免费文本熵/可预测性计算器。计算字符级和词级香农熵、N元语法熵和压缩率估算。
Use when you suspect a text is repetitive but can't spot why. Word entropy catches phrase-level repetition; character entropy catches alphabet-level oddities.
Lexical diversity measures unique-word *count*; entropy measures unique-word *distribution*. Two texts can have identical TTR but very different entropy.
Shannon entropy is computed as H = −Σ p(x) · log₂ p(x) over the chosen symbol set (characters, words, or word bigrams).
Predictability is 1 − H/Hₘₐₓ — higher means more repetitive and easier to compress.
Compare modes: AI-generated text often shows lower word entropy and higher word-bigram repetition than human writing of similar length.
A measurement of unpredictability. Higher entropy means each symbol (word or character) carries more information; lower means the text is more repetitive.
~9–10 bits per word for typical prose; ~4 bits per character. Highly templated text falls below.
Different views surface different patterns. Word-bigram entropy catches phrase-level repetition; character entropy catches alphabet quirks.
输入
The cat sat on the mat. The cat sat on the mat. The cat sat on the mat.
输出
Word entropy: 2.32 bits — Very predictable (predictability 51%).
Only 6 unique words across 18 — the same six repeated three times. Maximum possible entropy is log₂(6) ≈ 2.58 bits, so 2.32 is close to maximum-given-vocab but predictability vs uniform is low.