Update README.md (#1)

- Update README.md (4c85ed6ab76c5318f7050916bb5b743d01a9caa2)

README.md

@@ -1,21 +1,22 @@
 ---
 base_model: BAAI/bge-base-en-v1.5
 library_name: transformers.js
+license: mit
 ---
 
 https://huggingface.co/BAAI/bge-base-en-v1.5 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:
+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 @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 You can then use the model to compute embeddings, as follows:
 
 ```js
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 // Create a feature-extraction pipeline
 const extractor = await pipeline('feature-extraction', 'Xenova/bge-base-en-v1.5');
@@ -40,7 +41,7 @@ console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
 
 You can also use the model for retrieval. For example:
 ```js
-import { pipeline, cos_sim } from '@xenova/transformers';
+import { pipeline, cos_sim } from '@huggingface/transformers';
 
 // Create a feature-extraction pipeline
 const extractor = await pipeline('feature-extraction', 'Xenova/bge-small-en-v1.5');
@@ -76,5 +77,6 @@ console.log(scores);
 // ]
 ```
 
+---
 
 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`).