Popular GitHub Repositories for Gram Staining

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

基于Pytorch和torchtext的自然语言处理深度学习框架。

NLP 领域常见任务的实现，包括新词发现、以及基于pytorch的词向量、中文文本分类、实体识别、摘要文本生成、句子相似度判断、三元组抽取、预训练模型等。

cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information

A complete pytorch implementation of skip-gram

A C++ library providing fast language model queries in compressed space.

Machine learning based text classification in JavaScript using n-grams and cosine similarity

Training an n-gram based Language Model using KenLM toolkit for Deep Speech 2

A very simple, bare-bones, inefficient, implementation of skip-gram word2vec from scratch with Python

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

Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch

[EMNLP 2020] Keep CALM and Explore: Language Models for Action Generation in Text-based Games

TensorFlow implementation of word2vec applied on https://www.kaggle.com/tamber/steam-video-games dataset, using both CBOW and Skip-gram.

基于tensorflow1.x的预训练模型调用，支持单机多卡、梯度累积，XLA加速，混合精度。可灵活训练、验证、预测。

word2vec implementation for skip-gram in pytorch

PyTorch implementation of the Word2Vec (Skip-Gram Model) and visualizing the trained embeddings using TSNE

Implementing Skip-gram Negative Sampling with pytorch

基于机器学习的 Webshell 检测 (OPCode -> N-Gram -> TF-IDF -> XGBoost)

Next Word Prediction using n-gram Probabilistic Model with various Smoothing Techniques

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.)】