from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer('dpshade22/embeddinggemma-scripture-v1')

# Encode queries (use search_query: prefix)
query = "search_query: What is love?"
query_embedding = model.encode([query])

# Encode documents (use search_document: prefix)  
document = "search_document: Love is patient and kind"
doc_embedding = model.encode([document])

Prefixes

For optimal performance, use these prefixes:

Queries: "search_query: your question here"
Documents: "search_document: scripture text here"

Training Details

Training Data: 26,276 biblical text pairs
Learning Rate: 2.0e-04
Batch Size: 8
Training Strategy: Early stopping at 25 steps to prevent overfitting
Output Dimensions: 768D (supports Matryoshka 384D, 128D)

Intended Use

This model is designed for:

Biblical text search and retrieval
Finding relevant scripture passages
Semantic similarity of religious texts
Question answering on biblical topics