DataLab is a unified platform that allows for NLP researchers to perform a number of data-related tasks in an efficient and easy-to-use manner. In particular, DataLab supports the following functionalities:

• Data Diagnostics: DataLab allows for analysis and understanding of data to uncover undesirable traits such as hate speech, gender bias, or label imbalance.
• Operation Standardization: DataLab provides and standardizes a large number of data processing operations, including aggregating, preprocessing, featurizing, editing and prompting operations.
• Data Search: DataLab provides a semantic dataset search tool to help identify appropriate datasets given a textual description of an idea.
• Global Analysis: DataLab provides tools to perform global analyses over a variety of datasets.

DataLab can be installed from PyPi

pip install --upgrade pip pip install datalabs python -m nltk.downloader omw-1.4 # to support more feature calculation

or from the source

# This is suitable for SDK developers pip install --upgrade pip git clone git@github.com:ExpressAI/DataLab.git cd Datalab pip install -e .[dev] python -m nltk.downloader omw-1.4 # to support more feature calculation

By adding [dev], some extra libraries will be installed, such as pre-commit.

If you would like to contribute to DataLab, checking the code style and quality before your pull request is highly recommended. In this project, three types of checks will be expected: (a) black (2) flake8 (3) isort

you could achieve this in two ways:

pre-commit install git init . pre-commit run --all-files or

where pre-commit run -all-files is equivalent to

pre-commit run black # (this is also equivalent to python -m black .) pre-commit run isort # (this is also equivalent to isort .) pre-commit run flake8 # (this is also equivalent to flake8)

Notably, black and isort can help us fix code style automatically, while flake8 only provide hints with us, which means we need to fix these issues raised by flake8.

pre-commit install git init . git commit -m "your update message"

The git commit will automatically activate the command pre-commit run -all-files

Below we give several simple examples to showcase the usage of DataLab:

You can also view documentation:

• Online Tutorial
• Adding Datasets to the SDK
• Adding a new Task to the SDK

# pip install datalabs from datalabs import load_dataset dataset = load_dataset("ag_news") # Preprocessing operation from preprocess import * res=dataset["test"].apply(lower) print(next(res)) # Featurizing operation from featurize import * res = dataset["test"].apply(get_text_length) # get length print(next(res)) res = dataset["test"].apply(get_entities_spacy) # get entity print(next(res)) # Editing/Transformation operation from edit import * res = dataset["test"].apply(change_person_name) # change person name print(next(res)) # Prompting operation from prompt import * res = dataset["test"].apply(template_tc1) print(next(res)) # Aggregating operation from aggregate.text_classification import * res = dataset["test"].apply(get_statistics)

DataLab originated from a fork of the awesome Huggingface Datasets and TensorFlow Datasets. We highly thank the Huggingface/TensorFlow Datasets for building this amazing library. More details on the differences between DataLab and them can be found in the section. We thank Antonis Anastasopoulos for sharing the mapping data between countries and languages, and thank Alissa Ostapenko, Yulia Tsvetkov, Jie Fu, Ziyun Xu, Hiroaki Hayashi, and Zhengfu He for useful discussion and suggestions for the first version.