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

This repository implements different architectures for training word embeddings.

Yet another word2vec implementation from scratch

A framework for representing sequences as embeddings.

Pretained word2vec developed by Google is used to analyze the quality of the embedding.

Vectorization Techniques in Natural Language Processing Tutorial for Deep Learning Researchers

I performed sentiment analysis aimed at determining the sentiment of 50000 imDB movie reviews, whether they are positive, negative, or neutral. I employed various NLP approaches including lexicon based approaches, machine learning models, PLM models, and hybrid models, and assessed the performance on each type of model.

Word2Vec C model by Tomas Mikolov from svn Google's repo.

This is a novel Transformer network based approach to distinguish ChatGPT generated Text from Human text. The model was also deployed on local server using Flask where Docker was used to manage all dependencies.

Fork of github.com/tmikolov/word2vec

Skip-gram and FastText models to perform word embeddings for building a search engine for clinical trials dataset with a Streamlit user interface.

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…

Skip-gram and CBOW

Context-sensitive word embeddings with subwords. In Rust.

用skip-gram或者CBOW训练自己的词向量

A Basic Word2Vector WordEmbeddings Model. With image2Vector and Audio2Vector Encoding and decoding. (Audio is not great but works NEeds improvement - but can be reconstructred)

Language process with Python

An simple implementation of skip-gram word2vec

Arabic part of speech tagging using arabic PUD dataset using bidirectioanl LSTM for sequential labeling classification

Arabic Word Embedding models SkipGram, and GLoVE are trained over Arabic Wiki data Dump 2018 dataset from scratch using Gensim and GLoVE python libraries. Then the models are evaluated on three NLP tasks and its results are visualized in T-SNE