Instructions to use dchaplinsky/uk_ner_web_trf_best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use dchaplinsky/uk_ner_web_trf_best with spaCy:
!pip install https://huggingface.co/dchaplinsky/uk_ner_web_trf_best/resolve/main/uk_ner_web_trf_best-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("uk_ner_web_trf_best") # Importing as module. import uk_ner_web_trf_best nlp = uk_ner_web_trf_best.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
language: uk
datasets:
- ner-uk
license: mit
model-index:
- name: uk_ner_web_trf_best
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9278959811
- name: NER Recall
type: recall
value: 0.9127906977
- name: NER F Score
type: f_score
value: 0.9202813599
widget:
- text: >-
Президент Володимир Зеленський пояснив, що наразі діалог із режимом
Володимира путіна неможливий, адже агресор обрав курс на знищення
українського народу. За словами Зеленського цей режим РФ виявляє неповагу
до суверенітету і територіальної цілісності України.
uk_ner_web_trf_best
Model description
uk_ner_web_trf_best is a fine-tuned Roberta Large Ukrainian model that is ready to use for Named Entity Recognition and achieves a new SoA performance for the NER task for Ukrainian language. It outperforms another SpaCy model, uk_core_news_trf on a NER task.
It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC).
The model was fine-tuned on the NER-UK dataset, released by the lang-uk.
A smaller transformer-based model for the SpaCy is available here.
Copyright: Dmytro Chaplynskyi, lang-uk project, 2023