Release Your EGMN Artifacts On Hugging Face

Hey everyone! 👋 Niels from the Hugging Face open-source team reached out to @BestActionNow about their exciting work on Arxiv and suggested submitting it to hf.co/papers to boost its visibility. This is a fantastic opportunity to get more eyes on their research, and it’s something we should all consider when publishing our work.

Why Submit to Hugging Face Papers?

Submitting your paper to Hugging Face Papers offers several key advantages. Let's dive into the benefits of submitting your paper. This platform allows for discussions around your paper and makes it easier for people to find associated artifacts like models, datasets, and demos. You can also claim the paper as your own, which then displays it on your public profile on Hugging Face. Plus, you can add links to your GitHub repository and project pages, making it a central hub for all things related to your research. Enhancing the discoverability of your work is crucial in the field of machine learning, and Hugging Face Papers provides a great avenue for this.

Claiming Your Paper and Enhancing Your Profile

Claiming your paper on Hugging Face is a significant step towards building your professional presence in the AI community. By claiming your paper, it automatically gets linked to your Hugging Face profile, providing a direct connection between your research and your online identity. This not only helps in showcasing your work but also makes it easier for others to find and follow your contributions. Adding links to your GitHub repository and project pages further enriches your profile, creating a comprehensive view of your work. Building a strong profile is key for collaborations, job opportunities, and overall recognition in the field.

The Importance of Open Discussion

The discussion feature on Hugging Face Papers is invaluable for fostering community engagement and feedback. By allowing others to comment and discuss your paper directly on the platform, you open up avenues for valuable insights, suggestions, and potential collaborations. This interactive environment helps in refining your research, addressing concerns, and even identifying new directions for exploration. Encouraging open discussions can lead to significant advancements and a deeper understanding of your work within the AI community.

Making Artifacts Available on the Hub

Niels also pointed out that @BestActionNow mentioned in their GitHub README that additional datasets and in-depth details from their experiments would be synchronized if the manuscript is accepted. This is where the Hugging Face Hub comes in! Making these pre-trained model checkpoints and datasets available on the Hub can significantly improve their discoverability and visibility. Sharing your artifacts is essential for reproducibility and further research in the field. The Hugging Face Hub provides a centralized platform for this, ensuring that your work reaches a wider audience.

Leveraging Tags for Discoverability

Hugging Face allows you to add tags to your models and datasets, which helps users filter and find your work more easily. This is a crucial step in optimizing the discoverability of your artifacts. By using relevant tags, you ensure that your models and datasets appear in the appropriate search results, attracting the right audience. For example, you can tag your models based on the architecture, task, or specific application. Strategic tagging is a simple yet effective way to increase the visibility of your work on the Hugging Face Hub.

The Benefits of Download Stats

Pushing each model checkpoint to a separate repository on the Hugging Face Hub allows for accurate tracking of download statistics. This provides valuable insights into the impact and reach of your work. By monitoring download stats, you can gauge the popularity of your models, understand which checkpoints are most used, and potentially identify areas for improvement. These metrics are not only helpful for you but also for the community, as they highlight the most valuable resources. Tracking download stats is a key benefit of using the Hugging Face Hub for sharing your models.

Uploading Your Models to Hugging Face

So, how do you actually upload your models? Hugging Face provides excellent tools and guides to make this process smooth. Uploading models to the Hugging Face Hub is a straightforward process, thanks to the available resources and mixins. The platform offers flexible methods to suit different workflows, ensuring that your models can be easily shared and accessed by the community. Whether you prefer using mixins or direct file uploads, Hugging Face has you covered.

Utilizing PyTorchModelHubMixin

For PyTorch models, the PyTorchModelHubMixin class is a game-changer. This class adds from_pretrained and push_to_hub methods to any custom nn.Module, making it incredibly easy to upload and download models. The PyTorchModelHubMixin simplifies the model sharing process. By integrating this mixin into your model class, you can seamlessly push your model to the Hugging Face Hub with just a few lines of code. This not only saves time but also ensures consistency in how models are shared and loaded.

Alternative: hf_hub_download

Alternatively, you can use the hf_hub_download one-liner to download checkpoints directly from the Hub. This is perfect for quick access to specific files without needing the entire model repository. The hf_hub_download function offers a convenient way to fetch individual files from the Hugging Face Hub. This is particularly useful when you only need a specific checkpoint or configuration file. Its simplicity and efficiency make it a valuable tool for researchers and practitioners alike.

Best Practice: Separate Repositories for Checkpoints

Hugging Face recommends pushing each model checkpoint to a separate model repository. This allows for better organization and enables download stats to work correctly. Organizing checkpoints in separate repositories offers several benefits. It not only improves the clarity and structure of your Hugging Face Hub profile but also enables accurate tracking of download statistics for each checkpoint. This granular view of model usage is crucial for understanding the impact of different training runs and versions.

Uploading Datasets to Hugging Face

Sharing your datasets is just as important as sharing your models. The Hugging Face Hub makes this incredibly easy too! Sharing datasets on the Hugging Face Hub is a cornerstone of collaborative research. By making your datasets publicly available, you contribute to the collective knowledge of the community and facilitate reproducibility. The Hub offers a streamlined process for uploading and managing datasets, ensuring they are easily accessible to others.

The Power of load_dataset

Imagine how simple it is for others to use your dataset:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/your-dataset")

This one-liner makes your dataset instantly accessible to anyone using the datasets library. The load_dataset function is a game-changer for data accessibility. It allows users to seamlessly load datasets from the Hugging Face Hub with a single line of code. This not only simplifies the data loading process but also promotes standardization and collaboration within the AI community.

Exploring Datasets with the Dataset Viewer

Furthermore, the dataset viewer allows people to quickly explore the first few rows of your data directly in their browser. This makes it incredibly easy to get a feel for the data without downloading it. The Dataset Viewer provides an interactive way to explore datasets on the Hugging Face Hub. It allows users to preview the first few rows of the data, understand its structure, and get a sense of its content without needing to download the entire dataset. This feature is invaluable for data discovery and assessment.

Getting Involved and Seeking Help

Niels offered his help, and I encourage everyone to reach out if you're interested in uploading your work to Hugging Face! The Hugging Face community is incredibly supportive, so don't hesitate to ask for assistance. Community support is a hallmark of the Hugging Face ecosystem. Whether you're uploading models, datasets, or papers, you can always count on the community for guidance and assistance. Don't hesitate to reach out for help, as collaborative efforts often lead to the best outcomes.

Contributing to the Hugging Face Hub can significantly enhance the visibility and impact of your work. By sharing your models, datasets, and papers, you contribute to the collective knowledge of the community and facilitate further research. The Hub provides the tools and resources necessary to make this process seamless, encouraging collaboration and innovation in the field of AI.

So, what are you waiting for? Let's make our work more accessible and impactful by leveraging the Hugging Face Hub! 🎉