Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
deberta-v2
text-classification
Instructions to use cross-encoder/nli-deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-deberta-v3-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-deberta-v3-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cross-encoder/nli-deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-deberta-v3-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-v3-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-v3-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_O1.onnx from cross-encoder/nli-deberta-v3-base: direct link, hf CLI and curl.
- Browser
- Download file 774 MB
-
https://huggingface.co/cross-encoder/nli-deberta-v3-base/resolve/main/onnx/model_O1.onnx
- Command line
-
hf download hf://cross-encoder/nli-deberta-v3-base/onnx/model_O1.onnx
-
curl -L -o model_O1.onnx https://huggingface.co/cross-encoder/nli-deberta-v3-base/resolve/main/onnx/model_O1.onnx
774 MB
- Xet hash:
- 933f1b000f03e0ddc6ec5e60d2b32c9d4e4b2d05be90903518e1308769a82c57
- Size of remote file:
- 774 MB
- SHA256:
- fbaee5c2dbfc455261954875ae0c755f0393bfbf928216fc21ef9366d3769857
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