metadata
base_model: InstaDeepAI/nucleotide-transformer-500m-1000g
library_name: transformers.js
pipeline_tag: feature-extraction
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 @xenova/transformers
Example: Retrieve embeddings from a dummy DNA sequence.
import { pipeline } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/nucleotide-transformer-500m-1000g', {
quantized: false, // Set to true to use the 8-bit quantized model.
});
// 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, ... ]
// ]
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).