QA Exact-Match & Token-F1 Calculator
SQuAD-style exact-match and token-overlap F1 between a predicted answer and the reference — the QA evaluation pair.
SQuAD reports both: exact match is strict (the normalized answer must match exactly), token F1 gives partial credit for overlapping words. Normalization lowercases, strips punctuation and removes articles (a/an/the) so 'the Eiffel Tower' and 'Eiffel Tower' score F1 = 1.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About QA Exact-Match & Token-F1 Calculator
Extractive question-answering systems are scored with two complementary metrics that this calculator computes: Exact Match, the strict fraction of predictions identical to the reference after normalization, and token-level F1, which gives partial credit for word overlap so a near-miss isn't scored the same as a wild miss. The normalization step — lowercasing, stripping punctuation, and removing the articles a/an/the — is what makes 'the Eiffel Tower' and 'Eiffel Tower' count as a perfect match. This is the exact evaluation protocol from SQuAD, the benchmark that shaped modern reading comprehension.
How to use QA Exact-Match & Token-F1 Calculator
- 1Enter your values into QA Exact-Match & Token-F1 Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use QA Exact-Match & Token-F1 Calculator?
- ✓Computes QA Exact-Match & Token-F1 instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: EM = 1 if normalized strings match exactly else 0.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why does QA evaluation need both EM and F1?+
Exact match is unforgiving — a correct answer phrased slightly differently scores zero, which understates a model that understood the question. Token F1 rewards partial overlap, capturing 'nearly right' answers. Reporting both gives a strict ceiling (EM) and a lenient floor (F1); the gap between them indicates how often the model is close but not exact.
What does SQuAD normalization do?+
Before comparing, it lowercases text, removes punctuation, collapses whitespace, and strips the articles a/an/the. This prevents trivial formatting differences ('The cat.' vs 'cat') from being scored as errors, focusing evaluation on whether the substantive answer is correct rather than punctuation or capitalization.
Are EM and token-F1 good for generative/abstractive QA?+
Less so — they reward lexical overlap with one reference, penalizing correct paraphrases that use different words. For free-form generative answers, they're a weak signal; semantic metrics (BERTScore), LLM-as-judge evaluation, or multiple reference answers are needed. EM/F1 shine for extractive QA where the answer is a span of the source text.
How are multiple acceptable answers handled?+
SQuAD provides several human reference answers per question and takes the maximum EM/F1 over them — crediting the model if it matches any acceptable answer. This calculator compares against one reference; for fair evaluation with multiple valid answers, compute the score against each and keep the best, as the benchmark does.
Related ML & AI tools
ROC-AUC Calculator (from TPR/FPR points)
Trapezoidal area under the ROC curve from your (FPR, TPR) operating points — the threshold-independent ranking score.
● LiveClassification Threshold Cost Calculator
Find the probability cutoff that minimizes expected cost given your false-positive and false-negative penalties.
● LiveSilhouette Score Calculator
Cluster cohesion vs separation for one point — the building block of the silhouette metric for choosing K.
● Live