icaoberg / What is Hugging Face?

Created Tue, 11 Aug 2026 00:00:00 +0000 Modified Tue, 11 Aug 2026 06:10:48 -0400
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machine-learning huggingface datasets tensorflow croissant

What is Hugging Face?

Hugging Face is a company and, more importantly, a platform that has become the de facto home for open machine learning [1]. If GitHub is where the world stores and shares code, Hugging Face is where the world stores and shares the artifacts of machine learning: trained models, datasets, and interactive demos. At its center is the Hugging Face Hub, a website and Git-backed storage system that hosts hundreds of thousands of these artifacts, each in its own versioned repository.

Alongside the Hub, Hugging Face maintains a family of open-source Python libraries that make those artifacts easy to use. The best known is Transformers, which provides a uniform interface to thousands of pretrained models; Datasets, which loads and processes data efficiently; and Hub client tools that push and pull repositories. The combination—a shared registry plus batteries-included libraries—is what made Hugging Face the connective tissue of the modern ML ecosystem.

TL;DR

Imagine an app store, but for the ingredients of machine learning instead of finished apps. On the shelves you find models (pretrained brains you can download and run), datasets (the labeled data used to train and test them), and Spaces (live demos you can click on). Each item lives in its own repository with a description, version history, and a standard way to load it in one or two lines of code.

That is Hugging Face. Instead of hunting across dozens of university websites and FTP servers for a model or a dataset—and then writing custom code to load each one—you go to one place, and everything speaks a common language. You can download a state-of-the-art language model, grab a benchmark dataset, and run them together in an afternoon.

What is it for?

Hugging Face solves a very practical problem: machine-learning artifacts used to be scattered and inconsistent. A model might live on a lab’s server as a folder of weights with no documentation; a dataset might be a zip file with a README nobody kept up to date. Reusing someone else’s work meant reverse-engineering their format every time. Hugging Face standardizes all of this.

Concretely, people use Hugging Face to:

  • Find and download pretrained models. Rather than training from scratch (which can cost enormous amounts of compute), you start from a model someone already trained and fine-tune it for your task. The Hub hosts models for text, vision, audio, and multimodal tasks.
  • Share models and datasets. Publishing to the Hub gives your work a stable home, a model card or dataset card documenting it, version control via Git, and immediate discoverability.
  • Load datasets uniformly. The datasets library streams, caches, and batches data with a consistent API, so switching from one dataset to another is a one-line change.
  • Run and deploy demos. Spaces let you host an interactive app (often with Gradio or Streamlit) so others can try a model in the browser without installing anything.
  • Build production pipelines. Inference endpoints, integrations with cloud providers, and the transformers pipeline() abstraction take a model from the Hub to a running service.

The unifying idea is interoperability: a common registry, common metadata, and common loading code so that models and data flow freely between people, projects, and frameworks.

Hugging Face, Croissant, and TensorFlow

A frequent point of confusion is how Hugging Face relates to two other names you meet constantly: Croissant and TensorFlow. They operate at different layers, and they fit together rather than compete.

The three layers

  • TensorFlow is a framework—the engine that actually does the math of machine learning. It builds and trains neural networks: defining layers, running them on GPUs/TPUs, computing gradients, and updating weights. (PyTorch and JAX are its main peers; Hugging Face supports all three.) TensorFlow is where computation happens.
  • Hugging Face is a platform and library layer—where artifacts live and how they are loaded. It hosts the models and datasets and provides the code to fetch them, but it is not itself a training engine; it hands the actual tensors to a framework like TensorFlow, PyTorch, or JAX.
  • Croissant is a metadata format—a description of what a dataset is and how it is structured [2]. It is a JSON-LD file that names a dataset’s files, records, and typed fields, so any tool can understand a dataset without custom parsing code. Croissant is data about data; it computes nothing.

A useful way to hold the three in mind: Croissant describes the data, Hugging Face distributes it, and TensorFlow trains on it.

How they connect in practice

These layers are wired together directly:

  • Hugging Face ↔ Croissant. Every dataset on the Hugging Face Hub automatically exposes a Croissant metadata file [3]. Hugging Face co-developed and adopted Croissant as the standard, machine-readable description of its datasets, so a dataset published on the Hub can be discovered and understood by any Croissant-aware tool—not just Hugging Face’s own libraries. You can fetch a dataset’s Croissant JSON-LD straight from its Hub page or API.

  • Hugging Face ↔ TensorFlow. The datasets library can hand data to TensorFlow in the format it expects—for example, converting a dataset into a tf.data.Dataset (via to_tf_dataset) that a Keras/TensorFlow model can train on directly [4]. Likewise, many models on the Hub ship with TensorFlow weights, and transformers exposes them as native Keras models. The same model or dataset can usually be used from TensorFlow, PyTorch, or JAX with a one-line change.

  • Croissant ↔ TensorFlow. Croissant grew out of earlier dataset-description efforts including TensorFlow Datasets (TFDS), and the reference mlcroissant loader can materialize a Croissant-described dataset into framework-ready structures, including TensorFlow. So a Croissant file authored anywhere can feed a TensorFlow training loop [2].

Putting it together

A typical modern workflow ties all three together:

# 1. Load a dataset from the Hugging Face Hub.
#    (The Hub also publishes a Croissant JSON-LD description of this same dataset.)
from datasets import load_dataset
ds = load_dataset("glue", "mrpc", split="train")

# 2. Hand it to TensorFlow as a tf.data.Dataset.
tf_ds = ds.to_tf_dataset(
    columns=["input_ids", "attention_mask"],
    label_cols=["label"],
    batch_size=16,
    shuffle=True,
)

# 3. Load a TensorFlow/Keras model from the Hub and train on it.
from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy")
model.fit(tf_ds, epochs=1)

Here Hugging Face supplies the dataset and the model, Croissant is the standardized description that makes that dataset portable and discoverable, and TensorFlow is the engine that actually trains. Each layer does one job, and they hand off cleanly to the next.

Summary

Hugging Face is the hub-and-toolkit that hosts open machine-learning models and datasets and makes them trivial to share and load. It sits between the description of data and the computation on it: Croissant is the JSON-LD metadata format that describes a dataset (and which every Hugging Face dataset now exposes), while TensorFlow is the framework that performs the training. Croissant describes, Hugging Face distributes, and TensorFlow (or PyTorch, or JAX) computes—three cooperating layers of the same pipeline.

References

[1] Hugging Face. “Hugging Face – The AI community building the future.” Documentation and Hub available at huggingface.co and huggingface.co/docs.

[2] Mubashara Akhtar et al. “Croissant: A Metadata Format for ML-Ready Datasets.” Proceedings of the 2024 Workshop on Data Management for End-to-End Machine Learning (DEEM ‘24), 2024. doi:10.1145/3650203.3663326

[3] Hugging Face. “Croissant — Datasets documentation.” Available at huggingface.co/docs/datasets/croissant.

[4] Hugging Face. “Use with TensorFlow — Datasets documentation.” Available at huggingface.co/docs/datasets/use_with_tensorflow.

[5] Thomas Wolf et al. “Transformers: State-of-the-Art Natural Language Processing.” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45, 2020. doi:10.18653/v1/2020.emnlp-demos.6