Package: ja-ginza
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|5.1.2|spaCy|>=3.2.0,<3.5.0|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>### AccuracyType | 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 |