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 | 3.7.0 |
spaCy | >=3.7.0,<3.8.0 |
Default Pipeline | tok2vec , morphologizer , parser , attribute_ruler , ner |
Components | tok2vec , morphologizer , parser , senter , attribute_ruler , ner |
Vectors | 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 |
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 |
96.13 |
MORPH_ACC |
0.00 |
MORPH_MICRO_P |
34.01 |
MORPH_MICRO_R |
98.04 |
MORPH_MICRO_F |
50.51 |
SENTS_P |
98.04 |
SENTS_R |
98.62 |
SENTS_F |
98.33 |
DEP_UAS |
91.95 |
DEP_LAS |
90.48 |
TAG_ACC |
97.13 |
LEMMA_ACC |
96.70 |
ENTS_P |
71.09 |
ENTS_R |
57.23 |
ENTS_F |
63.41 |
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Evaluation results
- NER Precisionself-reported0.711
- NER Recallself-reported0.572
- NER F Scoreself-reported0.634
- TAG (XPOS) Accuracyself-reported0.971
- POS (UPOS) Accuracyself-reported0.961
- Morph (UFeats) Accuracyself-reported0.000
- Lemma Accuracyself-reported0.967
- Unlabeled Attachment Score (UAS)self-reported0.920
- Labeled Attachment Score (LAS)self-reported0.905
- Sentences F-Scoreself-reported0.983