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---
license: apache-2.0
language:
- uk
metrics:
- f1
- precision
- recall
base_model:
- 51la5/roberta-large-NER
pipeline_tag: token-classification
library_name: spacy
model-index:
- name: roberta-large-ner-uk
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9468
- name: NER Recall
type: recall
value: 0.9416
- name: NER F1
type: f1
value: 0.9442
tags:
- ner
- uk
datasets:
- lang-uk/UberText-NER-Silver
---
# roberta-large-ner-uk
A transformer-based NER model for Ukrainian, trained on a combination of human-annotated data (NER-UK 2.0) and high-quality silver-standard annotations (UberText-NER-Silver). Based on `roberta-large-NER`, this model achieves state-of-the-art performance on a wide range of named entities in Ukrainian.
## Model Details
- **Model type:** Transformer-based encoder (spaCy pipeline)
- **Language (NLP):** Ukrainian
- **License:** Apache 2.0
- **Finetuned from model:** `51la5/roberta-large-NER`
- **Entity Types (13):** `PERS`, `ORG`, `LOC`, `DATE`, `TIME`, `JOB`, `MON`, `PCT`, `PERIOD`, `DOC`, `QUANT`, `ART`, `MISC`
## Usage
```python
import spacy
nlp = spacy.load("roberta-large-ner-uk")
doc = nlp("Президент України Володимир Зеленський виступив у Брюсселі.")
print([(ent.text, ent.label_) for ent in doc.ents])
```
## Authors
[Vladyslav Radchenko](https://huggingface.co/pofce), [Nazarii Drushchak](https://huggingface.co/ndrushchak) |