https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-2-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 @xenova/transformers
Example: Information Retrieval w/ Xenova/ms-marco-MiniLM-L-2-v2
.
import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';
const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-MiniLM-L-2-v2');
const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-MiniLM-L-2-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: [ 9.063430786132812, -11.72588062286377 ]
// unquantized: [ 8.843852043151855, -11.74354362487793 ]
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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cross-encoder/ms-marco-MiniLM-L-2-v2