Duplicated from opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill
How to use seerware/opensearch-neural-sparse-encoding-doc-v3-distill with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seerware/opensearch-neural-sparse-encoding-doc-v3-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 seerware/opensearch-neural-sparse-encoding-doc-v3-distill with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="seerware/opensearch-neural-sparse-encoding-doc-v3-distill")
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seerware/opensearch-neural-sparse-encoding-doc-v3-distill") model = AutoModelForMaskedLM.from_pretrained("seerware/opensearch-neural-sparse-encoding-doc-v3-distill")
