Revert inadvertent config, tokenizer updates f375a3f
Tom Aarsen commited on
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]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")