metadata
base_model: distilbert-base-uncased-finetuned-sst-2-english
library_name: transformers.js
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 }
// ]
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).