Upload ONNX weights (+ quantizations) + Transformers.js support (#1)

- Upload ONNX weights (+ quantizations) (31a9a46f0afbbbe34a1668adcd72a03ce37d5b9b)
- Create config.json (a05d487c0285b3c35898c17db40f374058b42d81)
- Create preprocessor_config.json (8690aa35ac67dacfbc214cdff245602eaa83b5e2)
- Update tokenizer_config.json (cc93df47ec2c676dc83e0d3e92e3f56f57366066)
- Update README.md (4a45991a7a637ae0cf9ff467bcd6db7e166247d7)


Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>

README.md

@@ -5,6 +5,7 @@ tags:
 - fashion
 - multimodal retrieval
 - siglip
+- transformers.js
 library_name: open_clip
 pipeline_tag: zero-shot-image-classification
 license: apache-2.0
@@ -25,6 +26,9 @@ The model was fine-tuned from ViT-B-16-SigLIP (webli).
 
 
 ## Usage
+
+### OpenCLIP
+
 The model can be seamlessly used with [OpenCLIP](https://github.com/mlfoundations/open_clip) by
 
 ```python
@@ -49,6 +53,55 @@ with torch.no_grad(), torch.cuda.amp.autocast():
 print("Label probs:", text_probs)
 ```
 
+### Transformers.js
+
+You can also run the model in JavaScript with the [Transformers.js](https://huggingface.co/docs/transformers.js) library.
+
+First, install it from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+
+```bash
+npm i @huggingface/transformers
+```
+
+Then, compute embeddings as follows:
+```js
+import { SiglipTextModel, SiglipVisionModel, AutoTokenizer, AutoProcessor, RawImage, softmax, dot } from '@huggingface/transformers';
+
+const model_id = 'Marqo/marqo-fashionSigLIP';
+
+// Load tokenizer and text model
+const tokenizer = await AutoTokenizer.from_pretrained(model_id);
+const text_model = await SiglipTextModel.from_pretrained(model_id);
+
+// Load processor and vision model
+const processor = await AutoProcessor.from_pretrained(model_id);
+const vision_model = await SiglipVisionModel.from_pretrained(model_id);
+
+// Run tokenization
+const texts = ['a hat', 'a t-shirt', 'shoes'];
+const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
+
+// Compute text embeddings
+const { text_embeds } = await text_model(text_inputs);
+
+// Read image and run processor
+const image = await RawImage.read('https://raw.githubusercontent.com/marqo-ai/marqo-FashionCLIP/main/docs/fashion-hippo.png');
+const image_inputs = await processor(image);
+
+// Compute vision embeddings
+const { image_embeds } = await vision_model(image_inputs);
+
+// Compute similarity scores
+const normalized_text_embeds = text_embeds.normalize().tolist();
+const normalized_image_embeds = image_embeds.normalize().tolist()[0];
+
+const text_probs = softmax(normalized_text_embeds.map((text_embed) => 
+    100.0 * dot(normalized_image_embeds, text_embed)
+));
+console.log(text_probs);
+// [0.9860219105287394, 0.00777916527489097, 0.006198924196369721]
+```
+
 ## Benchmark Results
 Average evaluation results on 6 public multimodal fashion datasets ([Atlas](https://huggingface.co/datasets/Marqo/atlas), [DeepFashion (In-shop)](https://huggingface.co/datasets/Marqo/deepfashion-inshop), [DeepFashion (Multimodal)](https://huggingface.co/datasets/Marqo/deepfashion-multimodal), [Fashion200k](https://huggingface.co/datasets/Marqo/fashion200k), [KAGL](https://huggingface.co/datasets/Marqo/KAGL), and [Polyvore](https://huggingface.co/datasets/Marqo/polyvore)) are reported below: 
 

config.json

@@ -0,0 +1,3 @@
+{
+  "model_type": "siglip"
+}

onnx/text_model.onnx

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onnx/text_model_bnb4.onnx

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onnx/text_model_fp16.onnx

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onnx/text_model_quantized.onnx

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onnx/vision_model.onnx

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preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_processor_type": "SiglipImageProcessor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ]
+}

tokenizer_config.json

@@ -931,7 +931,7 @@
   "eos_token": "</s>",
   "extra_ids": 100,
   "legacy": false,
-  "model_max_length": 1000000000000000019884624838656,
+  "model_max_length": 64,
   "pad_token": "</s>",
   "sp_model_kwargs": {},
   "tokenizer_class": "T5Tokenizer",