Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
Russian
bert
Generated from Trainer
named-entity-recognition
russian
ner
Eval Results (legacy)
Instructions to use viktoroo/sberbank-rubert-base-collection3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viktoroo/sberbank-rubert-base-collection3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="viktoroo/sberbank-rubert-base-collection3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("viktoroo/sberbank-rubert-base-collection3") model = AutoModelForTokenClassification.from_pretrained("viktoroo/sberbank-rubert-base-collection3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb6934d69ac89926b839292704a5e0e65f38285478c6de5757b96046a640d7cf
- Size of remote file:
- 3.5 kB
- SHA256:
- 8e166b062912b2853970f17d2f73335473b5283c3ea939b3018aada8b933906a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.