Upload ONNX weights + Transformers.js sample code (#2)

- Upload ONNX weights (9e516c87c3f3ffcc035f58104788261cb7e01979)
- Update README.md (a26bf084e094b8e24fa09ea031416eebee559673)


Co-authored-by: Joshua <[email protected]>

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+onnx/model.onnx_data filter=lfs diff=lfs merge=lfs -text

README.md

@@ -9187,6 +9187,39 @@ Query: Where can I get the best tacos?
 tensor(0.2797) Mexico City of Course!
 tensor(0.1250) The Data Cloud!
 ```
+
+### Using Huggingface 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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+You can then use the model for retrieval, as follows:
+
+```js
+import { pipeline, dot } from '@huggingface/transformers';
+
+// Create feature extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', {
+    dtype: 'q8',
+});
+
+// Generate sentence embeddings
+const sentences = [
+    'query: what is snowflake?',
+    '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); // [0.24783534471401417, 0.05313122704326892]
+```
+
+
 ## Contact
 
 

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