How to use liddlefish/privacy_embedding_rag with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("liddlefish/privacy_embedding_rag") 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 liddlefish/privacy_embedding_rag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liddlefish/privacy_embedding_rag")
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liddlefish/privacy_embedding_rag") model = AutoModel.from_pretrained("liddlefish/privacy_embedding_rag")
