
Package index
-
check_aif_py_modules()
- Check if all necessary python modules are available
-
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
-
prepare_session()
- Function for setting up a python environment within R.
-
set_transformers_logger()
- Sets the level for logging information of the 'transformers' library
-
update_aifeducation()
- Updates an existing installation of 'aifeducation' on a machine
-
start_aifeducation_studio()
- Aifeducation Studio
-
load_from_disk()
- Loading objects created with 'aifeducation'
-
save_to_disk()
- Saving objects created with 'aifeducation'
-
EmbeddedText
- Abstract class for small data sets containing text embeddings
-
LargeDataSetForText
- Abstract class for large data sets containing raw texts
-
LargeDataSetForTextEmbeddings
- Abstract class for large data sets containing text embeddings
-
AIFETrType
- Transformer types
-
aife_transformer.make()
- Make a transformer
-
TEFeatureExtractor
- Feature extractor for reducing the number for dimensions of text embeddings.
-
TextEmbeddingModel
- Text embedding model
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TEClassifierParallel
- Text embedding classifier with a neural net
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TEClassifierParallelPrototype
- Text embedding classifier with a ProtoNet
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TEClassifierProtoNet
- Text embedding classifier with a ProtoNet
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TEClassifierRegular
- Text embedding classifier with a neural net
-
TEClassifierSequential
- Text embedding classifier with a neural net
-
TEClassifierSequentialPrototype
- Text embedding classifier with a ProtoNet
-
knnor()
- K-Nearest Neighbor OveRsampling approach (KNNOR)
-
calc_standard_classification_measures()
- Calculate recall, precision, and f1-scores
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cohens_kappa()
- Calculate Cohen's Kappa
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fleiss_kappa()
- Calculate Fleiss' Kappa
-
get_coder_metrics()
- Calculate reliability measures based on content analysis
-
gwet_ac()
- Calculate Gwet's AC1 and AC2
-
kendalls_w()
- Calculate Kendall's coefficient of concordance w
-
kripp_alpha()
- Calculate Krippendorff's Alpha
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AIFEBaseModel
- Base class for models using neural nets
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ClassifiersBasedOnTextEmbeddings
- Abstract class for all classifiers that use numerical representations of texts instead of words.
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LargeDataSetBase
- Abstract base class for large data sets
-
ModelsBasedOnTextEmbeddings
- Base class for models using neural nets
-
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
-
DataManagerClassifier
- Data manager for classification tasks
-
.AIFEBaseTransformer
- Base
R6
class for creation and definition of.AIFE*Transformer-like
classes
-
.AIFEBertTransformer
- Child
R6
class for creation and training ofBERT
transformers
-
.AIFEFunnelTransformer
- Child
R6
class for creation and training ofFunnel
transformers
-
.AIFELongformerTransformer
- Child
R6
class for creation and training ofLongformer
transformers
-
.AIFEMpnetTransformer
- Child
R6
class for creation and training ofMPNet
transformers
-
.AIFERobertaTransformer
- Child
R6
class for creation and training ofRoBERTa
transformers
-
aife_transformer.load_model_mlm()
- Load a MLM-model
-
aife_transformer.load_tokenizer()
- Load a tokenizer
-
.AIFEModernBertTransformer
- Child
R6
class for creation and training ofModernBERT
transformers
-
calc_tokenizer_statistics()
- Estimate tokenizer statistics
-
knnor_is_same_class()
- Validate a new point
-
get_called_args()
- Called arguments
-
get_depr_obj_names()
- Get names of deprecated objects
-
get_magnitude_values()
- Magnitudes of an argument
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get_param_def()
- Definition of an argument
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get_param_dict()
- Get dictionary of all parameters
-
get_param_doc_desc()
- Description of an argument
-
get_TEClassifiers_class_names()
- Get names of classifiers
-
create_dir()
- Create directory if not exists
-
get_file_extension()
- Get file extension
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auto_n_cores()
- Number of cores for multiple tasks
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create_object()
- Create object
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create_synthetic_units_from_matrix()
- Create synthetic units
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generate_id()
- Generate ID suffix for objects
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get_n_chunks()
- Get the number of chunks/sequences for each case
-
get_synthetic_cases_from_matrix()
- Create synthetic cases for balancing training data
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matrix_to_array_c()
- Reshape matrix to array
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tensor_to_matrix_c()
- Transform tensor to matrix
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to_categorical_c()
- Transforming classes to one-hot encoding
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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
-
get_desc_for_core_model_architecture()
- Generate documentation for core models
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get_layer_documentation()
- Generate layer documentation
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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
-
load_all_py_scripts()
- Load and re-load all python scripts
-
load_py_scripts()
- Load and re-load python scripts
-
run_py_file()
- Run python file
-
class_vector_to_py_dataset()
- Convert class vector to arrow data set
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data.frame_to_py_dataset()
- Convert data.frame to arrow data set
-
get_batches_index()
- Assign cases to batches
-
prepare_r_array_for_dataset()
- Convert R array for arrow data set
-
py_dataset_to_embeddings()
- Convert arrow data set to an arrow data set
-
reduce_to_unique()
- Reduce to unique cases
-
tensor_list_to_numpy()
- Convert list of tensors into numpy arrays
-
tensor_to_numpy()
- Tensor_to_numpy
-
cat_message()
- Print message (
cat()
)
-
clean_pytorch_log_transformers()
- Clean pytorch log of transformers
-
output_message()
- Print message
-
print_message()
- Print message (
message()
)
-
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.
-
reset_log()
- Function that resets a log file.
-
reset_loss_log()
- Reset log for loss information
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write_log()
- Write log
-
get_alpha_3_codes()
- Country Alpha 3 Codes
-
check_all_args()
- Check arguments automatically
-
check_class_and_type()
- Check class and type
-
add_missing_args()
- Add missing arguments to a list of arguments
-
long_load_target_data()
- Load target data for long running tasks
-
summarize_args_for_long_task()
- Summarize arguments from shiny input
-
check_adjust_n_samples_on_CI()
- Set sample size for argument combinations
-
generate_args_for_tests()
- Generate combinations of arguments
-
generate_embeddings()
- Generate test embeddings
-
generate_tensors()
- Generate test tensors
-
get_current_args_for_print()
- Print arguments
-
get_fixed_test_tensor()
- Generate static test tensor
-
get_test_data_for_classifiers()
- Get test data
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random_bool_on_CI()
- Random bool on Continuous Integration