Add/update the quantized ONNX model files and README.md for Transformers.js v3

## Applied Quantizations

### ✅ Based on `model.onnx` *with* slimming

↳ ❌ `int8`: `model_int8.onnx` (added but JS-based E2E test failed)
```
/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:25
__classPrivateFieldGet(this, _OnnxruntimeSessionHandler_inferenceSession, "f").loadModel(pathOrBuffer, options);
^

Error: Could not find an implementation for ConvInteger(10) node with name '/model/backbone/conv_encoder/model/conv1/Conv_quant'
at new OnnxruntimeSessionHandler (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:25:92)
at Immediate.<anonymous> (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/[email protected]/node_modules/onnxruntime-node/dist/backend.js:67:29)
at process.processImmediate (node:internal/timers:485:21)

Node.js v22.16.0
```
↳ ✅ `uint8`: `model_uint8.onnx` (added)
↳ ✅ `q4`: `model_q4.onnx` (added)
↳ ✅ `q4f16`: `model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `model_bnb4.onnx` (added)

README.md

@@ -5,4 +5,21 @@ library_name: transformers.js
 
 https://huggingface.co/microsoft/table-transformer-structure-recognition 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
+```
+
+**Example:** Run object-detection.
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+const detector = await pipeline('object-detection', 'Xenova/table-transformer-structure-recognition');
+const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
+const output = await detector(img, { threshold: 0.9 });
+```
+
 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`).

onnx/model_bnb4.onnx

@@ -0,0 +1,3 @@
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+oid sha256:29c742fccf6a03d363acbfdeea8483ef2d56a499cd765e6bb3e065683d86ace9
+size 55844137

onnx/model_q4.onnx

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+oid sha256:6255f0419f4caafa1da48862d0b2c017069ce30e44ed9175a0df30490fe4fc3f
+size 56933076

onnx/model_q4f16.onnx

@@ -0,0 +1,3 @@
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+oid sha256:92e6cbdd9c4f45887e2e4d078ddee81e9991d3d6685624926e3a992f21d4924e
+size 33042107

onnx/model_uint8.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62d0711a672d7eae51e7164debd2df92c9fee377097dad4b485d70a882b3a695
+size 29722333