Fuzzy Match: Approximate Text Similarity Evaluation

result = evaluator.evaluate(
    eval_templates="fuzzy_match",
    inputs={
        "expected": "The Eiffel Tower is a famous landmark in Paris, built in 1889 for the World's Fair. It stands 324 meters tall.",
        "output": "The Eiffel Tower, located in Paris, was built in 1889 and is 324 meters high."
    },
    model_name="turing_flash"
)

print(result.eval_results[0].output)
print(result.eval_results[0].reason)

import { Evaluator, Templates } from "@future-agi/ai-evaluation";

const evaluator = new Evaluator();

const result = await evaluator.evaluate(
  "fuzzy_match",
  {
    expected: "The Eiffel Tower is a famous landmark in Paris, built in 1889 for the World's Fair. It stands 324 meters tall.",
    output: "The Eiffel Tower, located in Paris, was built in 1889 and is 324 meters high."
  },
  {
    modelName: "turing_flash",
  }
);

console.log(result);

What to Do When Fuzzy Match Score is Low

• Ensure that both input texts are properly formatted and contain meaningful content
• This evaluation works best with texts that convey similar information but might have different wording
• For very short texts (1-2 words), results may be less reliable
• If you need more precise matching, consider using Levenshtein Similarity instead

Comparing Fuzzy Match with Similar Evals

• Levenshtein Similarity: Fuzzy Match uses approximate text matching, while Levenshtein Similarity provides a stricter character-by-character comparison.
• Embedding Similarity: Fuzzy Match compares surface-level text, while Embedding Similarity compares semantic meaning.
• Semantic List Contains: Fuzzy Match evaluates overall text similarity, while Semantic List Contains checks if specific semantic concepts are present.
• ROUGE Score: Fuzzy Match uses approximate matching, while ROUGE Score evaluates based on n-gram overlap, especially useful for summarization.