deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/evaluators/sasevaluator.mdx

---
title: "SASEvaluator"
id: sasevaluator
slug: "/sasevaluator"
description: "The `SASEvaluator` evaluates answers predicted by Haystack pipelines using ground truth labels. It checks the semantic similarity of a predicted answer and the ground truth answer using a fine-tuned language model. This metric is called semantic answer similarity."
---

# SASEvaluator

The `SASEvaluator` evaluates answers predicted by Haystack pipelines using ground truth labels. It checks the semantic similarity of a predicted answer and the ground truth answer using a fine-tuned language model. This metric is called semantic answer similarity.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | On its own or in an evaluation pipeline. To be used after a separate pipeline that has generated the inputs for the Evaluator. |
| **Mandatory init variables** | `token`: A HF API token. Can be set with `HF_API_TOKEN` or `HF_TOKEN` env var. |
| **Mandatory run variables** | `ground_truth_answers`: A list of strings containing the ground truth answers  <br /> <br />`predicted_answers`: A list of strings containing the predicted answers to be evaluated |
| **Output variables** | A dictionary containing:  <br /> <br />\- `score`: A number from 0.0 to 1.0 representing the mean SAS score for all pairs of predicted answers and ground truth answers  <br /> <br />- `individual_scores`: A list of the SAS scores ranging from 0.0 to 1.0 of all pairs of predicted answers and ground truth answers |
| **API reference** | [Evaluators](/reference/evaluators-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/evaluators/sas_evaluator.py |

</div>

## Overview

You can use the `SASEvaluator` component to evaluate answers predicted by a Haystack pipeline, such as a RAG pipeline, against ground truth labels.

You can provide a bi-encoder or cross-encoder model to initialize a `SASEvaluator`. By default, `sentence-transformers/paraphrase-multilingual-mpnet-base-v2` model is used.

Note that only _one_ predicted answer is compared to _one_ ground truth answer at a time. The component does not support multiple ground truth answers for the same question or multiple answers predicted for the same question.

## Usage

### On its own

Below is an example of using a `SASEvaluator` component to evaluate two answers and compare them to ground truth answers. We need to call `warm_up()` before `run()` to load the model.

```python
from haystack.components.evaluators import SASEvaluator

sas_evaluator = SASEvaluator()
sas_evaluator.warm_up()
result = sas_evaluator.run(
    ground_truth_answers=["Berlin", "Paris"],
    predicted_answers=["Berlin", "Lyon"],
)
print(result["individual_scores"])
## [[array([[0.99999994]], dtype=float32), array([[0.51747656]], dtype=float32)]
print(result["score"])
## 0.7587383
```

### In a pipeline

Below is an example where we use an `AnswerExactMatchEvaluator` and a `SASEvaluator` in a pipeline to evaluate two answers and compare them to ground truth answers. Running a pipeline instead of the individual components simplifies calculating more than one metric.

```python
from haystack import Pipeline
from haystack.components.evaluators import AnswerExactMatchEvaluator, SASEvaluator

pipeline = Pipeline()
em_evaluator = AnswerExactMatchEvaluator()
sas_evaluator = SASEvaluator()
pipeline.add_component("em_evaluator", em_evaluator)
pipeline.add_component("sas_evaluator", sas_evaluator)

ground_truth_answers = ["Berlin", "Paris"]
predicted_answers = ["Berlin", "Lyon"]

result = pipeline.run(
    {
        "em_evaluator": {
            "ground_truth_answers": ground_truth_answers,
            "predicted_answers": predicted_answers,
        },
        "sas_evaluator": {
            "ground_truth_answers": ground_truth_answers,
            "predicted_answers": predicted_answers,
        },
    },
)

for evaluator in result:
    print(result[evaluator]["individual_scores"])
## [1, 0]
## [array([[0.99999994]], dtype=float32), array([[0.51747656]], dtype=float32)]

for evaluator in result:
    print(result[evaluator]["score"])
## 0.5
## 0.7587383
```

## Additional References

🧑‍🍳 Cookbook: [Prompt Optimization with DSPy](https://haystack.deepset.ai/cookbook/prompt_optimization_with_dspy)