Datasets and evaluation metrics for natural language processing and more
Compatible with NumPy, Pandas, PyTorch and TensorFlow

🎓 Documentation: https://huggingface.co/docs/datasets/

🕹 Colab demo: https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb

🔎 Online dataset explorer: https://huggingface.co/nlp/viewer

🤗Datasets is a lightweight and extensible library to easily share and access datasets and evaluation metrics for Natural Language Processing (NLP) and more.

🤗Datasets has many interesting features (beside easy sharing and accessing datasets/metrics):

Built-in interoperability with NumPy, pandas, PyTorch and Tensorflow 2
Lightweight and fast with a transparent and pythonic API
Strive on large datasets: 🤗Datasets naturally frees the user from RAM memory limitation, all datasets are memory-mapped on drive by default.
Smart caching: never wait for your data to process several times

🤗Datasets currently provides access to ~100 NLP datasets and ~10 evaluation metrics and is designed to let the community easily add and share new datasets and evaluation metrics. You can browse the full set of datasets with the live datasets viewer.

🤗Datasets originated from a fork of the awesome TensorFlow Datasets and the HuggingFace team want to deeply thank the TensorFlow Datasets team for building this amazing library. More details on the differences between datasets and tfds can be found in the section Main differences between 🤗Datasets and tfds.

Contributors

Installation

🤗Datasets can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance)

pip install datasets

For more details on installation, check the installation page in the documentation: https://huggingface.co/docs/datasets/installation.html

Using with PyTorch/TensorFlow/pandas

If you plan to use 🤗Datasets with PyTorch (1.0+), TensorFlow (2.2+) or pandas, you should also install PyTorch, TensorFlow or pandas.

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 is made to be very simple to use. The main methods are:

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 examples 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')

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 🤗Datasets is the tutorial on Google Colab here:

Main differences between `datasets` and `tfds`

If you are familiar with the great Tensorflow Datasets, here are the main differences between datasets and 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 a tf.data.Dataset but a built-in framework-agnostic dataset class with methods inspired by what we like in tf.data (like a map() method). It basically wraps a memory-mapped Arrow table cache.

Disclaimers

Similar to TensorFlow Datasets, 🤗Datasets is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use them. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

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!

If you're interested in learning more about responsible AI practices, including fairness, please see Google AI's Responsible AI Practices.

giangdn/datasets

Datasets and evaluation metrics for natural language processing and more Compatible with NumPy, Pandas, PyTorch and TensorFlow