Serialization

Functionality to save / load your active learner.

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In some active learning applications, an active learner might have a longer lifespan. For example, you might want to save your active learner to resume your annotation process at a later time. This process in which the current object is saved to disk is called serialization.

To allow saving / loading, PoolBasedActiveLearner provides save() and load(). The only mandataroy argument is a folder to which the active learner is saved to or loaded from respectively.

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Usage

(De-)serialization is straightforward to use. Models on the GPU need to be transferred to the CPU first to avoid errors during deserialization.

Note

• Serialization has changed in v2.0.0 and is not backwards compatible with files saved using small-text v1.x.

• You likely need the same small-text version (and depending on the model also similar dependencies). This might be improved in future releases.

See

• save()

• load()

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Example

from small_text import PoolBasedActiveLearner

active_learner = <...>   # care, this does not run;
                         # active_learner is assumed to be a trained active learner

# Only for models on the GPU: transfer them to the CPU before serialization
active_learner.classifier.model = active_learner.classifier.model.cpu()

project_folder = '/tmp/active-learning-project'
active_learner.save(project_folder)

active_learner = PoolBasedActiveLearner.load(project_folder)