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--- |
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library_name: "transformers.js" |
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--- |
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https://huggingface.co/thenlper/gte-small 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/gte-small'); |
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// Compute sentence embeddings |
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const sentences = ['That is a happy person', 'That is a very happy person']; |
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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, 384 ], |
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// type: 'float32', |
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// data: Float32Array(768) [ -0.053555335849523544, 0.00843878649175167, ... ], |
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// size: 768 |
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// } |
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// Compute cosine similarity |
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import { cos_sim } from '@xenova/transformers'; |
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console.log(cos_sim(output[0].data, output[1].data)) |
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// 0.9798319649182318 |
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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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// [ -0.053555335849523544, 0.00843878649175167, 0.06234041228890419, ... ], |
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// [ -0.049980051815509796, 0.03879701718688011, 0.07510733604431152, ... ] |
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// ] |
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``` |
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By default, an 8-bit quantized version of the model is used, but you can choose to use the full-precision (fp32) version by specifying `{ quantized: false }` in the `pipeline` function: |
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```js |
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const extractor = await pipeline('feature-extraction', 'Xenova/gte-small', { quantized: false }); |
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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`). |
