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---
dataset_info:
 features:
 - name: image
 dtype: image
 - name: item_ID
 dtype: string
 - name: query
 dtype: string
 - name: title
 dtype: string
 - name: position
 dtype: int64
 splits:
 - name: data
 num_bytes: 22251545141.2
 num_examples: 982700
 download_size: 21955883446
 dataset_size: 22251545141.2
configs:
- config_name: default
 data_files:
 - split: data
 path: data/data-*
---
<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-Join_Marqo_Community-purple?logo=Slack" alt=Slack Community">
 </a>
</div>
# Marqo Ecommerce Embedding Models
**In this work, we introduce the GoogleShopping-1m 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}
 }
```