en_core_med7_lg / README.md
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metadata
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
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

Label Scheme

View label scheme (7 labels for 1 components)
Component Labels
ner DOSAGE, DRUG, DURATION, FORM, FREQUENCY, ROUTE, STRENGTH

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

@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}
}