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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
Categories
Machine LearningLicense
Apache License V2.0Follow Learning Interpretability Tool
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