Checksum .tar.gz:
df16f2f54b644836f8305dca08f5208ff1a2ec0655e1e5a907533ee1b0f59dde
Checksum .whl:abe7de8a5653288f79100243b208be37fc0ade5cd07e6bfdfc8b784424cfcf06
Details: https://spacy.io/models/ja#ja_core_news_sm
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.
| Feature | Description |
|---|---|
| Name | ja_core_news_sm
|
| Version | 3.3.0
|
| spaCy | >=3.3.0.dev0,<3.4.0
|
| Default Pipeline | tok2vec, morphologizer, parser, attribute_ruler, ner
|
| Components | tok2vec, morphologizer, parser, senter, attribute_ruler, ner
|
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | UD Japanese GSD v2.8 (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 v2.8 NER (Megagon Labs Tokyo) |
| License | CC BY-SA 4.0
|
| Author | Explosion |
| Model size | 11 MB |
Label Scheme
View label scheme (64 labels for 3 components)
| Component | Labels |
|---|---|
morphologizer
| POS=NOUN, POS=ADP, POS=VERB, POS=SCONJ, POS=AUX, POS=PUNCT, POS=PART, POS=DET, POS=NUM, POS=ADV, POS=PRON, POS=ADJ, POS=PROPN, POS=CCONJ, POS=SYM, POS=NOUN|Polarity=Neg, POS=AUX|Polarity=Neg, POS=INTJ, POS=SCONJ|Polarity=Neg
|
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 |
|---|---|
TOKEN_ACC
| 99.69 |
TOKEN_P
| 97.65 |
TOKEN_R
| 97.90 |
TOKEN_F
| 97.77 |
POS_ACC
| 96.17 |
MORPH_ACC
| 0.00 |
MORPH_MICRO_P
| 34.01 |
MORPH_MICRO_R
| 98.04 |
MORPH_MICRO_F
| 50.51 |
SENTS_P
| 97.27 |
SENTS_R
| 98.22 |
SENTS_F
| 97.74 |
DEP_UAS
| 92.07 |
DEP_LAS
| 90.61 |
TAG_ACC
| 97.12 |
LEMMA_ACC
| 96.50 |
ENTS_P
| 69.97 |
ENTS_R
| 56.86 |
ENTS_F
| 62.73 |
Installation
pip install spacy
python -m spacy download ja_core_news_sm