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

# BLEU

The BLEU (Bilingual Evaluation Understudy) Evaluator is a specialized tool designed to assess the quality of text by comparing it against reference text. It uses n-gram precision to measure how well the generated text matches the reference text.

## Purpose

The BLEU Evaluator is particularly useful when you need to:

* Measure the similarity between generated and reference text
* Evaluate machine translation quality
* Assess text generation quality
* Compare different text generation models
* Set quality thresholds for text generation

## How It Works

The evaluator calculates a BLEU score between 0 and 1 (or 0-100 when converted to percentage), where:

* **Score: 0**: The generated text is completely different from the reference
* **Score: 1**: The generated text perfectly matches the reference

The score is calculated using:

* N-gram precision (default: 1-gram)
* Smoothing method (default: method1)
* Customizable weights for different n-gram orders
* Configurable threshold (default: 0.7)

## Usage Example

```python theme={null}
import asyncio

from trusttest.evaluation_contexts import ExpectedResponseContext
from trusttest.evaluators import BleuEvaluator


async def evaluate():
    evaluator = BleuEvaluator(
        threshold=0.7,
        n_grams=4,
        smoothing_method="method1"
    )
    result = await evaluator.evaluate(
        response="The capital of France is Paris.",
        context=ExpectedResponseContext(
            expected_response="Paris is the capital of France."
        )
    )
    print(result)

if __name__ == "__main__":
    asyncio.run(evaluate())
```

The evaluator returns a tuple containing:

* A score (0-100) indicating the BLEU score percentage
* A list of explanations including the BLEU score, n-gram configuration, and threshold comparison

## When to Use

Use the BLEU Evaluator when you need to:

* Evaluate machine translation systems
* Assess text generation quality
* Compare different text generation models
* Set quality thresholds for automated text generation
* Measure similarity between generated and reference text
* Evaluate the performance of language models