# Downloading datasets

## Integrated libraries

If a dataset on the Hub is tied to a [supported library](./datasets-libraries), loading the dataset can be done in just a few lines. For information on accessing the dataset, you can click on the "Use this dataset" button on the dataset page to see how to do so. For example, [`samsum`](https://huggingface.co/datasets/knkarthick/samsum?library=datasets) shows how to do so with `datasets` below.

## Using the Hugging Face Client Library

You can use the [`huggingface_hub`](/docs/huggingface_hub) library to create, delete, update and retrieve information from repos. For example, to download the `HuggingFaceH4/ultrachat_200k` dataset from the command line, run

```bash
hf download HuggingFaceH4/ultrachat_200k --repo-type dataset
```

See the [HF CLI download documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli#download-a-dataset-or-a-space) for more information.

You can also integrate this into your own library! For example, you can quickly load a CSV dataset with a few lines using Pandas.
```py
from huggingface_hub import hf_hub_download
import pandas as pd

REPO_ID = "YOUR_REPO_ID"
FILENAME = "data.csv"

dataset = pd.read_csv(
    hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset")
)
```

## Using Git

Since all datasets on the Hub are Xet-backed Git repositories, you can clone the datasets locally by [installing git-xet](./xet/using-xet-storage#git-xet) and running:

```bash
git xet install
git lfs install
git clone git@hf.co:datasets/<dataset ID> # example: git clone git@hf.co:datasets/allenai/c4
```

If you have write-access to the particular dataset repo, you'll also have the ability to commit and push revisions to the dataset.

Add your SSH public key to [your user settings](https://huggingface.co/settings/keys) to push changes and/or access private repos.

## Using hf-mount

For large datasets, you can mount a repo as a local filesystem with [hf-mount](https://github.com/huggingface/hf-mount) instead of downloading the full repo. Files are fetched lazily — only the bytes your code reads hit the network. Useful when your workflow expects local file paths (e.g. `tarfile`, `zipfile`, `imagefolder`) rather than Python iterators.

```bash
brew install hf-mount
hf-mount start repo datasets/stanfordnlp/imdb /tmp/imdb
```

Repos are mounted read-only. See [Mount as a Local Filesystem](./storage-buckets-access#mount-as-a-local-filesystem) for full setup details, backend options, and caching.