sift-finetuned

Fine-tuned EmbeddingGemma-300M for personalized content scoring with Sift.

License: This model is a derivative of Google's Gemma and is subject to the Gemma Terms of Use. By using this model, you agree to those terms.

What is this?

This is a sentence embedding model fine-tuned on personal browsing labels collected with the Sift Chrome extension. It scores feed items (Hacker News, Reddit, X) against interest categories using cosine similarity, running entirely in the browser via Transformers.js.

Training

• Base model: google/embeddinggemma-300m
• Loss: MultipleNegativesRankingLoss (contrastive)
• Task prompt: task: classification | query:
• Epochs: 4
• Learning rate: 2e-5
• Framework: sentence-transformers

ONNX Variants

File	Format	Use case
onnx/model.onnx	FP32	Reference
onnx/model_quantized.onnx	INT8	Smaller download
onnx/model_q4.onnx	4-bit	WASM inference
onnx/model_no_gather_q4.onnx	4-bit	WebGPU inference

Usage with Sift

Set this model ID (shreyask/sift-finetuned) in Sift's popup settings under Model Source. The extension loads it directly from HuggingFace — no authentication needed.

Usage with Transformers.js

import { pipeline } from "@huggingface/transformers";
const extractor = await pipeline("feature-extraction", "shreyask/sift-finetuned", { dtype: "q4" });
const output = await extractor("Your text here", { pooling: "mean", normalize: true });

Privacy

ONNX files contain only numerical weights and tokenizer data — no training examples, user labels, or personal information.