# Artificial Intelligence for Education

## Documentation for package ‘aifeducation’ version 1.1.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A B C D E F G H I K L M O P R S T U W

## -- A --

add_missing_args | Add missing arguments to a list of arguments
AIFEBaseModel | Base class for objects using a pytorch model as core model.
AIFEMaster | Base class for most objects
auto_n_cores | Number of cores for multiple tasks

## -- B --

BaseModelBert | BERT-Transformer
BaseModelCore | Abstract class for all BaseModels
BaseModelDebertaV2 | DeBERTa V2
BaseModelFunnel | Funnel transformer
BaseModelModernBert | ModernBert
BaseModelMPNet | MPNet
BaseModelRoberta | RoBERTa
BaseModelsIndex | List of all available BaseModels
build_documentation_for_model | Generate documentation for a classifier class
build_layer_stack_documentation_for_vignette | Generate documentation of all layers for an vignette or article

## -- C --

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
ClassifiersBasedOnTextEmbeddings | Abstract class for all classifiers that use numerical representations of texts instead of words.
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

## -- D --

data.frame_to_py_dataset | Convert data.frame to arrow data set
DataManagerClassifier | Data manager for classification tasks
DataSetsIndex | List of all available types of data sets

## -- E --

EmbeddedText | Abstract class for small data sets containing text embeddings
extract_column_from_py_dataset | Extract column

## -- F --

fleiss_kappa | Calculate Fleiss' Kappa

## -- G --

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_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_parameter_documentation | Generate layer documentation
get_param_def | Definition of an argument
get_param_dict | Get dictionary of all parameters
get_param_doc_desc | Description of an argument
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_TEClassifiers_class_names | Get names of classifiers
get_test_data_for_classifiers | Get test data
get_time_stamp | Time stamp
gwet_ac | Calculate Gwet's AC1 and AC2

## -- H --

HuggingFaceTokenizer | HuggingFaceTokenizer

## -- I --

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_py_modules | Installing necessary python modules to an environment

## -- K --

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

## -- L --

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
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

## -- M --

matrix_to_array_c | Reshape matrix to array
ModelsBasedOnTextEmbeddings | Base class for models using neural nets
monitor_test_time_on_CI | Print duration of a test on CI

## -- O --

output_message | Print message

## -- P --

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

## -- R --

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

## -- S --

save_to_disk | Saving objects created with 'aifeducation'
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

## -- T --

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
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
TEClassifierSequential | Text embedding classifier with a neural net
TEClassifierSequentialPrototype | Text embedding classifier with a ProtoNet
TEFeatureExtractor | Feature extractor for reducing the number for dimensions of text embeddings.
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
TextEmbeddingModel | Text embedding model
TokenizerBase | Base class for tokenizers
TokenizerIndex | List of all available Tokenizers
to_categorical_c | Transforming classes to one-hot encoding

## -- U --

update_aifeducation | Updates an existing installation of 'aifeducation' on a machine

## -- W --

WordPieceTokenizer | WordPieceTokenizer
write_log | Write log