README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - ja
license: cc-by-sa-4.0
model-index:
  - name: ja_core_news_md
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.7266576455
          - name: NER Recall
            type: recall
            value: 0.6754716981
          - name: NER F Score
            type: f_score
            value: 0.7001303781
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9713282143
      - task:
          name: POS
          type: token-classification
        metrics:
          - name: POS (UPOS) Accuracy
            type: accuracy
            value: 0.9712018326
      - task:
          name: MORPH
          type: token-classification
        metrics:
          - name: Morph (UFeats) Accuracy
            type: accuracy
            value: 0
      - task:
          name: LEMMA
          type: token-classification
        metrics:
          - name: Lemma Accuracy
            type: accuracy
            value: 0.9670499959
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.9221346544
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.9092381767
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.9696376102

Details: https://spacy.io/models/ja#ja_core_news_md

Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler.

Feature	Description
Name	ja_core_news_md
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, 20000 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 (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	97.12
MORPH_ACC	0.00
MORPH_MICRO_P	34.01
MORPH_MICRO_R	98.04
MORPH_MICRO_F	50.51
SENTS_P	96.30
SENTS_R	97.63
SENTS_F	96.96
DEP_UAS	92.21
DEP_LAS	90.92
TAG_ACC	97.13
LEMMA_ACC	96.70
ENTS_P	72.67
ENTS_R	67.55
ENTS_F	70.01