ja-ginza 5.2.0

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.

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.

Feature | Description |

— | — |

Name | ja_ginza |

Version | 5.1.0 |

spaCy | >=3.2.0,<3.3.0 |

Default Pipeline | tok2vec, parser, attribute_ruler, ner, morphologizer, compound_splitter, bunsetu_recognizer |

Components | tok2vec, parser, attribute_ruler, ner, morphologizer, compound_splitter, bunsetu_recognizer |

Vectors | 480443 keys, 20000 unique vectors (300 dimensions) |

Sources | [UD_Japanese-BCCWJ r2.8](https://github.com/UniversalDependencies/UD_Japanese-BCCWJ) (Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S., Matsumoto, Y., Omura, M., & Murawaki, Y.)<br />[GSK2014-A(2019)](https://www.gsk.or.jp/catalog/gsk2014-a/) (Tokyo Institute of Technology)<br />[SudachiDict_core](https://github.com/WorksApplications/SudachiDict) (Works Applications Enterprise Co., Ltd.)<br />[chiVe](https://github.com/WorksApplications/chiVe) (Works Applications Enterprise Co., Ltd.) |

License | MIT License |

Author | [Megagon Labs Tokyo.](https://github.com/megagonlabs/ginza) |

### Label Scheme

<details>

<summary>View label scheme (241 labels for 3 components)</summary>

Component | Labels |

— | — |

`parser` | ROOT, acl, acl_bunsetu, advcl, advcl_bunsetu, advmod, advmod_bunsetu, amod, amod_bunsetu, aux, aux_bunsetu, case, case_bunsetu, cc, cc_bunsetu, ccomp_bunsetu, compound, compound_bunsetu, cop, csubj_bunsetu, dep, dep_bunsetu, det_bunsetu, discourse_bunsetu, dislocated_bunsetu, fixed, mark, nmod, nmod_bunsetu, nsubj_bunsetu, nummod, obj_bunsetu, obl_bunsetu, punct, punct_bunsetu |

`ner` | Academic, Age, Aircraft, Airport, Amphibia, Amusement_Park, Animal_Disease, Animal_Part, Archaeological_Place_Other, Art_Other, Astral_Body_Other, Award, Bay, Bird, Book, Bridge, Broadcast_Program, Cabinet, Calorie, Canal, Car, Car_Stop, Character, City, Class, Clothing, Color_Other, Company, Company_Group, Compound, Conference, Constellation, Continental_Region, Corporation_Other, Country, Countx_Other, County, Culture, Date, Day_Of_Week, Disease_Other, Dish, Doctrine_Method_Other, Domestic_Region, Drug, Earthquake, Element, Email, Era, Ethnic_Group_Other, Event_Other, Facility_Other, Facility_Part, Family, Fish, Flora, Flora_Part, Food_Other, Frequency, Fungus, GOE_Other, GPE_Other, Game, Geological_Region_Other, God, Government, ID_Number, Incident_Other, Insect, Intensity, International_Organization, Island, Lake, Language_Other, Latitude_Longtitude, Law, Line_Other, Living_Thing_Other, Living_Thing_Part_Other, Location_Other, Magazine, Mammal, Material, Measurement_Other, Military, Mineral, Mollusc_Arthropod, Money, Money_Form, Mountain, Movement, Movie, Multiplication, Museum, Music, N_Animal, N_Country, N_Event, N_Facility, N_Flora, N_Location_Other, N_Natural_Object_Other, N_Organization, N_Person, N_Product, Name_Other, National_Language, Nationality, Natural_Disaster, Natural_Object_Other, Natural_Phenomenon_Other, Nature_Color, Newspaper, Numex_Other, Occasion_Other, Offense, Ordinal_Number, Organization_Other, Park, Percent, Period_Day, Period_Month, Period_Time, Period_Week, Period_Year, Periodx_Other, Person, Phone_Number, Physical_Extent, Plan, Planet, Point, Political_Organization_Other, Political_Party, Port, Position_Vocation, Postal_Address, Printing_Other, Pro_Sports_Organization, Product_Other, Province, Public_Institution, Railroad, Rank, Region_Other, Religion, Religious_Festival, Reptile, Research_Institute, River, Road, Rule_Other, School, School_Age, Sea, Ship, Show, Show_Organization, Spa, Space, Spaceship, Speed, Sport, Sports_Facility, Sports_League, Sports_Organization_Other, Station, Style, Temperature, Theater, Theory, Time, Time_Top_Other, Timex_Other, Title_Other, Train, Treaty, Tumulus, Tunnel, URL, Unit_Other, Vehicle_Other, Volume, War, Water_Route, Weapon, Weight, Worship_Place, Zoo |

`morphologizer` | POS=PUNCT, POS=NUM, POS=NOUN, POS=ADP, POS=AUX, POS=VERB, POS=CCONJ, POS=PART, POS=SCONJ, POS=SYM, POS=ADJ, POS=DET, POS=PRON, POS=PROPN, POS=ADV, POS=X, POS=INTJ |

</details>

### Accuracy

Type | Score |

— | — |

DEP_UAS | 90.95 |

DEP_LAS | 89.07 |

SENTS_P | 83.03 |

SENTS_R | 83.03 |

SENTS_F | 83.03 |

ENTS_F | 55.40 |

ENTS_P | 58.37 |

ENTS_R | 52.72 |

POS_ACC | 97.44 |

MORPH_MICRO_F | 0.00 |

MORPH_PER_FEAT | 0.00 |

MORPH_ACC | 0.00 |

TAG_ACC | 0.00 |

TOK2VEC_LOSS | 20475934.74 |

PARSER_LOSS | 855491.84 |

NER_LOSS | 124114.99 |

MORPHOLOGIZER_LOSS | 26714.42 |