A low dimension static embedding model (3d) to be used as a text encoder in ML pipelines
Installation
Install model2vec using pip:
pip install model2vec
from sentence_transformers import SentenceTransformer
# Load a pretrained Sentence Transformer model
model = SentenceTransformer("cnmoro/low-dimension-static-model")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
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from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/low-dimension-static-model") 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]