👨🏽🏫 Use Active Learning#
Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. Wikipedia
Supervised machine learning often requires large amounts of labeled data that are expensive to generate. Active Learning systems attempt to overcome this labeling bottleneck. The underlying idea is that not all data points are equally important for training the model. The Active Learning system tries to query only the most relevant data from a pool of unlabeled data to be labeled by a so-called oracle, which is often a human annotator. Therefore, Active Learning systems are usually much more sample efficient and need far less training data than traditional supervised systems.
Active Learning systems can be a bit overwhelming to set-up, therefore we have defined several step-by-step tutorials with some of the most popular libraries like classy-classification, small-text and modAL .
Next steps#
If you want to continue learning Argilla:
🙋♀️ Join the Argilla Slack community!
⭐ Argilla Github repo to stay updated.
📚 Argilla documentation for more guides and tutorials.