This module converts English text into numbers. It supports both ordinal and cardinal numbers, negative numbers, and very large numbers.

Chewing is an intelligent phonetic (Zhuyin/Bopomofo) input method, one of the most popular choices for Traditional Chinese users.

libskk is a library to deal with Japanese kana-to-kanji conversion method.

Liblouis is a braille translator and back-translator named in honor of Louis Braille. It features support for computer and literary braille, supports contracted and uncontracted translation for many languages and has support for hyphenation. New languages can easily be added through tables that support a rule- or dictionary based approach. Tools for testing and debugging tables are also included. Liblouis also supports math braille, Nemeth and Marburg.

This module applies the Porter Stemming Algorithm to its parameters, returning the stemmed Italian word.

This module provides a utility method, "to_identifier" for converting an arbitrary string into a readable representation using the ASCII subset of "\w" for use as an identifier in a computer program. The intent is to make unique identifier names from which the content of the original string can be easily inferred by a human just by reading the identifier.

DParser is scannerless GLR parser generator. The form of the text to be parsed can be specified using a combination of regular expressions and grammar productions. Because of the parsing technique, a scannerless GLR parser based on the Tomita algorithm the grammar can be ambiguous, right or left recursive, have any number of null productions, and because there is no separate tokenizer, can include whitespace in terminals and have terminals which are prefixes of other terminals.

Mecab is a morphological analysis engine developed as a collaboration between the Kyoto university and Nippon Telegraph and Telephone Corporation. The engine is independent of any language, dictionary or corpus.

This module applies the Porter Stemming Algorithm to its parameters, returning the stemmed Russian (KOI8-R only) word.

This routine applies stemming algorithms to its parameters, returning the stemmed words as appropriate to the selected locale.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

This is a smaller version of the T1 neural network, which is currently one of the best neural networks for Leela Chess Zero.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

This is an official neural network of the Leela Chess Zero project that was finished being trained in April of 2022.

T2 is currently one of the best neural networks for Leela Chess Zero, superseding the neural network T1.

T1 is currently one of the best neural networks for Leela Chess Zero, however, it was superseded by the neural network T2.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

Leela Chess Zero is a UCI-compliant chess engine designed to play chess using neural networks. This package does not provide a neural network, which is necessary to use Leela Chess Zero and should be installed separately.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

This is an official neural network of a ``main run'' of the Leela Chess Zero project. The network was finished being trained in September of 2023.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

This is an official neural network of a ``main run'' of the Leela Chess Zero project that was finished being trained in January of 2022.