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---
base_model: cross-encoder/ms-marco-TinyBERT-L-2-v2
library_name: transformers.js
---
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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```bash
npm i @xenova/transformers
```
**Example:** Information Retrieval w/ `Xenova/ms-marco-TinyBERT-L-2-v2`.
```js
import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |