I’m trying to do sentence embeddings using a huggingface model similar to python example here: sentence-transformers/all-MiniLM-L6-v2 · Hugging Face.
So far I have this
using Transformers.HuggingFace using Transformers.TextEncoders sentTrans = hgf"sentence-transformers/all-MiniLM-L6-v2" enc = sentTrans model = sentTrans sentences = [ "This framework generates embeddings for each input sentence", "Sentences are passed as a list of string.", "The quick brown fox jumps over the lazy dog." ] out = model(encode(enc,sentences))
out is a 384 element vector for each sentence, which is what I expected to get, but the vectors don’t match what I get when I use the Python implementation.
I have a strong suspicion I’m just missing a step, looking for, and appreciative of, any guidance anyone may be able to offer.