Instructions to use qwazer/rubert-address-elements with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qwazer/rubert-address-elements with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="qwazer/rubert-address-elements")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("qwazer/rubert-address-elements") model = AutoModelForTokenClassification.from_pretrained("qwazer/rubert-address-elements", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: cointegrated/rubert-tiny2
language:
- ru
tags:
- address
library_name: transformers
pipeline_tag: token-classification
widget:
- text: город Москва, улица 8 Марта
- text: >-
Ставропольский край г Лермонтов территория садоводческого некоммерческого
товарищества имени И.В. Мичурина, ул массив 3 линия 3
- text: >-
Респ Северная Осетия - Алания, р-н Пригородный, тер. Кавказ автомобильная
дорога М-4 Дон-Владикавказ-Грозный-Махачкала-граница с Азербайджанской
Республикой, км 564-ый
Model for https://github.com/qwazer/ruaddress-elements-classification research project