Xenova/UAE-Large-V1

https://huggingface.co/WhereIsAI/UAE-Large-V1 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

You can then use the model to compute embeddings like this:

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

// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/UAE-Large-V1', {
    quantized: true, // Set this to false to use the full (unquantized) model
});

// Compute sentence embeddings
const sentences = ['That is a happy person', 'That is a very happy person'];
const output = await extractor(sentences, { pooling: 'cls' });
console.log(output);
// Tensor {
//   dims: [ 2, 1024 ],
//   type: 'float32',
//   data: Float32Array(2048) [ -0.1308155655860901, 0.44334232807159424, ... ],
//   size: 2048
// }

Compute cosine similarity between the two sentences:

import { cos_sim } from '@xenova/transformers';
console.log(cos_sim(output[0].data, output[1].data))
// 0.9586893906734091

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

console.log(output.tolist());
// [
//   [ -0.1308155655860901, 0.44334232807159424, -0.12212765961885452, ... ],
//   [ 0.03931744396686554,   0.30553528666496277,  -0.19462820887565613, ... ]
// ]

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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).