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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

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Evaluation results

NER Precision
self-reported

0.850
NER Recall
self-reported

0.831
NER F Score
self-reported

0.840
POS Accuracy
self-reported

0.000
SENTER Precision
self-reported

0.920
SENTER Recall
self-reported

0.955
SENTER F Score
self-reported

0.937
Unlabeled Dependencies Accuracy
self-reported

0.937
Labeled Dependencies Accuracy
self-reported

0.937

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