dpshade22/embeddinggemma-scripture-v1-onnx

EmbeddingGemma-300M Fine-tuned for Biblical Text Search (ONNX)

This is the ONNX version of our fine-tuned EmbeddingGemma-300M model specialized for biblical text search and retrieval. This version is optimized for web deployment using transformers.js.

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 with Transformers.js

import { AutoTokenizer, AutoModel } from '@huggingface/transformers';

// Load the model
const model = await AutoModel.from_pretrained('dpshade22/embeddinggemma-scripture-v1-onnx');
const tokenizer = await AutoTokenizer.from_pretrained('dpshade22/embeddinggemma-scripture-v1-onnx');

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

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

Prefixes

For optimal performance, use these prefixes:

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

Model Details

• Base Model: google/embeddinggemma-300m
• Training Data: 26,276 biblical text pairs
• Training Steps: 25 steps (optimal stopping point)
• Learning Rate: 2.0e-04
• Batch Size: 8
• Output Dimensions: 768D (supports Matryoshka 384D, 128D)
• ONNX Conversion: Using nixiesearch/onnx-convert specialized tool

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 in web applications
• Finding relevant scripture passages
• Semantic similarity of religious texts
• Question answering on biblical topics
• Offline PWA applications using transformers.js

Conversion Details

• Converted using: nixiesearch/onnx-convert specialized tool
• ONNX Opset: 17
• Optimization Level: 1
• Max difference from original: 1.9e-05 (within acceptable tolerance)

Related Models

• Original PyTorch version: dpshade22/embeddinggemma-scripture-v1
• Base model: google/embeddinggemma-300m
• Reference ONNX: onnx-community/embeddinggemma-300m-ONNX