nucleotide-transformer-500m-1000g

https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g 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

Example: Retrieve embeddings from a dummy DNA sequence.

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

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/nucleotide-transformer-500m-1000g', {
    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) [ -0.591946005821228, -0.8283093571662903, ... ],
//   size: 2560
// }

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

console.log(output.tolist());
// [
//   [ -0.591946005821228, -0.8283093571662903, -0.49790817499160767, ... ],
//   [ -0.5775232315063477, -0.8485714793205261, -0.5186372995376587, ... ]
// ]

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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).