README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - ja
license: cc-by-sa-3.0
model-index:
  - name: ja_core_news_trf
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8227383863
          - name: NER Recall
            type: recall
            value: 0.8465408805
          - name: NER F Score
            type: f_score
            value: 0.8344699318
      - 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: 0.979409718
      - 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: 0.9304880245
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.9178365731
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.9507246377

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

Japanese transformer pipeline (Transformer(name='cl-tohoku/bert-base-japanese-char-v2', piece_encoder='char', stride=160, type='bert', width=768, window=216, vocab_size=6144)). Components: transformer, morphologizer, parser, ner.

Feature	Description
Name	ja_core_news_trf
Version	3.7.2
spaCy	>=3.7.0,<3.8.0
Default Pipeline	transformer, morphologizer, parser, attribute_ruler, ner
Components	transformer, morphologizer, parser, 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) cl-tohoku/bert-base-japanese-char-v2 (Inui Laboratory, Tohoku University)
License	CC BY-SA 3.0
Author	Explosion

Label Scheme

View label scheme (64 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=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	97.94
MORPH_ACC	0.00
MORPH_MICRO_P	34.01
MORPH_MICRO_R	98.04
MORPH_MICRO_F	50.51
SENTS_P	93.18
SENTS_R	97.04
SENTS_F	95.07
DEP_UAS	93.05
DEP_LAS	91.78
TAG_ACC	97.13
LEMMA_ACC	96.70
ENTS_P	82.27
ENTS_R	84.65
ENTS_F	83.45