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# Learning Interpretability Tool (LIT)

Welcome to 🔥LIT, the Learning Interpretability Tool!

If you want to jump in and start playing with the LIT UI, check out the hosted demos at https://pair-code.github.io/lit/demos/.

## Research

Found LIT useful in your research? Please cite our system demonstration paper!

    @misc{tenney2020language,
        title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models},
        author={Ian Tenney and James Wexler and Jasmijn Bastings and Tolga Bolukbasi and Andy Coenen and Sebastian Gehrmann and Ellen Jiang and Mahima Pushkarna and Carey Radebaugh and Emily Reif and Ann Yuan},
        booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
        year = "2020",
        publisher = "Association for Computational Linguistics",
        pages = "107--118",
        url = "https://www.aclweb.org/anthology/2020.emnlp-demos.15",
    }

- Main Site
- Getting Started
  - Installation
  - Hosted demos
  - LIT with your model
  - Using LIT in notebooks
  - Stand-alone components
  - Run an existing example
- Examples
  - Classification
  - Regression / Scoring
  - Sequence-to-Sequence
  - Language Modeling
  - Structured Prediction
  - Multimodal
- UI Guide
  - General Layout
  - Datapoint Selections
  - Toolbars
  - Comparing Models
  - Comparing Datapoints
  - Slices
  - Module Details
  - User Journeys
- Components & Features
  - Framework and Model Support
  - Input and Output Types
  - Token-based Salience
  - Sequence Salience
  - Salience Clustering
  - Tabular Feature Attribution
  - Pixel-based Salience
  - Attention
  - Embedding Projector
  - Aggregate Analysis
  - TCAV
  - Counterfactual Analysis
- Python API
  - Design Overview
  - Adding Models and Data
  - Datasets
  - Models
  - Interpretation Components
  - Type System
  - Server Configuration
  - Accessing the LIT UI in Notebooks
  - Using LIT components outside of LIT
- Frontend Development
  - High Level Overview
  - Application Architecture
  - Modules (LitModule)
  - Style Guide
  - Development Tips (open-source)
  - Custom Client / Modules
- Running in Docker
  - Basic Usage
  - Integrating Custom LIT Instances with the Default Docker Image
  - Building Your Own Image
- Glossary
- FAQ
  - Model and Data Types
  - Languages
  - Scale
  - Privacy and Security
  - Workflow and Integrations
- GitHub