This model is a 62.3% smaller version of google/embeddinggemma-300m optimized for Piedmontese language via vocabulary trimming mined on Lumberjackk/fineweb-2-trimming.

Model Statistics

Original vocabulary size: 262,144 tokens
Trimmed vocabulary size: 16,384 tokens
Vocabulary reduction: 93.7%
Original model size: 302,863,104 parameters
Trimmed model size: 114,119,424 parameters
Size reduction: 62.3%

Usage

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("embeddinggemma-pms-16384")

# Run inference with queries and documents
query = "My query"
documents = [
    "Chunk 1",
    "Chunk 2",
    "Chunk 3",
]
query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)

# Compute similarities to determine a ranking
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)

alphaedge-ai/embeddinggemma-pms-16384

embeddinggemma-pms-16384

Model Statistics

Usage

alphaedge-ai/
embeddinggemma-pms-16384