--- 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: 0.0 - 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](https://github.com/UniversalDependencies/UD_Japanese-GSD)
[UD_Japanese-GSD r2.8+NE](https://github.com/megagonlabs/UD_Japanese-GSD/releases/tag/r2.8-NE)
[SudachiDict_core](https://github.com/WorksApplications/SudachiDict)
[cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking)
[unidic_lite](https://github.com/polm/unidic-lite) | | **License** | `CC BY-SA 4.0` | | **Author** | [Megagon Labs Tokyo.](https://github.com/megagonlabs/UD_japanese_GSD) | ### 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 |