Instructions to use DeepPavlov/rubert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/rubert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepPavlov/rubert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/rubert-base-cased") model = AutoModel.from_pretrained("DeepPavlov/rubert-base-cased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d752d2aba8aab987e60d5cbf789df62cb34a3b4dbe0e6f92a8a20f2a36245134
- Size of remote file:
- 714 MB
- SHA256:
- 8da346601df87881d568b074d00dd9346ef528b3b77edcf57f2d5ed682256902
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.