Update README.md

README.md

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   - split: data
     path: data/data-*
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+
+
+<div style="display: flex; align-items: center; gap: 10px;">
+  <a href="https://www.marqo.ai/blog/introducing-marqos-ecommerce-embedding-models">
+    <img src="https://img.shields.io/badge/Model_Release-Blog-blue?logo=font-awesome&logoColor=white&style=flat&logo=pencil-alt" alt="Blog">
+  </a>
+  <a href="https://github.com/marqo-ai/marqo-ecommerce-embeddings">
+    <img src="https://img.shields.io/badge/GitHub-Repo-black?logo=github" alt="GitHub Repo">
+  </a>
+  <a href="https://www.marqo.ai/blog/how-to-build-an-ecommerce-image-search-application">
+    <img src="https://img.shields.io/badge/Ecommerce Search-Blog-red?logo=font-awesome&logoColor=white&style=flat&logo=pencil-alt" alt="Blog">
+  </a>
+  <a href="https://join.slack.com/t/marqo-community/shared_invite/zt-2b4nsvbd2-TDf8agPszzWH5hYKBMIgDA">
+    <img src="https://img.shields.io/badge/Slack-4A154B?style=for-the-badge&logo=slack&logoColor=white" alt=Slack Community">
+  </a>
+</div>
+
+# Marqo Ecommerce Embedding Models
+**In this work, we introduce the AmazonProducts-3m dataset for evaluation.** This dataset comes with the release of our state-of-the-art embedding models for ecommerce products: [Marqo-Ecommerce-B](https://huggingface.co/Marqo/marqo-ecommerce-embeddings-B) and [Marqo-Ecommerce-L](https://huggingface.co/Marqo/marqo-ecommerce-embeddings-L). 
+
+**Released Content**: 
+1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models
+2) GoogleShopping-1m and AmazonProducts-3m for evaluation
+3) Evaluation Code
+
+The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset.
+
+<img src="https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/main/performance.png" alt="multi split visual" width="700"/>
+
+More benchmarking results can be found below.
+
+## Models
+
+| **Embedding Model** | **#Params (m)** | **Dimension** | **HuggingFace**                    | **Download .pt**                                                                                            |
+|---------------------| --- |---------------|------------------------------------|-------------------------------------------------------------------------------------------------------------|
+| Marqo-Ecommerce-B   | 203 | 768           | [Marqo/marqo-ecommerce-embeddings-B](https://huggingface.co/Marqo/marqo-ecommerce-embeddings-B) | [link](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/marqo-general-ecomm/marqo-ecomm-embeddings-b.pt) |
+| Marqo-Ecommerce-L   | 652 | 1024          | [Marqo/marqo-ecommerce-embeddings-L](https://huggingface.co/Marqo/marqo-ecommerce-embeddings-L) | [link](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/marqo-general-ecomm/marqo-ecomm-embeddings-l.pt)                                       |
+
+### Load from HuggingFace with transformers
+To load the models in Transformers, see below. The models are hosted on [Hugging Face](https://huggingface.co/collections/Marqo/marqo-ecommerce-embeddings-66f611b9bb9d035a8d164fbb) and loaded using [Transformers](https://github.com/huggingface/transformers).
+
+```python
+from transformers import AutoModel, AutoProcessor
+import torch
+from PIL import Image
+import requests
+
+model_name= 'Marqo/marqo-ecommerce-embeddings-L'
+# model_name = 'Marqo/marqo-ecommerce-embeddings-B'
+
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+img = Image.open(requests.get('https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/refs/heads/main/images/dining-chairs.png', stream=True).raw).convert("RGB")
+image = [img]
+text = ["dining chairs", "a laptop", "toothbrushes"]
+processed = processor(text=text, images=image, padding='max_length', return_tensors="pt")
+processor.image_processor.do_rescale = False
+with torch.no_grad():
+    image_features = model.get_image_features(processed['pixel_values'], normalize=True)
+    text_features = model.get_text_features(processed['input_ids'], normalize=True)
+
+    text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
+    
+print(text_probs)
+# [1.0000e+00, 8.3131e-12, 5.2173e-12]
+```
+
+### Load from HuggingFace with OpenCLIP
+To load the models in OpenCLIP, see below. The models are hosted on [Hugging Face](https://huggingface.co/collections/Marqo/marqo-ecommerce-embeddings-66f611b9bb9d035a8d164fbb) and loaded using [OpenCLIP](https://github.com/mlfoundations/open_clip). You can also find this code inside `run_models.py`.
+
+```
+pip install open_clip_torch
+```
+```python
+from PIL import Image
+import open_clip
+import requests
+import torch
+
+# Specify model from Hugging Face Hub
+model_name = 'hf-hub:Marqo/marqo-ecommerce-embeddings-L'
+# model_name = 'hf-hub:Marqo/marqo-ecommerce-embeddings-B'
+
+model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms(model_name)
+tokenizer = open_clip.get_tokenizer(model_name)
+
+# Preprocess the image and tokenize text inputs
+# Load an example image from a URL
+img = Image.open(requests.get('https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/refs/heads/main/images/dining-chairs.png', stream=True).raw)
+image = preprocess_val(img).unsqueeze(0)
+text = tokenizer(["dining chairs", "a laptop", "toothbrushes"])
+
+# Perform inference
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features = model.encode_image(image, normalize=True)
+    text_features = model.encode_text(text, normalize=True)
+
+    # Calculate similarity probabilities
+    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+
+# Display the label probabilities
+print("Label probs:", text_probs)
+# [1.0000e+00, 8.3131e-12, 5.2173e-12]
+```
+
+### Evaluation
+[Generalised Contrastiove Learning](https://github.com/marqo-ai/GCL) (GCL) is used for the evaluation. The following code can also be found in `scripts`.
+
+```
+git clone https://github.com/marqo-ai/GCL
+```
+Install the packages required by GCL. 
+
+**1. GoogleShopping-Text2Image Retrieval.**
+```
+cd ./GCL
+MODEL=hf-hub:Marqo/marqo-ecommerce-B
+outdir=/MarqoModels/GE/marqo-ecommerce-B/gs-title2image
+hfdataset=Marqo/google-shopping-general-eval
+python  evals/eval_hf_datasets_v1.py \
+      --model_name $MODEL \
+      --hf-dataset $hfdataset \
+      --output-dir $outdir \
+      --batch-size 1024 \
+      --num_workers 8 \
+      --left-key "['title']" \
+      --right-key "['image']" \
+      --img-or-txt "[['txt'], ['img']]" \
+      --left-weight "[1]" \
+      --right-weight "[1]" \
+      --run-queries-cpu \
+      --top-q 4000 \
+      --doc-id-key item_ID \
+      --context-length "[[64], [0]]"
+```
+
+**2. GoogleShopping-Category2Image Retrieval.**
+```
+cd ./GCL
+MODEL=hf-hub:Marqo/marqo-ecommerce-B
+outdir=/MarqoModels/GE/marqo-ecommerce-B/gs-cat2image
+hfdataset=Marqo/google-shopping-general-eval
+python  evals/eval_hf_datasets_v1.py \
+      --model_name $MODEL \
+      --hf-dataset $hfdataset \
+      --output-dir $outdir \
+      --batch-size 1024 \
+      --num_workers 8 \
+      --left-key "['query']" \
+      --right-key "['image']" \
+      --img-or-txt "[['txt'], ['img']]" \
+      --left-weight "[1]" \
+      --right-weight "[1]" \
+      --run-queries-cpu \
+      --top-q 4000 \
+      --doc-id-key item_ID \
+      --context-length "[[64], [0]]"
+```
+
+**3. AmazonProducts-Category2Image Retrieval.**
+```
+cd ./GCL
+MODEL=hf-hub:Marqo/marqo-ecommerce-B
+outdir=/MarqoModels/GE/marqo-ecommerce-B/ap-title2image
+hfdataset=Marqo/amazon-products-eval
+python  evals/eval_hf_datasets_v1.py \
+      --model_name $MODEL \
+      --hf-dataset $hfdataset \
+      --output-dir $outdir \
+      --batch-size 1024 \
+      --num_workers 8 \
+      --left-key "['title']" \
+      --right-key "['image']" \
+      --img-or-txt "[['txt'], ['img']]" \
+      --left-weight "[1]" \
+      --right-weight "[1]" \
+      --run-queries-cpu \
+      --top-q 4000 \
+      --doc-id-key item_ID \
+      --context-length "[[64], [0]]"
+```
+
+## Detailed Performance
+Our benchmarking process was divided into two distinct regimes, each using different datasets of ecommerce product listings: marqo-ecommerce-hard and marqo-ecommerce-easy. Both datasets contained product images and text and only differed in size. The "easy" dataset is approximately 10-30 times smaller (200k vs 4M products), and designed to accommodate rate-limited models, specifically Cohere-Embeddings-v3 and GCP-Vertex (with limits of 0.66 rps and 2 rps respectively). The "hard" dataset represents the true challenge, since it contains four million ecommerce product listings and is more representative of real-world ecommerce search scenarios.
+
+Within both these scenarios, the models were benchmarked against three different tasks:
+
+* Google Shopping Text-to-Image
+* Google Shopping Category-to-Image
+* Amazon Products Text-to-Image
+
+### Marqo-Ecommerce-Hard
+Marqo-Ecommerce-Hard looks into the comprehensive evaluation conducted using the full 4 million dataset, highlighting the robust performance of our models in a real-world context.
+
+**GoogleShopping-Text2Image Retrieval.**
+
+| **Embedding Model**         | **mAP**  | **R@10**  | **MRR**  | **nDCG@10** |
+|-------------------------|------|-------|------|---------|
+| **Marqo-Ecommerce-L**       | **0.682**| **0.878** | **0.683**| **0.726**   |
+| Marqo-Ecommerce-B       | 0.623| 0.832 | 0.624| 0.668   |
+| ViT-SO400M-14-SigLip    | 0.573| 0.763 | 0.574| 0.613   |
+| ViT-L-16-SigLip         | 0.540| 0.722 | 0.540| 0.577   |
+| ViT-B-16-SigLip         | 0.476| 0.660 | 0.477| 0.513   |
+| Amazon-Titan-MultiModal | 0.475| 0.648 | 0.475| 0.509   |
+| Jina-V1-CLIP            | 0.285| 0.402 | 0.285| 0.306   |
+
+**GoogleShopping-Category2Image Retrieval.**  
+
+| **Embedding Model**         | **mAP** | **P@10** | **MRR** | **nDCG@10** |
+|-----------------------------|---------|----------|---------|-------------|
+| **Marqo-Ecommerce-L**           | **0.463**   | **0.652**    | **0.822**   | **0.666**       |
+| Marqo-Ecommerce-B           | 0.423   | 0.629    | 0.810   | 0.644       |
+| ViT-SO400M-14-SigLip        | 0.352   | 0.516    | 0.707   | 0.529       |
+| ViT-L-16-SigLip             | 0.324   | 0.497    | 0.687   | 0.509       |
+| ViT-B-16-SigLip             | 0.277   | 0.458    | 0.660   | 0.473       |
+| Amazon-Titan-MultiModal     | 0.246   | 0.429    | 0.642   | 0.446       |
+| Jina-V1-CLIP                | 0.123   | 0.275    | 0.504   | 0.294       |
+
+**AmazonProducts-Text2Image Retrieval.** 
+
+| **Embedding Model**         | **mAP** | **R@10** | **MRR** | **nDCG@10** |
+|-----------------------------|---------|----------|---------|-------------|
+| **Marqo-Ecommerce-L**           | **0.658**   | **0.854**    | **0.663**   | **0.703**       |
+| Marqo-Ecommerce-B           | 0.592   | 0.795    | 0.597   | 0.637       |
+| ViT-SO400M-14-SigLip        | 0.560   | 0.742    | 0.564   | 0.599       |
+| ViT-L-16-SigLip             | 0.544   | 0.715    | 0.548   | 0.580       |
+| ViT-B-16-SigLip             | 0.480   | 0.650    | 0.484   | 0.515       |
+| Amazon-Titan-MultiModal     | 0.456   | 0.627    | 0.457   | 0.491       |
+| Jina-V1-CLIP                | 0.265   | 0.378    | 0.266   | 0.285       |
+
+### Marqo-Ecommerce-Easy
+This dataset is about 10-30 times smaller than the Marqo-Ecommerce-Hard, and  designed to accommodate rate-limited models, specifically Cohere-Embeddings-v3 and GCP-Vertex.
+
+**GoogleShopping-Text2Image Retrieval.**
+
+| **Embedding Model**         | **mAP** | **R@10** | **MRR** | **nDCG@10** |
+|-----------------------------|---------|----------|---------|-------------|
+| **Marqo-Ecommerce-L**           | **0.879**   | **0.971**    | **0.879**   | **0.901**       |
+| Marqo-Ecommerce-B           | 0.842   | 0.961    | 0.842   | 0.871       |
+| ViT-SO400M-14-SigLip        | 0.792   | 0.935    | 0.792   | 0.825       |
+| GCP-Vertex                  | 0.740   | 0.910    | 0.740   | 0.779       |
+| ViT-L-16-SigLip             | 0.754   | 0.907    | 0.754   | 0.789       |
+| ViT-B-16-SigLip             | 0.701   | 0.870    | 0.701   | 0.739       |
+| Amazon-Titan-MultiModal     | 0.694   | 0.868    | 0.693   | 0.733       |
+| Jina-V1-CLIP                | 0.480   | 0.638    | 0.480   | 0.511       |
+| Cohere-embedding-v3         | 0.358   | 0.515    | 0.358   | 0.389       |
+
+**GoogleShopping-Category2Image Retrieval.**  
+
+| **Embedding Model**         | **mAP** | **P@10** | **MRR** | **nDCG@10** |
+|-----------------------------|---------|----------|---------|-------------|
+| **Marqo-Ecommerce-L**           | **0.515**   | **0.358**    | **0.764**   | **0.590**       |
+| Marqo-Ecommerce-B           | 0.479   | 0.336    | 0.744   | 0.558       |
+| ViT-SO400M-14-SigLip        | 0.423   | 0.302    | 0.644   | 0.487       |
+| GCP-Vertex                  | 0.417   | 0.298    | 0.636   | 0.481       |
+| ViT-L-16-SigLip             | 0.392   | 0.281    | 0.627   | 0.458       |
+| ViT-B-16-SigLip             | 0.347   | 0.252    | 0.594   | 0.414       |
+| Amazon-Titan-MultiModal     | 0.308   | 0.231    | 0.558   | 0.377       |
+| Jina-V1-CLIP                | 0.175   | 0.122    | 0.369   | 0.229       |
+| Cohere-embedding-v3         | 0.136   | 0.110    | 0.315   | 0.178       |
+
+**AmazonProducts-Text2Image Retrieval.** 
+
+| **Embedding Model**         | **mAP** | **R@10** | **MRR** | **nDCG@10** |
+|-----------------------------|---------|----------|---------|-------------|
+| **Marqo-Ecommerce-L**           | **0.92**   | **0.978**    | **0.928**   | **0.940**       |
+| Marqo-Ecommerce-B           | 0.897   | 0.967    | 0.897   | 0.914       |
+| ViT-SO400M-14-SigLip        | 0.860   | 0.954    | 0.860   | 0.882       |
+| ViT-L-16-SigLip             | 0.842   | 0.940    | 0.842   | 0.865       |
+| GCP-Vertex                  | 0.808   | 0.933    | 0.808   | 0.837       |
+| ViT-B-16-SigLip             | 0.797   | 0.917    | 0.797   | 0.825       |
+| Amazon-Titan-MultiModal     | 0.762   | 0.889    | 0.763   | 0.791       |
+| Jina-V1-CLIP                | 0.530   | 0.699    | 0.530   | 0.565       |
+| Cohere-embedding-v3         | 0.433   | 0.597    | 0.433   | 0.465       |
+
+## Citation
+```
+@software{zhu2024marqoecommembed_2024,
+        author = {Tianyu Zhu and and Jesse Clark},
+        month = oct,
+        title = {{Marqo Ecommerce Embeddings - Foundation Model for Product Embeddings}},
+        url = {https://github.com/marqo-ai/marqo-ecommerce-embeddings/},
+        version = {1.0.0},
+        year = {2024}
+        }
+```