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uaritm/uk-cardio-ner
uk-cardio-ner is a token classification model from uaritm. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as apache-2.0.
Named Entity Recognition (NER) model for Ukrainian medical discharge summaries Trained on real cardiology cases from the cardiovascular surgery clinic. The dataset used for training underwent rigorous cleaning, thorou…
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Updated Jul 31, 2026
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From the Hugging Face model README
Named Entity Recognition (NER) model for Ukrainian medical discharge summaries Trained on real cardiology cases from the cardiovascular surgery clinic. The dataset used for training underwent rigorous cleaning, thorough depersonalization, and complete anonymization to ensure strict compliance with medical data privacy standards.
This model extracts medication-related entities from Ukrainian clinical texts.
It was trained using spaCy with the youscan/ukr-roberta-base transformer
as the backbone.
| Label | Description | Example |
|---|---|---|
| DRUG | Drug name (trade or INN) | Бісопролол, Ксарелто |
| DOSE | Dosage | 5 мг, 10/2,5/5 мг |
| FREQ | Frequency | щоранку, 2 р/д |
| DUR | Duration | тривало, 14 діб |
Evaluated on 1,171 held-out examples (20% split):
| Metric | Score |
|---|---|
| F1 | 90.21 |
| Precision | 91.02 |
| Recall | 89.42 |
import spacy
nlp = spacy.load("uk-cardio-ner")
doc = nlp("Бісопролол 5мг щоранку, тривало.")
for ent in doc.ents:
print(ent.text, ent.label_)
# Бісопролол DRUG
# 5мг DOSE
# щоранку FREQ
# тривало DUR
If you use this model, please cite:
TAIS DSS — Trusted AI for Intelligent Specialists IntracomUA OÜ, 2026 https://tais.esemi.org