---
base_model: colbert-ir/colbertv2.0
library_name: transformers.js
pipeline_tag: feature-extraction
---

https://huggingface.co/colbert-ir/colbertv2.0 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
```

You can then use the model to compute embeddings like this:

```js
import { pipeline } from '@xenova/transformers';

// Create a feature-extraction pipeline
const extractor = await pipeline('feature-extraction', 'Xenova/colbertv2.0');

// Compute sentence embeddings
const sentences = ['Hello world', 'This is a sentence'];
const output = await extractor(sentences, { pooling: 'mean', normalize: true });
console.log(output);
// Tensor {
//   dims: [ 2, 768 ],
//   type: 'float32',
//   data: Float32Array(768) [ -0.008133978582918644, 0.00663341861218214, ... ],
//   size: 768
// }
```

You can convert this Tensor to a nested JavaScript array using `.tolist()`:
```js
console.log(output.tolist());
// [
//   [ -0.008133978582918644, 0.00663341861218214, 0.06555338203907013, ... ],
//   [ -0.02630571834743023, 0.011146597564220428, 0.008737687021493912, ... ]
// ]
```

---

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`).