xailib.xailib_text

Text data explainability classes for XAI-Lib.

This module provides base classes for explaining predictions on text data. It extends the base Explainer and Explanation classes with text-specific functionality.

Text explanations typically highlight which words or phrases contributed most to a model’s prediction. Common use cases include:

• Sentiment analysis explanation

• Text classification explanation

• Named entity recognition explanation

Classes:

TextExplainer: Base class for text data explainers. TextExplanation: Base class for text data explanations.

Example

Using LIME for text explanation:

from xailib.explainers.lime_explainer import LimeXAITextExplainer
from xailib.models.sklearn_classifier_wrapper import sklearn_classifier_wrapper

# Wrap your model
bb = sklearn_classifier_wrapper(your_text_classifier)

# Create and fit explainer
explainer = LimeXAITextExplainer(bb)
explainer.fit(class_names=['negative', 'positive'])

# Generate explanation
explanation = explainer.explain("This movie was great!")

See also

xailib.explainers.lime_explainer: LIME implementation for text data.

Note

Text explanation support is currently being expanded. Additional methods will be added in future releases.

Classes

TextExplainer()	Abstract base class for text data explainers.
TextExplanation()	Abstract base class for text data explanations.