Buckets:

hf-doc-build/doc-dev / hub /pr_2521 /en /datasets-usage.md
HuggingFaceDocBuilder's picture
|
download
raw
1.32 kB
# Using ๐Ÿค— Datasets
Once you've found an interesting dataset on the Hugging Face Hub, you can load the dataset using ๐Ÿค— Datasets. You can click on the [**Use this dataset** button](https://huggingface.co/datasets/nyu-mll/glue?library=datasets) to copy the code to load a dataset.
First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
```
hf auth login
```
And then you can load a dataset from the Hugging Face Hub using
```python
from datasets import load_dataset
dataset = load_dataset("username/my_dataset")
# or load the separate splits if the dataset has train/validation/test splits
train_dataset = load_dataset("username/my_dataset", split="train")
valid_dataset = load_dataset("username/my_dataset", split="validation")
test_dataset = load_dataset("username/my_dataset", split="test")
```
You can also upload datasets to the Hugging Face Hub:
```python
my_new_dataset.push_to_hub("username/my_new_dataset")
```
This creates a dataset repository `username/my_new_dataset` containing your Dataset in Parquet format, that you can reload later.
For more information about using ๐Ÿค— Datasets, check out the [tutorials](/docs/datasets/tutorial) and [how-to guides](/docs/datasets/how_to) available in the ๐Ÿค— Datasets documentation.

Xet Storage Details

Size:
1.32 kB
ยท
Xet hash:
61e6cb8fe90bdf3654ec7505e173589efbe0ee30992a5afa73be3bb2e1256975

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.