readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - en
  license: mit
  model-index:
  - name: en_core_web_sm
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8508041869
      - name: NER Recall
        type: recall
        value: 0.8344851763
      - name: NER F Score
        type: f_score
        value: 0.8425656714
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.9726545475
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.9180803841
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.8996666011
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.9060200669
  ---
  ### Details: https://spacy.io/models/en#en_core_web_sm
  
  English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `en_core_web_sm` |
  | **Version** | `3.3.0` |
  | **spaCy** | `>=3.3.0.dev0,<3.4.0` |
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `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 />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
  | **License** | `MIT` |
  | **Author** | [Explosion](https://explosion.ai) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (112 labels for 3 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
  | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `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` | 99.93 |
  | `TOKEN_P` | 99.57 |
  | `TOKEN_R` | 99.58 |
  | `TOKEN_F` | 99.57 |
  | `TAG_ACC` | 97.27 |
  | `SENTS_P` | 91.89 |
  | `SENTS_R` | 89.35 |
  | `SENTS_F` | 90.60 |
  | `DEP_UAS` | 91.81 |
  | `DEP_LAS` | 89.97 |
  | `ENTS_P` | 85.08 |
  | `ENTS_R` | 83.45 |
  | `ENTS_F` | 84.26 |