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--- |
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library_name: transformers.js |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- transformers.js |
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- onnx |
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- biblical-search |
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- semantic-search |
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- embeddinggemma |
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- fine-tuned |
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license: apache-2.0 |
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datasets: |
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- biblical-text-pairs |
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metrics: |
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- accuracy@1: 12.00% |
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- accuracy@3: 15.00% |
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- accuracy@10: 31.00% |
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language: |
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- en |
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--- |
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# EmbeddingGemma-300M Fine-tuned for Biblical Text Search (ONNX) |
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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. |
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## Model Performance |
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- **Accuracy@1**: 12.00% (13x improvement over base model) |
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- **Accuracy@3**: 15.00% |
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- **Accuracy@10**: 31.00% |
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- **Training Steps**: 25 (optimal stopping point) |
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- **Base Model Accuracy@1**: 0.91% |
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## Usage with Transformers.js |
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```javascript |
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import { AutoTokenizer, AutoModel } from '@huggingface/transformers'; |
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// Load the model |
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const model = await AutoModel.from_pretrained('dpshade22/embeddinggemma-scripture-v1-onnx'); |
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const tokenizer = await AutoTokenizer.from_pretrained('dpshade22/embeddinggemma-scripture-v1-onnx'); |
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// Encode queries (use search_query: prefix) |
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const query = "search_query: What is love?"; |
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const query_embedding = await model.encode([query]); |
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// Encode documents (use search_document: prefix) |
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const document = "search_document: Love is patient and kind"; |
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const doc_embedding = await model.encode([document]); |
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``` |
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## Prefixes |
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For optimal performance, use these prefixes: |
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- **Queries**: `"search_query: your question here"` |
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- **Documents**: `"search_document: scripture text here"` |
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## Model Details |
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- **Base Model**: `google/embeddinggemma-300m` |
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- **Training Data**: 26,276 biblical text pairs |
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- **Training Steps**: 25 steps (optimal stopping point) |
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- **Learning Rate**: 2.0e-04 |
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- **Batch Size**: 8 |
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- **Output Dimensions**: 768D (supports Matryoshka 384D, 128D) |
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- **ONNX Conversion**: Using nixiesearch/onnx-convert specialized tool |
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## Training Details |
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- **Training Data**: 26,276 biblical text pairs |
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- **Learning Rate**: 2.0e-04 |
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- **Batch Size**: 8 |
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- **Training Strategy**: Early stopping at 25 steps to prevent overfitting |
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- **Output Dimensions**: 768D (supports Matryoshka 384D, 128D) |
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## Intended Use |
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This model is designed for: |
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- Biblical text search and retrieval in web applications |
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- Finding relevant scripture passages |
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- Semantic similarity of religious texts |
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- Question answering on biblical topics |
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- Offline PWA applications using transformers.js |
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## Conversion Details |
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- **Converted using**: nixiesearch/onnx-convert specialized tool |
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- **ONNX Opset**: 17 |
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- **Optimization Level**: 1 |
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- **Max difference from original**: 1.9e-05 (within acceptable tolerance) |
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## Related Models |
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- **Original PyTorch version**: dpshade22/embeddinggemma-scripture-v1 |
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- **Base model**: google/embeddinggemma-300m |
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- **Reference ONNX**: onnx-community/embeddinggemma-300m-ONNX |
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