How to use Marqjeev/aizen-vault-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Marqjeev/aizen-vault-embeddings") sentences = [ "how do I take my coffee for office meetings", "syn-coffee-preference-note\nSYNTHETIC EXAMPLE — a standing personal preference about coffee orders for meetings hosted at the office\nStanding preference: black coffee, no sugar, for any meeting hosted at the office; guests get asked their own preference rather than assumed the same.\n\n**Why this matters**: a small, low-stakes personal-preference fact, the kind that's easy to forget and mildly annoying to re-ask every time.", "syn-coffee-preference-note\nSYNTHETIC EXAMPLE — a standing personal preference about coffee orders for meetings hosted at the office\nStanding preference: black coffee, no sugar, for any meeting hosted at the office; guests get asked their own preference rather than assumed the same.\n\n**Why this matters**: a small, low-stakes personal-preference fact, the kind that's easy to forget and mildly annoying to re-ask every time.", "syn-velmoria-charter-amendments\nSYNTHETIC — chronological list of amendments to Velmoria's Founding Charter, 1962-2024\nMajor amendments to Velmoria's Founding Charter: 1962 (judicial review powers expanded), 1979 (voting age lowered to 18), 1994 (provincial resource-revenue sharing formula rewritten), 2011 (Crown's emergency powers narrowed), 2024 (digital-privacy rights added).\n\n**Why this matters**: fictional analog to a real constitutional-amendments tracking note, same structure, different content." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
The community tab is the place to discuss and collaborate with the HF community!
