Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
Russian
bert
Generated from Trainer
named-entity-recognition
russian
ner
Eval Results (legacy)
Instructions to use viktor-shcherb/sberbank-rubert-base-collection3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viktor-shcherb/sberbank-rubert-base-collection3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="viktor-shcherb/sberbank-rubert-base-collection3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("viktor-shcherb/sberbank-rubert-base-collection3") model = AutoModelForTokenClassification.from_pretrained("viktor-shcherb/sberbank-rubert-base-collection3") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "name_or_path": "sberbank-ai/ruBert-base", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |