{ "_id": "6a153d71acfb0bcc41d54743", "Type": "Package", "Package": "aifeducation", "Title": "Artificial Intelligence for Education", "Version": "1.1.5", "Authors@R": "c(\nperson(\"Berding\", \"Florian\", , \"florian.berding@uni-hamburg.de\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-3593-1695\")),\nperson(\"Tykhonova\", \"Yuliia\", , \"yuliia.tykhonova@uni-hamburg.de\", role = \"aut\",\ncomment = c(ORCID = \"0009-0006-9015-1006\")),\nperson(\"Pargmann\", \"Julia\", , \"julia.pargmann@uni-hamburg.de\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-3616-0172\")),\nperson(\"Leube\", \"Anna\", , \"anna.leube@uni-hamburg.de\", role = \"ctb\",\ncomment = c(ORCID = \"0009-0001-6949-1608\")),\nperson(\"Riebenbauer\", \"Elisabeth\", , \"elisabeth.riebenbauer@uni-graz.at\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-8535-3694\")),\nperson(\"Rebmann\", \"Karin\", , \"karin.rebmann@uni-oldenburg.de\", role = \"ctb\"),\nperson(\"Slopinski\", \"Andreas\", , \"andreas.slopinski@uni-oldenburg.de\", role = \"ctb\")\n)", "Description": "In social and educational settings, the use of Artificial\nIntelligence (AI) is a challenging task. Relevant data is often\nonly available in handwritten forms, or the use of data is\nrestricted by privacy policies. This often leads to small data\nsets. Furthermore, in the educational and social sciences, data\nis often unbalanced in terms of frequencies. To support\neducators as well as educational and social researchers in\nusing the potentials of AI for their work, this package\nprovides a unified interface for neural nets in 'PyTorch' to\ndeal with natural language problems. In addition, the package\nships with a shiny app, providing a graphical user interface.\nThis allows the usage of AI for people without skills in\nwriting python/R scripts. The tools integrate existing\nmathematical and statistical methods for dealing with small\ndata sets via pseudo-labeling (e.g. Cascante-Bonilla et al.\n(2020) ) and imbalanced data via\nthe creation of synthetic cases (e.g. Islam et al. (2012)\n). Performance evaluation of\nAI is connected to measures from content analysis which\neducational and social researchers are generally more familiar\nwith (e.g. Berding & Pargmann (2022) , Gwet\n(2014) , Krippendorff (2019)\n). Estimation of energy consumption\nand CO2 emissions during model training is done with the\n'python' library 'codecarbon'. Finally, all objects created\nwith this package allow to share trained AI models with other\npeople.", "License": "GPL-3", "URL": "https://fberding.github.io/aifeducation/", "BugReports": "https://github.com/FBerding/aifeducation/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE, r6 = TRUE)", "RoxygenNote": "7.3.3", "SystemRequirements": "PyTorch (see vignette \"Get started\")", "Config/Needs/website": "rmarkdown", "Config/pak/sysreqs": "libicu-dev libpng-dev python3", "Repository": "https://fberding.r-universe.dev", "Date/Publication": "2026-04-26 14:05:56 UTC", "RemoteUrl": "https://github.com/fberding/aifeducation", "RemoteRef": "HEAD", "RemoteSha": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-26 06:19:03 UTC", "User": "root" }, "Author": "Berding Florian [aut, cre] (ORCID:\n),\nTykhonova Yuliia [aut] (ORCID: ),\nPargmann Julia [ctb] (ORCID: ),\nLeube Anna [ctb] (ORCID: ),\nRiebenbauer Elisabeth [ctb] (ORCID:\n),\nRebmann Karin [ctb],\nSlopinski Andreas [ctb]", "Maintainer": "Berding Florian ", "MD5sum": "e4f15cc48facd4419bc8f7975a88c5ae", "_user": "fberding", "_type": "src", "_file": "aifeducation_1.1.5.tar.gz", "_fileid": "cf7d5b39f5b738050ea567ff21c6b9776855ae5ff6f5d30cc54ab0d7a78bee31", "_filesize": 2930212, "_sha256": "cf7d5b39f5b738050ea567ff21c6b9776855ae5ff6f5d30cc54ab0d7a78bee31", "_created": "2026-05-26T06:19:03.000Z", "_published": "2026-05-26T06:28:01.173Z", "_distro": "noble", "_jobs": [ { "job": 77818520258, "time": 216, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7210225666" }, { "job": 77818520248, "time": 252, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7210233815" }, { "job": 77818520284, "time": 193, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7210220280" }, { "job": 77818520265, "time": 218, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7210226229" }, { "job": 77818520416, "time": 104, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7210260489" }, { "job": 77818520283, "time": 222, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7210243177" }, { "job": 77818520227, "time": 184, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7210272926" }, { "job": 77818520257, "time": 368, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7210280545" }, { "job": 77817935780, "time": 312, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7210176559" }, { "job": 77818520272, "time": 152, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7210210884" }, { "job": 77818520244, "time": 253, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7210235187" }, { "job": 77818520302, "time": 217, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7210226953" }, { "job": 77818520252, "time": 188, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7210219350" } ], "_buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/fberding/aifeducation", "_commit": { "id": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "author": "FBerding <69512012+FBerding@users.noreply.github.com>", "committer": "GitHub ", "message": "Merge pull request #23 from FBerding/version_1_1_5_dev\n\nVersion 1 1 5", "time": 1777212356 }, "_maintainer": { "name": "Berding Florian", "email": "florian.berding@uni-hamburg.de", "login": "fberding", "description": "", "uuid": 69512012, "orcid": "0000-0002-3593-1695" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "doParallel", "role": "Imports" }, { "package": "foreach", "role": "Imports" }, { "package": "iotarelr", "version": ">= 0.1.5", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "Rcpp", "version": ">= 1.0.10", "role": "Imports" }, { "package": "reshape2", "role": "Imports" }, { "package": "reticulate", "version": ">= 1.42.0", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "stringi", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "bslib", "role": "Suggests" }, { "package": "DT", "role": "Suggests" }, { "package": "fs", "role": "Suggests" }, { "package": "future", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "pkgdown", "role": "Suggests" }, { "package": "promises", "role": "Suggests" }, { "package": "readtext", "role": "Suggests" }, { "package": "readxl", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "shiny", "version": ">= 1.9.0", "role": "Suggests" }, { "package": "shinyFiles", "role": "Suggests" }, { "package": "shinyWidgets", "role": "Suggests" }, { "package": "shinycssloaders", "role": "Suggests" }, { "package": "sortable", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "fberding", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-25", "n": 3 }, { "week": "2025-27", "n": 4 }, { "week": "2025-28", "n": 17 }, { "week": "2025-29", "n": 23 }, { "week": "2025-30", "n": 11 }, { "week": "2025-31", "n": 3 }, { "week": "2025-32", "n": 2 }, { "week": "2025-33", "n": 3 }, { "week": "2025-34", "n": 1 }, { "week": "2025-35", "n": 1 }, { "week": "2025-36", "n": 2 }, { "week": "2025-37", "n": 16 }, { "week": "2025-38", "n": 16 }, { "week": "2025-39", "n": 3 }, { "week": "2025-40", "n": 7 }, { "week": "2025-41", "n": 10 }, { "week": "2025-42", "n": 1 }, { "week": "2025-47", "n": 1 }, { "week": "2026-10", "n": 1 }, { "week": "2026-17", "n": 1 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "fberding", "count": 737, "uuid": 69512012 }, { "user": "yuliiatykhonova", "count": 121, "uuid": 163284863 } ], "_userbio": { "uuid": 69512012, "type": "user", "name": "FBerding" }, "_downloads": { "count": 639, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/aifeducation" }, "_devurl": "https://github.com/fberding/aifeducation", "_pkgdown": "https://fberding.github.io/aifeducation/", "_searchresults": 13, "_topics": [ "openblas", "cpp", "openmp" ], "_rbuild": "4.6.0", "_assets": [ "extra/aifeducation.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/fberding/aifeducation", "_realowner": "fberding", "_cranurl": true, "_releases": [ { "version": "0.2.0", "date": "2023-08-15" }, { "version": "0.3.0", "date": "2023-10-10" }, { "version": "0.3.1", "date": "2024-02-18" }, { "version": "0.3.2", "date": "2024-03-15" }, { "version": "0.3.3", "date": "2024-04-22" }, { "version": "1.0.0", "date": "2024-12-20" }, { "version": "1.0.1", "date": "2025-01-28" }, { "version": "1.0.2", "date": "2025-02-05" }, { "version": "1.1.0", "date": "2025-08-19" }, { "version": "1.1.1", "date": "2025-08-23" }, { "version": "1.1.2", "date": "2025-10-14" }, { "version": "1.1.3", "date": "2025-11-19" }, { "version": "1.1.4", "date": "2026-03-02" }, { "version": "1.1.5", "date": "2026-04-26" } ], "_exports": [ "add_missing_args", "AIFEBaseModel", "AIFEMaster", "auto_n_cores", "BaseModelBert", "BaseModelCore", "BaseModelDebertaV2", "BaseModelFunnel", "BaseModelModernBert", "BaseModelMPNet", "BaseModelRoberta", "BaseModelsIndex", "build_documentation_for_model", "build_layer_stack_documentation_for_vignette", "calc_standard_classification_measures", "calc_tokenizer_statistics", "cat_message", "check_aif_py_modules", "check_all_args", "check_class_and_type", "class_vector_to_py_dataset", "ClassifiersBasedOnTextEmbeddings", "clean_pytorch_log_transformers", "cohens_kappa", "create_dir", "create_object", "create_synthetic_units_from_matrix", "data.frame_to_py_dataset", "DataManagerClassifier", "DataSetsIndex", "EmbeddedText", "extract_column_from_py_dataset", "fleiss_kappa", "generate_args_for_tests", "generate_embeddings", "generate_id", "generate_tensors", "get_alpha_3_codes", "get_batches_index", "get_called_args", "get_coder_metrics", "get_current_args_for_print", "get_depr_obj_names", "get_desc_for_core_model_architecture", "get_file_extension", "get_fixed_test_tensor", "get_layer_documentation", "get_n_chunks", "get_param_def", "get_param_dict", "get_param_doc_desc", "get_parameter_documentation", "get_py_package_version", "get_py_package_versions", "get_recommended_py_versions", "get_synthetic_cases_from_matrix", "get_TEClassifiers_class_names", "get_test_data_for_classifiers", "get_time_stamp", "gwet_ac", "HuggingFaceTokenizer", "inspect_tmp_dir", "install_aifeducation", "install_aifeducation_studio", "install_py_modules", "kendalls_w", "knnor", "knnor_is_same_class", "kripp_alpha", "LargeDataSetBase", "LargeDataSetForText", "LargeDataSetForTextEmbeddings", "load_all_py_scripts", "load_from_disk", "load_py_scripts", "long_load_target_data", "matrix_to_array_c", "ModelsBasedOnTextEmbeddings", "monitor_test_time_on_CI", "output_message", "prepare_r_array_for_dataset", "prepare_session", "print_message", "py_dataset_to_embeddings", "random_bool_on_CI", "read_log", "read_loss_log", "reduce_to_unique", "reset_log", "reset_loss_log", "run_py_file", "save_to_disk", "set_transformers_logger", "start_aifeducation_studio", "summarize_args_for_long_task", "TEClassifierParallel", "TEClassifierParallelPrototype", "TEClassifierProtoNet", "TEClassifierRegular", "TEClassifiersBasedOnProtoNet", "TEClassifiersBasedOnRegular", "TEClassifierSequential", "TEClassifierSequentialPrototype", "TEFeatureExtractor", "tensor_list_to_numpy", "tensor_to_matrix_c", "tensor_to_numpy", "TextEmbeddingModel", "to_categorical_c", "TokenizerBase", "TokenizerIndex", "update_aifeducation", "WordPieceTokenizer", "write_log" ], "_datasets": [ { "name": "imdb_movie_reviews", "title": "Standford Movie Review Dataset", "object": "imdb_movie_reviews", "class": [ "data.frame" ], "fields": [ "text", "label", "id" ], "rows": 300, "table": true, "tojson": true } ], "_help": [ { "page": "add_missing_args", "title": "Add missing arguments to a list of arguments", "concept": [ "Utils Studio Developers" ], "topics": [ "add_missing_args" ] }, { "page": "AIFEBaseModel", "title": "Base class for objects using a pytorch model as core model.", "concept": [ "R6 Classes for Developers" ], "topics": [ "AIFEBaseModel" ] }, { "page": "AIFEMaster", "title": "Base class for most objects", "concept": [ "R6 Classes for Developers" ], "topics": [ "AIFEMaster" ] }, { "page": "auto_n_cores", "title": "Number of cores for multiple tasks", "concept": [ "Utils Developers" ], "topics": [ "auto_n_cores" ] }, { "page": "BaseModelBert", "title": "BERT-Transformer", "concept": [ "Base Model" ], "topics": [ "BaseModelBert" ] }, { "page": "BaseModelCore", "title": "Abstract class for all BaseModels", "concept": [ "R6 Classes for Developers" ], "topics": [ "BaseModelCore" ] }, { "page": "BaseModelDebertaV2", "title": "DeBERTa V2", "concept": [ "Base Model" ], "topics": [ "BaseModelDebertaV2" ] }, { "page": "BaseModelFunnel", "title": "Funnel transformer", "concept": [ "Base Model" ], "topics": [ "BaseModelFunnel" ] }, { "page": "BaseModelModernBert", "title": "ModernBert", "concept": [ "Base Model" ], "topics": [ "BaseModelModernBert" ] }, { "page": "BaseModelMPNet", "title": "MPNet", "concept": [ "Base Model" ], "topics": [ "BaseModelMPNet" ] }, { "page": "BaseModelRoberta", "title": "RoBERTa", "concept": [ "Base Model" ], "topics": [ "BaseModelRoberta" ] }, { "page": "BaseModelsIndex", "title": "List of all available BaseModels", "concept": [ "Parameter Dictionary" ], "topics": [ "BaseModelsIndex" ] }, { "page": "build_documentation_for_model", "title": "Generate documentation for a classifier class", "concept": [ "Utils Documentation" ], "topics": [ "build_documentation_for_model" ] }, { "page": "build_layer_stack_documentation_for_vignette", "title": "Generate documentation of all layers for an vignette or article", "concept": [ "Utils Documentation" ], "topics": [ "build_layer_stack_documentation_for_vignette" ] }, { "page": "calc_standard_classification_measures", "title": "Calculate recall, precision, and f1-scores", "concept": [ "performance measures" ], "topics": [ "calc_standard_classification_measures" ] }, { "page": "calc_tokenizer_statistics", "title": "Estimate tokenizer statistics", "concept": [ "Utils Transformers Developers" ], "topics": [ "calc_tokenizer_statistics" ] }, { "page": "cat_message", "title": "Print message ('cat()')", "concept": [ "Utils Log Developers" ], "topics": [ "cat_message" ] }, { "page": "check_adjust_n_samples_on_CI", "title": "Set sample size for argument combinations", "concept": [ "Utils TestThat Developers" ], "topics": [ "check_adjust_n_samples_on_CI" ] }, { "page": "check_aif_py_modules", "title": "Check if all necessary python modules are available", "concept": [ "Installation and Configuration" ], "topics": [ "check_aif_py_modules" ] }, { "page": "check_all_args", "title": "Check arguments automatically", "concept": [ "Utils Checks Developers" ], "topics": [ "check_all_args" ] }, { "page": "check_class_and_type", "title": "Check class and type", "concept": [ "Utils Checks Developers" ], "topics": [ "check_class_and_type" ] }, { "page": "class_vector_to_py_dataset", "title": "Convert class vector to arrow data set", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "class_vector_to_py_dataset" ] }, { "page": "ClassifiersBasedOnTextEmbeddings", "title": "Abstract class for all classifiers that use numerical representations of texts instead of words.", "concept": [ "R6 Classes for Developers" ], "topics": [ "ClassifiersBasedOnTextEmbeddings" ] }, { "page": "clean_pytorch_log_transformers", "title": "Clean pytorch log of transformers", "concept": [ "Utils Log Developers" ], "topics": [ "clean_pytorch_log_transformers" ] }, { "page": "cohens_kappa", "title": "Calculate Cohen's Kappa", "concept": [ "performance measures" ], "topics": [ "cohens_kappa" ] }, { "page": "create_dir", "title": "Create directory if not exists", "concept": [ "Utils File Management Developers" ], "topics": [ "create_dir" ] }, { "page": "create_object", "title": "Create object#'", "concept": [ "Utils Developers" ], "topics": [ "create_object" ] }, { "page": "create_py_dataset_cache_file_path", "title": "File path for caching data sets", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "create_py_dataset_cache_file_path" ] }, { "page": "create_synthetic_units_from_matrix", "title": "Create synthetic units", "concept": [ "Utils Developers" ], "topics": [ "create_synthetic_units_from_matrix" ] }, { "page": "data.frame_to_py_dataset", "title": "Convert data.frame to arrow data set", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "data.frame_to_py_dataset" ] }, { "page": "DataManagerClassifier", "title": "Data manager for classification tasks", "concept": [ "R6 Classes for Developers" ], "topics": [ "DataManagerClassifier" ] }, { "page": "DataSetsIndex", "title": "List of all available types of data sets", "concept": [ "Parameter Dictionary" ], "topics": [ "DataSetsIndex" ] }, { "page": "EmbeddedText", "title": "Abstract class for small data sets containing text embeddings", "concept": [ "Data Management" ], "topics": [ "EmbeddedText" ] }, { "page": "extract_column_from_py_dataset", "title": "Extract column", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "extract_column_from_py_dataset" ] }, { "page": "fleiss_kappa", "title": "Calculate Fleiss' Kappa", "concept": [ "performance measures" ], "topics": [ "fleiss_kappa" ] }, { "page": "generate_args_for_tests", "title": "Generate combinations of arguments", "concept": [ "Utils TestThat Developers" ], "topics": [ "generate_args_for_tests" ] }, { "page": "generate_embeddings", "title": "Generate test embeddings", "concept": [ "Utils TestThat Developers" ], "topics": [ "generate_embeddings" ] }, { "page": "generate_id", "title": "Generate ID suffix for objects", "concept": [ "Utils Developers" ], "topics": [ "generate_id" ] }, { "page": "generate_tensors", "title": "Generate test tensors", "concept": [ "Utils TestThat Developers" ], "topics": [ "generate_tensors" ] }, { "page": "get_alpha_3_codes", "title": "Country Alpha 3 Codes", "concept": [ "Utils Sustainability Developers" ], "topics": [ "get_alpha_3_codes" ] }, { "page": "get_batches_index", "title": "Assign cases to batches", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "get_batches_index" ] }, { "page": "get_called_args", "title": "Called arguments", "concept": [ "Parameter Dictionary" ], "topics": [ "get_called_args" ] }, { "page": "get_coder_metrics", "title": "Calculate reliability measures based on content analysis", "concept": [ "performance measures" ], "topics": [ "get_coder_metrics" ] }, { "page": "get_current_args_for_print", "title": "Print arguments", "concept": [ "Utils TestThat Developers" ], "topics": [ "get_current_args_for_print" ] }, { "page": "get_depr_obj_names", "title": "Get names of deprecated objects", "concept": [ "Parameter Dictionary" ], "topics": [ "get_depr_obj_names" ] }, { "page": "get_desc_for_core_model_architecture", "title": "Generate documentation for core models", "concept": [ "Utils Documentation" ], "topics": [ "get_desc_for_core_model_architecture" ] }, { "page": "get_file_extension", "title": "Get file extension", "concept": [ "Utils File Management Developers" ], "topics": [ "get_file_extension" ] }, { "page": "get_fixed_test_tensor", "title": "Generate static test tensor", "concept": [ "Utils TestThat Developers" ], "topics": [ "get_fixed_test_tensor" ] }, { "page": "get_layer_documentation", "title": "Generate layer documentation", "concept": [ "Utils Documentation" ], "topics": [ "get_layer_documentation" ] }, { "page": "get_magnitude_values", "title": "Magnitudes of an argument", "concept": [ "Parameter Dictionary" ], "topics": [ "get_magnitude_values" ] }, { "page": "get_n_chunks", "title": "Get the number of chunks/sequences for each case", "concept": [ "Utils Developers" ], "topics": [ "get_n_chunks" ] }, { "page": "get_param_def", "title": "Definition of an argument", "concept": [ "Parameter Dictionary" ], "topics": [ "get_param_def" ] }, { "page": "get_param_dict", "title": "Get dictionary of all parameters", "concept": [ "Parameter Dictionary" ], "topics": [ "get_param_dict" ] }, { "page": "get_param_doc_desc", "title": "Description of an argument", "concept": [ "Parameter Dictionary" ], "topics": [ "get_param_doc_desc" ] }, { "page": "get_parameter_documentation", "title": "Generate layer documentation", "concept": [ "Utils Documentation" ], "topics": [ "get_parameter_documentation" ] }, { "page": "get_py_package_version", "title": "Get versions of a specific python package", "concept": [ "Utils Python Developers" ], "topics": [ "get_py_package_version" ] }, { "page": "get_py_package_versions", "title": "Get versions of python components", "concept": [ "Utils Python Developers" ], "topics": [ "get_py_package_versions" ] }, { "page": "get_recommended_py_versions", "title": "Recommended version of python packages", "concept": [ "Installation and Configuration" ], "topics": [ "get_recommended_py_versions" ] }, { "page": "get_synthetic_cases_from_matrix", "title": "Create synthetic cases for balancing training data", "concept": [ "Utils Developers" ], "topics": [ "get_synthetic_cases_from_matrix" ] }, { "page": "get_TEClassifiers_class_names", "title": "Get names of classifiers", "concept": [ "Parameter Dictionary" ], "topics": [ "get_TEClassifiers_class_names" ] }, { "page": "get_test_data_for_classifiers", "title": "Get test data", "concept": [ "Utils TestThat Developers" ], "topics": [ "get_test_data_for_classifiers" ] }, { "page": "get_time_stamp", "title": "Time stamp", "concept": [ "Utils Developers" ], "topics": [ "get_time_stamp" ] }, { "page": "gwet_ac", "title": "Calculate Gwet's AC1 and AC2", "concept": [ "performance measures" ], "topics": [ "gwet_ac" ] }, { "page": "HuggingFaceTokenizer", "title": "HuggingFaceTokenizer", "concept": [ "Tokenizer" ], "topics": [ "HuggingFaceTokenizer" ] }, { "page": "inspect_tmp_dir", "title": "Inspect Temporary directory", "concept": [ "Memory Cache" ], "topics": [ "inspect_tmp_dir" ] }, { "page": "install_aifeducation", "title": "Install aifeducation on a machine", "concept": [ "Installation and Configuration" ], "topics": [ "install_aifeducation" ] }, { "page": "install_aifeducation_studio", "title": "Install 'AI for Education - Studio' on a machine", "concept": [ "Installation and Configuration" ], "topics": [ "install_aifeducation_studio" ] }, { "page": "install_py_modules", "title": "Installing necessary python modules to an environment", "concept": [ "Installation and Configuration" ], "topics": [ "install_py_modules" ] }, { "page": "kendalls_w", "title": "Calculate Kendall's coefficient of concordance w", "concept": [ "performance measures" ], "topics": [ "kendalls_w" ] }, { "page": "knnor", "title": "K-Nearest Neighbor OveRsampling approach (KNNOR)", "concept": [ "oversampling_approaches" ], "topics": [ "knnor" ] }, { "page": "knnor_is_same_class", "title": "Validate a new point", "concept": [ "oversampling_approaches Developers" ], "topics": [ "knnor_is_same_class" ] }, { "page": "kripp_alpha", "title": "Calculate Krippendorff's Alpha", "concept": [ "performance measures" ], "topics": [ "kripp_alpha" ] }, { "page": "LargeDataSetBase", "title": "Abstract base class for large data sets", "concept": [ "R6 Classes for Developers" ], "topics": [ "LargeDataSetBase" ] }, { "page": "LargeDataSetForText", "title": "Abstract class for large data sets containing raw texts", "concept": [ "Data Management" ], "topics": [ "LargeDataSetForText" ] }, { "page": "LargeDataSetForTextEmbeddings", "title": "Abstract class for large data sets containing text embeddings", "concept": [ "Data Management" ], "topics": [ "LargeDataSetForTextEmbeddings" ] }, { "page": "load_all_py_scripts", "title": "Load and re-load all python scripts", "concept": [ "Utils Python Developers" ], "topics": [ "load_all_py_scripts" ] }, { "page": "load_from_disk", "title": "Loading objects created with 'aifeducation'", "concept": [ "Saving and Loading" ], "topics": [ "load_from_disk" ] }, { "page": "load_py_scripts", "title": "Load and re-load python scripts", "concept": [ "Utils Python Developers" ], "topics": [ "load_py_scripts" ] }, { "page": "long_load_target_data", "title": "Load target data for long running tasks", "concept": [ "Utils Studio Developers" ], "topics": [ "long_load_target_data" ] }, { "page": "matrix_to_array_c", "title": "Reshape matrix to array", "concept": [ "Utils Developers" ], "topics": [ "matrix_to_array_c" ] }, { "page": "ModelsBasedOnTextEmbeddings", "title": "Base class for models using neural nets", "concept": [ "R6 Classes for Developers" ], "topics": [ "ModelsBasedOnTextEmbeddings" ] }, { "page": "monitor_test_time_on_CI", "title": "Print duration of a test on CI", "concept": [ "Utils TestThat Developers" ], "topics": [ "monitor_test_time_on_CI" ] }, { "page": "output_message", "title": "Print message", "concept": [ "Utils Log Developers" ], "topics": [ "output_message" ] }, { "page": "prepare_r_array_for_dataset", "title": "Convert R array for arrow data set", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "prepare_r_array_for_dataset" ] }, { "page": "prepare_session", "title": "Function for setting up a python environment within R.", "concept": [ "Installation and Configuration" ], "topics": [ "prepare_session" ] }, { "page": "print_message", "title": "Print message ('message()')", "concept": [ "Utils Log Developers" ], "topics": [ "print_message" ] }, { "page": "py_dataset_to_embeddings", "title": "Convert arrow data set to an arrow data set", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "py_dataset_to_embeddings" ] }, { "page": "random_bool_on_CI", "title": "Random bool on Continuous Integration", "concept": [ "Utils TestThat Developers" ], "topics": [ "random_bool_on_CI" ] }, { "page": "read_log", "title": "Function for reading a log file in R", "concept": [ "Utils Log Developers" ], "topics": [ "read_log" ] }, { "page": "read_loss_log", "title": "Function for reading a log file containing a record of the loss during training.", "concept": [ "Utils Log Developers" ], "topics": [ "read_loss_log" ] }, { "page": "reduce_to_unique", "title": "Reduce to unique cases", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "reduce_to_unique" ] }, { "page": "reset_log", "title": "Function that resets a log file.", "concept": [ "Utils Log Developers" ], "topics": [ "reset_log" ] }, { "page": "reset_loss_log", "title": "Reset log for loss information", "concept": [ "Utils Log Developers" ], "topics": [ "reset_loss_log" ] }, { "page": "run_py_file", "title": "Run python file", "concept": [ "Utils Python Developers" ], "topics": [ "run_py_file" ] }, { "page": "save_to_disk", "title": "Saving objects created with 'aifeducation'", "concept": [ "Saving and Loading" ], "topics": [ "save_to_disk" ] }, { "page": "set_transformers_logger", "title": "Sets the level for logging information of the 'transformers' library", "concept": [ "Installation and Configuration" ], "topics": [ "set_transformers_logger" ] }, { "page": "start_aifeducation_studio", "title": "Aifeducation Studio", "concept": [ "Graphical User Interface" ], "topics": [ "start_aifeducation_studio" ] }, { "page": "summarize_args_for_long_task", "title": "Summarize arguments from shiny input", "concept": [ "Utils Studio Developers" ], "topics": [ "summarize_args_for_long_task" ] }, { "page": "TEClassifierParallel", "title": "Text embedding classifier with a neural net", "concept": [ "Classification" ], "topics": [ "TEClassifierParallel" ] }, { "page": "TEClassifierParallelPrototype", "title": "Text embedding classifier with a ProtoNet", "concept": [ "Classification" ], "topics": [ "TEClassifierParallelPrototype" ] }, { "page": "TEClassifierProtoNet", "title": "Text embedding classifier with a ProtoNet", "concept": [ "Classification" ], "topics": [ "TEClassifierProtoNet" ] }, { "page": "TEClassifierRegular", "title": "Text embedding classifier with a neural net", "concept": [ "Classification" ], "topics": [ "TEClassifierRegular" ] }, { "page": "TEClassifiersBasedOnProtoNet", "title": "Base class for classifiers relying on numerical representations of texts instead of words that use the architecture of Protonets and its corresponding training techniques.", "concept": [ "R6 Classes for Developers" ], "topics": [ "TEClassifiersBasedOnProtoNet" ] }, { "page": "TEClassifiersBasedOnRegular", "title": "Base class for regular classifiers relying on EmbeddedText or LargeDataSetForTextEmbeddings as input", "concept": [ "R6 Classes for Developers" ], "topics": [ "TEClassifiersBasedOnRegular" ] }, { "page": "TEClassifierSequential", "title": "Text embedding classifier with a neural net", "concept": [ "Classification" ], "topics": [ "TEClassifierSequential" ] }, { "page": "TEClassifierSequentialPrototype", "title": "Text embedding classifier with a ProtoNet", "concept": [ "Classification" ], "topics": [ "TEClassifierSequentialPrototype" ] }, { "page": "TEFeatureExtractor", "title": "Feature extractor for reducing the number for dimensions of text embeddings.", "concept": [ "Text Embedding" ], "topics": [ "TEFeatureExtractor" ] }, { "page": "tensor_list_to_numpy", "title": "Convert list of tensors into numpy arrays", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "tensor_list_to_numpy" ] }, { "page": "tensor_to_matrix_c", "title": "Transform tensor to matrix", "concept": [ "Utils Developers" ], "topics": [ "tensor_to_matrix_c" ] }, { "page": "tensor_to_numpy", "title": "Tensor_to_numpy", "concept": [ "Utils Python Data Management Developers" ], "topics": [ "tensor_to_numpy" ] }, { "page": "TextEmbeddingModel", "title": "Text embedding model", "concept": [ "Text Embedding" ], "topics": [ "TextEmbeddingModel" ] }, { "page": "to_categorical_c", "title": "Transforming classes to one-hot encoding", "concept": [ "Utils Developers" ], "topics": [ "to_categorical_c" ] }, { "page": "TokenizerBase", "title": "Base class for tokenizers", "concept": [ "R6 Classes for Developers" ], "topics": [ "TokenizerBase" ] }, { "page": "TokenizerIndex", "title": "List of all available Tokenizers", "concept": [ "Parameter Dictionary" ], "topics": [ "TokenizerIndex" ] }, { "page": "update_aifeducation", "title": "Updates an existing installation of 'aifeducation' on a machine", "concept": [ "Installation and Configuration" ], "topics": [ "update_aifeducation" ] }, { "page": "WordPieceTokenizer", "title": "WordPieceTokenizer", "concept": [ "Tokenizer" ], "topics": [ "WordPieceTokenizer" ] }, { "page": "write_log", "title": "Write log", "concept": [ "Utils Log Developers" ], "topics": [ "write_log" ] } ], "_readme": "https://github.com/fberding/aifeducation/raw/HEAD/README.md", "_rundeps": [ "cli", "codetools", "cpp11", "doParallel", "dplyr", "farver", "foreach", "generics", "ggalluvial", "ggplot2", "glue", "gridExtra", "gtable", "here", "iotarelr", "isoband", "iterators", "jsonlite", "labeling", "lattice", "lazyeval", "lifecycle", "magrittr", "Matrix", "pillar", "pkgconfig", "plyr", "png", "purrr", "R6", "rappdirs", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppTOML", "reshape2", "reticulate", "rlang", "rprojroot", "S7", "scales", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "liblapack", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.26+ds-1ubuntu0.1", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libblas", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.26+ds-1ubuntu0.1", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" }, { "shlib": "libgomp", "package": "libgomp1", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "openmp", "homepage": "http://gcc.gnu.org/", "description": "GCC OpenMP (GOMP) support library" } ], "_vignettes": [ { "source": "aifeducation.Rmd", "filename": "aifeducation.html", "title": "01 Get started", "engine": "knitr::rmarkdown", "headings": [ "1) Installation and Technical Requirements", "Introduction", "Step 1 - Install the R package", "Step 2 - Install Python and optional R packages", "2) Starting a new session", "3) Tutorials and Guides", "4) Update aifeducation", "5) Recommended Version of Python Packages" ], "created": "2023-04-14 12:49:12", "modified": "2025-11-20 17:23:06", "commits": 36 } ], "_score": 5.113943352306837, "_indexed": true, "_nocasepkg": "aifeducation", "_universes": [ "fberding" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.1.5", "date": "2026-05-26T06:22:31.000Z", "distro": "noble", "arch": "aarch64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "56b9c2334aa68adb94c0c058eb9129d4bde8c758e5143ef5f528fd04df9d08f2", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.7.0", "os": "linux", "version": "1.1.5", "date": "2026-05-26T06:23:05.000Z", "distro": "noble", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "39c1a54f52e99bcb51c3fbff62422262aa797274099d6504bfb5d32bb635f609", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.6.0", "os": "linux", "version": "1.1.5", "date": "2026-05-26T06:22:18.000Z", "distro": "noble", "arch": "aarch64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "f1dd2a8f2dad6ddf7deff8aa5cd8ec9aae3ba40dc8ea49981e5841697dc06f89", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.6.0", "os": "linux", "version": "1.1.5", "date": "2026-05-26T06:22:30.000Z", "distro": "noble", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "74cd43940af93ed2149970bfb625570e9e9bd6061de13f3ba3b6c7e0355bcda4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.5.3", "os": "mac", "version": "1.1.5", "date": "2026-05-26T06:25:36.000Z", "arch": "aarch64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "236f2b61c619aea005fd593013540f2456c225227e49c04e1c4c5f233404ec88", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.5.3", "os": "mac", "version": "1.1.5", "date": "2026-05-26T06:23:24.000Z", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "5b6574895356bf2c3b80b7a724b7d24c98ac4b14a70162c6832a1cecead7728f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.6.0", "os": "mac", "version": "1.1.5", "date": "2026-05-26T06:25:56.000Z", "arch": "aarch64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "6395bff6925eeab28418b6a44b0e8c55f919d75e5870ff54d82adfa1f0731a9f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.6.0", "os": "mac", "version": "1.1.5", "date": "2026-05-26T06:24:49.000Z", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "7db05418b292386435283ba588c5bac24a12ecd9fe686ddded7775f8d08b8339", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.6.0", "os": "wasm", "version": "1.1.5", "date": "2026-05-26T06:22:27.000Z", "arch": "emscripten", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "8f2e368e20d91ffbbd3c3abde4ee2a3ee62d0cfabdebf8adbcf178ae63d31e3b", "status": "success", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.7.0", "os": "win", "version": "1.1.5", "date": "2026-05-26T06:22:20.000Z", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "4b442fa2226c92b48ad966ec88d39eff099c423875f38a5e55e5666e83c1d94b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.5.3", "os": "win", "version": "1.1.5", "date": "2026-05-26T06:21:34.000Z", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "74193c795c469c84961b5ef07fd00870273a536659357393c6f759b39e46593e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" }, { "r": "4.6.0", "os": "win", "version": "1.1.5", "date": "2026-05-26T06:21:28.000Z", "arch": "x86_64", "commit": "e5d96bf0960d0bd0c1349c7356b8129d348dee07", "fileid": "dba324ac56d6de7a5ebe2b12dd1c4116214aabbab7f85644c5dc2513f0c27ec5", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/fberding/actions/runs/26435689316" } ] }