Xenova/distilbert-base-uncased-finetuned-sst-2-english

https://huggingface.co./distilbert-base-uncased-finetuned-sst-2-english 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 classify text like this:

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

// Create a sentiment analysis pipeline
const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english');

// Classify input text
const output = await classifier('I love transformers!');
console.log(output);
// [{ label: 'POSITIVE', score: 0.999788761138916 }]

// Classify input text (and return all classes)
const output2 = await classifier('I love transformers!', { topk: null });
console.log(output2);
// [
//   { label: 'POSITIVE', score: 0.999788761138916 },
//   { label: 'NEGATIVE', score: 0.00021126774663571268 }
// ]

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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).