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Add transformers.js support (#7)
Browse files- Add transformers.js support (fe06939db5b8d3db45f633108099e8accd7066e4)
Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>
README.md
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- mteb
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- arctic
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- snowflake-arctic-embed
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model-index:
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- name: snowflake-snowflake-arctic-embed-xs
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results:
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print(score, document)
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```
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## FAQ
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- mteb
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- arctic
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- snowflake-arctic-embed
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- transformers.js
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model-index:
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- name: snowflake-snowflake-arctic-embed-xs
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results:
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print(score, document)
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```
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### Using 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) by running:
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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, dot } from '@xenova/transformers';
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// Create feature extraction pipeline
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const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-xs', {
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quantized: false, // Comment out this line to use the quantized version
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});
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// Generate sentence embeddings
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const sentences = [
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'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
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'The Data Cloud!',
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'Mexico City of Course!',
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]
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const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
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// Compute similarity scores
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const [source_embeddings, ...document_embeddings ] = output.tolist();
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const similarities = document_embeddings.map(x => dot(source_embeddings, x));
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console.log(similarities); // [0.5044895661144148, 0.5636021124426508]
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```
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## FAQ
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