How to use opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill") 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 opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill")
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill") model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill", device_map="auto")
