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
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "do_basic_tokenize": true, "tokenizer_class": "BertTokenizerFast"} | |