Checksum .tar.gz:
1fe511cc33e3d6d2008339663a0d2a9275bba4218fd0c93060b765e832eccc3b
Checksum .whl:1aa64940154d4c04423c309463862d417ab7e232fbf55f790e25f89bb92912bf
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 | 3.2.0
|
| spaCy | >=3.2.0,<3.3.0
|
| 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 | 530 MB |
Label Scheme
View label scheme (66 labels for 4 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
|
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 |
TOKEN_P
| 97.65 |
TOKEN_R
| 97.90 |
TOKEN_F
| 97.77 |
POS_ACC
| 97.36 |
MORPH_ACC
| 0.40 |
MORPH_MICRO_P
| 34.01 |
MORPH_MICRO_R
| 98.04 |
MORPH_MICRO_F
| 50.51 |
SENTS_P
| 98.62 |
SENTS_R
| 98.82 |
SENTS_F
| 98.72 |
DEP_UAS
| 92.14 |
DEP_LAS
| 90.81 |
TAG_ACC
| 97.16 |
LEMMA_ACC
| 96.59 |
ENTS_P
| 74.02 |
ENTS_R
| 69.18 |
ENTS_F
| 71.52 |
Installation
pip install spacy
python -m spacy download ja_core_news_lg