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---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_core_med7_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8822157434
- name: NER Recall
type: recall
value: 0.925382263
- name: NER F Score
type: f_score
value: 0.9032835821
---
| Feature | Description |
| --- | --- |
| **Name** | `en_core_med7_trf` |
| **Version** | `3.4.2.1` |
| **spaCy** | `>=3.4.2,<3.5.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `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` | 90.33 |
| `ENTS_P` | 88.22 |
| `ENTS_R` | 92.54 |
| `TRANSFORMER_LOSS` | 2502627.06 |
| `NER_LOSS` | 114576.77 |
### 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}
}
``` |