Token Classification
Transformers
PyTorch
bert
belarusian
bulgarian
macedonian
russian
serbian
ukrainian
pos
dependency-parsing
Instructions to use KoichiYasuoka/bert-base-slavic-cyrillic-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/bert-base-slavic-cyrillic-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/bert-base-slavic-cyrillic-upos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-slavic-cyrillic-upos") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-slavic-cyrillic-upos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7aa8d6c49432bc4f4b292cef5e0733f511ca4b986b4c609ea3171dfb685d9e3
- Size of remote file:
- 711 MB
- SHA256:
- d13d1eb5c54c01ac92304c3cce40ed1f105296b044d669e560ea2fee3fe345e2
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