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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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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 |
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This model is a fine-tuned version of [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) specialized for biblical text search and retrieval. |
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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 |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Load the model |
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model = SentenceTransformer('dpshade22/embeddinggemma-scripture-v1') |
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# Encode queries (use search_query: prefix) |
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query = "search_query: What is love?" |
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query_embedding = model.encode([query]) |
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# Encode documents (use search_document: prefix) |
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document = "search_document: Love is patient and kind" |
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doc_embedding = 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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## 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 |
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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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