How to use cross-encoder/nli-distilroberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-distilroberta-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]
How to use cross-encoder/nli-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-distilroberta-base")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-distilroberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-distilroberta-base", device_map="auto")
https://huggingface.co/cross-encoder/nli-distilroberta-base/resolve/refs%2Fpr%2F4/tokenizer.json
hf download hf://cross-encoder/nli-distilroberta-base@refs/pr/4/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/cross-encoder/nli-distilroberta-base/resolve/refs%2Fpr%2F4/tokenizer.json
