How To Release Models And Datasets On Hugging Face For Increased Visibility

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Hey guys! Today, we're diving deep into how you can boost the visibility and accessibility of your research work by releasing your models and datasets on the Hugging Face Hub. This guide is inspired by a real discussion between Niels from the Hugging Face open-source team and a researcher, @whiteinblue, about their project on Arxiv.

Why Release on Hugging Face?

The Hugging Face Hub is a fantastic platform for researchers to share their work with the broader community. By making your models and datasets available, you can:

  • Increase Discoverability: The Hub's search and filtering capabilities make it easier for others to find your work.
  • Improve Visibility: Your projects gain exposure through download stats and links to your paper page.
  • Facilitate Collaboration: Sharing your work encourages others to build upon your research.
  • Simplify Usage: The Hub provides tools for easy downloading and integration of your models and datasets.

Uploading Your Models to Hugging Face

Step-by-Step Guide

  1. Prepare Your Model: Before uploading, ensure your model is in a compatible format. For PyTorch models, the PyTorchModelHubMixin class is incredibly useful. This mixin adds from_pretrained and push_to_hub methods to your custom nn.Module, simplifying the upload process. Alternatively, you can use the hf_hub_download one-liner to download checkpoints.

    Main keywords: Uploading models, PyTorchModelHubMixin, hf_hub_download

    When preparing your models for release on Hugging Face, it's essential to ensure they are in a format that is both accessible and easy to use for the community. The PyTorchModelHubMixin class is a powerful tool that simplifies this process, especially for those working with PyTorch. This mixin adds two crucial methods—from_pretrained and push_to_hub—directly to your custom nn.Module, streamlining the workflow for uploading and sharing your models. By leveraging this class, you can seamlessly integrate your models with the Hugging Face Hub, making them readily available for others to download and utilize in their own projects. Furthermore, the PyTorchModelHubMixin ensures that your models are compatible with the Hugging Face ecosystem, enhancing their discoverability and usability. Alternatively, if you need a quick way to download a specific checkpoint, the hf_hub_download function offers a simple one-liner solution. This function allows you to directly fetch a model checkpoint from the Hub, making it a convenient option for accessing and testing different versions of a model or for incorporating specific components into your work. By offering these versatile methods, Hugging Face provides researchers and developers with the flexibility to manage and share their models effectively, fostering collaboration and accelerating progress in the field. Remember, clear and concise model preparation is the foundation for a successful release on the Hugging Face Hub, ensuring your work reaches a wider audience and has a greater impact.

  2. Create a Repository: Create a new model repository on the Hugging Face Hub. This repository will house your model files and metadata.

  3. Push Your Model: Use the push_to_hub method or the Hugging Face CLI to upload your model checkpoints and configuration files to the repository.

    Main keywords: Push Your Model, Hugging Face CLI, repository

    Once you've prepared your model and created a repository on the Hugging Face Hub, the next crucial step is to push your model to the Hub, making it accessible to the broader community. This process involves uploading your model checkpoints, configuration files, and any other necessary metadata to your repository. One of the most straightforward methods for accomplishing this is by using the push_to_hub method, which seamlessly integrates with your PyTorch workflow, especially if you've utilized the PyTorchModelHubMixin class. This method simplifies the upload process, allowing you to push your model directly from your training environment to the Hugging Face Hub with minimal effort. Alternatively, the Hugging Face CLI offers a powerful command-line interface for managing your models and datasets on the Hub. The CLI provides a range of commands specifically designed for uploading, downloading, and managing your repositories. Whether you prefer the programmatic approach of the push_to_hub method or the flexibility of the command-line interface, the Hugging Face Hub provides the tools necessary to efficiently share your work. By effectively pushing your model to the Hub, you're not only making it available for others to use but also contributing to the growing ecosystem of open-source models and datasets. This collaborative environment fosters innovation and accelerates the pace of research and development in the field of artificial intelligence.

  4. Separate Checkpoints: It's highly recommended to push each model checkpoint to a separate repository. This approach allows for better tracking of download statistics and version control.

    Main keywords: Separate Checkpoints, download statistics, version control

    When it comes to managing your models on the Hugging Face Hub, the practice of using separate checkpoints and separate repositories is highly recommended for several compelling reasons. By pushing each model checkpoint to its own repository, you gain significant advantages in terms of download statistics and version control. Download statistics become more accurate and granular when each checkpoint has its own repository, allowing you to track the popularity and usage of specific versions of your model. This detailed insight is invaluable for understanding how your model is being adopted and used within the community. Furthermore, separating checkpoints enhances version control by providing a clear and organized structure for managing different iterations of your model. Each repository effectively acts as a snapshot of your model at a particular stage of development, making it easier to revert to previous versions if needed or to compare the performance of different checkpoints. This level of organization is especially crucial for collaborative projects where multiple researchers may be working on the same model. By adopting the strategy of separate checkpoints and repositories, you ensure that your models are not only easily accessible but also well-managed and effectively tracked, contributing to a more streamlined and transparent research process. This approach aligns with best practices for model sharing and fosters a collaborative environment within the Hugging Face community.

Uploading Your Dataset to Hugging Face

Step-by-Step Guide

  1. Prepare Your Dataset: Format your dataset into a compatible format for the datasets library. This typically involves creating a directory structure with data files (e.g., CSV, JSON, Parquet) and a dataset script.

    Main keywords: Prepare Your Dataset, datasets library, dataset script

    When you're ready to share your valuable data with the world via the Hugging Face Hub, the first crucial step is to prepare your dataset properly. This involves ensuring that your data is in a format that the datasets library can easily handle. The datasets library is a powerful tool within the Hugging Face ecosystem, designed to streamline the process of loading, processing, and sharing datasets for machine learning tasks. To make your dataset compatible, you typically need to organize it into a specific directory structure, which includes your data files and a dataset script. Data files can come in various formats, such as CSV, JSON, or Parquet, depending on the nature of your data and your specific needs. The key to successful dataset preparation lies in structuring your data in a way that allows the datasets library to efficiently access and utilize it. Additionally, the dataset script is a vital component of this process. This script is essentially a Python file that contains instructions on how to load and process your data. It defines the structure of your dataset, specifies data types, and handles any necessary preprocessing steps. By creating a well-structured dataset and a comprehensive dataset script, you ensure that your data can be seamlessly integrated into the Hugging Face Hub, making it accessible and user-friendly for the community. This thoughtful preparation not only enhances the discoverability of your dataset but also promotes its adoption and use in a wide range of machine-learning projects.

  2. Create a Dataset Script: Write a Python script that defines how to load and process your dataset using the datasets library. This script will be used by others to easily access your data.

  3. Upload to the Hub: Use the Hugging Face CLI or the push_to_hub method to upload your dataset and script to a new dataset repository on the Hub.

    Main keywords: Upload to the Hub, Hugging Face CLI, push_to_hub

    Once you've meticulously prepared your dataset and crafted a comprehensive dataset script, the next exciting step is to upload your work to the Hub, making it readily available to the global community of researchers and developers. The Hugging Face CLI offers a robust and efficient way to manage your datasets on the Hub. This command-line interface provides a suite of powerful tools specifically designed for uploading, downloading, and manipulating datasets, giving you precise control over your data management workflow. Whether you're dealing with a small dataset or a massive collection of data, the Hugging Face CLI can streamline the upload process, ensuring that your data is securely and effectively stored on the Hub. Alternatively, you can leverage the push_to_hub method, which integrates seamlessly with the datasets library and provides a programmatic way to upload your dataset. This method is particularly useful if you prefer to automate the upload process or if you need to integrate it into your existing data pipeline. By uploading your dataset to the Hugging Face Hub, you're not only sharing your valuable data but also contributing to the growth of open-source resources for machine learning. This collaborative environment fosters innovation and accelerates the pace of research by enabling others to build upon your work and create new and exciting applications. So, take the leap and share your dataset with the world, and become a part of this dynamic and ever-expanding community.

  4. Leverage the Dataset Viewer: The Hugging Face Hub provides a dataset viewer that allows users to explore the first few rows of your data directly in their browser. This feature makes it easy for others to understand your dataset's structure and content.

    Main keywords: Dataset Viewer, explore data, browser

    One of the standout features of the Hugging Face Hub that truly enhances the user experience is the Dataset Viewer. This invaluable tool allows users to explore data in an incredibly intuitive and accessible way, all directly within their browser. Imagine being able to peek into the structure and content of a dataset without having to download it, write custom code, or navigate complex file systems. The Dataset Viewer makes this a reality, providing a streamlined and efficient way to understand the essence of a dataset at a glance. By presenting the first few rows of your data in a clear and organized manner, the Viewer enables researchers and developers to quickly assess whether a particular dataset aligns with their needs and project goals. This feature saves valuable time and effort, as users can make informed decisions about data usage without committing to a full download. Furthermore, the Dataset Viewer promotes transparency and collaboration within the Hugging Face community. By allowing anyone to easily examine the contents of a dataset, it fosters a deeper understanding of the data and encourages the development of innovative solutions. Whether you're a data scientist, a machine learning engineer, or simply someone curious about exploring the world of data, the Hugging Face Hub's Dataset Viewer is an indispensable tool that empowers you to make the most of the available resources and contribute to the collective knowledge of the community.

Example Usage

Once your dataset is on the Hub, others can easily load it using the load_dataset function:

from datasets import load_dataset

dataset = load_dataset(