PAIR-code/lit

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

What it solves

LIT provides a visual, interactive way to understand and debug machine learning models. It helps developers identify where a model performs poorly, understand why specific predictions were made, and test if a model behaves consistently across different variations of input data.

How it works

LIT operates as a browser-based UI that can be run as a standalone server or integrated into notebook environments like Colab and Jupyter. It is framework-agnostic, supporting TensorFlow and PyTorch. Users can connect their own models and datasets via a custom data loader and model wrapper using the tool's API.

Who it’s for

ML engineers and researchers who need to analyze and interpret the same-model performance on text, image, and tabular data across various tasks like classification, regression, and language modeling.

Highlights

  • Local explanations: Uses salience maps and rich visualizations to explain individual predictions.
  • Aggregate analysis: Supports slicing, binning, and embedding space visualization to analyze model behavior at scale.
  • Counterfactual generation: Allows users to manually edit inputs or use plugins to create new examples for dynamic evaluation.
  • Side-by-side comparison: Compare multiple models or a single model's response to different examples.
  • Extensible architecture: Compatible with various model types (seq2seq, span labeling, etc.) and supports multi-head models.

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