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Developed a skip-gram model in Python using a large corpus, focusing on efficient memory management, runtime optimization, and model performance. The model captures and quantifies semantic relationships between words by analyzing their contextual proximity within the text.
Java implementation of the Word2Vec algorithm supporting CBOW and Skip-gram models. Features include training on custom corpora, vector operations, and similarity calculations. Ideal for NLP tasks like finding similar words and word analogies.
Icelandic Word Embeddings. Here you can find pre-trained corpora of word embeddings. Current methods: CBOW, Skip-Gram, Fast-Text (from Gensim library). The .model file are available for download.
Conducted intrinsic evaluation of word embeddings through analogy and similarity tests, comparing the custom Skip-gram model’s performance against Google’s pre-trained Word2Vec model to assess the quality of the generated word representations.
This repository provides implementations of Skip-gram and SVD word embedding models, and demonstrates their use in an RNN (LSTM) classification task on a news dataset.
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.
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.
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…