Seeking Guidance on Extracting Bidding Data from Procurement Documents
Hello Hugging Face Community,I’m working on a project that involves extracting company names and their bid amounts from diverse public procurement announcements, with the aim to organize this information into a structured format like this:{'announcement_id': xxx, bids: [{'company': 'A', 'bid_value': 500}, {'company': 'B', 'bid_value': 600}]}.Each announcement can include none or multiple companies and their bids. This structured information would very much help me with my research project. I’ve manually prepared a training dataset from a selection of these announcements. However, I’m quite new to NLP and I am unsure about the best methodologies to employ.Could you please  point me to a good direction and offer advice or resources on the following points?Model Choice:Should I use Named Entity Recognition (NER), a Generative Model, or another approach for this task?Data Preparation:What are best practices for preparing and structuring my data to handle multiple entries per document?Model Training:How can I effectively fine-tune a model to recognize and extract this specific type of data? (Using my manually extracted sample of companies and bids from announcements).I would greatly appreciate any insights or examples of similar projects, as I’m not sure which aspects to focus on more deeply.Thank you!