How to use naver/splade-code-06B with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/splade-code-06B", trust_remote_code=True) queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities)
How to use naver/splade-code-06B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver/splade-code-06B", trust_remote_code=True)
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("naver/splade-code-06B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("naver/splade-code-06B", trust_remote_code=True, device_map="auto")
