Changelog

Version 1.3.3 - 2023-12-29

Changed

  • An errata section was added to the documentation.

Fixed

  • Fixed a deviation from the paper, where DeltaFScore also took into account the agreement in predictions of the negative label. (#51)

  • Fixed a bug in KappaAverage that affected the stopping behavior. (#52)

Version 1.3.2 - 2023-08-19

Fixed

  • Fixed a bug in TransformerBasedClassification, where validations_per_epoch>=2 left the model in eval mode. (#40)

Version 1.3.1 - 2023-07-22

Fixed

  • Fixed a bug where parameter groups were omitted when using TransformerBasedClassification‘s layer-specific fine-tuning functionality. (#36, #38)

  • Fixed a bug where class weighting resulted in nan values. (#39)

Contributors

@JP-SystemsX

Version 1.3.0 - 2023-02-21

Added

Fixed

  • Fixed broken link in README.md.

  • Fixed typo in README.md. (#26)

Changed

  • The ClassificationChange stopping criterion now supports multi-label classification.

  • Documentation:

    • Updated the active learning setup figure.

    • The documentation of integrations has been reorganized.

Contributors

@rmitsch

Version 1.2.0 - 2023-02-04

Added

  • Added new classifier: SetFitClassification which wraps huggingface/setfit.

  • Active Learner:

    • PoolBasedActiveLearner now handles keyword arguments passed to the classifier’s fit() during the update() step.

  • Query Strategies:

  • Notebook Examples:

    • Revised both existing notebook examples.

    • Added a notebook example for active learning with SetFit classifiers.

    • Added a notebook example for cold start initialization with SetFit classifiers.

  • Documentation:

    • A showcase section has been added to the documentation.

Fixed

  • Distances in lightweight_coreset were not correctly projected onto the [0, 1] interval (but ranking was unaffected).

Changed

Version 1.1.1 - 2022-10-14

Fixed

  • Model selection raised an error in cases where no model was available for selection (#21).

Version 1.1.0 - 2022-10-01

Added

  • General:

    • Small-Text package is now available via conda-forge.

    • Imports have been reorganized. You can import all public classes and methods from the top-level package (small_text):

      from small_text import PoolBasedActiveLearner

  • Classification:

    • All classifiers now support weighting of training samples.

    • Early stopping has been reworked, improved, and documented (#18).

    • Model selection has been reworked and documented.

    • [!] KimCNNClassifier.__init()__: The default value of the (now deprecated) keyword argument early_stopping_acc has been changed from 0.98 to -1 in order to match TransformerBasedClassification.

    • [!] Removed weight renormalization after gradient clipping.

  • Datasets:

    • The target_labels keyword argument in __init()__ will now raise a warning if not passed.

    • Added from_arrays() to SklearnDataset, PytorchTextClassificationDataset, and TransformersDataset to construct datasets more conveniently.

  • Query Strategies:

  • Stopping Criteria:

Deprecated

  • small_text.integrations.pytorch.utils.misc.default_tensor_type() is deprecated without replacement (#2).

  • TransformerBasedClassification and KimCNNClassifier: The keyword arguments for early stopping (early_stopping / early_stopping_no_improvement, early_stopping_acc) that are passed to __init__() are now deprecated. Use the early_stopping keyword argument in the fit() method instead (#18).

Fixed

  • Classification:

    • KimCNNClassifier.fit() and TransformerBasedClassification.fit() now correctly process the scheduler keyword argument (#16).

Removed

  • Removed the strict check that every target label has to occur in the training data. (This is intended for multi-label settings with many labels; apart from that it is still recommended to make sure that all labels occur.)

Version 1.0.1 - 2022-09-12

Minor bug fix release.

Fixed

Links to notebooks and code examples will now always point to the latest release instead of the latest main branch.

Version 1.0.0 - 2022-06-14

First stable release.

Changed

  • Datasets:

    • SklearnDataset now checks if the dimensions of the features and labels match.

  • Query Strategies:

  • Documentation:

    • The documentation is now available in full width.

  • Repository:

    • Versions in this can now be referenced using the respective Zenodo DOI.

[1.0.0b4] - 2022-05-04

Added

  • General:

    • We now have a concept for optional dependencies which allows components to rely on soft dependencies, i.e. python dependencies which can be installed on demand (and only when certain functionality is needed).

  • Datasets:

    • The Dataset interface now has a clone() method that creates an identical copy of the respective dataset.

  • Query Strategies:

Changed

  • Datasets:

    • Separated the previous DatasetView implementation into interface (DatasetView) and implementation (SklearnDatasetView).

    • Added clone() method which creates an identical copy of the dataset.

  • Query Strategies:

    • EmbeddingBasedQueryStrategy now only embeds instances that are either in the label or in the unlabeled pool (and no longer the entire dataset).

  • Code examples:

    • Code structure was unified.

    • Number of iterations can now be passed via an cli argument.

  • small_text.integrations.pytorch.utils.data:

    • Method get_class_weights() now scales the resulting multi-class weights so that the smallest class weight is equal to 1.0.

[1.0.0b3] - 2022-03-06

Added

Changed

  • Cleaned up and unified argument naming: The naming of variables related to datasets and indices has been improved and unified. The naming of datasets had been inconsistent, and the previous x_ notation for indices was a relict of earlier versions of this library and did not reflect the underlying object anymore.

    • PoolBasedActiveLearner:

      • attribute x_indices_labeled was renamed to indices_labeled

      • attribute x_indices_ignored was unified to indices_ignored

      • attribute queried_indices was unified to indices_queried

      • attribute _x_index_to_position was named to _index_to_position

      • arguments x_indices_initial, x_indices_ignored, and x_indices_validation were renamed to indices_initial, indices_ignored, and indices_validation. This affects most methods of the PoolBasedActiveLearner.

    • QueryStrategy

      • old: query(self, clf, x, x_indices_unlabeled, x_indices_labeled, y, n=10)

      • new: query(self, clf, dataset, indices_unlabeled, indices_labeled, y, n=10)

    • StoppingCriterion

      • old: stop(self, active_learner=None, predictions=None, proba=None, x_indices_stopping=None)

      • new: stop(self, active_learner=None, predictions=None, proba=None, indices_stopping=None)

  • Renamed environment variable which sets the small-text temp folder from ALL_TMP to SMALL_TEXT_TEMP

[1.0.0b2] - 2022-02-22

Bugfix release.

Fixed

  • Fix links to the documentation in README.md and notebooks.

[1.0.0b1] - 2022-02-22

First beta release with multi-label functionality and stopping criteria.

Added

  • Added a changelog.

  • All provided classifiers are now capable of multi-label classification.

Changed

  • Documentation has been overhauled considerably.

  • PoolBasedActiveLearner: Renamed incremental_training kwarg to reuse_model.

  • SklearnClassifier: Changed __init__(clf) to __init__(model, num_classes, multi_Label=False)

  • SklearnClassifierFactory: __init__(clf_template, kwargs={}) to __init__(base_estimator, num_classes, kwargs={}).

  • Refactored KimCNNClassifier and TransformerBasedClassification.

Removed

  • Removed device kwarg from PytorchDataset.__init__(), PytorchTextClassificationDataset.__init__() and TransformersDataset.__init__().