Release DCR Artifacts On Hugging Face

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Introduction

As a researcher, making your work discoverable and accessible to the community is crucial for its impact and adoption. Hugging Face, a leading platform for natural language processing (NLP) and artificial intelligence (AI), offers a unique opportunity to share your research with a broader audience. In this article, we will explore how to release DCR artifacts on Hugging Face, making your models, datasets, and demos easily accessible to the community.

What is Hugging Face?

Hugging Face is an open-source platform that provides a suite of tools and resources for NLP and AI research. Its mission is to democratize access to AI and make it easier for researchers and developers to build and share their models. With Hugging Face, you can easily upload, share, and discover models, datasets, and demos, making it an ideal platform for researchers and developers to collaborate and advance the field.

Benefits of Releasing Artifacts on Hugging Face

Releasing your artifacts on Hugging Face offers several benefits, including:

  • Improved discoverability: By making your artifacts available on Hugging Face, you increase their visibility and discoverability, making it easier for others to find and use your work.
  • Community engagement: Hugging Face provides a platform for discussion and feedback, allowing you to engage with the community and receive valuable insights and suggestions.
  • Collaboration: By sharing your artifacts, you open up opportunities for collaboration and co-creation with other researchers and developers.
  • Credibility: Publishing your work on Hugging Face demonstrates your commitment to sharing your research and contributing to the community.

Uploading Models

Uploading models to Hugging Face is a straightforward process. Here's a step-by-step guide:

Using the PyTorchModelHubMixin Class

You can leverage the PyTorchModelHubMixin class to add from_pretrained and push_to_hub to any custom nn.Module. This class provides a convenient way to upload your models to Hugging Face.

from huggingface_hub import PyTorchModelHubMixin

class MyModel(nn.Module, PyTorchModelHubMixin):
    def __init__(self):
        super(MyModel, self).__init__()
        # Initialize your model here
        self.fc1 = nn.Linear(5, 10)
        self.fc2 = nn.Linear(10, 5)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Create an instance of your model
model = MyModel()

# Push your model to Hugging Face
model.push_to_hub("my-model")

Using the hf_hub_download Function

Alternatively, you can use the hf_hub_download function to download a checkpoint from the hub.

import torch
from huggingface_hub import hf_hub_download

# Download a checkpoint from the hub
checkpoint = hf_hub_download("my-model", "model.pth")

# Load the checkpoint
model = torch.load(checkpoint)

Uploading Datasets

Uploading datasets to Hugging Face is also a straightforward process. Here's a step-by-step guide:

Using the Datasets Library

You can use the datasets library to load and upload your datasets to Hugging Face.

from datasets import load_dataset

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

# Push your dataset to Hugging Face
dataset.push_to_hub("your-dataset")

Using the Dataset Viewer

The dataset viewer allows people to quickly explore the first few rows of the data in the browser.

from datasets import load_dataset

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

# View the first few rows of the data
dataset.show()

Conclusion

Introduction

In our previous article, we explored how to release DCR artifacts on Hugging Face, making your models, datasets, and demos easily accessible to the community. In this article, we will answer some frequently asked questions (FAQs) about releasing artifacts on Hugging Face.

Q: What is the benefit of releasing my artifacts on Hugging Face?

A: Releasing your artifacts on Hugging Face offers several benefits, including improved discoverability, community engagement, collaboration, and credibility. By making your work available on Hugging Face, you increase its visibility and discoverability, making it easier for others to find and use your work.

Q: How do I upload my models to Hugging Face?

A: Uploading models to Hugging Face is a straightforward process. You can use the PyTorchModelHubMixin class to add from_pretrained and push_to_hub to any custom nn.Module. Alternatively, you can use the hf_hub_download function to download a checkpoint from the hub.

Q: How do I upload my datasets to Hugging Face?

A: Uploading datasets to Hugging Face is also a straightforward process. You can use the datasets library to load and upload your datasets to Hugging Face. The dataset viewer allows people to quickly explore the first few rows of the data in the browser.

Q: Can I link my checkpoints to the paper page?

A: Yes, you can link your checkpoints to the paper page. By pushing each model checkpoint to a separate model repository, you can link the checkpoints to the paper page, making it easier for others to find and use your work.

Q: How do I claim my paper as mine on Hugging Face?

A: To claim your paper as yours on Hugging Face, you need to submit it to the Hugging Face paper page. You can do this by following the instructions on the Hugging Face website.

Q: Can I add tags to my models and datasets on Hugging Face?

A: Yes, you can add tags to your models and datasets on Hugging Face. This makes it easier for others to find and use your work by filtering models and datasets based on specific tags.

Q: How do I get help with releasing my artifacts on Hugging Face?

A: If you need help with releasing your artifacts on Hugging Face, you can contact the Hugging Face support team. They will be happy to assist you with any questions or issues you may have.

Conclusion

Releasing your DCR artifacts on Hugging Face is a great way to make your work discoverable and accessible to the community. By following the steps outlined in this article and answering the FAQs, you can easily upload your models, datasets, and demos to Hugging Face, increasing their visibility and discoverability. We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page. Let us know if you're interested/need any help regarding this!