metadata
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
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])