ExactMatchScorer
Evaluates whether the prediction exactly matches the ground truth. Strictest form of accuracy measurement for tasks requiring exact output like code generation or classification labels.
Overview
Evaluates whether the prediction exactly matches the ground truth. Strictest form of accuracy measurement for tasks requiring exact output like code generation or classification labels.
Use Cases
- Accuracy benchmarking and validation
How It Works
This scorer uses deterministic rule-based evaluation to validate outputs against specific criteria. It applies predefined rules and patterns to assess the response, providing consistent and reproducible results without requiring LLM inference.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| prediction | str | Yes | Generated output |
| ground_truth | str | Yes | Expected exact output |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | 10.0 if exact match, 0.0 otherwise |
| passed | bool | True if exact match |
| reasoning | str | Match result |
| metadata | dict | Comparison details |
Score Interpretation
Default threshold: 10/10
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Frequently Asked Questions
When should I use this scorer?
Use ExactMatchScorer when you need to evaluate accuracy and rule-based aspects of your AI outputs. It's particularly useful for accuracy benchmarking and validation.
Why does this scorer need expected output?
This scorer compares the generated output against a known expected result to calculate accuracy metrics.
Can I customize the threshold?
Yes, the default threshold of 10 can be customized when configuring the scorer.
Quick Info
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