Datasets:

Marqo
/

marqo-GS-10M

---
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<n<10M
libraries:
- datasets
- pandas
---


# Marqo-GS-10M
This dataset is our multimodal, fine-grained, ranking dataset, **Marqo-GS-10M** 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

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

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

**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
<img src="https://raw.githubusercontent.com/marqo-ai/GCL/main/assets/ms1.png" alt="multi split visual" width="500"/>

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](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())
```
<!---
### Using VITB32/VITL14/E5 with **marqo** vector search. 
Using model download url for VIT models
```python
import marqo
# create an index with your custom model
mq = marqo.Client(url='http://localhost:8882')
settings = {
    "treatUrlsAndPointersAsImages": True,
    "model": "generic-clip-test-model-1",
    "modelProperties": {
        "name": "ViT-B-32",
        "dimensions": 512,
        "url": "https://marqo-gcl-public.s3.us-west-2.amazonaws.com/v1/marqo-gcl-vitb32-127-gs-full_states.pt",
        "type": "open_clip",
    },
    "normalizeEmbeddings": True,
}

response = mq.create_index("my-own-clip", settings_dict=settings)
```
Using Hugging Face for our finetuned E5 models
```python
import marqo
# create an index with your custom model
mq = marqo.Client(url='http://localhost:8882')
model_properties = {
    "name": "Marqo/marqo-gcl-e5-large-v2-130",
    "dimensions": 1024,
    "type": "hf"
}

mq.create_index("test_e5", model="my_custom_e5", model_properties=model_properties)
```
-->

## 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}
}
```