> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gaussia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Explainability

> Analyze token-level attributions to understand which input tokens drive model outputs

## Overview

The **Explainability** module provides token attribution analysis to understand which parts of the input influence model outputs. It uses the [interpreto](https://github.com/gaussia-labs/interpreto) library for token-level attribution.

## Use case

Explainability helps you understand:

* Which tokens in the input have the most influence on the output
* Whether the model is attending to the right parts of the context
* Potential biases in token-level attention patterns

## Usage

```python theme={null}
from gaussia.explainability import TokenAttribution

attribution = TokenAttribution(
    model_name="bert-base-uncased",
)

results = attribution.analyze(
    text="The weather in Paris is sunny today.",
    target="sunny",
)

for token, score in results.attributions:
    print(f"{token}: {score:.4f}")
```

<Note>
  Requires the `explainability` extra: `pip install "gaussia[explainability]"`.
</Note>