Deep abstract generator

Natural language processing (NLP) has substantially evolved thanks to deep learning algorithms, enabling computers to understand, analyse, and produce human language more precisely. This dissertation applies and evaluates unique deep-learning methods for NLP tasks focusing on text production and model interpretation. The core concepts of NLP, including its underlying linguistic principles, the NLP pipeline, and the most well-liked tools and frameworks, are covered in detail in this thesis. In addition, critical NLP methodologies, applications, difficulties, and prospective new advancements are also covered. Strategy components are data preparation and collection, model training and evaluation, hyperparameter optimisation, model interpretation, and transfer learning. We detail dataset selection, data cleaning, pre-processing, and representation techniques. The PyTorch Transformer is discussed, along with attention mechanisms and several model interpretation techniques, including Local Interpretable Model-Agnostic Explanations (LIME), Recall- Oriented Understudy for Gisting Evaluation (ROUGE), BiLingual Evaluation Understudy (BLEU), and Metric for Evaluation of Translation with Explicit Ordering (METEOR). Data pre-processing, lemmatization, stemming, tokenization, and vectorization are investigated in implementing an NLP pipeline. The data structure and model training implementation are discussed for a customised Transformer and a tuned GPT-2 model. The models are then assessed using a variety of indicators, and their implications, constraints, and possibilities for future study improvement are examined. This thesis contributes significantly to the continuing developments in NLP by providing a thorough grasp of modern methods, illustrating the use and assessment of cutting-edge models, and promoting transparency and interpretability in deep learning.