# Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

arXiv: https://arxiv.org/abs/2208.01483 · HTML: https://mlsanity.com/paper/2208.01483/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy](https://mlsanity.com/paper/2205.11966/) (2022)

## Built on by (most cited first, 8 of 8)

- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [Parameter Competition Balancing for Model Merging](https://mlsanity.com/paper/2410.02396/) (2024)
- [MEGAnno+: A Human-LLM Collaborative Annotation System](https://mlsanity.com/paper/2402.18050/) (2024)
- [Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations](https://mlsanity.com/paper/2403.06009/) (2024)
- [Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data](https://mlsanity.com/paper/2506.09093/) (2025)
- [Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey](https://mlsanity.com/paper/2503.09701/) (2025)
- [ALANNO: An Active Learning Annotation System for Mortals](https://mlsanity.com/paper/2211.06224/) (2022)
- [Gamified crowd-sourcing of high-quality data for visual fine-tuning](https://mlsanity.com/paper/2410.04038/) (2024)

## Abstract (arXiv)

Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically 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 to non-experts, (b) guiding users through the entire labeling process until they obtain a custom classifier, making the process efficient -- from cold start to classifier in a few hours, and (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 broaden the utilization of NLP models.