{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic/2"}],"enrichment":{"capability":"Extracts quantities, measurements, and their units from unstructured text, disambiguating between similar units using vector similarity and Wikipedia context.","skillfed_tags":["nlp","information-extraction","unit-parsing"],"use_cases":["Parse scientific or technical documents to identify measurements and their units for indexing or validation.","Disambiguate currency amounts from weight or distance in mixed-domain text.","Build a dimensionless number extractor for cases where only numeric values matter.","Convert measurement text to structured data for downstream unit conversion or comparison.","Extract and classify quantities from product descriptions or technical specifications."],"what_it_does":"Quantulum3 is a Python library that identifies and extracts numeric quantities and their units from plain text. It parses both standard units (litre, kilogram, terawatt) and spelled-out numbers, ranges, and uncertainties, then reconciles them against Wikipedia to determine their entity type (volume, mass, energy, etc.). When multiple units could match the same text, it uses a classifier trained on GloVe word vectors and Wikipedia context to disambiguate\u2014for example, distinguishing currency from weight based on surrounding words.\n\nThe library returns Quantity objects containing the parsed value, unit name, entity classification, and span positions in the original text. It also supports inline parsing for debugging, export to JSON or dictionaries, and conversion to spoken form. Two lightweight runtime dependencies (inflect and num2words) handle number inflection and text-to-speech conversion. Optional classifier training is available for custom models.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific NLP problem (quantity extraction with disambiguation) that is difficult to implement from scratch. MIT licensing removes legal friction. Use it when you need to reliably extract and classify quantities from unstructured text; skip it if you only need simple regex-based number extraction."},"id":"quantulum3","links":{"html":"https://skillfed.io/packages/quantulum3","md":"https://skillfed.io/packages/quantulum3.md","pypi":"https://pypi.org/project/quantulum3/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-09","license_spdx":"MIT","license_treatment":"permissive","name":"quantulum3","python_support":"supports_current","summary":"Extract quantities from unstructured text."},"popularity":{"monthly_downloads":226851,"position":9194,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.10.0"}