readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - en
  license: mit
  model-index:
  - name: en_core_med7_lg
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8649613325
      - name: NER Recall
        type: recall
        value: 0.8892966361
      - name: NER F Score
        type: f_score
        value: 0.876960193
  duplicated_from: kormilitzin/en_core_med7_lg
  ---
  | Feature | Description |
  | --- | --- |
  | **Name** | `en_core_med7_lg` |
  | **Version** | `3.4.2.1` |
  | **spaCy** | `>=3.4.2,<3.5.0` |
  | **Default Pipeline** | `tok2vec`, `ner` |
  | **Components** | `tok2vec`, `ner` |
  | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
  | **Sources** | n/a |
  | **License** | `MIT` |
  | **Author** | [Andrey Kormilitzin](https://www.kormilitzin.com/) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (7 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`ner`** | `DOSAGE`, `DRUG`, `DURATION`, `FORM`, `FREQUENCY`, `ROUTE`, `STRENGTH` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `ENTS_F` | 87.70 |
  | `ENTS_P` | 86.50 |
  | `ENTS_R` | 88.93 |
  | `TOK2VEC_LOSS` | 226109.53 |
  | `NER_LOSS` | 302222.55 |
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{kormilitzin2021med7,
    title={Med7: A transferable clinical natural language processing model for electronic health records},
    author={Kormilitzin, Andrey and Vaci, Nemanja and Liu, Qiang and Nevado-Holgado, Alejo},
    journal={Artificial Intelligence in Medicine},
    volume={118},
    pages={102086},
    year={2021},
    publisher={Elsevier}
  }
  ```