Token Classification
Transformers
PyTorch
bert
belarusian
bulgarian
macedonian
russian
serbian
ukrainian
pos
dependency-parsing
Instructions to use KoichiYasuoka/bert-large-slavic-cyrillic-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/bert-large-slavic-cyrillic-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/bert-large-slavic-cyrillic-upos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-slavic-cyrillic-upos") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-slavic-cyrillic-upos") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16ba0e26ab9036ebd8617ed7caee6f2b1da212cfc17833ff282616eed4c75af6
- Size of remote file:
- 1.7 GB
- SHA256:
- 3461cbf80e8b00d7dde0f1f5f2e0444cf905a49a0c60218d05de1c04696f6035
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