README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - ja
license: CC-BY-SA-4.0
model-index:
  - name: ja_gsd_bert_wwm_unidic_lite
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8496143959
          - name: NER Recall
            type: recall
            value: 0.8314465409
          - name: NER F Score
            type: f_score
            value: 0.840432295
      - task:
          name: POS
          type: token-classification
        metrics:
          - name: POS Accuracy
            type: accuracy
            value: 0
      - task:
          name: SENTER
          type: token-classification
        metrics:
          - name: SENTER Precision
            type: precision
            value: 0.9201520913
          - name: SENTER Recall
            type: recall
            value: 0.9546351085
          - name: SENTER F Score
            type: f_score
            value: 0.9370764763
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Dependencies Accuracy
            type: accuracy
            value: 0.9367795389
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Dependencies Accuracy
            type: accuracy
            value: 0.9367795389

Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.

Feature	Description
Name	ja_gsd_bert_wwm_unidic_lite
Version	3.1.1
spaCy	>=3.1.0,<3.2.0
Default Pipeline	transformer, parser, ner
Components	transformer, parser, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	UD_Japanese-GSD UD_Japanese-GSD r2.8+NE SudachiDict_core cl-tohoku/bert-base-japanese-whole-word-masking unidic_lite
License	CC BY-SA 4.0
Author	Megagon Labs Tokyo.

Label Scheme

View label scheme (45 labels for 2 components)

Component	Labels
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
DEP_UAS	93.68
DEP_LAS	92.61
SENTS_P	92.02
SENTS_R	95.46
SENTS_F	93.71
ENTS_F	84.04
ENTS_P	84.96
ENTS_R	83.14
TAG_ACC	0.00
TRANSFORMER_LOSS	28861.67
PARSER_LOSS	1306248.63
NER_LOSS	13993.36