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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from viktor-shcherb/sberbank-rubert-base-collection3: direct link, hf CLI and curl.
- Browser
- Download file 3.71 MB
-
https://huggingface.co/viktor-shcherb/sberbank-rubert-base-collection3/resolve/main/tokenizer.json
- Command line
-
hf download hf://viktor-shcherb/sberbank-rubert-base-collection3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/viktor-shcherb/sberbank-rubert-base-collection3/resolve/main/tokenizer.json
3.71 MB
File too large to display, you can check the raw version instead.