{"categories":[{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic/3"}],"enrichment":{"capability":"A pre-trained Japanese NLP model for spaCy that performs tokenization, dependency parsing, named entity recognition, part-of-speech tagging, and morphological analysis on Japanese text.","skillfed_tags":["japanese-nlp","spacy-model","pretrained"],"use_cases":["Extract named entities (people, places, organizations, dates) from Japanese documents for information extraction pipelines","Parse Japanese sentence structure to understand grammatical relationships for machine translation or semantic analysis","Tokenize and tag Japanese text with parts of speech for downstream NLP tasks like text classification or sentiment analysis","Identify compound words and phrase boundaries (bunsetu) to improve Japanese text segmentation for search or indexing","Build Japanese chatbots or question-answering systems that need to understand sentence structure and entity types"],"what_it_does":"ja_ginza is a pre-trained spaCy model specialized for Japanese text analysis. It bundles a CNN trained on UD-Japanese BCCWJ r2.8 and GSK2014-A corpora, providing a complete NLP pipeline with seven components: tok2vec, parser, attribute_ruler, ner, morphologizer, compound_splitter, and bunsetu_recognizer. The model includes word2vec token vectors with 300 dimensions.\n\nYou install it as a Python package and load it through spaCy's standard interface. It works by processing Japanese text through its pipeline components, assigning linguistic annotations to each token and entity. The model achieves high accuracy on dependency parsing (90.95 UAS) and POS tagging (97.44), moderate accuracy on sentence segmentation (83.03 F), and lower accuracy on named entity recognition (55.40 F). It is maintained by Megagon Labs and actively used in production, though releases are infrequent.","worth_installing":"Yes, if you need Japanese NLP and are comfortable with spaCy 3.2.x. The model is actively maintained, permissively licensed, and provides a complete pipeline with strong parsing and tagging performance. Be aware that NER accuracy is moderate (55.40 F-score), so validate it on your data before relying on entity extraction in production. Install friction is low and no known vulnerabilities exist."},"id":"ja-ginza","links":{"html":"https://skillfed.io/packages/ja-ginza","md":"https://skillfed.io/packages/ja-ginza.md","pypi":"https://pypi.org/project/ja-ginza/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-03-30","license_spdx":null,"license_treatment":"permissive","name":"ja-ginza","python_support":"unspecified","summary":"Japanese multi-task CNN trained on UD-Japanese BCCWJ r2.8 + GSK2014-A(2019). Assigns word2vec token vectors. Components: tok2vec, parser, ner, morphologizer, atteribute_ruler, compound_splitter, bunsetu_recognizer."},"popularity":{"monthly_downloads":83718,"position":14053,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.2.0"}