Effectively Annotate Text Data for Transformers via Active Learning + Re-labeling

Active learning is a technique used in machine learning to select the most informative data points to annotate, reducing the need for human labeling and resulting in cost savings and faster model training times. In this article, ActiveLab is presented as an active learning algorithm that can be particularly useful when dealing with noisy annotators. The authors demonstrate the effectiveness of ActiveLab in improving the accuracy of a fine-tuned Hugging Face Transformer for text classification while keeping the total number of collected labels from human annotators low. By using ActiveLab, the authors achieve 90% model accuracy at only 35% of the label spend as standard training, outperforming random selection by a significant margin. The technique is particularly useful in text classification tasks, where annotating data can be time-consuming and expensive. Overall, ActiveLab offers a promising approach to improving datasets with minimal labeling effort.

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