https://huggingface.co/BAAI/bge-reranker-large 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: Text classification using a reranker model.
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('text-classification', 'Xenova/bge-reranker-large');
const output = await classifier('I love transformers!');
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).
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Model tree for Xenova/bge-reranker-large
Base model
BAAI/bge-reranker-large
// npm i @huggingface/transformers import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers'; const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bge-reranker-large'); const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/bge-reranker-large'); const query = 'Which planet is known as the Red Planet?'; const documents = [ 'Mars, known for its reddish appearance, is often referred to as the Red Planet.', 'Venus is often called the twin of Earth because of its similar size and proximity.', ]; const inputs = tokenizer(new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true }); const { logits } = await model(inputs); console.log(logits.sigmoid().tolist()); // one relevance score per document