---
language: en
license: mit
library_name: spacy
pipeline_tag: token-classification
tags:
- spacy
- token-classification
- clinical-nlp
- medication-ner
- med7
- word-vectors
model-index:
- name: en_core_med7_lg
  results:
  - task:
      type: token-classification
      name: Medication named entity recognition
    dataset:
      name: Med7 held-out evaluation split
      type: private
    metrics:
    - type: f1
      value: 0.8886872353
      name: NER F1
    - type: precision
      value: 0.8796407186
      name: NER precision
    - type: recall
      value: 0.8979217604
      name: NER recall
---

## Installation

```bash
pip install -U pip setuptools wheel
pip install "en-core-med7-lg @ https://huggingface.co/kormilitzin/en_core_med7_lg/resolve/main/en_core_med7_lg-1.1.0-py3-none-any.whl"
```


# en_core_med7_lg

`en_core_med7_lg` is a non-transformer, large-vector spaCy pipeline for medication-related named entity recognition in English clinical text.

The model extracts the following entity types:

- `DRUG`
- `STRENGTH`
- `DOSAGE`
- `DURATION`
- `FREQUENCY`
- `FORM`
- `ROUTE`

## Version

Current release: `1.1.0`

This release was retrained and repackaged for the modern spaCy/transformers stack:

- Python `3.12.13`
- spaCy `3.8.14`

Unlike `en_core_med7_trf`, this model does not use a transformer backend and does not require `spacy-transformers`, `transformers`, `tokenizers`, or `torch` at runtime.


## Citation

If you use this model, please cite the original Med7 paper:

Kormilitzin, A., Vaci, N., Liu, Q., & Nevado-Holgado, A. (2021). Med7: A transferable clinical natural language processing model for electronic health records. *Artificial Intelligence in Medicine*, 118, 102086. https://doi.org/10.1016/j.artmed.2021.102086

BibTeX:

```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},
  doi = {10.1016/j.artmed.2021.102086},
  publisher = {Elsevier}
}
```