Sentence Similarity
Transformers.js
ONNX
sentence-transformers
English
gemma3_text
feature-extraction
biblical-search
semantic-search
embeddinggemma
fine-tuned
text-embeddings-inference
Instructions to use dpshade22/embeddinggemma-scripture-v1-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use dpshade22/embeddinggemma-scripture-v1-onnx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'dpshade22/embeddinggemma-scripture-v1-onnx'); - sentence-transformers
How to use dpshade22/embeddinggemma-scripture-v1-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dpshade22/embeddinggemma-scripture-v1-onnx") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4485e3079650f17465947d4b537ecf30d2627371580946c1f2ed8223fcc683be
- Size of remote file:
- 33.4 MB
- SHA256:
- c79a190be01275b078b3574d02188abc5784e5651a101b20d826371ba8e897dc
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