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:
      - tasks:
          name: NER
          type: token-classification
          metrics:
            - name: Precision
              type: precision
              value: 0.8496143959
            - name: Recall
              type: recall
              value: 0.8314465409
            - name: F Score
              type: f_score
              value: 0.840432295
      - tasks:
          name: POS
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0
      - tasks:
          name: SENTER
          type: token-classification
          metrics:
            - name: Precision
              type: precision
              value: 0.9201520913
            - name: Recall
              type: recall
              value: 0.9546351085
            - name: F Score
              type: f_score
              value: 0.9370764763
      - tasks:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0.9367795389
      - tasks:
          name: LABELED_DEPENDENCIES
          type: token-classification
          metrics:
            - name: 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