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

# Text Match (Deprecated)

> Determines whether the generated text matches the reference with high character-level similarity using fuzzy matching given by the [Text Similarity](/concepts/metric/text-similarity) metric. Returns a binary outcome based on a threshold, making it ideal for simple pass/fail evaluations where exact wording is not required.

<Warning>
  **Deprecated:** Text Match is deprecated in favor of [Text Similarity](/concepts/metric/text-similarity), which provides a continuous similarity score using the same underlying algorithm. Text Match will be removed in a future release. Please migrate your evaluations to use `text_similarity` instead.
</Warning>

The Text Match metric is part of the [Deterministic Metric](/concepts/metric) options in Galtea. It leverages character-level fuzzy string comparison to assess whether two texts are similar enough, based on a predefined threshold. Rather than producing a continuous similarity score, this metric returns a boolean-style score, which is useful for scenarios where a definitive match decision is needed despite minor wording differences.

## Evaluation Parameters

To compute the text\_match metric, the following parameters must be provided:

* **`actual_output`**: The generated text from the model.
* **`expected_output`**: The target or reference text to compare against.

## How Is It Calculated?

The metric uses a fuzzy string matching algorithm (based on edit distance) to compute the similarity ratio between the actual and expected outputs. If the similarity ratio exceeds 85%, the output is considered a match, otherwise; it is marked as a non-match.

## Interpretation of Scores

* **1.0** – Texts are considered a match (similarity > 85%).
* **0.0** – Texts are not considered a match (similarity ≤ 85%).

This binary scoring makes the metric particularly useful for simple pass/fail validation use cases.

## Suggested Test Case Types

Use Text Match when evaluating:

* **Simple equivalence checks** where light paraphrasing or minor differences are allowed.
* **Rule-based or heuristic outputs** where exact matches aren't expected but alignment is necessary.
* **Pass/fail QA checks** for generated text.