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--- |
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library_name: transformers.js |
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pipeline_tag: feature-extraction |
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--- |
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https://huggingface.co/colbert-ir/colbertv2.0 with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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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: |
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```bash |
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npm i @xenova/transformers |
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``` |
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You can then use the model to compute embeddings like this: |
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```js |
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import { pipeline } from '@xenova/transformers'; |
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// Create a feature-extraction pipeline |
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const extractor = await pipeline('feature-extraction', 'Xenova/colbertv2.0'); |
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// Compute sentence embeddings |
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const sentences = ['Hello world', 'This is a sentence']; |
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const output = await extractor(sentences, { pooling: 'mean', normalize: true }); |
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console.log(output); |
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// Tensor { |
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// dims: [ 2, 768 ], |
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// type: 'float32', |
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// data: Float32Array(768) [ -0.008133978582918644, 0.00663341861218214, ... ], |
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// size: 768 |
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// } |
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``` |
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You can convert this Tensor to a nested JavaScript array using `.tolist()`: |
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```js |
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console.log(output.tolist()); |
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// [ |
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// [ -0.008133978582918644, 0.00663341861218214, 0.06555338203907013, ... ], |
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// [ -0.02630571834743023, 0.011146597564220428, 0.008737687021493912, ... ] |
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// ] |
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``` |
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--- |
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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`). |
