MultiPatternAccuracyScorer
Evaluates prediction accuracy against multiple acceptable patterns or answers. Ideal for tasks where multiple correct answers exist, with regex support for flexible pattern matching.
Overview
Evaluates prediction accuracy against multiple acceptable patterns or answers. Ideal for tasks where multiple correct answers exist, with regex support for flexible pattern matching.
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 | Primary expected output |
| context.patterns | list[str] | No | Additional acceptable patterns (regex supported) |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | 10.0 if any pattern matches, 0.0 otherwise |
| passed | bool | True if any pattern matched |
| reasoning | str | Pattern match details |
| metadata | dict | Which patterns matched |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use MultiPatternAccuracyScorer 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 7 can be customized when configuring the scorer.
Quick Info
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