Upload ONNX weights + Transformers.js sample code (#4)

- Upload ONNX weights (8e62bfddef78b0294a7f06a897ba8154f7997847)
- Add Transformers.js sample code (78fe1aae637a5bec38ec168e3ce03c6e299edaf3)


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

README.md

@@ -9173,6 +9173,35 @@ for query, query_scores in zip(queries, scores):
         print(score, document)
 ```
 
+### 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');
+
+// 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); // [0.32719788157046004, 0.06960141111667434]
+```
+
 
 ## Contact
 

onnx/model.onnx

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onnx/model_fp16.onnx

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onnx/model_q4f16.onnx

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onnx/model_quantized.onnx

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onnx/model_uint8.onnx

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