github explosion/spacy-models ja_core_news_lg-3.7.0

Downloads Downloads (wheel)

Checksum .tar.gz: 6f73a27239b07e4900bc78fa398cc2773ae7d0e15546c4758f1cf8bfe33c67d9
Checksum .whl: f08eecb4d40523045c9478ce59a67564fd71edd215f32c076fa91dc1f05cc7fd

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 3.7.0
spaCy >=3.7.0,<3.8.0
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 97.64
TOKEN_R 97.88
TOKEN_F 97.76
POS_ACC 97.42
MORPH_ACC 0.00
MORPH_MICRO_P 34.01
MORPH_MICRO_R 98.04
MORPH_MICRO_F 50.51
SENTS_P 95.56
SENTS_R 97.63
SENTS_F 96.59
DEP_UAS 92.12
DEP_LAS 90.90
TAG_ACC 97.13
LEMMA_ACC 96.70
ENTS_P 73.88
ENTS_R 68.68
ENTS_F 71.19

Installation

pip install spacy
python -m spacy download ja_core_news_lg

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