Add transformers.js support (#5)

- Add transformers.js support (f7fcb031ba14dbfae3270403cff4f6f859a21360)


Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>

README.md

@@ -7,6 +7,7 @@ tags:
 - mteb
 - arctic
 - snowflake-arctic-embed
+- transformers.js
 model-index:
 - name: snowflake-snowflake-arctic-embed-s
   results:
@@ -3012,6 +3013,37 @@ for query, query_scores in zip(queries, scores):
         print(score, document)
 ```
 
+### Using 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) by running:
+```bash
+npm i @xenova/transformers
+```
+
+You can then use the model to compute embeddings as follows:
+
+```js
+import { pipeline, dot } from '@xenova/transformers';
+
+// Create feature extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-s', {
+    quantized: false, // Comment out this line to use the quantized version
+});
+
+// Generate sentence embeddings
+const sentences = [
+    'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
+    'The Data Cloud!',
+    'Mexico City of Course!',
+]
+const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
+
+// Compute similarity scores
+const [source_embeddings, ...document_embeddings ] = output.tolist();
+const similarities = document_embeddings.map(x => dot(source_embeddings, x));
+console.log(similarities); // [0.48255123876493394, 0.5659250100112143]
+```
+
 ## FAQ