Marqo/marqo-ecommerce-embeddings-L

Marqo E-commerce Embedding Models

In this work, we introduce two state-of-the-art embedding models for e-commerce: Marqo-Ecommerce-B and Marqo-Ecommerce-L. Trained on over 60M unique products, they are over 30% better compared to Amazon Titan Embedding services for e-commerce retrieval tasks.

Released Content:

1. Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models
2. GoogleShopping-1m and AmazonProducts-3m for evaluation
3. Evaluation Code

Models

Two models are available. The B model is smaller and faster for inference with a snaller embedding dimension. The L model is larger, has a larger embedding dimension, but has better retrieval performance. The models can be loaded in several ways. See below for available options.

Embedding Model	#Params (m)	Dimension	HuggingFace	Download .pt
Marqo-Ecommerce-B	203	768	Marqo/marqo-ecommerce-embeddings-B	link
Marqo-Ecommerce-L	652	1024	Marqo/marqo-ecommerce-embeddings-L	link

HuggingFace with transformers

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-FashionCLIP/main/docs/fashion-hippo.png', stream=True).raw).convert("RGB")
image = [img]
text = ["a hat", "a t-shirt", "shoes"]
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)
# [9.9955e-01, 4.4712e-04, 4.4010e-06]]

HuggingFace with OpenCLIP

pip install open_clip_torch

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, 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-FashionCLIP/main/docs/fashion-hippo.png', stream=True).raw)
image = preprocess_val(img).unsqueeze(0)
text = tokenizer(["a hat", "a t-shirt", "shoes"])

# 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)
# [9.9955e-01, 4.4712e-04, 4.4010e-06]]

Evaluation with GCL

git clone https://github.com/marqo-ai/GCL

Install the packages required by GCL.

cd ./GCL
MODEL=hf-hub:Marqo/marqo-ecommerce-L
outdir=/MarqoModels/GE/marqo-ecommerce-L/gs-title2image2
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]]"

Detailed Performance

GoogleShopping-Text2Image Retrieval.

Embedding Model	mAP	P@10	R@10	MRR
Marqo-Ecommerce-L	0.682	0.089	0.878	0.683
Marqo-Ecommerce-B	0.623	0.084	0.832	0.624
Amazon-Titan-MultiModal	0.475	0.065	0.648	0.475
ViT-B-16-SigLip	0.476	0.067	0.660	0.477
ViT-L-16-SigLip	0.540	0.073	0.722	0.540

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
Amazon-Titan-MultiModal	0.246	0.429	0.642	0.446
ViT-B-16-SigLip	0.277	0.458	0.660	0.473
ViT-L-16-SigLip	0.324	0.497	0.687	0.509

AmazonProducts-Text2Image Retrieval.

Embedding Model	mAP	P@10	R@10	MRR
Marqo-Ecommerce-L	0.658	0.096	0.854	0.663
Marqo-Ecommerce-B	0.592	0.089	0.795	0.597
Amazon-Titan-MultiModal	0.456	0.064	0.627	0.457
ViT-B-16-SigLip	0.480	0.070	0.650	0.484
ViT-L-16-SigLip	0.544	0.077	0.715	0.548