Feature Extraction
Transformers
PyTorch
Safetensors
Russian
English
roberta
text-embeddings-inference
Instructions to use deepvk/roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepvk/roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="deepvk/roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepvk/roberta-base") model = AutoModel.from_pretrained("deepvk/roberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c60ce92f6483c7fe92c9bbab03ced0ad8721ba1f783cb30dfba56d1b87abe968
- Size of remote file:
- 499 MB
- SHA256:
- 091fa81cf9c756eedbc4032d919b37ebaf05d4469515df540e22f37777266e7c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.