Update README.md

README.md

@@ -6,4 +6,61 @@ pipeline_tag: feature-extraction
 
 https://huggingface.co/Qwen/Qwen3-Embedding-0.6B with ONNX weights to be compatible with Transformers.js.
 
+## Usage (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 compute embeddings as follows:
+```js
+import { pipeline, matmul } from "@huggingface/transformers";
+
+// Create a feature extraction pipeline
+const extractor = await pipeline(
+  "feature-extraction",
+  "onnx-community/Qwen3-Embedding-0.6B-ONNX",
+  {
+    dtype: "fp32", // Options: "fp32", "fp16", "q8"
+    // device: "webgpu",
+  },
+);
+
+function get_detailed_instruct(task_description, query) {
+  return `Instruct: ${task_description}\nQuery:${query}`;
+}
+
+// Each query must come with a one-sentence instruction that describes the task
+const task = "Given a web search query, retrieve relevant passages that answer the query";
+const queries = [
+  get_detailed_instruct(task, "What is the capital of China?"),
+  get_detailed_instruct(task, "Explain gravity"),
+];
+
+// No need to add instruction for retrieval documents
+const documents = [
+  "The capital of China is Beijing.",
+  "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
+];
+const input_texts = [...queries, ...documents];
+
+// Extract embeddings for queries and documents
+const output = await extractor(input_texts, {
+  pooling: "last_token",
+  normalize: true,
+});
+const scores = await matmul(
+  output.slice([0, queries.length]), // Query embeddings
+  output.slice([queries.length, null]).transpose(1, 0), // Document embeddings
+);
+console.log(scores.tolist());
+// [
+//   [ 0.7645590305328369, 0.14142560958862305 ],
+//   [ 0.13549776375293732, 0.599955141544342 ]
+// ]
+```
+
+---
+
 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`).