Search Results for "text"

UI Component Library for Neovim.

Library of 40+ independent Lua modules improving overall Neovim (version 0.8 and higher) experience with minimal effort. They all share same configuration approaches and general design principles. Think about this project as "Swiss Army knife" among Neovim plugins: it has many different independent tools (modules) suitable for most common tasks. Each module can be used separately without any startup and usage overhead.

refactoring.nvim is a Neovim plugin developed to bring powerful automated code refactoring capabilities to one of the most popular text editors among programmers, giving developers a suite of refactoring operations that streamline repetitive restructuring tasks inside the editor. Built around an intuitive set of commands and a Lua API, the plugin allows users to extract and inline variables or functions, pull blocks of code into new files, and modify code structure without leaving the comfort of Neovim’s modal interface. ...

The torch-rnn project is a lightweight and efficient implementation of recurrent neural networks built on the Torch framework, focusing on flexibility and reusability for sequence modeling tasks. It provides implementations of standard RNNs and long short-term memory networks, enabling users to train models for tasks such as text generation, language modeling, and sequence prediction. The repository emphasizes simplicity and performance, offering a streamlined pipeline for preprocessing data, training models, and sampling outputs. It includes tools for handling datasets, converting text into structured formats, and managing checkpoints during training. By leveraging Torch’s modular design, the project allows users to experiment with different architectures and hyperparameters with minimal overhead. ...

char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. ...