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

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/nucleotide-transformer-500m-human-ref', {
    dtype: "fp32"  // Options: "fp32", "fp16", "q8", "q4"
});

// Perform feature extraction
const sequences = ["ATTCCGATTCCGATTCCG", "ATTTCTCTCTCTCTCTGAGATCGATCGATCGAT"];
const output = await extractor(sequences, { pooling: 'mean' });
console.log(output);
// Tensor {
//   dims: [ 2, 1280 ],
//   type: 'float32',
//   data: Float32Array(2560) [ -0.4544594883918762, 0.33294573426246643, ... ],
//   size: 2560
// }

You can convert the output Tensor to a nested JavaScript array using .tolist():

console.log(output.tolist());
// [
//   [ -0.4544594883918762, 0.33294573426246643, -0.06337763369083405, ... ],
//   [ 0.05060688406229019, -0.21165050566196442, -0.32883304357528687, ... ]
// ]

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).