Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
deberta
text-classification
Instructions to use cross-encoder/nli-deberta-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-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-deberta-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-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-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download bpe_encoder.bin from cross-encoder/nli-deberta-base: direct link, hf CLI and curl.
- Browser
- Download file 3.92 MB
-
https://huggingface.co/cross-encoder/nli-deberta-base/resolve/main/bpe_encoder.bin
- Command line
-
hf download hf://cross-encoder/nli-deberta-base/bpe_encoder.bin
-
curl -L -o bpe_encoder.bin https://huggingface.co/cross-encoder/nli-deberta-base/resolve/main/bpe_encoder.bin
3.92 MB
- Xet hash:
- cb74d3b609260018677cb0cf01a10aa8e0e940230d9752094a6aabacc9987cb4
- Size of remote file:
- 3.92 MB
- SHA256:
- caff5a98d15fd439255194f3fcfd41a7b276b500cdf69caba890ae242c498797
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