marqo-ai/GCL

Generalised Contrastive Learning. This is a Repository for Google Shopping Dataset and Benchmarks followed by our novel fine-grained contrastive learning framework.

Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking

This is the repository for Marqo-GS-10M (a multi-modal fine-grained ranking dataset), followed by our novel training framework: Generalized Contrastive Learning (GCL).

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

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

This work aims to improve and measure the ranking performance of information retrieval models, especially for retrieving relevant products given a search query.

Released Content: 1) Full Dataset, 2) Evaluation Code and Benchmarks, 3) Models Finetuned by Marqo GCL.

Release WIP: GCL Training Framework.

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

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/
├── marqo_gs_full_10m/
│   ├── corpus_1.json
│   ├── corpus_2.json
│   ├── query_0_product_id_0.csv
│   ├── query_0_product_id_0_gt_dev.json
│   ├── query_0_product_id_0_gt_test.json
│   ├── query_0_product_id_0_queries.json
│   ├── query_0_product_id_1.csv
│   ├── query_0_product_id_1_gt_dev.json
│   ├── query_0_product_id_1_gt_test.json
│   ├── query_0_product_id_1_queries.json
│   ├── query_1_product_id_0.csv
│   ├── query_1_product_id_0_gt_dev.json
│   ├── query_1_product_id_0_gt_test.json
│   ├── query_1_product_id_0_queries.json
│   ├── query_1_product_id_1.csv
│   ├── query_1_product_id_1_gt_dev.json
│   ├── query_1_product_id_1_gt_test.json
│   └── query_1_product_id_1_queries.json
├── marqo_gs_fashion_5m/
├── 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.

• 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.

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

5. Run the eval script:

bash ./scripts/eval-vitb32-ckpt.sh

4. GCL Training Framework and Models

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('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}
}