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 training_args.bin from viktor-shcherb/sberbank-rubert-base-collection3: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/viktor-shcherb/sberbank-rubert-base-collection3/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://viktor-shcherb/sberbank-rubert-base-collection3@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/viktor-shcherb/sberbank-rubert-base-collection3/resolve/refs%2Fpr%2F2/training_args.bin
3.5 kB
- Xet hash:
- eb6934d69ac89926b839292704a5e0e65f38285478c6de5757b96046a640d7cf
- Size of remote file:
- 3.5 kB
- SHA256:
- 8e166b062912b2853970f17d2f73335473b5283c3ea939b3018aada8b933906a
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