Towards Automating the AI Operations Lifecycle
Matthew Arnold, Jeffrey Boston, et al.
MLSys 2020
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for; (a) being a no-code system, making NLP accessible for non-experts, (b) guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours, (c) being open for configuration and extension by developers.
By open sourcing Label Sleuth we hope to build a community of users and developers that will widen the utilization of NLP models.
Matthew Arnold, Jeffrey Boston, et al.
MLSys 2020
Shiqiang Wang, Nathalie Baracaldo Angel, et al.
NeurIPS 2022
Ingkarat Rak-amnouykit, Ana Milanova, et al.
ICLR 2021
George Kour, Samuel Ackerman, et al.
EMNLP 2022