readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - zh
  license: mit
  model-index:
  - name: zh_core_web_sm
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.7203462148
      - name: NER Recall
        type: recall
        value: 0.6493406593
      - name: NER F Score
        type: f_score
        value: 0.6830029475
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.8933253054
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.6960047338
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.640776699
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.7514211886
  ---
  ### Details: https://spacy.io/models/zh#zh_core_web_sm
  
  Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `zh_core_web_sm` |
  | **Version** | `3.5.0` |
  | **spaCy** | `>=3.5.0,<3.6.0` |
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `ner` |
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `ner` |
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
  | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[CoreNLP Universal Dependencies Converter](https://nlp.stanford.edu/software/stanford-dependencies.html) (Stanford NLP Group) |
  | **License** | `MIT` |
  | **Author** | [Explosion](https://explosion.ai) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (100 labels for 3 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`tagger`** | `AD`, `AS`, `BA`, `CC`, `CD`, `CS`, `DEC`, `DEG`, `DER`, `DEV`, `DT`, `ETC`, `FW`, `IJ`, `INF`, `JJ`, `LB`, `LC`, `M`, `MSP`, `NN`, `NR`, `NT`, `OD`, `ON`, `P`, `PN`, `PU`, `SB`, `SP`, `URL`, `VA`, `VC`, `VE`, `VV`, `X`, `_SP` |
  | **`parser`** | `ROOT`, `acl`, `advcl:loc`, `advmod`, `advmod:dvp`, `advmod:loc`, `advmod:rcomp`, `amod`, `amod:ordmod`, `appos`, `aux:asp`, `aux:ba`, `aux:modal`, `aux:prtmod`, `auxpass`, `case`, `cc`, `ccomp`, `compound:nn`, `compound:vc`, `conj`, `cop`, `dep`, `det`, `discourse`, `dobj`, `etc`, `mark`, `mark:clf`, `name`, `neg`, `nmod`, `nmod:assmod`, `nmod:poss`, `nmod:prep`, `nmod:range`, `nmod:tmod`, `nmod:topic`, `nsubj`, `nsubj:xsubj`, `nsubjpass`, `nummod`, `parataxis:prnmod`, `punct`, `xcomp` |
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `TOKEN_ACC` | 95.85 |
  | `TOKEN_P` | 94.58 |
  | `TOKEN_R` | 91.36 |
  | `TOKEN_F` | 92.94 |
  | `TAG_ACC` | 89.33 |
  | `SENTS_P` | 77.85 |
  | `SENTS_R` | 72.62 |
  | `SENTS_F` | 75.14 |
  | `DEP_UAS` | 69.60 |
  | `DEP_LAS` | 64.08 |
  | `ENTS_P` | 72.03 |
  | `ENTS_R` | 64.93 |
  | `ENTS_F` | 68.30 |