A highly efficient, isomorphic, full-featured, multilingual text search engine library, providing full-text search, fuzzy matching, phonetic scoring, document indexing and more, with micro JSON state hydration/dehydration in-browser and server-side.
plugin for nlp-compromise
A JavaScript library for escaping CSS strings and identifiers while generating the shortest possible ASCII-only output.
跨项目知识库 MCP 服务，支持三层搜索（文本/TF-IDF/语义向量），提供 Web UI 管理界面
For ruby and ruby on rails
A trainable Hidden Markov Model with Gaussian emissions using TensorFlow.js
Ruby SemVer in TypeScript.
TensorFlow layers API in JavaScript
Convention over configuration for using Vite in Ruby apps
Like ruby's abbrev module, but in js
Ruby grammar for tree-sitter
prettier plugin for the Ruby programming language
完善依赖和相对的功能
Vanilla JavaScript backend for TensorFlow.js
WebSocket framework for Ruby on Rails.
Cross-session memory and recall for AI agents — git-synced knowledge base, knowledge graph, confidence scoring, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing
GPU accelerated WebGL backend for TensorFlow.js
Full-text search with TF-IDF ranking — index files, search with relevance scoring, suggest completions.
JS Search is an efficient, client-side search library for JavaScript and JSON objects
Tensorflow model converter for javascript
JavaScript client for graphql-ruby
bootstrap-sass is a Sass-powered version of Bootstrap 3, ready to drop right into your Sass powered applications.
Semantic search using TF-IDF vector space — cosine similarity, intent matching, document similarity.
Convention over configuration for using Vite in Rails apps
Term Frequency - Inverse Document Frequency
A TF-IDF in ruby - http://en.wikipedia.org/wiki/Tf–idf
Term Frequency - Inverse Document Frequency
Pure Ruby keyword and keyphrase extraction library. Implements RAKE, YAKE, and TF-IDF algorithms for extracting keywords from text.
Lightweight gem for document retrieval using tf-idf based algorithms for Ruby
A Ruby library for text classification featuring Naive Bayes, LSI (Latent Semantic Indexing), Logistic Regression, and k-Nearest Neighbors classifiers. Includes TF-IDF vectorization, streaming/incremental training, pluggable persistence backends, thread safety, and a native C extension for fast LSI operations.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.