Chatbot normalized exact match
You are scoring a chatbot's short answers against a reference answer key using normalized exact match. Given equal-length lists outputs and references, normalize each string by lowercasing it, trimming leading and trailing whitespace, and collapsing every internal run of whitespace to a single space. Return the number of positions where the normalized output equals the normalized reference. Example: outputs = [" Paris ", "london"], references = ["paris", "Berlin"] returns 1.
Implement
exact_match_count(outputs: list[str], references: list[str]) → intExamples
in
[[" Paris ","london"],["paris","Berlin"]]out1What a strong answer looks like
State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
0:00 of about 12 min
solution.py
InputExpectedGot
[[" Paris ","london"],["paris","Berlin"]]1not run yetsample