groq-docs

https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with 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:

Create val
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():

Create val
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).