How to use Netizine/icis_commodity_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Netizine/icis_commodity_embedding") sentences = [ "How is demand from blown film converters trending for natural-colour rLDPE pellets sourced from production scrap in Germany?", "For a tender closing Friday, market participants indicated post-industrial, food-grade HDPE bales could be workable around €1,030-1,110/t DAP Valencia for prompt-to-March delivery, depending on lot size and delivery flexibility.", "Demand from German blown-film converters for natural rLDPE pellets sourced from production scrap was steady to slightly firmer week on week, though buyers continued to push back on offers above the low-to-mid €1,200s/t FCA level.", "Europe recycled high-density polyethylene (R-HDPE) blow-moulding natural pellet demand continues to increase on the back of new packaging projects and increased recycled content use from the packaging sector." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
