Details: https://nightly.spacy.io/models/ja#ja_core_news_sm
File checksum:
bf3edac225a8c528a51ea0fa6840914f63f849767899a124cdca894bebd7d5e4
Japanese pipeline optimized for CPU. Components: tok2vec, parser, senter, ner, attribute_ruler.
| Feature | Description |
|---|---|
| Name | ja_core_news_sm
|
| Version | 3.0.0a0
|
| spaCy | >=3.0.0a41,<3.1.0
|
| Model size | 17 MB |
| Default Pipeline | tok2vec, parser, ner, attribute_ruler
|
| Components | tok2vec, parser, senter, ner, attribute_ruler
|
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | All rights reserved
|
| Author | Explosion |
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 |
|---|---|
TOK
| 99.69 |
TAG
| 97.21 |
POS
| 96.39 |
UAS
| 90.04 |
LAS
| 87.93 |
NER_P
| 68.96 |
NER_R
| 55.04 |
NER_F
| 61.22 |
SENT_P
| 82.14 |
SENT_R
| 89.02 |
SENT_F
| 85.44 |
Installation
pip install spacy-nightly --pre
python -m spacy download ja_core_news_sm