The Learning Interpretability Tool (LIT) provides an interactive, extensible, and framework-agnostic interface for analyzing machine learning models across text, image, and tabular data. It empowers researchers and practitioners to understand model behavior, identify failure modes, and debug predictions through a browser-based UI.

How It Works

LIT offers a suite of debugging workflows, including local explanations (salience maps), aggregate analysis (custom metrics, embedding visualization), counterfactual generation, and side-by-side model comparison. Its framework-agnostic design supports TensorFlow, PyTorch, and various model types (classification, regression, seq2seq, etc.), facilitating deep dives into model decision-making.

Quick Start & Requirements

• Install: pip install lit-nlp
• Optional Dependencies: pip install 'lit-nlp[examples-discriminative-ai]' or pip install 'lit-nlp[examples-generative-ai]' for demo-specific packages.
• Python Version: 3.9+ required for building from source.
• Quickstart Demo: python -m lit_nlp.examples.glue.demo --port=5432
• Documentation: https://github.com/PAIR-code/lit
• Demos: https://pair-code.github.io/lit/

Highlighted Details

• Supports text, image, and tabular data.
• Framework-agnostic (TensorFlow, PyTorch).
• Extensible with custom components (interpretability methods, generators).
• Includes features for aggregate analysis, counterfactuals, and model comparison.

Maintenance & Community

LIT is an active research project with contributions from Google. Community engagement is encouraged via the Discussions page.

Licensing & Compatibility

The project is not explicitly licensed in the README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

LIT is a research project under active development, implying potential for breaking changes or incomplete features. The lack of explicit licensing information may pose a barrier to commercial adoption.