Update README.md

README.md

@@ -4,4 +4,76 @@ library_name: transformers.js
 
 https://huggingface.co/BAAI/bge-m3 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/@xenova/transformers) using:
+```bash
+npm i @xenova/transformers
+```
+
+You can then use the model to compute embeddings, as follows:
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Xenova/bge-m3');
+
+// Compute sentence embeddings
+const texts = ["What is BGE M3?", "Defination of BM25"]
+const embeddings = await extractor(texts, { pooling: 'cls', normalize: true });
+console.log(embeddings);
+// Tensor {
+//   dims: [ 2, 1024 ],
+//   type: 'float32',
+//   data: Float32Array(2048) [ -0.0340719036757946, -0.04478546231985092, ... ],
+//   size: 2048
+// }
+
+console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
+// [
+//   [ -0.0340719036757946, -0.04478546231985092, -0.004497686866670847, ... ],
+//   [ -0.015383965335786343, -0.041989751160144806, -0.025820579379796982, ... ]
+// ]
+```
+
+You can also use the model for retrieval. For example:
+```js
+import { pipeline, cos_sim } from '@xenova/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Xenova/bge-m3');
+
+// Define query to use for retrieval
+const query = 'What is BGE M3?';
+
+// List of documents you want to embed
+const texts = [
+  'BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.',
+  'BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document',
+];
+
+// Compute sentence embeddings
+const embeddings = await extractor(texts, { pooling: 'cls', normalize: true });
+
+// Compute query embeddings
+const query_embeddings = await extractor(query, { pooling: 'cls', normalize: true });
+
+// Sort by cosine similarity score
+const scores = embeddings.tolist().map(
+  (embedding, i) => ({
+    id: i,
+    score: cos_sim(query_embeddings.data, embedding),
+    text: texts[i],
+  })
+).sort((a, b) => b.score - a.score);
+console.log(scores);
+// [
+//   { id: 0, score: 0.62532672968664, text: 'BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.' },
+//   { id: 1, score: 0.33111060648806, text: 'BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document' },
+// ]
+```
+
+---
+
 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`).