Metadata-Version: 2.1
Name: datasets
Version: 2.4.0
Summary: HuggingFace community-driven open-source library of datasets
Home-page: https://github.com/huggingface/datasets
Author: HuggingFace Inc.
Author-email: thomas@huggingface.co
License: Apache 2.0
Download-URL: https://github.com/huggingface/datasets/tags
Description: <p align="center">
            <br>
            <img src="https://raw.githubusercontent.com/huggingface/datasets/main/docs/source/imgs/datasets_logo_name.jpg" width="400"/>
            <br>
        <p>
        <p align="center">
            <a href="https://circleci.com/gh/huggingface/datasets">
                <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/datasets/main">
            </a>
            <a href="https://github.com/huggingface/datasets/blob/main/LICENSE">
                <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
            </a>
            <a href="https://huggingface.co/docs/datasets/index.html">
                <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/datasets/index.html.svg?down_color=red&down_message=offline&up_message=online">
            </a>
            <a href="https://github.com/huggingface/datasets/releases">
                <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/datasets.svg">
            </a>
            <a href="https://huggingface.co/datasets/">
                <img alt="Number of datasets" src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/datasets&color=brightgreen">
            </a>
            <a href="CODE_OF_CONDUCT.md">
                <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
            </a>
            <a href="https://zenodo.org/badge/latestdoi/250213286"><img src="https://zenodo.org/badge/250213286.svg" alt="DOI"></a>
        </p>
        
        ** ⚠️ The "master" branch has been renamed "main", please update your forks with [these instructions](https://github.com/huggingface/datasets/issues/4629)**
        
        🤗 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 ![number of datasets](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/datasets&color=brightgreen) major public datasets (text datasets in 467 languages and dialects, image datasets, audio datasets, etc.) 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/PNG/JPEG/etc. With simple commands like `processed_dataset = dataset.map(process_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/main/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/main/ADD_NEW_DATASET.md)
        
        <h3 align="center">
            <a href="https://hf.co/course"><img src="https://raw.githubusercontent.com/huggingface/datasets/main/docs/source/imgs/course_banner.png"></a>
        </h3>
        
        🤗 Datasets also provides access to +40 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
        
        ## 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
        
        # 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
        
        This library can be used for text/image/audio/etc. datasets. Here is an example to load a text dataset:
        
        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
        - What's in a Dataset: https://huggingface.co/docs/datasets/access
        - Processing data with 🤗 Datasets: https://huggingface.co/docs/datasets/process
        - Processing audio data: https://huggingface.co/docs/datasets/audio_process
        - Processing image data: https://huggingface.co/docs/datasets/image_process
        - Writing your own dataset loading script: https://huggingface.co/docs/datasets/dataset_script
        - etc.
        
        Another introduction to 🤗 Datasets is the tutorial on Google Colab here:
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/datasets/blob/main/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 ![number of datasets](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/datasets&color=brightgreen) datasets already provided on the [HuggingFace Datasets Hub](https://huggingface.co/datasets).
        
        You will find [the step-by-step guide here](https://huggingface.co/docs/datasets/share.html) to add a dataset on the Hub.
        
        However if you prefer to add your dataset in this repository, you can find the guide [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
        
        # 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 library, you can use our [paper](https://arxiv.org/abs/2109.02846):
        
        ```bibtex
        @inproceedings{lhoest-etal-2021-datasets,
            title = "Datasets: A Community Library for Natural Language Processing",
            author = "Lhoest, Quentin  and
              Villanova del Moral, Albert  and
              Jernite, Yacine  and
              Thakur, Abhishek  and
              von Platen, Patrick  and
              Patil, Suraj  and
              Chaumond, Julien  and
              Drame, Mariama  and
              Plu, Julien  and
              Tunstall, Lewis  and
              Davison, Joe  and
              {\v{S}}a{\v{s}}ko, Mario  and
              Chhablani, Gunjan  and
              Malik, Bhavitvya  and
              Brandeis, Simon  and
              Le Scao, Teven  and
              Sanh, Victor  and
              Xu, Canwen  and
              Patry, Nicolas  and
              McMillan-Major, Angelina  and
              Schmid, Philipp  and
              Gugger, Sylvain  and
              Delangue, Cl{\'e}ment  and
              Matussi{\`e}re, Th{\'e}o  and
              Debut, Lysandre  and
              Bekman, Stas  and
              Cistac, Pierric  and
              Goehringer, Thibault  and
              Mustar, Victor  and
              Lagunas, Fran{\c{c}}ois  and
              Rush, Alexander  and
              Wolf, Thomas",
            booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
            month = nov,
            year = "2021",
            address = "Online and Punta Cana, Dominican Republic",
            publisher = "Association for Computational Linguistics",
            url = "https://aclanthology.org/2021.emnlp-demo.21",
            pages = "175--184",
            abstract = "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.",
            eprint={2109.02846},
            archivePrefix={arXiv},
            primaryClass={cs.CL},
        }
        ```
        
        If you need to cite a specific version of our 🤗 Datasets library for reproducibility, you can use the corresponding version Zenodo DOI from this [list](https://zenodo.org/search?q=conceptrecid:%224817768%22&sort=-version&all_versions=True).
        
Keywords: datasets machine learning datasets metrics
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Provides-Extra: audio
Provides-Extra: vision
Provides-Extra: apache-beam
Provides-Extra: tensorflow
Provides-Extra: tensorflow_gpu
Provides-Extra: torch
Provides-Extra: s3
Provides-Extra: streaming
Provides-Extra: dev
Provides-Extra: tests
Provides-Extra: quality
Provides-Extra: benchmarks
Provides-Extra: docs
