Checksum .tar.gz:
058896d79a6efa8774b058ecb083cd7c6e416290994b8312eb095544ba416ee0
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Details: https://spacy.io/models/ja#ja_core_news_lg
Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.
| Feature | Description |
|---|---|
| Name | ja_core_news_lg
|
| Version | 3.6.0
|
| spaCy | >=3.6.0,<3.7.0
|
| Default Pipeline | tok2vec, morphologizer, parser, attribute_ruler, ner
|
| Components | tok2vec, morphologizer, parser, senter, attribute_ruler, ner
|
| Vectors | 480443 keys, 480443 unique vectors (300 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) chiVe: Japanese Word Embedding with Sudachi & NWJC (chive-1.1-mc90-500k) (Works Applications) |
| License | CC BY-SA 4.0
|
| Author | Explosion |
| Model size | 529 MB |
Label Scheme
View label scheme (65 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=SPACE, 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
| 99.37 |
TOKEN_P
| 97.65 |
TOKEN_R
| 97.90 |
TOKEN_F
| 97.77 |
POS_ACC
| 97.30 |
MORPH_ACC
| 0.00 |
MORPH_MICRO_P
| 34.01 |
MORPH_MICRO_R
| 98.04 |
MORPH_MICRO_F
| 50.51 |
SENTS_P
| 96.32 |
SENTS_R
| 98.03 |
SENTS_F
| 97.17 |
DEP_UAS
| 92.22 |
DEP_LAS
| 90.81 |
TAG_ACC
| 97.13 |
LEMMA_ACC
| 96.71 |
ENTS_P
| 74.97 |
ENTS_R
| 69.69 |
ENTS_F
| 72.23 |
Installation
pip install spacy
python -m spacy download ja_core_news_lg