readme: ---
  tags:
  - spacy
  language:
  - en
  model-index:
  - name: en_stonk_pipeline
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8565043157
      - name: NER Recall
        type: recall
        value: 0.8348858173
      - name: NER F Score
        type: f_score
        value: 0.8455569081
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.9726250474
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.9165718428
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.8978441095
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.9038596962
  ---
   pipeline to extract stonk names, need to adjust for general use as some stonk names are very short. Based on the standard spacy pipeline, but added a pipe and wanted to distribute it easily
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `en_stonk_pipeline` |
  | **Version** | `0.0.1` |
  | **spaCy** | `>=3.4.1,<3.5.0` |
  | **Default Pipeline** | `entity_ruler` |
  | **Components** | `entity_ruler` |
  | **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** | n/a |
  | **Author** | [FriendlyUser](friendlyuser.github.io) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (8 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`entity_ruler`** | `COMPANY`, `COUNTRY`, `DIVIDENDS`, `INDEX`, `MAYBE`, `STOCK`, `STOCK_EXCHANGE`, `THINGS` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `TOKEN_ACC` | 99.93 |
  | `TOKEN_P` | 99.57 |
  | `TOKEN_R` | 99.58 |
  | `TOKEN_F` | 99.57 |
  | `TAG_ACC` | 97.26 |
  | `SENTS_P` | 91.92 |
  | `SENTS_R` | 88.90 |
  | `SENTS_F` | 90.39 |
  | `DEP_UAS` | 91.66 |
  | `DEP_LAS` | 89.78 |
  | `ENTS_P` | 85.65 |
  | `ENTS_R` | 83.49 |
  | `ENTS_F` | 84.56 |