import { pipeline } from '@huggingface/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/conv-bert-base');

// Perform feature extraction
const output = await extractor('This is a test sentence.');
console.log(output)
// Tensor {
//   dims: [ 1, 8, 768 ],
//   type: 'float32',
//   data: Float32Array(6144) [ -0.13742968440055847, -0.6912388205528259, ... ],
//   size: 6144
// }

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).