Interactive ML model analysis tool for understanding model behavior
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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
pip install lit-nlp
pip install 'lit-nlp[examples-discriminative-ai]'
or pip install 'lit-nlp[examples-generative-ai]'
for demo-specific packages.python -m lit_nlp.examples.glue.demo --port=5432
Highlighted Details
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.
5 days ago
Inactive