https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
You can then use the model to compute embeddings like this:
import { pipeline } from '@huggingface/transformers';
// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
// Compute sentence embeddings
const sentences = ['This is an example sentence', 'Each sentence is converted'];
const output = await extractor(sentences, { pooling: 'mean', normalize: true });
console.log(output);
// Tensor {
// dims: [ 2, 384 ],
// type: 'float32',
// data: Float32Array(768) [ 0.04592696577310562, 0.07328180968761444, ... ],
// size: 768
// }
You can convert this Tensor to a nested JavaScript array using .tolist():
console.log(output.tolist());
// [
// [ 0.04592696577310562, 0.07328180968761444, 0.05400655046105385, ... ],
// [ 0.08188057690858841, 0.10760223120450974, -0.013241755776107311, ... ]
// ]
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).