Instructions to use dchaplinsky/uk_ner_web_trf_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use dchaplinsky/uk_ner_web_trf_base with spaCy:
!pip install https://huggingface.co/dchaplinsky/uk_ner_web_trf_base/resolve/main/uk_ner_web_trf_base-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("uk_ner_web_trf_base") # Importing as module. import uk_ner_web_trf_base nlp = uk_ner_web_trf_base.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
language: uk
datasets:
- ner-uk
license: mit
model-index:
- name: uk_ner_web_trf_base
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8987742191
- name: NER Recall
type: recall
value: 0.8810077519
- name: NER F Score
type: f_score
value: 0.8898023096
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_base
Model description
uk_ner_web_trf_base is a fine-tuned XLM-Roberta model that is ready to use for Named Entity Recognition and achieves a performance close to SoA for the NER task for Ukrainian language. 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 bigger model, trained on xlm-roberta-large with the State-of-the-Art performance is available here.
Copyright: Dmytro Chaplynskyi, lang-uk project, 2022