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aifeducation 0.3.3
Graphical User Interface Aifeducation Studio
- Fixed a bug concerning the ids of .pdf and .csv files. Now the ids are correctly saved within a text collection file.
- Fixed a bug while checking for the selection of at least one file type during creation of a text collection.
TextEmbeddingClassifiers
- Fixed the process for checking if TextEmbeddingModels are compatible.
Python Installation
- Fixed a bug which caused the installation of incompatible versions of keras and Tensorflow.
Further Changes
- Removed quanteda.textmodels as necessary library for testing the package.
- Added a dataset for testing the package based on Maas et al. (2011).
aifeducation 0.3.2
TextEmbeddingClassifiers
- Fixed a bug in GlobalAveragePooling1D_PT. Now the layer makes a correct pooling. This change has an effect on PyTorch models trained with version 0.3.1.
TextEmbeddingModel
- Replaced the parameter ‘aggregation’ with three new parameters allowing to explicitly choose the start and end layer to be included in the creation of embeddings. Furthermore, two options for the pooling method within each layer is added (“cls” and “average”).
- Added support for reporting the training and validation loss during training the corresponding base model.
Transformer Models
- Fixed a bug in the creation of all transformer models except funnel. Now choosing the number of layers is working.
- A file ‘history.log’ is now saved within the model’s folder reporting the loss and validation loss during training for each epoch.
EmbeddedText
- Changed the process for validating if EmbeddedTexts are compatible. Now only the model’s unique name is used for the validation.
- Added new fields and updated methods to account for the new options in creating embeddings (layer selection and pooling type).
Graphical User Interface Aifeducation Studio
- Adapted the interface according to the changes made in this version.
- Improved the read of raw texts. Reading now reduces multiple spaces characters to one single space character. Hyphenation is removed.
Python Installation
- Updated installation to account for the new version of keras.
aifeducation 0.3.1
Graphical User Interface Aifeducation Studio
- Added a shiny app to the package that serves as a graphical user interface.
Transformer Models
- Fixed a bug in all transformers except BERT concerning the unk_token.
- Switched from SentencePiece tokenizer to WordPiece tokenizer for DeBERTa_V2.
- Add the possibility to train DeBERTa_V2 and FunnelTransformer models with Whole Word Masking.
TextEmbeddingModel
- Added a method for ‘fill-mask’.
- Added a new argument to the method ‘encode’, allowing to chose between encoding into token ids or into token strings.
- Added a new argument to the method ‘decode’, allowing to chose between decoding into single tokens or into plain text.
- Fixed a bug for embedding texts when using pytorch. The fix should decrease computational time and enables gpu support (if available on machine).
- Fixed two missing columns for saving the results of sustainability tracking on machines without gpu.
- Implemented the advantages of datasets from the python library ‘datasets’ increasing computational speed and allowing the use of large datasets.
TextEmbeddingClassifiers
- Adding support for pytorch without the need for kerasV3 or keras-core. Classifiers for pytorch are now implemented in native pytorch.
- Changed the architecture for new classifiers and extended the abilities of neural nets by adding the possibility to add positional embedding.
- Changed the architecture for new classifiers and extended the abilities of neural nets by adding an alternative method for the self-attention mechanism via fourier transformation (similar to FNet).
- Added balanced_accuracy as the new metric for determining which state of a model predicts classes best.
- Fixed error that training history is not saved correctly.
- Added a record metric for the test dataset to training history with pytorch.
- Added the option to balance class weights for calculating training loss according to the Inverse Frequency method. Balance class weights is activated by default.
- Added a method for checking the compatibility of the underlying TextEmbeddingModels of a classifier and an object of class EmbeddedText.
- Added precision, recall, and f1-score as new metrics.
Python Installation
- Added an argument to ‘install_py_modules’, allowing to choose which machine learning framework should be installed.
- Updated ‘check_aif_py_modules’.
Further Changes
- Setting the machine learning framework at the start of a session is no longer necessary. The function for setting the global ml_framework remains active for convenience. The ml_framework can now be switched at any time during a session.
- Updated documentation.
aifeducation 0.3.0
- Added DeBERTa and Funnel-Transformer support.
- Fixed issues for installing the required python packages.
- Fixed issues in training transformer models.
- Fixed an issue for calculating the final iota values in classifiers if pseudo labeling is active.
- Added support for PyTorch and Tensorflow for all transformer models.
- Added support for PyTorch for classifier objects via keras 3 in the future.
- Removed augmentation of vocabulary from training BERT models.
- Updated documentation.
- Changed the reported values for kappa.
aifeducation 0.2.0
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