sentence-transformers How to use Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent with sentence-transformers:
from sentence_transformers import SparseEncoder
model = SparseEncoder("Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent", 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) Transformers How to use Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent", trust_remote_code=True) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Y-Research-Group/CSR-NV_Embed_v2-Classification-MTOPIntent", trust_remote_code=True, device_map="auto") 
