Python document model and CoreNLP's Java representations, allowing seamless use of CoreNLP processors alongside native Stanza processors. This enables leveraging CoreNLP's mature implementations of complex linguistic tasks while staying in Python.","intents":["I need to use CoreNLP's advanced features (Semgrex, Ssurgeon) from Python","I want to migrate from CoreNLP to Stanza while maintaining access to CoreNLP functionality","I need enhanced dependencies or other CoreNLP-specific annotations"],"best_for":["Teams migrating from CoreNLP to Stanza who need feature parity","Researchers using Semgrex patterns for linguistic rule-based extraction","Developers requiring enhanced dependencies or other CoreNLP-specific outputs"],"limitations":["Requires Java Runtime Environment (JRE) installed and configured","CoreNLP integration adds latency; Java startup overhead ~1-2 seconds per process","Not all CoreNLP features are exposed; some require direct Java API access","Maintenance burden; CoreNLP updates may require Stanza integration updates"],"requires":["Python 3.6+","Java Runtime Environment (JRE) 8+","Stanford CoreNLP JAR files (downloaded separately or via stanza)","CLASSPATH configured to include CoreNLP JARs"],"input_types":["Stanza Document objects or raw text"],"output_types":["Stanza Document objects with CoreNLP annotations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_11","uri":"capability://automation.workflow.training.and.fine.tuning.with.custom.datasets.and.dynamic.oracles","name":"training and fine-tuning with custom datasets and dynamic oracles","description":"Supports training custom NLP models on user-provided datasets using PyTorch, with utilities for dataset preparation, model configuration, and evaluation. The training framework includes dynamic oracles for transition-based parsers, which correct parser errors during training to improve robustness. Training pipelines handle data loading, batching, optimization, and evaluation metrics. Users can fine-tune pre-trained models on domain-specific data or train models from scratch for new languages or tasks.","intents":["I need to fine-tune Stanza models on domain-specific data for better accuracy","I want to train models for a new language or low-resource language","I need to evaluate model performance on custom test sets"],"best_for":["NLP researchers training models on custom datasets","Teams building domain-specific NLP systems (biomedical, legal, social media)","Developers working on low-resource language NLP"],"limitations":["Training requires significant computational resources (GPU recommended)","Requires annotated training data in CoNLL-U or similar format","Training time varies from hours to days depending on dataset size and model complexity","Limited documentation on training custom models; requires understanding of Stanza internals"],"requires":["Python 3.6+","PyTorch 1.3+ with CUDA support (GPU recommended)","Annotated training data in CoNLL-U format","Stanza source code or development installation"],"input_types":["CoNLL-U formatted training/test files"],"output_types":["Trained model files and evaluation metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_12","uri":"capability://data.processing.analysis.biomedical.and.clinical.nlp.models.with.domain.specific.training","name":"biomedical and clinical nlp models with domain-specific training","description":"Provides specialized pre-trained models for biomedical and clinical NLP tasks, trained on medical corpora and annotated with medical entity types and clinical terminology. These models include biomedical NER recognizing medical entities (drugs, diseases, procedures), POS tagging adapted for medical text, and dependency parsing trained on clinical notes. Models are available for English and trained on diverse medical sources (PubMed abstracts, clinical notes, biomedical literature).","intents":["I need to extract medical entities from clinical notes or biomedical literature","I want to analyze medical text with domain-specific NLP models","I need to process clinical documentation for information extraction"],"best_for":["Biomedical NLP researchers and clinical informatics teams","Healthcare organizations processing clinical notes and medical records","Pharmaceutical and life sciences companies analyzing biomedical literature"],"limitations":["Biomedical models are English-only; no support for other languages","Models trained on specific medical corpora; may not generalize to all medical domains","Clinical notes often contain abbreviations and non-standard language; accuracy may vary","No support for structured clinical data (ICD codes, medications); text-only processing"],"requires":["Python 3.6+","PyTorch 1.3+","Biomedical model downloads (separate from general models)"],"input_types":["Clinical notes or biomedical text"],"output_types":["Annotated documents with medical entity types and clinical annotations"],"categories":["data-processing-analysis","nlp-biomedical"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_2","uri":"capability://data.processing.analysis.named.entity.recognition.with.multi.token.entity.spans.and.language.specific.models","name":"named entity recognition with multi-token entity spans and language-specific models","description":"Identifies and classifies named entities (persons, organizations, locations, etc.) in text using neural sequence labeling models trained on language-specific corpora. The NER processor operates on tokenized input and produces entity spans that may cover multiple tokens, with each entity assigned a type label. Models are trained using BiLSTM-CRF or transformer-based architectures on diverse treebanks, with specialized biomedical/clinical models available for English medical text.","intents":["I need to extract named entities and their types from text","I want to identify person, organization, and location mentions for information extraction","I need biomedical entity recognition for clinical or scientific text processing"],"best_for":["Information extraction and knowledge graph construction teams","Biomedical NLP researchers processing clinical notes or scientific literature","Developers building entity-aware search or recommendation systems"],"limitations":["NER accuracy varies significantly by entity type; rare entity types have lower F1 scores","Biomedical models are English-only; general models available for 60+ languages","No entity linking or disambiguation; returns entity spans and types only, not canonical IDs"],"requires":["Python 3.6+","PyTorch 1.3+","Pre-trained NER models for target language/domain"],"input_types":["Document with tokenized sentences"],"output_types":["Entity objects with text, type, and character offsets"],"categories":["data-processing-analysis","nlp-information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_3","uri":"capability://data.processing.analysis.constituency.parsing.with.hierarchical.phrase.structure.trees","name":"constituency parsing with hierarchical phrase structure trees","description":"Constructs constituency parse trees that represent the hierarchical phrase structure of sentences, showing how words group into noun phrases, verb phrases, and other constituents. The parser uses a neural chart-based or transition-based approach to build trees bottom-up from tokens, trained on treebanks with constituency annotations. Output is a tree structure where each node represents a phrase with a syntactic label (NP, VP, PP, etc.) and children are sub-constituents or words.","intents":["I need to understand hierarchical phrase structure for syntactic analysis","I want to extract noun phrases, verb phrases, or other constituents from sentences","I need constituency trees for grammar-based information extraction or semantic parsing"],"best_for":["Computational linguists studying syntactic structure","NLP teams building grammar-based information extraction systems","Researchers working on semantic parsing or syntax-aware neural models"],"limitations":["Constituency parsing is slower than dependency parsing; adds ~100-200ms per sentence","Available for fewer languages than dependency parsing; primarily English and major languages","Tree structure can be ambiguous; parser produces single best parse without confidence scores"],"requires":["Python 3.6+","PyTorch 1.3+","Pre-trained constituency parser model (English and select languages only)"],"input_types":["Document with tokenized sentences"],"output_types":["Tree objects with hierarchical phrase structure and syntactic labels"],"categories":["data-processing-analysis","nlp-syntactic-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_4","uri":"capability://data.processing.analysis.lemmatization.with.morphological.analysis.and.language.specific.rules","name":"lemmatization with morphological analysis and language-specific rules","description":"Determines the base/dictionary form (lemma) of each word using a combination of neural models and morphological rules. The lemmatizer takes POS tags and morphological features as input to guide lemmatization, handling irregular forms and language-specific morphology. For some languages, it uses rule-based approaches; for others, neural sequence-to-sequence models trained on morphological analyzers. Output is a lemma attribute on each word, enabling downstream tasks to work with canonical word forms.","intents":["I need to normalize words to their base forms for text analysis","I want to group inflected forms together for frequency analysis or information retrieval","I need lemmas for cross-lingual analysis where inflections differ but lemmas align"],"best_for":["NLP teams building search or information retrieval systems","Linguists analyzing word frequency and morphological patterns","Developers working with morphologically rich languages (Finnish, Turkish, Arabic)"],"limitations":["Lemmatization accuracy depends on POS tag correctness; errors propagate from earlier pipeline stages","Ambiguous words may have multiple valid lemmas; returns single best lemma without alternatives","Rule-based approaches for some languages may not handle neologisms or domain-specific terms"],"requires":["Python 3.6+","PyTorch 1.3+","Pre-trained POS tagger (lemmatization depends on POS accuracy)"],"input_types":["Document with POS-tagged words"],"output_types":["Words with lemma attribute"],"categories":["data-processing-analysis","nlp-morphological-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_5","uri":"capability://data.processing.analysis.coreference.resolution.with.entity.linking.across.sentences","name":"coreference resolution with entity linking across sentences","description":"Identifies mentions of the same entity across a document and groups them into coreference chains, enabling tracking of who/what is being discussed. The resolver uses a neural mention-ranking model that scores pairs of mentions for coreference likelihood, building chains by linking mentions to their antecedents. It operates on the full document context, using word embeddings, syntactic features, and semantic similarity to determine if mentions refer to the same entity. Output is a mapping of mention spans to coreference cluster IDs.","intents":["I need to track entity mentions across a document to understand discourse structure","I want to resolve pronouns and definite descriptions to their referents","I need to build entity-level summaries by grouping all mentions of the same entity"],"best_for":["Information extraction teams building entity-centric knowledge bases","Summarization and question-answering systems requiring discourse understanding","Researchers analyzing narrative structure and entity tracking"],"limitations":["Coreference resolution is computationally expensive; adds significant latency for long documents","Accuracy degrades on out-of-domain text; trained primarily on news and fiction","English-only; no models available for other languages in current release"],"requires":["Python 3.6+","PyTorch 1.3+","Pre-trained coreference model (English only)","Full document context (cannot process streaming/incremental text)"],"input_types":["Document with tokenized sentences and NER annotations"],"output_types":["Coreference clusters mapping mention spans to cluster IDs"],"categories":["data-processing-analysis","nlp-discourse-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_6","uri":"capability://data.processing.analysis.sentiment.analysis.with.sentence.level.classification","name":"sentiment analysis with sentence-level classification","description":"Classifies the sentiment polarity (positive, negative, neutral) of sentences using neural classification models trained on sentiment-annotated corpora. The sentiment analyzer takes tokenized sentences as input and outputs a sentiment label and confidence score for each sentence. Models are typically fine-tuned LSTM or transformer-based classifiers trained on domain-specific data (e.g., movie reviews, product reviews, social media).","intents":["I need to classify sentiment of sentences for opinion mining","I want to analyze customer feedback or review sentiment at scale","I need to filter positive/negative content for downstream processing"],"best_for":["Teams building sentiment analysis pipelines for customer feedback","Content moderation systems requiring opinion classification","Researchers analyzing sentiment trends in social media or reviews"],"limitations":["Sentiment models are domain-specific; accuracy drops significantly on out-of-domain text","No aspect-based sentiment analysis; classifies overall sentence sentiment only","Limited language support; primarily English with select other languages","Sarcasm and irony often misclassified; requires additional context for accurate classification"],"requires":["Python 3.6+","PyTorch 1.3+","Pre-trained sentiment model for target domain/language"],"input_types":["Document with tokenized sentences"],"output_types":["Sentiment labels and confidence scores per sentence"],"categories":["data-processing-analysis","nlp-sentiment-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_7","uri":"capability://data.processing.analysis.hierarchical.document.model.with.structured.linguistic.annotations","name":"hierarchical document model with structured linguistic annotations","description":"Provides a unified data structure (Document → Sentence → Token/Word → Entity) that stores all linguistic annotations produced by pipeline processors. The model is hierarchical, with each level containing relevant metadata: Documents contain sentences, sentences contain tokens and words (tokens may expand to multiple words for MWT), and entities are associated with sentence spans. All annotations (POS tags, lemmas, dependencies, NER, sentiment, etc.) are stored as attributes on the appropriate level, enabling easy access and traversal of linguistic information.","intents":["I need a unified data structure to access all linguistic annotations produced by the pipeline","I want to traverse the document hierarchy to extract specific linguistic information","I need to serialize/deserialize annotated documents for storage or sharing"],"best_for":["NLP developers building downstream applications using Stanza annotations","Researchers analyzing linguistic structure across multiple annotation types","Teams requiring standardized document representation for pipeline integration"],"limitations":["Document model is immutable after creation; requires rebuilding for modifications","No built-in serialization to standard formats (CoNLL-U, XML); requires custom export code","Memory overhead for storing all annotations; large documents may consume significant RAM"],"requires":["Python 3.6+","Stanza library installed"],"input_types":["Processed text from pipeline"],"output_types":["Document objects with hierarchical structure and annotations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_8","uri":"capability://automation.workflow.pipeline.orchestration.with.processor.dependency.management.and.lazy.loading","name":"pipeline orchestration with processor dependency management and lazy loading","description":"Manages the initialization, configuration, and execution of NLP processors in correct dependency order, with automatic model downloading and caching. The Pipeline class coordinates processor dependencies (e.g., POS tagging must run before lemmatization), handles processor configuration via kwargs, and supports lazy loading where processors are only initialized when needed. The resource management system automatically downloads missing models from Stanford's servers on first use, caching them locally to avoid repeated downloads.","intents":["I need to initialize an NLP pipeline for a specific language with minimal configuration","I want to run only specific processors without initializing the full pipeline","I need automatic model downloading and caching for production deployments"],"best_for":["NLP engineers building production pipelines requiring reliable model management","Developers prototyping NLP applications who want minimal setup overhead","Teams deploying Stanza across multiple machines with shared model caches"],"limitations":["First-run initialization requires internet connection for model downloads (50-200MB per language)","Processor dependencies are fixed; cannot reorder or skip processors in middle of pipeline","No built-in batching; processes documents sequentially (can be slow for large corpora)"],"requires":["Python 3.6+","PyTorch 1.3+","Internet connection for initial model download","Disk space for model caching (500MB-2GB depending on languages)"],"input_types":["Configuration dict with language and processor list"],"output_types":["Pipeline object ready for processing text"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-stanza__cap_9","uri":"capability://data.processing.analysis.multi.language.support.with.60.language.models.and.universal.dependencies.standardization","name":"multi-language support with 60+ language models and universal dependencies standardization","description":"Provides pre-trained models for 60+ languages using Universal Dependencies (UD) treebanks as the standard annotation scheme, enabling consistent linguistic representations across languages. Models are trained on UD treebanks for each language, ensuring that POS tags, dependency relations, and morphological features follow the same standards. The unified API allows switching between languages by changing a single parameter, with all downstream code working identically regardless of language.","intents":["I need to process text in multiple languages with consistent annotation schemes","I want to build cross-lingual NLP applications without language-specific code","I need to analyze linguistic phenomena across languages using standardized annotations"],"best_for":["Multilingual NLP teams building applications for global audiences","Computational linguists studying cross-lingual phenomena","Researchers working on language transfer learning and zero-shot NLP"],"limitations":["Model quality varies by language; low-resource languages have lower accuracy","Not all processors available for all languages; some languages have only tokenization and POS","Universal Dependencies scheme may not capture language-specific linguistic phenomena","No support for code-switching or mixed-language text"],"requires":["Python 3.6+","PyTorch 1.3+","Language-specific models downloaded via stanza.download(lang='xx')"],"input_types":["Text in any of 60+ supported languages"],"output_types":["Annotated documents with UD-standard annotations"],"categories":["data-processing-analysis","nlp-multilingual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+","PyTorch 1.3+","Language-specific pre-trained models downloaded via stanza.download()","Pre-trained POS/dependency models for target language","Java Runtime Environment (JRE) 8+","Stanford CoreNLP JAR files (downloaded separately or via stanza)","CLASSPATH configured to include CoreNLP JARs","PyTorch 1.3+ with CUDA support (GPU recommended)","Annotated training data in CoNLL-U format","Stanza source code or development installation"],"failure_modes":["Tokenization quality varies by language; less-resourced languages may have lower accuracy","Requires downloading language-specific models (50-200MB per language)","No real-time streaming tokenization; processes complete documents","Dependency parsing accuracy degrades on out-of-domain text; typically 90-95% UAS on in-domain test sets","Morphological feature prediction requires sufficient training data; sparse languages have lower accuracy","No support for non-projective parsing in some language models; assumes mostly projective structures","Requires Java Runtime Environment (JRE) installed and configured","CoreNLP integration adds latency; Java startup overhead ~1-2 seconds per process","Not all CoreNLP features are exposed; some require direct Java API access","Maintenance burden; CoreNLP updates may require Stanza integration updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:25.058Z","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-stanza","compare_url":"https://unfragile.ai/compare?artifact=pypi-stanza"}},"signature":"2QTa/XnKh5iEcsOkRd0dwmF37QDwLpu44MrYDJ3Nznif7cj4DW7zv5jHio1LxiSNZ6y22qXVlirRPa5AV5LcBw==","signedAt":"2026-06-22T14:40:31.633Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-stanza","artifact":"https://unfragile.ai/pypi-stanza","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-stanza","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"}}