Advanced file format fuzzer based-on deep neural language models.
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Updated
Apr 13, 2023 - Python
Advanced file format fuzzer based-on deep neural language models.
Generating text sequences using attention-based Bi-LSTM
Language Modeling using Recurrent Neural Networks implemented over Tensorflow 2.0 (Keras) (GRU, LSTM)
Materials for the MSc Thesis "Interpreting Neural Language Models for Linguistic Complexity Assessment" and related works.
Implementation of "A Neural Probabilistic Language Model" by Yoshua Bengio et al. - Tensorflow
Generating High-Quality Query Suggestion Candidates for Task-Based Search - ECIR'18
Towards Comprehensive Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias
Classification of case.law cases by landmark cases from www.law.cornell.edu
Bengio's Neural Probabilistic Language Model implemented in Matlab which includes t-SNE representations for word embeddings.
Pytorch implementation of a simple GRU word-level language model trained on Donald Trump's tweets.
Projects for Data Mining and Analytics
Improving Language Model Performance through Smart Vocabularies
Creating a Neural Language Model using LSTMs and calculating perplexity score of the model.
Master Thesis Project - different phases of analyses developed for the LangLearn shared task (EVALITA 2023).
Natural Language Processing Lab Experiments
Implementation of a simple neural language model (multi-layer perceptron) from scratch for next word prediction
Pytorch based Neural Network Language Modeling (NNLM) Toolkit for easier and faster NNLM research and development. Result of my Master's Thesis work.
Basic concepts which are used in NLP such as : Language model, Neural Language model, Word embedding, Text classification, Bert, RNN, LSTM, GRU, Attention, Transformers.
Deep learning models in Python
N-gram and neural word-level language models
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