# datasets
**Repository Path**: AI-Mart/datasets
## Basic Information
- **Project Name**: datasets
- **Description**: 21-10-31
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-31
- **Last Updated**: 2021-10-31
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
🤗 Datasets is a lightweight library providing **two** main features:
- **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](https://huggingface.co/datasets). 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.
[🎓 **Documentation**](https://huggingface.co/docs/datasets/) [🕹 **Colab tutorial**](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb)
[🔎 **Find a dataset in the Hub**](https://huggingface.co/datasets) [🌟 **Add a new dataset to the Hub**](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)
🤗 Datasets also provides access to +15 evaluation metrics and is designed to let the community easily add and share new datasets and evaluation metrics.
🤗 Datasets has many additional interesting features:
- 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.
🤗 Datasets originated from a fork of the awesome [TensorFlow Datasets](https://github.com/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`](#main-differences-between--datasets-and-tfds).
# Installation
## With pip
🤗 Datasets can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance)
```bash
pip install datasets
```
## With conda
🤗 Datasets can be installed using conda as follows:
```bash
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 🤗 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 start page in the documentation: https://huggingface.co/docs/datasets/quickstart.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:
```python
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 start page in the documentation: https://huggingface.co/docs/datasets/quickstart.html and the specific pages on:
- Loading a dataset https://huggingface.co/docs/datasets/loading.html
- What's in a Dataset: https://huggingface.co/docs/datasets/access.html
- Processing data with 🤗 Datasets: https://huggingface.co/docs/datasets/process.html
- Writing your own dataset loading script: https://huggingface.co/docs/datasets/dataset_script.html
- etc.
Another introduction to 🤗 Datasets is the tutorial on Google Colab here:
[](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb)
# Add a new dataset to the Hub
We have a very detailed step-by-step guide to add a new dataset to the  datasets already provided on the [HuggingFace Datasets Hub](https://huggingface.co/datasets).
You will find [the step-by-step guide here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) 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](https://huggingface.co/docs/datasets/share.html).
# 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](https://rajpurkar.github.io/SQuAD-explorer/) or [GLUE](https://gluebenchmark.com/).
- the backend serialization of 🤗 Datasets is based on [Apache Arrow](https://arrow.apache.org/) 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](https://github.com/huggingface/datasets/issues/new). Thanks for your contribution to the ML community!
## BibTeX
If you want to cite our 🤗 Datasets [paper](https://arxiv.org/abs/2109.02846) and library, you can use these:
```bibtex
@misc{lhoest2021datasets,
title={Datasets: A Community Library for Natural Language Processing},
author={Quentin Lhoest and Albert Villanova del Moral and Yacine Jernite and Abhishek Thakur and Patrick von Platen and Suraj Patil and Julien Chaumond and Mariama Drame and Julien Plu and Lewis Tunstall and Joe Davison and Mario Šaško and Gunjan Chhablani and Bhavitvya Malik and Simon Brandeis and Teven Le Scao and Victor Sanh and Canwen Xu and Nicolas Patry and Angelina McMillan-Major and Philipp Schmid and Sylvain Gugger and Clément Delangue and Théo Matussière and Lysandre Debut and Stas Bekman and Pierric Cistac and Thibault Goehringer and Victor Mustar and François Lagunas and Alexander M. Rush and Thomas Wolf},
year={2021},
eprint={2109.02846},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@software{quentin_lhoest_2021_5579268,
author = {Quentin Lhoest and
Albert Villanova del Moral and
Patrick von Platen and
Thomas Wolf and
Mario Šaško 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: 1.14.0},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {1.14.0},
doi = {10.5281/zenodo.5579268},
url = {https://doi.org/10.5281/zenodo.5579268}
}
```