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This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.
Fake-Heart-Sensor-Data-Using-Python-and-Kafka is a GitHub project that provides a simple and easy-to-use way to generate simulated heart sensor data using Python and Kafka. This project is ideal for developers who want to test their applications with realistic heart sensor data or simulate a data stream for research purposes.
We use a tabular dataset which contains health information of patients to predict whether they suffer from a heart disease. Two notebooks are present currently in the repo, one focuses on data preprocessing, exploration and visualisation, while the other focuses on model creation, training and evaluation.