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Details: https://spacy.io/models/ja#ja_core_news_sm
Japanese pipeline optimized for CPU. Components: tok2vec, parser, senter, ner, attribute_ruler.
Feature | Description |
---|---|
Name | ja_core_news_sm
|
Version | 3.1.0
|
spaCy | >=3.1.0,<3.2.0
|
Default Pipeline | tok2vec , parser , attribute_ruler , ner
|
Components | tok2vec , parser , senter , attribute_ruler , ner
|
Vectors | 0 keys, 0 unique vectors (0 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 NER (Megagon Labs Tokyo) |
License | CC BY-SA 4.0
|
Author | Explosion |
Model size | 12 MB |
Label Scheme
View label scheme (47 labels for 3 components)
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
|
senter
| I , S
|
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 |
---|---|
TOKEN_ACC
| 99.69 |
TAG_ACC
| 97.22 |
POS_ACC
| 96.40 |
MORPH_ACC
| 0.00 |
DEP_UAS
| 91.62 |
DEP_LAS
| 89.41 |
ENTS_P
| 71.63 |
ENTS_R
| 57.73 |
ENTS_F
| 63.93 |
SENTS_P
| 98.61 |
SENTS_R
| 98.80 |
SENTS_F
| 98.70 |
Installation
pip install spacy
python -m spacy download ja_core_news_sm