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

javascript

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.