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