readme: ---
  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: 0.754691689
      - name: NER Recall
        type: recall
        value: 0.7081761006
      - name: NER F Score
        type: f_score
        value: 0.7306943543
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.9712488769
    - task:
        name: POS
        type: token-classification
      metrics:
      - name: POS (UPOS) Accuracy
        type: accuracy
        value: 0.9749672775
    - 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: 0.9670526831
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.9233540485
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.9101052821
    - 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** | `3.5.0` |
  | **spaCy** | `>=3.5.0,<3.6.0` |
  | **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)<br />[UD Japanese GSD v2.8 NER](https://github.com/megagonlabs/UD_Japanese-GSD) (Megagon Labs Tokyo)<br />[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
  
  <details>
  
  <summary>View label scheme (65 labels for 3 components)</summary>
  
  | 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` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `TOKEN_ACC` | 99.37 |
  | `TOKEN_P` | 97.65 |
  | `TOKEN_R` | 97.90 |
  | `TOKEN_F` | 97.77 |
  | `POS_ACC` | 97.50 |
  | `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` | 92.34 |
  | `DEP_LAS` | 91.01 |
  | `TAG_ACC` | 97.12 |
  | `LEMMA_ACC` | 96.71 |
  | `ENTS_P` | 75.47 |
  | `ENTS_R` | 70.82 |
  | `ENTS_F` | 73.07 |