Zero-Shot Classification
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
text-classification
Instructions to use cross-encoder/nli-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-roberta-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-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_O2.onnx from cross-encoder/nli-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/cross-encoder/nli-roberta-base/resolve/main/onnx/model_O2.onnx
- Command line
-
hf download hf://cross-encoder/nli-roberta-base/onnx/model_O2.onnx
-
curl -L -o model_O2.onnx https://huggingface.co/cross-encoder/nli-roberta-base/resolve/main/onnx/model_O2.onnx
499 MB
- Xet hash:
- c27bc06608bdcd090b0d9a0c1a79c80412e256d0c98bfd1881de977c9abb9416
- Size of remote file:
- 499 MB
- SHA256:
- 54ec3f780c5e28633d4a02f686b2a59ceb6a044b3ddc753cc6083283b41e2e55
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