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
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 |
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}
}