--- tags: - spacy - token-classification language: - ja license: cc-by-sa-4.0 model-index: - name: ja_core_news_lg results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.7388362652 - name: NER Recall type: recall value: 0.6867924528 - name: NER F Score type: f_score value: 0.7118644068 - 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: 0.9742268041 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.0 - 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: 0.9212481426 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.9089518668 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9658536585 --- ### Details: https://spacy.io/models/ja#ja_core_news_lg Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `ja_core_news_lg` | | **Version** | `3.7.0` | | **spaCy** | `>=3.7.0,<3.8.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | 480443 keys, 480443 unique vectors (300 dimensions) | | **Sources** | [UD Japanese GSD v2.8](https://github.com/UniversalDependencies/UD_Japanese-GSD) (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](https://github.com/megagonlabs/UD_Japanese-GSD) (Megagon Labs Tokyo)
[chiVe: Japanese Word Embedding with Sudachi & NWJC (chive-1.1-mc90-500k)](https://github.com/WorksApplications/chiVe) (Works Applications) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme
View label scheme (65 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=SPACE`, `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` | 97.42 | | `MORPH_ACC` | 0.00 | | `MORPH_MICRO_P` | 34.01 | | `MORPH_MICRO_R` | 98.04 | | `MORPH_MICRO_F` | 50.51 | | `SENTS_P` | 95.56 | | `SENTS_R` | 97.63 | | `SENTS_F` | 96.59 | | `DEP_UAS` | 92.12 | | `DEP_LAS` | 90.90 | | `TAG_ACC` | 97.13 | | `LEMMA_ACC` | 96.70 | | `ENTS_P` | 73.88 | | `ENTS_R` | 68.68 | | `ENTS_F` | 71.19 |