REQ-I: Automated Requirements Identifier

Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. REQ-I formulates the requirement identification problem as a binary text classification problem. It uses various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning for requirements identification in large tender documents. Results show that REQ-I could identify requirements in large documents with an average accuracy of 76%.

REQ-I identifies requirements in tender documents as follows:

• It extracts all the textual information from PDF tender documents using Optical Character Recognition (OCR)
• It queries a fine-tuned BERT large language model that classifies the text as either a requirement or not
• It then highlights the requirements in the PDF tender documents

Hugging Face Transformers, spaCy, NLTK, PyTorch, Numpy, Pandas, Tesseract

Requirements Engineers, Bid Managers

Partly open-source: https://github.com/a66as/REFSQ2023-ReqORNot