README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- biblical-search
- semantic-search
- embeddinggemma
- fine-tuned
license: apache-2.0
datasets:
- biblical-text-pairs
metrics:
- accuracy@1: 12.00%
- accuracy@3: 15.00%  
- accuracy@10: 31.00%
language:
- en
---

# EmbeddingGemma-300M Fine-tuned for Biblical Text Search

This model is a fine-tuned version of [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) specialized for biblical text search and retrieval.

## Model Performance

- **Accuracy@1**: 12.00% (13x improvement over base model)
- **Accuracy@3**: 15.00%
- **Accuracy@10**: 31.00%
- **Training Steps**: 25 (optimal stopping point)
- **Base Model Accuracy@1**: 0.91%

## Usage

```python
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