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ExamplesEvalsTone Consistency

Tone Consistency Evaluation

This example demonstrates how to use Mastra’s Tone Consistency metric to evaluate emotional tone patterns and sentiment consistency in text.

Overview

The example shows how to:

  1. Configure the Tone Consistency metric
  2. Compare sentiment between texts
  3. Analyze tone stability within text
  4. Handle different tone scenarios

Setup

Dependencies

Import the necessary dependencies:

src/index.ts
import { ToneConsistencyMetric } from '@mastra/evals/nlp';

Metric Configuration

Set up the Tone Consistency metric:

src/index.ts
const metric = new ToneConsistencyMetric();

Example Usage

Consistent Positive Tone Example

Evaluate texts with similar positive sentiment:

src/index.ts
const input1 = 'This product is fantastic and amazing!';
const output1 = 'The product is excellent and wonderful!';
 
console.log('Example 1 - Consistent Positive Tone:');
console.log('Input:', input1);
console.log('Output:', output1);
 
const result1 = await metric.measure(input1, output1);
console.log('Metric Result:', {
  score: result1.score,
  info: result1.info,
});
// Example Output:
// Metric Result: {
//   score: 0.8333333333333335,
//   info: {
//     responseSentiment: 1.3333333333333333,
//     referenceSentiment: 1.1666666666666667,
//     difference: 0.16666666666666652
//   }
// }

Tone Stability Example

Evaluate sentiment consistency within a single text:

src/index.ts
const input2 = 'Great service! Friendly staff. Perfect atmosphere.';
const output2 = ''; // Empty string for stability analysis
 
console.log('Example 2 - Tone Stability:');
console.log('Input:', input2);
console.log('Output:', output2);
 
const result2 = await metric.measure(input2, output2);
console.log('Metric Result:', {
  score: result2.score,
  info: result2.info,
});
// Example Output:
// Metric Result: {
//   score: 0.9444444444444444,
//   info: {
//     avgSentiment: 1.3333333333333333,
//     sentimentVariance: 0.05555555555555556
//   }
// }

Mixed Tone Example

Evaluate texts with varying sentiment:

src/index.ts
const input3 = 'The interface is frustrating and confusing, though it has potential.';
const output3 = 'The design shows promise but needs significant improvements to be usable.';
 
console.log('Example 3 - Mixed Tone:');
console.log('Input:', input3);
console.log('Output:', output3);
 
const result3 = await metric.measure(input3, output3);
console.log('Metric Result:', {
  score: result3.score,
  info: result3.info,
});
// Example Output:
// Metric Result: {
//   score: 0.4181818181818182,
//   info: {
//     responseSentiment: -0.4,
//     referenceSentiment: 0.18181818181818182,
//     difference: 0.5818181818181818
//   }
// }

Understanding the Results

The metric provides different outputs based on the mode:

  1. Comparison Mode (when output text is provided):

    • Score between 0 and 1 indicating tone consistency
    • Response sentiment: Emotional tone of input (-1 to 1)
    • Reference sentiment: Emotional tone of output (-1 to 1)
    • Difference: Absolute difference between sentiments

    Score interpretation:

    • 0.8-1.0: Very consistent tone
    • 0.6-0.7: Generally consistent
    • 0.4-0.5: Mixed tone
    • 0.0-0.3: Conflicting tone

  2. Stability Mode (when analyzing single text):

    • Score between 0 and 1 indicating internal consistency
    • Average sentiment: Overall emotional tone
    • Sentiment variance: How much tone varies between sentences

    Score interpretation:

    • 0.9-1.0: Very stable tone
    • 0.7-0.8: Mostly stable
    • 0.4-0.6: Variable tone
    • 0.0-0.3: Highly inconsistent




View Example on GitHub
Textual DifferenceToxicity