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--- |
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library_name: transformers.js |
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--- |
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https://huggingface.co/BAAI/bge-small-en-v1.5 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, as follows: |
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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/bge-small-en-v1.5'); |
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// Compute sentence embeddings |
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const texts = ['Hello world.', 'Example sentence.']; |
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const embeddings = await extractor(texts, { pooling: 'mean', normalize: true }); |
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console.log(embeddings); |
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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) [ -0.04314826801419258, -0.029488801956176758, ... ], |
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// size: 768 |
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// } |
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console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list |
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// [ |
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// [ -0.04314826801419258, -0.029488801956176758, 0.027080481871962547, ... ], |
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// [ -0.03605496883392334, 0.01643390767276287, 0.008982205763459206, ... ] |
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// ] |
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``` |
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You can also use the model for retrieval. For example: |
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```js |
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import { pipeline, cos_sim } from '@xenova/transformers'; |
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// Create a feature-extraction pipeline |
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const extractor = await pipeline('feature-extraction', 'Xenova/bge-small-en-v1.5'); |
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// List of documents you want to embed |
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const texts = [ |
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'Hello world.', |
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'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.', |
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'I love pandas so much!', |
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]; |
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// Compute sentence embeddings |
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const embeddings = await extractor(texts, { pooling: 'mean', normalize: true }); |
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// Prepend recommended query instruction for retrieval. |
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const query_prefix = 'Represent this sentence for searching relevant passages: ' |
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const query = query_prefix + 'What is a panda?'; |
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const query_embeddings = await extractor(query, { pooling: 'mean', normalize: true }); |
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// Sort by cosine similarity score |
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const scores = embeddings.tolist().map( |
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(embedding, i) => ({ |
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id: i, |
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score: cos_sim(query_embeddings.data, embedding), |
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text: texts[i], |
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}) |
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).sort((a, b) => b.score - a.score); |
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console.log(scores); |
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// [ |
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// { id: 1, score: 0.7995888037433755, text: 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.' }, |
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// { id: 2, score: 0.6911046766159414, text: 'I love pandas so much!' }, |
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// { id: 0, score: 0.39066192695524765, text: 'Hello world.' } |
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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`). |
