July 2021 Summaries

The Haystack question answering system relies on an intricate building block called the "Reader," which processes batches of preselected documents to find the best answers to natural-language queries. The reader object is an interface to a Transformer-based language model, such as BART, BORT, or CamemBERT, and can be fine-tuned to perform extractive QA tasks. Choosing the right reader model depends on factors like computational power, performance-speed tradeoff, multilingual support, and the specific task requirements. Haystack provides two classes of readers: TransformersReader and FARMReader, with FARMReader being recommended for its usability, performance optimization, and ability to remove duplicate answers automatically. By selecting the right reader-model combination, users can build efficient and effective question answering pipelines in their Haystack systems.

Harnessing the power of graphics processing units (GPUs) can significantly accelerate a Haystack question answering system. GPUs are ideal for deep learning's parallelizable operations, such as those found in transformer-based language models, making them a crucial component in the field's advancements. By leveraging GPUs, users can speed up their Haystack pipelines, including readers, retrievers, and indexing processes, resulting in faster inference times and improved overall performance. The use of GPUs can be achieved through Google Colab or Amazon Web Services (AWS), both of which provide accessible options for accelerating workflow. With the ability to harness GPU power, users can significantly improve their Haystack pipelines, making it a practical necessity for large-scale projects.