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Noticing the urgent need to provide tools for fast and user-friendlyqualitative analysis of large-scale textual corpora of the modern NLP, wepropose to turn to the mature and well-tested methods from the domain ofInformation Retrieval (IR) - a research field with a long history of tacklingTB-scale document collections. We discuss how Pyserini - a widely used toolkitfor reproducible IR research can be integrated with the Hugging Face ecosystemof open-source AI libraries and artifacts. We leverage the existingfunctionalities of both platforms while proposing novel features furtherfacilitating their integration. Our goal is to give NLP researchers tools thatwill allow them to develop retrieval-based instrumentation for their dataanalytics needs with ease and agility. We include a Jupyter Notebook-based walkthrough the core interoperability features, available on GitHub athttps://github.com/huggingface/gaia. We then demonstrate how the ideas wepresent can be operationalized to create a powerful tool for qualitative dataanalysis in NLP. We present GAIA Search - a search engine built followingpreviously laid out principles, giving access to four popular large-scale textcollections. GAIA serves a dual purpose of illustrating the potential ofmethodologies we discuss but also as a standalone qualitative analysis toolthat can be leveraged by NLP researchers aiming to understand datasets prior tousing them in training. GAIA is hosted live on Hugging Face Spaces -https://huggingface.co/spaces/spacerini/gaia.

GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration