datasets
- one-line dataloaders for many public datasets: one liners to download and pre-process any of the
major public datasets (in 467 languages and dialects!) provided on the HuggingFace Datasets Hub. With a simple command like
squad_dataset = load_dataset("squad"), get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX), - efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. With simple commands like
tokenized_dataset = dataset.map(tokenize_example), efficiently prepare the dataset for inspection and ML model evaluation and training.
- Thrive on large datasets:
🤗 Datasets naturally frees the user from RAM memory limitation, all datasets are memory-mapped using an efficient zero-serialization cost backend (Apache Arrow). - Smart caching: never wait for your data to process several times.
- Lightweight and fast with a transparent and pythonic API (multi-processing/caching/memory-mapping).
- Built-in interoperability with NumPy, pandas, PyTorch, Tensorflow 2 and JAX.
tfds can be found in the section Main differences between
tfds.
Installation
With pip
pip install datasets
With conda
conda install -c huggingface -c conda-forge datasets
Follow the installation pages of TensorFlow and PyTorch to see how to install them with conda.
For more details on installation, check the installation page in the documentation: https://huggingface.co/docs/datasets/installation.html
Installation to use with PyTorch/TensorFlow/pandas
If you plan to use
For more details on using the library with NumPy, pandas, PyTorch or TensorFlow, check the quick tour page in the documentation: https://huggingface.co/docs/datasets/quicktour.html
Usage
-
datasets.list_datasets()to list the available datasets -
datasets.load_dataset(dataset_name, **kwargs)to instantiate a dataset -
datasets.list_metrics()to list the available metrics -
datasets.load_metric(metric_name, **kwargs)to instantiate a metric
Here is a quick example:
from datasets import list_datasets, load_dataset, list_metrics, load_metric
# Print all the available datasets
print(list_datasets())
# Load a dataset and print the first example in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
# List all the available metrics
print(list_metrics())
# Load a metric
squad_metric = load_metric('squad')
# Process the dataset - add a column with the length of the context texts
dataset_with_length = squad_dataset.map(lambda x: {"length": len(x["context"])})
# Process the dataset - tokenize the context texts (using a tokenizer from the 🤗 Transformers library)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
tokenized_dataset = squad_dataset.map(lambda x: tokenizer(x['context']), batched=True)
For more details on using the library, check the quick tour page in the documentation: https://huggingface.co/docs/datasets/quicktour.html and the specific pages on:
- Loading a dataset https://huggingface.co/docs/datasets/loading_datasets.html
- What's in a Dataset: https://huggingface.co/docs/datasets/exploring.html
- Processing data with
🤗 Datasets: https://huggingface.co/docs/datasets/processing.html - Writing your own dataset loading script: https://huggingface.co/docs/datasets/add_dataset.html
- etc.
Another introduction to
Add a new dataset to the Hub
We have a very detailed step-by-step guide to add a new dataset to the
You will find the step-by-step guide here to add a dataset to this repository.
You can also have your own repository for your dataset on the Hub under your or your organization's namespace and share it with the community. More information in the documentation section about dataset sharing.
Main differences between
🤗
Datasets and tfds
If you are familiar with the great TensorFlow Datasets, here are the main differences between tfds:
- the scripts in
🤗 Datasets are not provided within the library but are queried, downloaded/cached and dynamically loaded upon request -
🤗 Datasets also provides evaluation metrics in a similar fashion to the datasets, i.e. as dynamically installed scripts with a unified API. This gives access to the pair of a benchmark dataset and a benchmark metric for instance for benchmarks like SQuAD or GLUE. - the backend serialization of
🤗 Datasets is based on Apache Arrow instead of TF Records and leverage python dataclasses for info and features with some diverging features (we mostly don't do encoding and store the raw data as much as possible in the backend serialization cache). - the user-facing dataset object of
🤗 Datasets is not atf.data.Datasetbut a built-in framework-agnostic dataset class with methods inspired by what we like intf.data(like amap()method). It basically wraps a memory-mapped Arrow table cache.
Disclaimers
Similar to TensorFlow Datasets,
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
BibTeX
If you want to cite this framework you can use this:
@software{quentin_lhoest_2021_5570305,
author = {Quentin Lhoest and
Albert Villanova del Moral and
Patrick von Platen and
Thomas Wolf and
Yacine Jernite and
Abhishek Thakur and
Lewis Tunstall and
Suraj Patil and
Mariama Drame and
Julien Chaumond and
Julien Plu and
Joe Davison and
Simon Brandeis and
Victor Sanh and
Teven Le Scao and
Kevin Canwen Xu and
Nicolas Patry and
Steven Liu and
Angelina McMillan-Major and
Philipp Schmid and
Sylvain Gugger and
Nathan Raw and
Sylvain Lesage and
Anton Lozhkov and
Matthew Carrigan and
Théo Matussière and
Leandro von Werra and
Lysandre Debut and
Stas Bekman and
Clément Delangue},
title = {huggingface/datasets: 1.13.2},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {1.13.2},
doi = {10.5281/zenodo.5570305},
url = {https://doi.org/10.5281/zenodo.5570305}
}