https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2 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: Information Retrieval w/ Xenova/ms-marco-MiniLM-L-6-v2.
import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers';
const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-MiniLM-L-6-v2');
const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-MiniLM-L-6-v2');
const features = tokenizer(
['How many people live in Berlin?', 'How many people live in Berlin?'],
{
text_pair: [
'Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.',
'New York City is famous for the Metropolitan Museum of Art.',
],
padding: true,
truncation: true,
}
)
const scores = await model(features);
console.log(scores);
// quantized: [ 8.663132667541504, -11.245542526245117 ]
// unquantized: [ 8.845855712890625, -11.245561599731445 ]
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).