Real world NLP models made easy

From task definition to working model in just a few hours

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"Anyone can take advantage of Label Sleuth to quickly annotate high-quality text datasets at a cost lower than ever before."

Prof. Toby Li, University of Notre Dame

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Developed by the research community

Developed by researchers across industry and academia, Label Sleuth incorporates latest research from human computer interaction, natural language processing, and artificial intelligence.

Read the publications
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Extensible architecture

Label Sleuth has been designed with an extensible architecture allowing the easy integration of new components, such as additional model architectures or active learning techniques.

System architecture
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Open source

Label Sleuth is an open source project welcoming contributions by the open source community.

How can I contribute?
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"It is critical for machines to learn in a label efficient manner. Label Sleuth achieves this goal for challenging text classification and NLP tasks using a unique combination of very good user-interface and good backend active learning algorithms."

Prof. Rishabh Iyer, UT Dallas

Use cases

Legal Document Understanding
Legal Document Understanding

Identify contract clauses of interest (e.g., clauses related to Warranties)

Fight Social Violence
Fight Social Violence

Identify within a large set of text messages bullying content as it begins in order to stop it

Customer Care Analytics
Customer Care Analytics

Classify customer interactions across different dimensions of interest (e.g., request types, sentiment, etc.)

Get started in 4 easy steps

1. Install Anaconda

Let's make sure you have a separate Python environment for Label Sleuth

Get Anaconda
2. Activate Environment

Let's setup your Python enviroment

Open a terminal or restart it if already open

conda create --yes -n label-sleuth python=3.8

conda activate label-sleuth

3. Install Label Sleuth

Install the system

pip install label-sleuth

4. Fire it all up

For a quick start, follow our tutorial (recommended)

View tutorial

To skip the tutorial, start Label Sleuth

python -m label_sleuth.start_label_sleuth

Access Label Sleuth on your browser

http://localhost:8000/

Label Sleuth collaborators include

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Label Sleuth is an open source project initiated by IBM Research in collaboration with leading universities

IBM Research