Making Your Work More Discoverable

Oct 13, 2025 by talynt.org 93 views

Introduction

As a researcher, making your work accessible to the community is crucial for its impact and adoption. Hugging Face provides a platform for researchers to share their models, datasets, and papers, making it easier for others to discover and build upon their work. In this article, we will guide you through the process of releasing your TrajectoryCrafter model and dataset on Hugging Face.

Discovering Your Work

I am Niels, a member of the open-source team at Hugging Face. I came across your work through Hugging Face's daily papers, where your paper was featured. This is a great opportunity to share your work with the community and get feedback from others. The paper page on Hugging Face allows people to discuss your paper, find artifacts related to it, and even claim the paper as yours, which will showcase on your public profile.

Making Your Work More Discoverable

To make your work more discoverable, we can add your checkpoints and dataset to the Hugging Face hub. This will improve their visibility and allow people to find them when filtering models and datasets on the hub. We can add tags to help people find your work more easily.

Uploading Models

To upload your models to the Hugging Face hub, you can follow the guide provided on the Hugging Face documentation: https://huggingface.co/docs/hub/models-uploading. In this case, you can leverage the PyTorchModelHubMixin class, which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, you can use the hf_hub_download one-liner to download a checkpoint from the hub.

Uploading Dataset

Making your dataset available on the Hugging Face hub will allow people to easily load and explore your data. You can follow the guide provided on the Hugging Face documentation: https://huggingface.co/docs/datasets/loading. This will enable people to load your dataset using the following code:

from datasets import load_dataset

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

Dataset Viewer

The Hugging Face hub also provides a dataset viewer, which allows people to quickly explore the first few rows of your data in the browser. This is a great way to give people a glimpse into your data and encourage them to explore it further.

Getting Help

If you need any help regarding this process, please don't hesitate to reach out. We are here to support you and help you make your work more accessible to the community.

Conclusion

Releasing your TrajectoryCrafter model and dataset on Hugging Face is a great way to share your work with the community and get feedback from others. By following the guides provided on the Hugging Face documentation, you can make your work more discoverable and accessible to others. We encourage you to take advantage of this opportunity and share your work with the world.

Additional Resources

Hugging Face documentation: https://huggingface.co/docs
Hugging Face hub: https://huggingface.co
Hugging Face daily papers: https://huggingface.co/papers

Contact Us

Introduction

In our previous article, we guided you through the process of releasing your TrajectoryCrafter model and dataset on Hugging Face. However, we understand that you may have questions and concerns about the process. In this article, we will address some of the most frequently asked questions about releasing your work on Hugging Face.

Q: What is the Hugging Face hub?

A: The Hugging Face hub is a platform that allows researchers to share their models, datasets, and papers with the community. It provides a centralized location for people to discover and access your work.

Q: How do I upload my model to the Hugging Face hub?

A: To upload your model to the Hugging Face hub, you can follow the guide provided on the Hugging Face documentation: https://huggingface.co/docs/hub/models-uploading. You can also leverage the PyTorchModelHubMixin class, which adds from_pretrained and push_to_hub to any custom nn.Module.

Q: How do I upload my dataset to the Hugging Face hub?

A: To upload your dataset to the Hugging Face hub, you can follow the guide provided on the Hugging Face documentation: https://huggingface.co/docs/datasets/loading. This will enable people to load your dataset using the following code:

from datasets import load_dataset

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

Q: What is the dataset viewer, and how does it work?

A: The dataset viewer is a tool that allows people to quickly explore the first few rows of your data in the browser. It is a great way to give people a glimpse into your data and encourage them to explore it further.

Q: How do I make my work more discoverable on the Hugging Face hub?

A: To make your work more discoverable on the Hugging Face hub, you can add tags to your model and dataset. This will help people find your work when filtering models and datasets on the hub.

Q: Can I use the Hugging Face hub for commercial purposes?

A: Yes, you can use the Hugging Face hub for commercial purposes. However, you must comply with the terms of service and ensure that you have the necessary permissions to use the models and datasets.

Q: How do I get help with releasing my work on the Hugging Face hub?

A: If you need help with releasing your work on the Hugging Face hub, please don't hesitate to reach out to us. We are here to support you and help you make your work more accessible to the community.

Q: What are the benefits of releasing my work on the Hugging Face hub?

A: Releasing your work on the Hugging Face hub provides several benefits, including:

Increased visibility and discoverability of your work
Ability to share your work with the community and get feedback
Opportunity to collaborate with other researchers and developers
Ability to use the Hugging Face hub for commercial purposes

Conclusion

Additional Resources

Hugging Face documentation: https://huggingface.co/docs
Hugging Face hub: https://huggingface.co
Hugging Face daily papers: https://huggingface.co/papers

Contact Us

If you have any questions or need help regarding this process, please don't hesitate to reach out to us at Hugging Face. We are here to support you and help you make your work more accessible to the community.