AIFEBaseModel           Base class for objects using a pytorch model as
                        core model.
AIFEMaster              Base class for most objects
BaseModelAlbert         ALBERT-Transformer
BaseModelBert           BERT-Transformer
BaseModelCore           Abstract class for all BaseModels
BaseModelDebertaV2      DeBERTa V2
BaseModelDistilBERT     DistilBERT
BaseModelEuroBert       EuroBERT
BaseModelFunnel         Funnel transformer
BaseModelMPNet          MPNet
BaseModelModernBert     ModernBert
BaseModelRoberta        RoBERTa
BaseModelRobertaXML     RoBERTa-XML
BaseModelXmod           Xmod-Transformer
BaseModelsIndex         List of all available BaseModels
ClassifiersBasedOnTextEmbeddings
                        Abstract class for all classifiers that use
                        numerical representations of texts instead of
                        words.
DataManagerClassifier   Data manager for classification tasks
DataSetsIndex           List of all available types of data sets
EmbeddedText            Abstract class for small data sets containing
                        text embeddings
HuggingFaceTokenizer    HuggingFaceTokenizer
LargeDataSetBase        Abstract base class for large data sets
LargeDataSetForText     Abstract class for large data sets containing
                        raw texts
LargeDataSetForTextEmbeddings
                        Abstract class for large data sets containing
                        text embeddings
ModelsBasedOnTextEmbeddings
                        Base class for models using neural nets
TEClassifierParallel    Text embedding classifier with a neural net
TEClassifierParallelPrototype
                        Text embedding classifier with a ProtoNet
TEClassifierProtoNet    Text embedding classifier with a ProtoNet
TEClassifierRegular     Text embedding classifier with a neural net
TEClassifierSequential
                        Text embedding classifier with a neural net
TEClassifierSequentialPrototype
                        Text embedding classifier with a ProtoNet
TEClassifiersBasedOnProtoNet
                        Base class for classifiers relying on numerical
                        representations of texts instead of words that
                        use the architecture of Protonets and its
                        corresponding training techniques.
TEClassifiersBasedOnRegular
                        Base class for regular classifiers relying on
                        EmbeddedText or LargeDataSetForTextEmbeddings
                        as input
TEFeatureExtractor      Feature extractor for reducing the number for
                        dimensions of text embeddings.
TextEmbeddingModel      Text embedding model
TokenizerBase           Base class for tokenizers
TokenizerIndex          List of all available Tokenizers
WordPieceTokenizer      WordPieceTokenizer
add_missing_args        Add missing arguments to a list of arguments
auto_n_cores            Number of cores for multiple tasks
build_documentation_for_model
                        Generate documentation for a classifier class
build_layer_overview_base_models
                        Generate documentation of supported BaseModels
                        for an vignette or article
build_layer_stack_documentation_for_vignette
                        Generate documentation of all layers for an
                        vignette or article
calc_standard_classification_measures
                        Calculate recall, precision, and f1-scores
calc_tokenizer_statistics
                        Estimate tokenizer statistics
cat_message             Print message ('cat()')
check_adjust_n_samples_on_CI
                        Set sample size for argument combinations
check_aif_py_modules    Check if all necessary python modules are
                        available
check_all_args          Check arguments automatically
check_class_and_type    Check class and type
class_vector_to_py_dataset
                        Convert class vector to arrow data set
clean_pytorch_log_transformers
                        Clean pytorch log of transformers
cohens_kappa            Calculate Cohen's Kappa
create_dir              Create directory if not exists
create_object           Create object#'
create_py_dataset_cache_file_path
                        File path for caching data sets
create_synthetic_units_from_matrix
                        Create synthetic units
data.frame_to_py_dataset
                        Convert data.frame to arrow data set
extract_column_from_py_dataset
                        Extract column
fleiss_kappa            Calculate Fleiss' Kappa
generate_args_for_tests
                        Generate combinations of arguments
generate_embeddings     Generate test embeddings
generate_id             Generate ID suffix for objects
generate_tensors        Generate test tensors
get_TEClassifiers_class_names
                        Get names of classifiers
get_alpha_3_codes       Country Alpha 3 Codes
get_batches_index       Assign cases to batches
get_called_args         Called arguments
get_coder_metrics       Calculate reliability measures based on content
                        analysis
get_current_args_for_print
                        Print arguments
get_depr_obj_names      Get names of deprecated objects
get_desc_for_core_model_architecture
                        Generate documentation for core models
get_file_extension      Get file extension
get_fixed_test_tensor   Generate static test tensor
get_layer_documentation
                        Generate layer documentation
get_magnitude_values    Magnitudes of an argument
get_n_chunks            Get the number of chunks/sequences for each
                        case
get_param_def           Definition of an argument
get_param_dict          Get dictionary of all parameters
get_param_doc_desc      Description of an argument
get_parameter_documentation
                        Generate layer documentation
get_py_package_version
                        Get versions of a specific python package
get_py_package_versions
                        Get versions of python components
get_recommended_py_versions
                        Recommended version of python packages
get_synthetic_cases_from_matrix
                        Create synthetic cases for balancing training
                        data
get_test_data_for_classifiers
                        Get test data
get_time_stamp          Time stamp
gwet_ac                 Calculate Gwet's AC1 and AC2
inspect_tmp_dir         Inspect Temporary directory
install_aifeducation    Install aifeducation on a machine
install_aifeducation_studio
                        Install 'AI for Education - Studio' on a
                        machine
install_protocol_buffer_compiler
                        Install protocol buffer compiler
install_py_modules      Installing necessary python modules to an
                        environment
kendalls_w              Calculate Kendall's coefficient of concordance
                        w
knnor                   K-Nearest Neighbor OveRsampling approach
                        (KNNOR)
knnor_is_same_class     Validate a new point
kripp_alpha             Calculate Krippendorff's Alpha
load_all_py_scripts     Load and re-load all python scripts
load_from_disk          Loading objects created with 'aifeducation'
load_py_scripts         Load and re-load python scripts
long_load_target_data   Load target data for long running tasks
matrix_to_array_c       Reshape matrix to array
monitor_test_time_on_CI
                        Print duration of a test on CI
output_message          Print message
prepare_r_array_for_dataset
                        Convert R array for arrow data set
prepare_session         Function for setting up a python environment
                        within R.
print_message           Print message ('message()')
py_dataset_to_embeddings
                        Convert arrow data set to an arrow data set
random_bool_on_CI       Random bool on Continuous Integration
read_log                Function for reading a log file in R
read_loss_log           Function for reading a log file containing a
                        record of the loss during training.
reduce_to_unique        Reduce to unique cases
reset_log               Function that resets a log file.
reset_loss_log          Reset log for loss information
run_py_file             Run python file
save_to_disk            Saving objects created with 'aifeducation'
set_codecarbon_logger   Sets the level for logging information of the
                        'codecarbon' library
set_transformers_logger
                        Sets the level for logging information of the
                        'transformers' library
start_aifeducation_studio
                        Aifeducation Studio
summarize_args_for_long_task
                        Summarize arguments from shiny input
tensor_list_to_numpy    Convert list of tensors into numpy arrays
tensor_to_matrix_c      Transform tensor to matrix
tensor_to_numpy         Tensor_to_numpy
to_categorical_c        Transforming classes to one-hot encoding
update_aifeducation     Updates an existing installation of
                        'aifeducation' on a machine
write_log               Write log
