Skip to main content

Japanese multi-task CNN trained on UD-Japanese BCCWJ r2.8 + GSK2014-A(2019) + transformers-ud-japanese-electra–base. Components: transformer, parser, atteribute_ruler, ner, morphologizer, compound_splitter, bunsetu_recognizer.

Feature | Description |
— | — |
Name | ja_ginza_bert_large |
Version | 5.3.0 |
spaCy | >=3.8.16,<4.0.0 |
Default Pipeline | transformer, parser, ner, morphologizer, compound_splitter, bunsetu_recognizer |
Components | transformer, parser, attribute_ruler, ner, morphologizer, compound_splitter, bunsetu_recognizer |
Vectors | 0 keys, 0 unique vectors (0 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>[cl-tohoku/bert-large-japanese-v2](https://huggingface.co/cl-tohoku/bert-large-japanese-v2) (Tohoku University) |
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 | 94.48 |
DEP_LAS | 93.35 |
SENTS_P | 89.73 |
SENTS_R | 90.44 |
SENTS_F | 90.08 |
ENTS_F | 71.12 |
ENTS_P | 71.73 |
ENTS_R | 70.52 |
POS_ACC | 98.67 |
MORPH_MICRO_F | 0.00 |
MORPH_PER_FEAT | 0.00 |
TAG_ACC | 0.00 |
TRANSFORMER_LOSS | 86315.36 |
PARSER_LOSS | 946823.28 |
NER_LOSS | 3432.99 |
MORPHOLOGIZER_LOSS | 2551.99 |

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ja_ginza_bert_large-5.3.0-py3-none-any.whl (2.9 MB view details)

Uploaded Python 3

File details

Details for the file ja_ginza_bert_large-5.3.0-py3-none-any.whl.

File metadata

File hashes

Hashes for ja_ginza_bert_large-5.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9114483c50613cce78be75499e994a3f50181867e89f7b67a5e8439c70e33268
MD5 0cf772a646fad0e41263c1ebcbfaacc3
BLAKE2b-256 fbf4c77d6720c5e0037dd75c4dc1585442f49994a7b90cc6b638677c0c276451

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

5.3.0 This release

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page