FILEQA: A Summarizer, Translator and Question Answerer using T5 Transformer

The FILEQA, a machine learning project aims to provide a flexible machine learning-based system that may be used for text summarisation, translation, and question answering (QA). With the use of cutting-edge techniques such as transformer-based models and neural networks, FILEQA improves information retrieval and text comprehension in a variety of linguistic and textual contexts. One of its most important features is that it can work with a variety of data sources, such as PDF documents, thanks to text extraction libraries like PyMuPDF. The T5 (Text-to-Text Transfer Transformer) model, which powers the summarisation capabilities, helps users rapidly assimilate important information by condensing long texts into brief summaries. Bidirectional Encoder Representations from Transformers (BERT) technology from Hugging Face powers the QA functionality, which gives consumers responses to natural language questions. Moreover, FILEQA is a master at translation, using the Google Translate API to provide precise, natural-sounding translations that improve accessibility and foster cross-cultural dialogue. PyTorch and Transformers are two components of the strong technology stack that the project depends on to provide dependable and scalable operations. With the help of language processing libraries, neural networks, and transformer-based models, FILEQA seeks to provide a versatile, effective, and user-friendly solution for information extraction, translation, and text processing. This tool has the potential to completely change how people and organisations in a variety of industries engage with textual data and gain insights from it.