Labeled data is key to train models, but data annotators often make mistakes. One can collect multiple annotations per datapoint to get a more reliable consensus label, but this is expensive! To train the best ML model with the least data labeling, a key question is: which new data should I label or which of my current labels should be checked again?
ActiveLab automatically answers this question for you, allowing you to train the most accurate ML model via a smaller number of total annotations than required to reach similar accuracy with popular active learning methods. ActiveLab is highly practical — it runs quickly and works with: any type of ML model, batch settings where many examples are (re)labeled before model retraining, and settings where multiple annotators can label an example (or just one annotator).
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[ 3.1 ms ] story [ 10.5 ms ] threadActiveLab automatically answers this question for you, allowing you to train the most accurate ML model via a smaller number of total annotations than required to reach similar accuracy with popular active learning methods. ActiveLab is highly practical — it runs quickly and works with: any type of ML model, batch settings where many examples are (re)labeled before model retraining, and settings where multiple annotators can label an example (or just one annotator).