metadata
tags:
- spacy
- token-classification
language:
- ja
license: cc-by-sa-4.0
model-index:
- name: ja_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7402422611
- name: NER Recall
type: recall
value: 0.6918238994
- name: NER F Score
type: f_score
value: 0.7152145644
- task:
name: POS
type: token-classification
metrics:
- name: POS Accuracy
type: accuracy
value: 0.9715755942
- task:
name: SENTER
type: token-classification
metrics:
- name: SENTER Precision
type: precision
value: 0.9862204724
- name: SENTER Recall
type: recall
value: 0.9881656805
- name: SENTER F Score
type: f_score
value: 0.9871921182
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Dependencies Accuracy
type: accuracy
value: 0.9214150689
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Dependencies Accuracy
type: accuracy
value: 0.9214150689
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 |
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 |