Language Interpretability Tool.
Project description
Language Interpretability Tool (LIT)
The Language Interpretability Tool (LIT) is a visual, interactive model-understanding tool for NLP models.
LIT is built to answer questions such as:
- What kind of examples does my model perform poorly on?
- Why did my model make this prediction? Can this prediction be attributed to adversarial behavior, or to undesirable priors in the training set?
- Does my model behave consistently if I change things like textual style, verb tense, or pronoun gender?
LIT supports a variety of debugging workflows through a browser-based UI. Features include:
- Local explanations via salience maps, attention, 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.
- Highly extensible to new model types, including classification, regression, span labeling, seq2seq, and language modeling. Supports multi-head models and multiple input features out of the box.
- Framework-agnostic and compatible with TensorFlow, PyTorch, and more.
The source code and documentation can be found at https://github.com/pair-code/lit.
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