An implementation of the original GloVe model with PyTorch and Lightning.

Improving Topic Models with Word Embeddings

KAG CTF EU 3 2017-2018 word embeddings

Some useful examples of Deep Learning (.py)

Kaggle competition to predict which Tweets are about real disasters and which ones are not

Sentiment Analysis + Category Classification using LSTM in Pytorch

Implemented conditional Generative adversarial networks for data augmentation to remove biases of data-set

This repository implements different architectures for training word embeddings.

Optimized talent acquisition processes by integrating a RankNet TensorFlow neural network (NLP) utilizing TF-IDF, BERT, GloVE, and Word2vec into a machine learning pipeline. The algorithm lists and ranks job candidates based on search terms and has the ability to refine future results based on feedback.

This project investigates similar word prediction using pre-trained word embeddings from the Gensim library.

GloVe model for distributed word representation

fork from: https://github.com/kefirski/pytorch_GloVe

From a given set of English sentences the program develops a vector representation in a high dimensional vector space for each word.

Celem projektu było wykonanie analizy budżetów partycypacyjnych polskich miast pod kątem istniejących w nich wspólnych tematów, trendów, zależności.

Deep learning prediction of twitter sentiment using GloVe

111-01 NTU CSIE5434 Applied Deep Learning (ADL)

Naive Bayes classifier and boolean retrieval done on the 20Newsgroups dataset that has been written from scratch. Extremely lightweight and produces decent results. Also currently working on classification using word embeddings.

A simple Python implementation of GloVe word embedding algorithm (Pennington et al., 2014)