Title: Table Question-Answering with TAPAS Model

• Developed a Python script leveraging the transformers library (version 4.4.2) to perform table question-answering tasks
• Utilized the TAPAS (Table-based Pre-training for Answering Selection) model (google/tapas-base-finetuned-wtq) for accurate and efficient question-answering on tabular data
• Employed the pipeline function to create a streamlined workflow for table question-answering tasks
• Processed tabular data from CSV files, performing data cleaning operations such as correcting typographical errors in specific columns (e.g., ‘location’)
• Generated insightful answers to complex queries posed to the tabular data, facilitating rapid data analysis and decision-making
• Demonstrated proficiency in data manipulation techniques using pandas library for data wrangling and preprocessing tasks
• Optimized code efficiency by saving modified data back to CSV files for future use and easy retrieval