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73
product_id
stringlengths
13
20
position
int64
1
100
title
stringlengths
4
86
pair_id
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22
94
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int64
1
100
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float64
1
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no_score
int64
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query_id
stringlengths
1
5
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La Carrie Faux Fur Headband with Stretch Womens Winter Earwarmer Earmuff (White)
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Ugg Women's Shearling Earmuffs - Black
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Prettylittlething Women's Cream Soft Faux Fur Ear Muffs
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Prettylittlething Women's Camel Soft Faux Fur Ear Muffs
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Michael Michael Kors Women's Embellished Faux-Fur Earmuffs - Black
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Dolls Kill x Spongebob Snazy Trail Earmuffs - One Size
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Hig Ear Warmer Unisex Classic Fleece Earmuffs Winter Accessory Outdoor
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Earmuffs
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GERGELLA Ear Muffs for Men Women - Winter Ear Warmers - Warm Ear Covers for Cold ...
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Genuine Sheepskin Earmuffs With Gift Box - Chocolate
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Vorkoi Ear Muffs Earmuff Fashion Unisex Women Men Windproof Winter Ear Warmer ...
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LCXSHYE Winter Ear Muffs Faux Fur Warm Earmuffs Cute Foldable Outdoor Ear Warmers ...
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180s Women's Down Ear Warmers, Black
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Ugg Sheepskin Earmuffs - Black
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Shein 1pc Ladies' Foldable Plush Ear Warmer Ear Muffs Suitable for Winter Outdoor ...
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Aroma Season Heated Ear Warmer Electric Earmuff, Size: One size, Brown
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Cozy Fur-Lined Winter Windproof Plush Earmuffs (4-Pack) - Assorted
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Turtle Fur Ear Muffin - Faux Fur Lined Ivory
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180s Behind-the-Head Earmuffs - Chesterfield Ear Warmer Men Charcoal Gray
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Pia Rossini Hepburn Earmuffs, Pink, Women's, Hair Accessories Earmuffs
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Shein 1pc Women's Foldable Plush Ear Muffs Ear Warmers for Winter Outdoor ...
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FRR Rabbit Fur Earmuffs with Velvet Band
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Lowtagz Women's Faux Fur Earmuffs with Pearl Accent Bow Knot, Foldable Fluffy Ear ...
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ProjecktNomad Brown Herringbone and Faux Fur Earmuffs
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LillSport Earmuffs (Black)
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Hyeeun Faux Fur Ear Muffs Pink One Size
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Vorkoi Foldable Ear Muffs for Women Cold Weather Fluffy Earmuffs Winter Warm ...
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180s - Lush Ear Warmer - Dusty Rose
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Shein 1pc Women's Cute Solid Color Ear Muffs, Winter Ear Warmers for Riding, One ...
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Western Safety Noise Canceling Electronic Ear Muffs
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Damelio Ear Muffs | Womens | Princess Polly
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Bad Piggies Badpiggies 6 Pairs Earbags Bandless Ear Muffs Soft Fleece Winter Ear ...
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Winter Warm Foldable Earmuffs Trendy Color Block Plush Earmuffs Trendy Faux Fur ...
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AWESAFE Electronic Shooting Earmuffs Ear Hearing Protection Headphones for ...
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Techwear Storm Docker Hat Saita, Camel
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Carhartt Acrylic Watch Hat, Black
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Bass Pro Shops Mesh Cap - Light Blue - OSFM - Men's
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Pandaize Tech Aviator Hat: Outdoor Cold-Weather Protection, Water-Resistant ...
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Gelante Plain Blank Baseball Caps Adjustable Back Strap Wholesale Lot 6 Pack
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Besufy Women Girl Solid Color Warm Winter Beret French Artist Beanie Hat Ski Cap ...
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100% Real Sheep Shearing Fur Hat Thicken Winter Warm Baseball Cap
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Goorin Bros. Panthuroy Panther Patch Corduroy Trucker Hat in Whiskey
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Melin Hydro Odyssey Stacked Snapback Hat Navy / Classic
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PURFUN Winter Cozy Plush Ushanka Russian Hat Windproof Thermal Full Hood Earflap ...
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Yirtree Women Girl Solid Color Warm Winter Beret French Artist Beanie Hat Ski Cap ...
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32 Degrees Unisex Waffle Sherpa-Lined Beanie Cement / One Size
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New Era Men's Basic 59FIFTY Fitted Hat
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Ogvpe Leather Cap for Men,Beret HatNewsboy Hat Hunting Fishing Hat Genuine ...
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Holzlrgus Soft Beanies for Women with Brim Berets for Women Bonnet Bucket Hat ...
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Hats Scarves & More Fleece Flower Cloche Hat for Women Cancer Headwear Chemo ...
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Public Lands Adult Core Beanie, Men's, No size, Cream
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Premium Wear Premium Imports Beanie Hat | Fashion Skully Cap for Men & Women with ...
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Varsity Headwear - Soft Front Cashmere Cap Large / Flint Grey
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Men's Herringbone Ivy Beret - Goodfellow & Co Light Gray L/XL
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Original Beanie Cap Soft Knit Beanie Hat Warm and Durable for Winter
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Walbest Men Summer Beret Vintage British Style Solid Color Adjustable Men Beret ...
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Pileus Society Logo Men's Knit Soft Headwear Warm Beanie Cap Black / One Size
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Buy Alpine Beanie Online - Men's Alpine Beanie | Slyk Shades
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CAPLORD Satin Bonnet Lined Sleeping Beanie Hat Bamboo Headwear Frizzy Natural ...
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KANGOLS American Style Kangaroo Wool Felt Baseball Cap Hat - High Quality Real ...
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Haxmnou Womens Knit Cap Big Head Hat Striped Beanie Soft Knit Slouchy Wool Hats ...
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Stylish Fedora Hat with Removable Band - Ideal for Outdoor Activities Navy Blue ...
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Stylish Fedora Hats - Trendy Headwear for Every Occasion
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Luxury Knitted Beanie for Men and Women - Designer Wool Skull Cap Beanie with ...
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Tsalei Y2K Beanie Spider Beanie Double-Layer Knitted Hat Streetwear Headwear ...
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Stylish Flat Brim Fedora Hat with Kerchief - Perfect for Outdoors Khaki / F(56-58)
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Aviator Hat, Women's, Size: One size, Plaid
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Falari 2 Pcs Set Men Women Knitted Beanie Hat Ski Skully Cap Plain Solid Color ...
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Winter Outdoor Padded Adjustable Head Circumference Ski Hat Warm Ear Protected ...
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Sam's Rock Headwear Womens Fuzzy Fedora w/ Band - Black - Small/Medium, Women's ...
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TheLittleTichelLady Quilted Velour Velvet Black Snood for Women, Lined, Comfort ...
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TheLittleTichelLady Knitted Beret Hat for Women, Multiple Options, Cotton Lurex ...
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AMIEHAIRLLC Cover All Head Wrap -Slouchy Beanies Womens Headwear Sleeping Beanies ...
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Phaiy Trapper Hat for Men Women, Winter Hats with Ear Flaps, Full Face Windproof ...
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Avant Garde x Hysteric Glamour Sports Mountain Casual Hat(Bass Cap)(Headwear)Nike ...
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All Seasons Adjustable Size Baseball Cap Men Women - Classic Plain Hat
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SATINIOR Unisex Trapper Hat Faux Fur Plaid Earflaps Hat with Chin Strap Winter ...
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Premium Adjustable Baseball Caps for Men and Women - Hat for Hikers and Bikers
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KUTTOR 3 Pack Winter Knitted Beanie Hats Fleece Lined Skull Cap Beanie Knit Cap ...
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High-Quality Luxury High Profile Baseball Caps for Men and Women - Perfect for ...
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SATINIOR 5 Pieces Women's Slouchy Beanie Hat Stretch Turban Hats Cancer Headwear ...
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Vgogfly Cuffed Beanie for Men Women Knit Skull Cap Warm Stocking Hats Guys Winter ...
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ROYBENS Swag Wool Knit Cuff Short Fisherman Beanie for Men Women, Winter Warm Hats
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Syhood 4 Pieces Men Skull Caps Soft Cotton Beanie Hats Stretchy Helmet Liner ...
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Zowya Winter Visor Beanie Hat for Men & Women Brim Knit Skully Hats Billed Skull ...
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1

Marqo-GS-10M

This dataset is our multimodal, fine-grained, ranking Google Shopping dataset, Marqo-GS-10M, followed by our novel training framework: Generalized Contrastive Learning (GCL). GCL aims to improve and measure the ranking performance of information retrieval models, especially for retrieving relevant products given a search query.

Blog post: https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking

Paper: https://arxiv.org/pdf/2404.08535.pdf

GitHub: https://github.com/marqo-ai/GCL

from datasets import load_dataset

ds = load_dataset("Marqo/marqo-GS-10M")

Questions?

If you have any questions about this dataset, please join our Community and ask one of our team!

Slack Community: https://join.slack.com/t/marqo-community/shared_invite/zt-2b4nsvbd2-TDf8agPszzWH5hYKBMIgDA

Table of Content

  1. Motivation
  2. Dataset and Benchmarks
  3. Instructions to evaluate with the GCL Benchmarks
  4. GCL Training Framwork and Models
  5. Example Usage of Models

1. Motivation

Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular contrastive frameworks typically learn from binary relevance, making them ineffective at incorporating direct fine-grained rankings.

In this paper, we curate a large-scale dataset: Marqo-GS-10M, featuring detailed relevance scores for each query-document pair to facilitate future research and evaluation.

Subsequently, we propose Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking (GCL), which is designed to learn from fine-grained rankings beyond binary relevance score.

Our results show that GCL achieves a 94.5% increase in NDCG@10 for in-domain and 26.3 to 48.8% increases for cold-start evaluations, measured relative to the CLIP baseline within our curated ranked dataset.

2. Dataset and Benchmarks

Dataset Structure

multi split visual

Illustration of multi-dimensional split along both query and document dimensions resulting in 4 splits: training split with 80% of queries and 50% of documents, novel query splitwith the other 20% of queries and the same documents as the training split, novel corpus split with the same queries as the training split and unseen documents with the equal size of the training corpus, and zero-shot split with unseen queries and documents.

Dataset Structure

In this section, we show the dataset structure.

marqo-gs-dataset/
β”œβ”€β”€ marqo_gs_full_10m/
β”‚   β”œβ”€β”€ corpus_1.json
β”‚   β”œβ”€β”€ corpus_2.json
β”‚   β”œβ”€β”€ query_0_product_id_0.csv
β”‚   β”œβ”€β”€ query_0_product_id_0_gt_dev.json
β”‚   β”œβ”€β”€ query_0_product_id_0_gt_test.json
β”‚   β”œβ”€β”€ query_0_product_id_0_queries.json
β”‚   β”œβ”€β”€ query_0_product_id_1.csv
β”‚   β”œβ”€β”€ query_0_product_id_1_gt_dev.json
β”‚   β”œβ”€β”€ query_0_product_id_1_gt_test.json
β”‚   β”œβ”€β”€ query_0_product_id_1_queries.json
β”‚   β”œβ”€β”€ query_1_product_id_0.csv
β”‚   β”œβ”€β”€ query_1_product_id_0_gt_dev.json
β”‚   β”œβ”€β”€ query_1_product_id_0_gt_test.json
β”‚   β”œβ”€β”€ query_1_product_id_0_queries.json
β”‚   β”œβ”€β”€ query_1_product_id_1.csv
β”‚   β”œβ”€β”€ query_1_product_id_1_gt_dev.json
β”‚   β”œβ”€β”€ query_1_product_id_1_gt_test.json
β”‚   └── query_1_product_id_1_queries.json
β”œβ”€β”€ marqo_gs_fashion_5m/
β”œβ”€β”€ marqo_gs_wfash_1m/

For each dataset such as marqo_gs_full_10m, there are 4 splits as discussed before.

  • query_0_product_id_0 represents in-domain set,
  • query_1_product_id_0 represents novel query set,
  • query_0_product_id_1 represents novel document set,
  • query_1_product_id_1 represents zero shot set, For each split, there is a ground truth csv containing triplet information, a set of validation ground truth and a set of test ground truth.

Dataset Downloads

The Marqo-GS-10M dataset is available for direct download. This dataset is pivotal for training and benchmarking in Generalized Contrastive Learning (GCL) frameworks and other multi-modal fine-grained ranking tasks.

You can use the dataset with Hugging Face's datasets:

from datasets import load_dataset

ds = load_dataset("Marqo/marqo-GS-10M")

Alternatively:

  • Full Dataset: Download - Link contains the entire Marqo-GS-10M dataset except for the images.
  • Full Images: Download - Link contains the images of the entire Marqo-GS-10M dataset.
  • Sample Images: Download - Link contains the images for woman fashion category, it corresponds to the woman fashion sub-dataset.

Dataset Visualization

Visualization of the collected triplet dataset containing search queries (top row), documents and scores, showcasing thumbnails of returned products with scores that decrease linearly according to their positions. Dataset Qualitative

3. Instructions to use the GCL Benchmarks

Install environment

conda create -n gcl python=3.8
conda activate gcl
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install jupyterlab pandas matplotlib beir pytrec_eval braceexpand webdataset wandb notebook open_clip_torch
pip install --force-reinstall numpy==1.23.2

Evaluate using GCL benchmarks

  1. Download the Dataset, links above. We recommend try out the Sample set first.
  2. Either prepare your own model or download our finetuned model down below.
  3. Modify eval-vitb32-ckpt.sh to add image dir, eval dir and model path.
  4. Use change_image_paths.py to modify image paths in the csv.
python change_image_paths.py /dataset/csv/dir/path /image/root/path
# Example:
python change_image_paths.py /data/marqo-gs-dataset/marqo_gs_wfash_1m /data/marqo-gs-dataset/images_wfash
  1. Run the eval script:
bash ./scripts/eval-vitb32-ckpt.sh

4. GCL Training Framework and Models

Main Figure Overview of our Generalized Contrastive Learning (GCL) approach. GCL integrates ranking information alongside multiple input fields for each sample (e.g., title and image) across both left-hand-side (LHS) and right-hand-side (RHS). Ground-truth ranking scores are transformed into weights, which are used for computing contrastive losses, ensuring that pairs with higher weights incur greater penalties. Please refer to the paper for full explanation.

Results and Model Downloads

Retrieval and ranking performance comparison of GCL versus publicly available contrastive learning methods assessed by NDCG@10, ERR, and RBP metrics on the GSFull-10M dataset for the In-Domain category. The methods are based on multi-modal approaches:

Multi-Field/Text-Image

Methods Models Size nDCG ERR RBP Downloads
CLIP ViT-L-14 1.6G 0.310 0.093 0.252 model
GCL (ours) ViT-B-32 577M 0.577 0.554 0.446 model
GCL (ours) ViT-L-14 1.6G 0.603 0.562 0.467 model
GCL (ours) ViT-B-32 577M 0.683 0.689 0.515 model
GCL (ours) ViT-L-14 1.6G 0.690 0.630 0.535 model

Text-only

Methods Models nDCG ERR RBP Downloads
BM25 - 0.071 0.028 0.052
E5 e5-large-v2 0.335 0.095 0.289
Cross Entropy xlm-roberta-base-ViT-B-32 0.332 0.099 0.288
GCL (ours) e5-large-v2 0.431 0.400 0.347 model
GCL (ours) xlm-roberta-base-ViT-B-32 0.441 0.404 0.355 model
E5 e5-large-v2 0.470 0.457 0.374 Marqo/marqo-gcl-e5-large-v2-130

5. Example Usage of Models

Quick Demo with OpenCLIP

Here is a quick example to use our model if you have installed open_clip_torch.

python demos/openclip_demo.py 

or

import torch
from PIL import Image
import open_clip
import wget

model_url = "https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/gcl-vitb32-117-gs-full-states.pt"
wget.download(model_url, "gcl-vitb32-117-gs-full-states.pt")
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='gcl-vitb32-117-gs-full-states.pt')
tokenizer = open_clip.get_tokenizer('ViT-B-32')

image = preprocess(Image.open('https://raw.githubusercontent.com/marqo-ai/GCL/main/assets/oxford_shoe.png')).unsqueeze(0)
text = tokenizer(["a dog", "Vintage Style Women's Oxfords", "a cat"])
logit_scale = 10
with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)

    text_probs = (logit_scale * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)

Quick Demo with Hugging Face for E5 models.

Here is a quick example to load our finetuned e5 text models from Hugging Face directly.

import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: Espresso Pitcher with Handle',
               'query: Women’s designer handbag sale',
               "passage: Dianoo Espresso Steaming Pitcher, Espresso Milk Frothing Pitcher Stainless Steel",
               "passage: Coach Outlet Eliza Shoulder Bag - Black - One Size"]

tokenizer = AutoTokenizer.from_pretrained('Marqo/marqo-gcl-e5-large-v2-130')
model_new = AutoModel.from_pretrained('Marqo/marqo-gcl-e5-large-v2-130')

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=77, padding=True, truncation=True, return_tensors='pt')

outputs = model_new(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

Citation

@misc{zhu2024generalized,
      title={Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking}, 
      author={Tianyu Zhu and Myong Chol Jung and Jesse Clark},
      year={2024},
      eprint={2404.08535},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
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