Temel Kavramlar
Spacerini simplifies the deployment of search engines by integrating Pyserini with Hugging Face, enabling easy access to state-of-the-art retrieval models.
Özet
Abstract:
Spacerini integrates Pyserini with Hugging Face for interactive search engines.
Enables effortless construction and deployment of search interfaces.
Introduction:
Data commoditization transforms ML and NLP, emphasizing large language models.
Spacerini aids in understanding and validating research through qualitative analysis.
Background and Related Work:
Large-scale text datasets proliferate in NLP, necessitating data understanding and governance.
Spacerini:
Modular framework streamlining indexing, preprocessing, indexing, and deployment of search interfaces.
Use Cases and Demonstrations:
Benefits NLP researchers, IR researchers, linguists, digital humanists, IR students, shared task organizers, tech journalists.
Limitations and Future Plans:
Disk space limit on Hugging Face Spaces is a constraint; planned improvements include better documentation.
Conclusion:
Spacerini facilitates quick deployment of template-based search indexes for qualitative dataset exploration.
İstatistikler
"We demonstrate a portfolio of 13 search engines created with Spacerini for different use cases."
"The disk space limit imposed by Hugging Face Spaces is currently set to 50 GB for the free tier."