Convert numbers to approximated text expressions like 'less than a quarter' for natural numbers, percentages, and fractions in multiple languages.
Number Words is a Clojure/Java library that converts numeric values into human-readable text approximations for natural numbers, percentages, and fractions. It solves the problem of presenting precise numeric data in a more interpretable, natural language format, such as turning 0.231 into 'less than a quarter' or 102 into 'over one hundred'.
Developers building text generation systems, data reporting tools, or applications that need to present numeric data in a human-friendly way, especially those requiring multi-language support.
Developers choose Number Words for its flexible approximation scales, configurable hedges, and built-in support for multiple languages, making it ideal for generating natural numerical expressions in reports or automated text systems.
Convert a number to an approximated text expression: from '0.23' to 'less than a quarter'.
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Supports six languages including English, French, and Russian using ICU4J, making it versatile for international applications as highlighted in the languages section.
Allows custom scales like 1/4 or multiples of 10, enabling domain-specific precision control as described in the numerical approximations feature.
Offers relational words like 'over' and 'under' that can be customized via an EDN file, providing tailored natural language expressions.
Supports words, bite-size shortening, or plain numbers, giving flexibility in how approximations are presented, such as '1k' for large numbers.
Only supports six languages; adding new ones requires manual configuration changes in numwords.edn, which can be tedious and error-prone.
Relies on ICU4J for number-to-text translation, increasing library size and potential compatibility issues in environments with strict dependency management.
Customizing hedges and favorite numbers involves editing an EDN file, which may not be intuitive for developers unfamiliar with Clojure or configuration-based setups.