Zipf's law observes that in natural language the frequency of a word is inversely proportional to its rank: the most common word appears about twice as often as the second, three times as often as the third, and so on. The exponent controls how sharp that decline is.

How is each frequency calculated?

For a word at rank r the unnormalized weight is 1 divided by r raised to the exponent s. Those weights are then scaled so they sum to the chosen total count, giving integer frequencies.

No — the words are generated pronounceable tokens so the tool works for any vocabulary size without shipping a dictionary. The distribution of frequencies is what matters for testing ranking and NLP pipelines.

What exponent should I use?

Real English text sits near an exponent of 1.0. Use values above 1 for a steeper, more top-heavy distribution and below 1 for a flatter one.

Is anything sent to a server?

No. The list is computed entirely in your browser with JavaScript, so nothing you generate leaves your device.

Word Frequency List Generator

Generate realistic frequency data

This tool builds a synthetic word frequency list that follows Zipf’s law, the empirical rule that word frequency is roughly inversely proportional to rank. It is handy for stress-testing search ranking, building NLP preprocessing fixtures, and demoing text-analysis tools when you do not want to ship a real corpus.

How it works

For a vocabulary of size N, each word at rank r (from 1 to N) is given an unnormalized weight:

weight(r) = 1 / r^s

where s is the Zipf exponent. The weights are summed and each is scaled so the frequencies add up to a chosen total count, then rounded to integers:

frequency(r) = round( totalCount * weight(r) / sum_of_weights )

Words themselves are generated as short pronounceable tokens, so the tool works for any size without a dictionary. The output gives you rank, word, frequency, and each word’s percentage share of the corpus.

Tips and notes

An exponent near 1.0 mimics natural English; raise it for a steeper head and lower it for a flatter tail.
Larger vocabularies produce a long, thin tail — exactly the shape real text exhibits.
The percentages always sum to roughly 100 percent; small rounding differences are expected at large N.