Zero-Shot Classification
Transformers
PyTorch
ONNX
Safetensors
Russian
bert
text-classification
rubert
russian
nli
rte
Instructions to use cointegrated/rubert-base-cased-nli-threeway with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-base-cased-nli-threeway with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cointegrated/rubert-base-cased-nli-threeway")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-base-cased-nli-threeway") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/rubert-base-cased-nli-threeway", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cointegrated/rubert-base-cased-nli-threeway: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cointegrated/rubert-base-cased-nli-threeway/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 8392afc77df50263e33fe4bd2a7e565b5e28b404721c9fd3983cc37329b706ea
- Size of remote file:
- 712 MB
- SHA256:
- 7821ef90a2f24ef93603668bbfb75682217ba3ba7d9d93b27756c199fe8d2326
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