Boost UCO Discoverability: Models & Datasets On Hugging Face

Hey there! 👋 Niels from the open-source team at Hugging Face here. I stumbled upon your awesome work on Arxiv and was super intrigued. I thought, wouldn't it be fantastic to make your research even more visible? That's why I'm reaching out to explore the possibility of sharing your UCO artifacts – specifically your models and datasets – on the Hugging Face Hub.

Enhancing Discoverability: Why Hugging Face? 🚀

Hugging Face is a vibrant community and a central hub for all things related to machine learning and natural language processing. Think of it as the go-to place where researchers, developers, and enthusiasts come together to share, discover, and collaborate on cutting-edge projects. By making your UCO model checkpoints and datasets available on the Hub, you'll significantly increase their discoverability. This means more people will find your work, use it, build upon it, and cite it. It's a win-win! 🙌

I noticed in your GitHub README that you're planning to release the code and data soon – that's fantastic news! I believe that sharing these artifacts on the Hugging Face Hub would be a game-changer for your project. We can add tags to your models and datasets, making them easily searchable on the Hugging Face models and datasets pages. This helps people who are specifically looking for UCO-related resources to find your work quickly. This also helps with the SEO (Search Engine Optimization) of your content.

Furthermore, submitting your paper to hf.co/papers is another great way to enhance visibility. This platform allows people to discuss your paper, find its artifacts (like your models, datasets, or demos), and even claim the paper as their own, which will show up on your public profile on Hugging Face. You can also link your GitHub and project page URLs. It's an excellent way to create a centralized location for all things related to your research.

The Benefits of Using Hugging Face

• Increased Visibility: Reach a global audience of researchers and practitioners.
• Enhanced Collaboration: Encourage others to use, build upon, and contribute to your work.
• Easy Access: Provide a simple way for others to download and use your models and datasets.
• Community Support: Benefit from the vast resources and community support offered by Hugging Face.

Uploading Your Models: A Step-by-Step Guide 🛠️

Let's talk about how to get your UCO model checkpoints up on the Hub. Hugging Face offers a smooth and straightforward process. Here's a simplified guide, with links to the official documentation for more detailed instructions.

1. Prepare Your Model: Ensure your model is ready for sharing. This means making sure it's well-documented, includes a clear license, and is free of any sensitive data. It's good practice to create a README.md file in your model repository to provide information about the model, its usage, and how to cite your work.

2. Choose Your Method: Hugging Face provides several ways to upload your models. One popular method involves using the PyTorchModelHubMixin class. This adds from_pretrained and push_to_hub methods to your custom nn.Module. This simplifies the process, allowing you to easily load and upload your model to the Hub.

3. Create a Repository: Consider pushing each model checkpoint to a separate model repository. This is recommended because it allows for things like download statistics to work correctly. You can then link the checkpoints to your paper page on Hugging Face, creating a complete resource for your work.

4. Upload Your Model: Using push_to_hub, you can upload your trained model to the Hugging Face Hub. Make sure to include all necessary files, such as the model weights, configuration files, and tokenizer (if applicable).

5. Add Tags and Metadata: When uploading, you can add relevant tags to your model repository to make it easier for people to find. This could include tags like