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Xenova/
ms-marco-TinyBERT-L-2-v2

Name: ms-marco-TinyBERT-L-2-v2
Availability: InStock

cross-encoder/ms-marco-TinyBERT-L2-v2

quantized

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https://huggingface.co/cross-encoder/ms-marco-TinyBERT-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 @huggingface/transformers

Example: Information Retrieval w/ Xenova/ms-marco-TinyBERT-L-2-v2.

import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers';

const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-TinyBERT-L-2-v2');
const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-TinyBERT-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:   [ 7.210887908935547, -11.559350967407227 ]
// unquantized: [ 7.235750675201416, -11.562294006347656 ]

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).