The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks.

Features

  • Documentation available
  • Local explanations via salience maps and rich visualization of model predictions
  • Aggregate analysis including custom metrics, slicing and binning, and visualization of embedding spaces
  • Counterfactual generation via manual edits or generator plug-ins to dynamically create and evaluate new examples
  • Side-by-side mode to compare two or more models, or one model on a pair of examples
  • Framework-agnostic and compatible with TensorFlow, PyTorch, and more

Project Samples

Project Activity

See All Activity >

Categories

Machine Learning

License

Apache License V2.0

Follow Learning Interpretability Tool

Learning Interpretability Tool Web Site

Other Useful Business Software
Go From AI Idea to AI App Fast Icon
Go From AI Idea to AI App Fast

One platform to build, fine-tune, and deploy ML models. No MLOps team required.

Access Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
Try Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of Learning Interpretability Tool!

Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

TypeScript

Related Categories

TypeScript Machine Learning Software

Registered

2024-08-05