--- license: apache-2.0 dataset_info: features: - name: image dtype: image - name: query dtype: string - name: product_id dtype: string - name: position dtype: int64 - name: title dtype: string - name: pair_id dtype: string - name: score_linear dtype: int64 - name: score_reciprocal dtype: float64 - name: no_score dtype: int64 - name: query_id dtype: string configs: - config_name: default data_files: - split: in_domain path: data/in_domain-* - split: novel_document path: data/novel_document-* - split: novel_query path: data/novel_query-* - split: zero_shot path: data/zero_shot-* language: - en tags: - multimodal - GCL pretty_name: marqo-GS-10M size_categories: - 1M

# 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)](https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking). 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 ```python 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](https://join.slack.com/t/marqo-community/shared_invite/zt-2b4nsvbd2-TDf8agPszzWH5hYKBMIgDA) 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/ ├── 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. You can use the dataset with Hugging Face's `datasets`: ```python from datasets import load_dataset ds = load_dataset("Marqo/marqo-GS-10M") ``` Alternatively: - **Full Dataset**: [Download](https://marqo-gcl-public.s3.amazonaws.com/v1/marqo-gs-dataset.tar) - Link contains the entire Marqo-GS-10M dataset except for the images. - **Full Images**: [Download](https://marqo-gcl-public.s3.amazonaws.com/v1/images_archive.tar) - Link contains the images of the entire Marqo-GS-10M dataset. - **Sample Images**: [Download](https://marqo-gcl-public.s3.amazonaws.com/v1/images_wfash.tar) - 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](https://raw.githubusercontent.com/marqo-ai/GCL/main/assets/visual_dataset_4.png) ## 3. Instructions to use the GCL Benchmarks ### Install environment ```bash 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](./scripts/eval-vitb32-ckpt.sh) to add image dir, eval dir and model path. 4. Use [change_image_paths.py](./evals/change_image_paths.py) to modify image paths in the csv. ```bash 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 bash ./scripts/eval-vitb32-ckpt.sh ``` ## 4. GCL Training Framework and Models ![Main Figure](https://raw.githubusercontent.com/marqo-ai/GCL/main/assets/main_figure1.png) 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](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/clip-vitl14-110-gs-full-states.pt) | | GCL (ours) | ViT-B-32 | 577M | 0.577 | 0.554 | 0.446 | [model](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/gcl-vitb32-117-gs-full-states.pt) | | GCL (ours) | ViT-L-14 | 1.6G | 0.603 | 0.562 | 0.467 | [model](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/gcl-vitl14-120-gs-full-states.pt) | | GCL (ours) | ViT-B-32 | 577M | 0.683 | 0.689 | 0.515 | [model](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/marqo-gcl-vitb32-127-gs-full_states.pt) | | GCL (ours) | ViT-L-14 | 1.6G | **0.690** | **0.630** | **0.535** | [model](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/marqo-gcl-vitl14-124-gs-full_states.pt) | ### 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](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/gcl-e5l-113-gs-full-states.pt) | | GCL (ours) | xlm-roberta-base-ViT-B-32 | 0.441 | 0.404 | 0.355 | [model](https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/gcl-robbxlm-105-gs-full-states.pt) | | 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. ```bash python demos/openclip_demo.py ``` or ```python 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. ```python 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} } ```