Bidirectional Encoder Representations from Transformers (BERT)
Scientific research often involves analyzing extensive articles, which can be time-consuming. This project addresses the challenge by leveraging BERT, a transformer-based model, to summarize scientific papers into concise, meaningful representations. The system can process large-scale research datasets to extract key insights, helping researchers and readers focus on essential information. The project combines state-of-the-art NLP techniques with domain-specific datasets to demonstrate the practical applications of machine learning in academia and knowledge management.
Basic Knowledge of Programming Languages like Python, R, TensorFlow etc.
Before Commencing the project the following links have to be examined.
https://www.coursera.org/
https://www.semanticscholar.org/
https://arxiv.org/
https://github.com/
