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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb74d3b609260018677cb0cf01a10aa8e0e940230d9752094a6aabacc9987cb4
- Size of remote file:
- 3.92 MB
- SHA256:
- caff5a98d15fd439255194f3fcfd41a7b276b500cdf69caba890ae242c498797
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.