Update README.md

README.md

@@ -44,20 +44,28 @@ size_categories:
 - 1M<n<10M
 ---
 
+<div style="display: flex; align-items: center; gap: 10px;">
+  <a href="https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking">
+    <img src="https://img.shields.io/badge/Marqo-Blog-blue?logo=font-awesome&logoColor=white&style=flat&logo=pencil-alt" alt="Blog">
+  </a>
+  <a href="https://arxiv.org/pdf/2404.08535.pdf">
+    <img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv" alt="arXiv Paper">
+  </a>
+  <a href="https://github.com/marqo-ai/GCL">
+    <img src="https://img.shields.io/badge/GitHub-Repo-lightgrey?logo=github" alt="GitHub Repo">
+  </a>
+</div>
 
 # 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
+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.
 
-GitHub: https://github.com/marqo-ai/GCL
+```python
+from datasets import load_dataset
 
-This work aims to improve and measure the **ranking** performance of information retrieval models, 
-especially for retrieving relevant **products** given a search query.
+ds = load_dataset("Marqo/marqo-GS-10M")
+```
 
-**Release WIP**: GCL Training Framework.
 ## Table of Content
 1. Motivation
 2. Dataset and Benchmarks
@@ -124,6 +132,16 @@ 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.