Update README.md

README.md

@@ -8,4 +8,34 @@ pipeline_tag: image-to-text
 
 https://huggingface.co/facebook/nougat-base 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 convert images of scientific PDFs into markdown like this:
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create an image-to-text pipeline
+const pipe = await pipeline('image-to-text', 'Xenova/nougat-base');
+
+// Generate markdown
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/nougat_paper.png';
+const output = await pipe(url, {
+  min_length: 1,
+  max_new_tokens: 40,
+  bad_words_ids: [[pipe.tokenizer.unk_token_id]],
+});
+console.log(output);
+// [{ generated_text: "# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blecher\n\nCorrespondence to: liblecher@meta.com\n\nGuillem Cucurull" }]
+```
+
+
+---
+
 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`).