Improve dataset card: Add paper and code links, sample usage, and refine tags

This PR enhances the dataset card for "ABC-Pretraining-Data" by:

- Updating the initial summary to better describe the dataset's purpose.
- Adding explicit links to the official Hugging Face paper page (`https://huggingface.co/papers/2503.00329`) and the GitHub repository (`https://github.com/TIGER-AI-Lab/ABC`).
- Including a "Sample Usage" section based on the project's GitHub README, guiding users on how to quickly get started with the model and fetch the dataset.
- Expanding the metadata tags with `multimodal`, `vision-language-model`, and `retrieval` to improve discoverability and accurately reflect the dataset's context within the ABC model.

These changes provide more comprehensive and accessible information for users interacting with the dataset.

README.md

@@ -33,31 +33,57 @@ dataset_info:
   dataset_size: 2289772991
 tags:
 - visual
+- multimodal
+- vision-language-model
+- retrieval
 ---
 
 ## ABC Pretraining Data
 
-<!-- Provide a quick summary of the dataset. -->
-This the the pretraining data for ABC. This dataset is derived from Google's [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/) dataset.
-The each item in the dataset contain a URL where the corresponding image can be downloaded and mined negatives for each item. Full dataaset is ~300 GB of images. For a detailed description of how we mined the negatives please check out our ppaer ;).   
-**Update** I have added the images to this repository, for an example of how to use and download this dataset see our [repository](https://github.com/TIGER-AI-Lab/ABC).
+This dataset contains the pretraining data for ABC, an open-source multimodal embedding model that uses a vision-language model backbone to deeply integrate image features with natural language instructions, advancing the state of visual embeddings with natural language control.
 
-## Paper and Website
+This dataset is derived from Google's [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/) dataset.
+Each item in the dataset contains a URL where the corresponding image can be downloaded and mined negatives for each item. The full dataset is ~300 GB of images. For a detailed description of how we mined the negatives, please check out our paper.
+**Update**: The images have been added to this repository. For an example of how to use and download this dataset, see our [repository](https://github.com/TIGER-AI-Lab/ABC).
 
-For more information, please refer to [Website](https://tiger-ai-lab.github.io/ABC/).
+## Paper, Project Page, and Code
 
-## Citation
+- Paper: [ABC: Achieving Better Control of Multimodal Embeddings using VLMs](https://huggingface.co/papers/2503.00329)
+- Project Page: [https://tiger-ai-lab.github.io/ABC/](https://tiger-ai-lab.github.io/ABC/)
+- Code: [https://github.com/TIGER-AI-Lab/ABC](https://github.com/TIGER-AI-Lab/ABC)
 
-If you find any of our work helpful please connsider citing:
+## Sample Usage
 
+### Quick Start
+First, install the necessary dependencies by cloning the repository and installing requirements:
+```bash
+git clone https://github.com/TIGER-AI-Lab/ABC
+cd ABC
+pip install -r requirements.txt
+```
+Then, you can start making multimodal embeddings:
+```python
+python -i ./quick_start.py
 ```
+
+### Fetching Datasets from 🤗 Hub
+Our datasets are hosted on HuggingFace Hub. The text data and dataset metadata can be fetched using HF's `load_dataset` utility.
+To fetch the images from our datasets, we provide scripts in the `fetch_datasets` directory.
+These scripts will pull the pretraining/finetuning image data off the hub and unpack them in your huggingface datasets cache (under a directory called `tigerlab`).
+Run `python ./fetch_datasets/pretrain.py` to get the pretraining dataset and `python ./fetch_datasets/instruct.py` to get the finetuning dataset, respectively.
+
+## Citation
+
+If you find any of our work helpful, please consider citing:
+
+```bibtex
 @misc{schneider2025abcachievingbettercontrol,
-      title={ABC: Achieving Better Control of Multimodal Embeddings using VLMs}, 
+      title={ABC: Achieving Better Control of Multimodal Embeddings using VLMs},
       author={Benjamin Schneider and Florian Kerschbaum and Wenhu Chen},
       year={2025},
       eprint={2503.00329},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2503.00329}, 
+      url={https://arxiv.org/abs/2503.00329},
 }
 ```