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
| 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 |