Details: https://spacy.io/models/ja#ja_core_news_md
File checksum:
ad86d09d9cfa5a070cd48a74893924cc91f9ec8a1a96d46463b0171c6d65c21c
Japanese multi-task CNN trained on UD_Japanese-GSD v2.6-NE. Assigns word2vec token vectors, POS tags, dependency parses and named entities.
| Feature | Description |
|---|---|
| Name | ja_core_news_md
|
| Version | 2.3.0
|
| spaCy | >=2.3.0,<2.4.0
|
| Model size | 37 MB |
| Pipeline | parser, ner
|
| Vectors | 480443 keys, 20000 unique vectors (300 dimensions) |
| Sources | UD_Japanese-GSD v2.6 (Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel) UD_Japanese-GSD v2.6-NE (Megagon Labs Tokyo) chiVe: Japanese Word Embedding with Sudachi & NWJC (chive-1.1-mc90-500k) (Works Applications) SudachiPy (Works Applications) SudachiDict (Works Applications) |
| License | CC BY-SA 4.0
|
| Author | Explosion and Megagon Labs Tokyo |
Label Scheme
| Component | Labels |
|---|---|
parser
| ROOT, acl, advcl, advmod, amod, aux, case, cc, ccomp, compound, cop, csubj, dep, det, dislocated, fixed, mark, nmod, nsubj, nummod, obj, obl, punct
|
ner
| CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, MOVEMENT, NORP, ORDINAL, ORG, PERCENT, PERSON, PET_NAME, PHONE, PRODUCT, QUANTITY, TIME, TITLE_AFFIX, WORK_OF_ART
|
Accuracy
| Type | Score |
|---|---|
LAS
| 87.76 |
UAS
| 89.26 |
TOKEN_ACC
| 97.67 |
ENTS_F
| 67.68 |
ENTS_P
| 69.84 |
ENTS_R
| 65.64 |
Installation
pip install spacy
python -m spacy download ja_core_news_md