Abstract:

In the digital age, the ability to process and comprehend information rapidly is increasingly vital. This project explores the integration of Natural Language Processing (NLP) techniques to enhance speed reading capabilities. By utilizing NLP algorithms, we aim to develop a system that analyzes textual content, identifies key phrases, and summarizes essential information without sacrificing comprehension. The proposed method employs advanced techniques such as tokenization, named entity recognition, and semantic analysis to extract relevant data from various text formats, including articles, academic papers, and online resources. Through user studies, we will evaluate the effectiveness of the speed-reading system in improving reading speed and retention rates. The findings from this research will contribute to the development of educational tools that empower users to manage information overload and foster lifelong learning in an era characterized by an abundance of data.

Keywords:
NLP(Natural language processing),
Token frequency vectorizer(TFV),
Machine learning(ML)

OBJECTIVE:

The objective of this project is to fine-tune a pre-trained natural language processing (NLP) model using a curated dataset to enhance its performance on specific tasks, such as text classification or sentiment analysis. To achieve this, we will first evaluate the baseline performance of the selected model and then curate a diverse dataset tailored to our objectives. The fine-tuning process will involve training the model on this dataset while monitoring key performance metrics to avoid overfitting. Upon completion of the training, we will analyze the results by comparing the model's performance before and after fine-tuning, using metrics such as accuracy and F1 score to quantify improvements. Through error analysis, we will identify the types of mistakes made by the model and highlight areas for further enhancement. The findings of this project will not only demonstrate the effectiveness of fine-tuning NLP models with curated datasets but will also provide insights for future research and applications in the field, paving the way for improved performance in real-world NLP tasks.