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 (Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S., Matsumoto, Y., Omura, M., & Murawaki, Y.) GSK2014-A(2019) (Tokyo Institute of Technology) SudachiDict_core (Works Applications Enterprise Co., Ltd.) chiVe (Works Applications Enterprise Co., Ltd.) |
| License | MIT License |
| Author | Megagon Labs Tokyo. |
| 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 |
| 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 |
FAQs
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.
We found that ja-ginza demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.
