  Noticing the urgent need to provide tools for fast and user-friendly
qualitative analysis of large-scale textual corpora of the modern NLP, we
propose to turn to the mature and well-tested methods from the domain of
Information Retrieval (IR) - a research field with a long history of tackling
TB-scale document collections. We discuss how Pyserini - a widely used toolkit
for reproducible IR research can be integrated with the Hugging Face ecosystem
of open-source AI libraries and artifacts. We leverage the existing
functionalities of both platforms while proposing novel features further
facilitating their integration. Our goal is to give NLP researchers tools that
will allow them to develop retrieval-based instrumentation for their data
analytics needs with ease and agility. We include a Jupyter Notebook-based walk
through the core interoperability features, available on GitHub at
https://github.com/huggingface/gaia. We then demonstrate how the ideas we
present can be operationalized to create a powerful tool for qualitative data
analysis in NLP. We present GAIA Search - a search engine built following
previously laid out principles, giving access to four popular large-scale text
collections. GAIA serves a dual purpose of illustrating the potential of
methodologies we discuss but also as a standalone qualitative analysis tool
that can be leveraged by NLP researchers aiming to understand datasets prior to
using them in training. GAIA is hosted live on Hugging Face Spaces -
https://huggingface.co/spaces/spacerini/gaia.
