Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

Skipgram Negative Sampling in PyTorch

结合python一起学习自然语言处理 (nlp): 语言模型、HMM、PCFG、Word2vec、完形填空式阅读理解任务、朴素贝叶斯分类器、TFIDF、PCA、SVD

This repository contains the "tensorflow" implementation of our paper "graph2vec: Learning distributed representations of graphs".

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 and query pattern models.

Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling

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

Context-sensitive word embeddings with subwords. In Rust.

A word2vec port for Windows.

Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.

This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper

Finds out symptoms similar to a given symptom, from a symptom-disease data set.

Efficient learning of word representations

Bengali Word Embedding

This repo contains my solution to the Stanford course "NLP with Deep Learning" under CS224n code by prof. Richard Socher and Prof. Christopher Manning in 2017/2018.

This repo specifically set up for students of Computer Engineering Department at University of Guilan studying NLP

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

A regularized version of RBM for unsupervised feature selection.

This is a practical implementation implementing neural networks on top of fasttext as well as word2vec word embeddings.

SkipGram model implemented in Tensorflow and PyTorch

Skipgram with Hierarchical Softmax

word2vec implementation (for skip-gram and cbow) and simple application of word2vec in sentiment analysis

[SCiL 2020] DialectGram: Detection of Dialectal Changes with Multi-geographic Resolution Analysis

Implemented the skip-gram model for Word2Vec, complete with data pre-processing and a sweet word embedding visualizer in tensorboard

A PyTorch Implementation of the Skipgram Negative Sampling Word2Vec Model as Described in Mikolov et al.

SLM is a Bayesian skipgram language model based on hierarchical Pitman-Yor processes. It facilitates multiple backoff strategies, interpolation strategies, and offers tools to train and evaluate language models, and rescore nbest lists

For all your n-gram and skip-gram needs 🔠

Implementation of the Mineral algorithm as described in the paper, Mineral: Multi-modal Network Representation Learning.

Implementing skip gram model of word2vec with visualization of embedding in tensorboard.