Four word embedding models implemented in Python. Supporting arbitrary context features

A Lite Bert For Self-Supervised Learning Language Representations

A TUI tool to help you type faster and learn new layouts. Includes a free cat.

Touch typing trainer using N-grams as data source, with options to customize the auto-generated lessons and specify the minimum typing performance needed. There are sound/color effects as well.

datagrand 2019 information extraction competition rank9

Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate a…

Cluster and merge similar string values: an R implementation of Open Refine clustering algorithms

Python implementation of an N-gram language model with Laplace smoothing and sentence generation.

A fuzzy matching string distance library for Scala and Java that includes Levenshtein distance, Jaro distance, Jaro-Winkler distance, Dice coefficient, N-Gram similarity, Cosine similarity, Jaccard similarity, Longest common subsequence, Hamming distance, and more..

Get n-grams from text

Fast n-Gram Tokenization

Top-k Approximate String Matching.

Mirror of SRILM

natural language processing

利用传统方法（N-gram，HMM等）、神经网络方法（CNN，LSTM等）和预训练方法（Bert等）的中文分词任务实现【The word segmentation task is realized by using traditional methods (n-gram, HMM, etc.), neural network methods (CNN, LSTM, etc.) and pre training methods (Bert, etc.)】

Create n-grams of wordlists based on words, characters, or charsets to use in offline password attacks and data analysis

multiprocess unsupervised chinese_detect_words ngram_combination

Ngrams with Basic Smoothings

🔤 Lightweight R package for manipulating [string] characters

This project is an auto-filling text program implemented in Python using N-gram models. The program suggests the next word based on the input given by the user. It utilizes N-gram models, specifically Trigrams and Bigrams, to generate predictions.