Nearly every deep learning workflow begins with loading a dataset, which makes it one of the most important steps. With 🤗 Datasets, there are more than 900 datasets available to help you get started with your NLP task. All you have to do is call: load_dataset() to take your first step. This function is a true workhorse in every sense because it builds and loads every dataset you use.
Let’s begin with a basic Explain Like I’m Five.
A dataset is a directory that contains:
- Some data files in generic formats (JSON, CSV, Parquet, text, etc.)
- An optional dataset script if it requires some code to read the data files. This is used to load files of all formats and structures.
The load_dataset() function fetches the requested dataset locally or from the Hugging Face Hub. The Hub is a central repository where all the Hugging Face datasets and models are stored.
If the dataset only contains data files, then load_dataset() automatically infers how to load the data files from their extensions (json, csv, parquet, txt, etc.). If the dataset has a dataset script, then it downloads and imports it from the Hugging Face Hub. Code in the dataset script defines the dataset information (description, features, URL to the original files, etc.), and tells 🤗 Datasets how to generate and display examples from it.
Read the Share section to learn more about how to share a dataset. This section also provides a step-by-step guide on how to write your own dataset loading script!
The dataset script downloads the dataset files from the original URL, generates the dataset and caches it in an Arrow table on your drive. If you’ve downloaded the dataset before, then 🤗 Datasets will reload it from the cache to save you the trouble of downloading it again.
Now that you have a high-level understanding about how datasets are built, let’s take a closer look at the nuts and bolts of how all this works.
When you load a dataset for the first time, 🤗 Datasets takes the raw data file and builds it into a table of rows and typed columns. There are two main classes responsible for building a dataset: BuilderConfig and DatasetBuilder.
||Short name of the dataset.|
||Dataset version identifier.|
||Stores the path to a local folder containing the data files.|
||Stores paths to local data files.|
||Description of the dataset.|
If you want to add additional attributes to your dataset such as the class labels, you can subclass the base BuilderConfig class. There are two ways to populate the attributes of a BuilderConfig class or subclass:
Provide a list of predefined BuilderConfig class (or subclass) instances in the datasets
When you call load_dataset(), any keyword arguments that are not specific to the method will be used to set the associated attributes of the BuilderConfig class. This will override the predefined attributes if a specific configuration was selected.
There are three main methods in DatasetBuilder:
DatasetBuilder._info()is in charge of defining the dataset attributes. When you call
dataset.info, 🤗 Datasets returns the information stored here. Likewise, the Features are also specified here. Remember, the Features are like the skeleton of the dataset. It provides the names and types of each column.
DatasetBuilder._split_generatordownloads or retrieves the requested data files, organizes them into splits, and defines specific arguments for the generation process. This method has a DownloadManager that downloads files or fetches them from your local filesystem. Within the DownloadManager, there is a DownloadManager.download_and_extract() method that accepts a dictionary of URLs to the original data files, and downloads the requested files. Accepted inputs include: a single URL or path, or a list/dictionary of URLs or paths. Any compressed file types like TAR, GZIP and ZIP archives will be automatically extracted.
Once the files are downloaded, SplitGenerator organizes them into splits. The SplitGenerator contains the name of the split, and any keyword arguments that are provided to the
DatasetBuilder._generate_examplesmethod. The keyword arguments can be specific to each split, and typically comprise at least the local path to the data files for each split.
DownloadManager.download_and_extract() can download files from a wide range of sources. If the data files are hosted on a special access server, you should use
DownloadManger.download_custom. Refer to the reference of DownloadManager for more details.
DatasetBuilder._generate_examplesreads and parses the data files for a split. Then it yields dataset examples according to the format specified in the
DatasetBuilder._info(). The input of
DatasetBuilder._generate_examplesis actually the
filepathprovided in the keyword arguments of the last method.
The dataset is generated with a Python generator, which doesn’t load all the data in memory. As a result, the generator can handle large datasets. However, before the generated samples are flushed to the dataset file on disk, they are stored in an
ArrowWriterbuffer. This means the generated samples are written by batch. If your dataset samples consumes a lot of memory (images or videos), then make sure to specify a low value for the
DEFAULT_WRITER_BATCH_SIZEattribute in DatasetBuilder. We recommend not exceeding a size of 200 MB.
As a user, you want to be able to quickly use a dataset. Implementing a dataset loading script can sometimes get in the way, or it may be a barrier for some people without a developer background. 🤗 Datasets removes this barrier by making it possible to load any dataset from the Hub without a dataset loading script. All a user has to do is upload the data files (see upload_dataset_repo for a list of supported file formats) to a dataset repository on the Hub, and they will be able to load that dataset without having to create a loading script. This doesn’t mean we are moving away from loading scripts because they still offer the most flexibility in controlling how a dataset is generated.
The loading script-free method uses the huggingface_hub library to list the files in a dataset repository. You can also provide a path to a local directory instead of a repository name, in which case 🤗 Datasets will use glob instead. Depending on the format of the data files available, one of the data file builders will create your dataset for you. If you have a CSV file, the CSV builder will be used and if you have a Parquet file, the Parquet builder will be used. The drawback of this approach is it’s not possible to simultaneously load a CSV and JSON file. You will need to load the two file types separately, and then concatenate them.
To ensure a dataset is complete, load_dataset() will perform a series of tests on the downloaded files to make sure everything is there. This way, you don’t encounter any surprises when your requested dataset doesn’t get generated as expected. load_dataset() verifies:
- The list of downloaded files.
- The number of bytes of the downloaded files.
- The SHA256 checksums of the downloaded files.
- The number of splits in the generated
- The number of samples in each split of the generated
If the dataset doesn’t pass the verifications, it is likely that the original host of the dataset made some changes in the data files.
In this case, an error is raised to alert that the dataset has changed.
To ignore the error, one needs to specify
ignore_verifications=True in load_dataset().
Anytime you see a verification error, feel free to open an issue on GitHub so that we can update the integrity checks for this dataset.
The dataset repositories on the Hub are scanned for malware, see more information here.
Moreover the datasets that were constributed on our GitHub repository have all been reviewed by our maintainers. The code of these datasets is considered safe. It concerns datasets that are not under a namespace, e.g. “squad” or “glue”, unlike the other datasets that are named “username/dataset_name” or “org/dataset_name”.