nixsearch / pkg / timblserver

timblserver

This server for TiMBL implements several memory-based learning algorithms

GPL-3.0 github.com timblserver 2 releases newest changed 15 days ago

version1.19 attributetimblserver rev

aarch64-darwin 8d5d270900d3fc75655ea2d9d248b234f6631439 2026-09-27

aarch64-linux 8d5d270900d3fc75655ea2d9d248b234f6631439 2026-09-27

x86_64-darwin 3d46470bb3030020f7e1361f33514854f5bfa86d 2026-06-27

x86_64-linux 8d5d270900d3fc75655ea2d9d248b234f6631439 2026-09-27

systems✓ aarch64-darwin✓ aarch64-linux✓ x86_64-darwin✓ x86_64-linux

$ devbox add timblserver@1.19

$ nix shell github:NixOS/nixpkgs/8d5d270900d3fc75655ea2d9d248b234f6631439#timblserver  # aarch64-darwin

$ nix shell github:NixOS/nixpkgs/8d5d270900d3fc75655ea2d9d248b234f6631439#timblserver  # aarch64-linux

$ nix shell github:NixOS/nixpkgs/3d46470bb3030020f7e1361f33514854f5bfa86d#timblserver  # x86_64-darwin

$ nix shell github:NixOS/nixpkgs/8d5d270900d3fc75655ea2d9d248b234f6631439#timblserver  # x86_64-linux

JSON: /v2/resolve?name=timblserver&version=latest

Releases

newest first, as /v2/pkg orders them

Version	arm mac	arm linux	x86 mac	x86 linux	Attribute	Commit	Date	Outputs
1.19resolved	✓	✓	✓	✓	`timblserver`	varies	2026-09-13
1.11	✓	✓	✓	✓	`timblserver`	2dad7af78a	2025-10-09

Prereleases are listed but latest never picks one. “varies” means each system last changed at a different nixpkgs commit — the release page lists them.

Description

This implements a server for TiMBL. TiMBL is an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. For over fifteen years TiMBL has been mostly used in natural language processing as a machine learning classifier component, but its use extends to virtually any supervised machine learning domain. Due to its particular decision-tree-based implementation, TiMBL is in many cases far more efficient in classification than a standard k-nearest neighbor algorithm would be.