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NEWS

• Improved selection of examples in textExamples()
• On MacOS setting OMP settings at start-up.

• added bootstrap_difference and replaced “bootstrap” with “bootstrap_overlap” in the textPredictTest() function.

• changing textTrainExamples() to textExamples() and improving the filter_word function.

• adding matrix/box legend for textTrainExamples().
• improving functionality of textTopics().

• added save_output = “no_plot” in textTrainRegression() for “logistic” and “multinomial” to reduce model size of saved objects.

• fixing bug in textAssess() for implicit motives where the participant id was incorrect.

• Added checks for matching word_embeddings and model requirements in the textPredict() function. This is controlled via the new check_matching_word_embeddings parameter, which validates compatibility of model type, layers, and aggregation settings.
• Added a name parameter to the textDimName() function, allowing users to specify or change the name suffix for word embedding dimensions.
• Improved the dim_names = FALSE behavior in the textDimName() function to also ignore model-required dimension suffixes. Now includes clearer and more informative warnings when dimension mismatches occur.

• updating from the depracated rsample::function validation_split() to initial_validation_split(). However, this changes some results in textTrainRegression() and textTrainRandomForrest().
• updating textLBAM() to take construct_start parameter.

• removing objects in the environment of textTrainRegression() to reduce saved model sizes.

• fixing bug in layer selection in textEmbedRawLayers() (when using default -2, layer 11 was selected even for large models). This was never a problem in textEmbed().

• adding the dlatk_method to the textEmbed() function.

• adding cv_method = “group_cv” in the textTrainRegression() function.

• fixing python dependency (aiohappyeyeballs)
• adding parameter plot_n_word_random and legend_number_colour in textPlot.
• removed nltk warning when running the functions requiring pyhon.
• anchouring group word embeddings in the textProjection() function.
• adding cohen’s d to the output of the textProjection() function

• harmonizing wordclouds with topics-package
• implementing textTrainExamples()
• updating legend plots.

• Result change: Updating the workflows package from version “1.1.4” to “1.2.0” result in changes in the random forest function tests.

• included parameter highest_parameter and lowest_parameter when parameters are tied.

• Alias function: textPredict(), textAssess() and textClassify().
• LBAM integration with textLBAM().
• Full support of implicit motives models.
• Text cleaning functionality with textClean() (removing common personal information).
• Compatability with the topics-package, see www.r-topics.org.

• textLBAM() returns the library as a datafram

• textPredict() detects model_type.
• Instead of having to specify the URL, one can now specify the model name from the Language-Based Assessmet Model (L-BAM) library.
• Including default option to download an updated version of the L-BAM file

• fixing bugs related to text prediction functions
• adding method_typ = “texttrained” and “finetuned”
• streamlining code for implicit motives output
• adding textFindNonASCII() function and feature in textEmbed() to warn and clean non-ASCII characters. This may change results slightly.
• removed type parameter in textPredict() and instead giving both probability and class.

• textClassify() is now called textClassifyPipe()
• textPredict() is now called textPredictR()
• Making textAssess(), textPredict() and textClassify() works the same, now taking the parameter method with the string “text” to using textPredict(), and “huggingface” to using textClassifyPipe().

• updating python code, including adding parameters hg_gated, hg_token, and trust_remote_code.
• changed parameter name from return_incorrect_results to force_return_results
• changed default of function_to_apply = NULL instead of “none”; this is to mimic huggingface default.
• textWordPrediction since it is under development and note tested.

• updating security issues with python packages.
• updating the default range of penalties in textTrain() functions.
• updating textPredict() functionality

• Improving textTrainN() including subsets sampling (new: default change from random to subsets), use_same_penalty_mixture (new:default change from FALSE to TRUE) and std_err (new output).
• Improving textTrainPlot()

• Improving textPredict() functionality.
• Implementing experimental features related to textTopics()

• textTopics() trains a BERTopic model with different modules and returns the model, data, and topic_document distributions based on c-td-idf
• textTopicsTest() can perform multiple tests (correlation, t-test, regression) between a BERTopic model from textTopics() and data
• textTopicsWordcloud() can plot word clouds of topics tested with textTopicsTest()
• textTopicsTree() prints out a tree structure of the hierarchical topic structure

• textEmbed() is now fully embedding one column at the time; and reducing word_types for each column. This can break some code; and produce different results in plots where word_types are based on several embedded columns.
• textTrainN() and textTrainNPlot() evaluates prediction accuracy across number of cases.
• textTrainRegression() and textTrainRandomForest now takes tibble as input in strata.

• multinomial regression in textTrainRegression()
• textPredictTest() can handle auc
• textEmbed() is faster (thanks to faster handling of aggregating layers)
• Added sort parameter in textEmbedRawLayers().

• Tests using training with random forest was updated since outcomes changed when updating from R 4.2 to R 4.3.1. (see test_2_textTrain.R in tests/testthat folder)

Possibility to use GPU for MacOS M1 and M2 chip using device = “mps” in textEmbed()

textFineTune() as an experimental function is implemented max_length implemented in textTranslate()

• textEmbedReduce() implemented

• Fixing textEmbed error for many non-BERT models.

• fixed textEmbed(decontextualize=TRUE), which gave error.

• Removing textSimialirtyTest() for version 1.0 because it needs more evaluations.

• changed hard coded “bert-base-uncased” to model, so that layers = -2 works in textEmbed().
• Update logging level critical using integer 50 with set_verbosity.
• changed in sorting_xs_and_x_append from Dim to Dim0 when renaming x_appended variables.
• changed first to append_first and made it an option in textTrainRegression() and textTrainRandomForest().

• The default setting of textEmbed() is now providing token-level embeddings and text-level embeddings. Word_type embeddings are optional.
• In textEmbed() layers = 11:12 is now second_to_last.
• In textEmbedRawLayers default is now second_to_last.
• In textEmbedLayerAggregation() layers = 11:12 is now layers = "all".
• In textEmbed() and textEmbedRawLayers() x is now called texts.
• textEmbedLayerAggregation() now uses layers = "all", aggregation_from_layers_to_tokens, aggregation_from_tokens_to_texts.

• textZeroShot() is implemented.
• textDistanceNorm() and textDistanceMatrix()
• textDistance() can compute cosine distance.
• textModelLayers() provides N layers for a given model

max_token_to_sentence in textEmbed()

• aggregate_layers is now called aggregation_from_layers_to_tokens.
• aggregate_tokens is now called aggregation_from_tokens_to_texts. single_word_embeddings is now called word_types_embeddings

• textEmbedLayersOutput() is now called textEmbedRawLayers()

• adding textDimName()
• DEFAULT CHANGE in textEmbed(): dim_name = TRUE
• DEFAULT CHANGE in textEmbed(): single_context_embeddings = TRUE
• DEFAULT CHANGE in textEmbed(): device = “gpu”
• Adding specific layer aggregations for explore_words in textPlot()
• Adding x_append_target in textPredict() function

• updating textClassify(), textGeneration(), textNER(), textSum(), textQA(), and textTranslate().

• harmonizing x_add to x_append across functions
• adding set_seed to language analysis tasks

• abstracting function for sorting out x' in training and prediction
• textPredict does not take word_embeddings and x_append (not new_data)

• textClassify() (under development)
• textGeneration() (under development)
• textNER() (under development)
• textSum() (under development)
• textQA() (under development)
• textTranslate() (under development)

• New function: textSentiment(), from huggingface transformers models.
• add progression for time consuming functions including textEmbed(), textTrainRegression(), textTrainRandomForest() and textProjection().

• Option dim_names to set unique dimension names in textEmbed() and textEmbedStatic().
• textPreictAll() function that can take several models, word embeddings, and variables as input to provide multiple outputs.
• option to add variables to the embeddings in textTrain() functions with x_append.

• text version is printed from DESCRIPTION-file (rather than manual update)
• textPredict related functions are located in its own file
• textEmbed comment includes text_version number
• textEmbedLayersOutput and textEmbed can provide single_context_embeddings

• Removed return_tokens option from textEmbed (since it is only relevant for textEmbedLayersOutput)
• removed the empty list $single_we when decontexts is FALSE.

• Visualization of the download process of language models
• Can set error level from python
• Logistic regression is default for classification in textTrain.
• Megatron language model functionality

• When GPU is not found, CPU is used.

• Option to set model_max_length in textEmbed().
• textModels() show downloaded models.
• textModelsRemove() deletes specified models.

• Fixed error for unpaired textSimilarityTest() when uneven number of cases are tested.

• Inclusion of textDistance() function with distance measures.
• Adding more measures to textSimilarity().
• Adding functionality from textSimilarity() in textSimilarityTest(), textProjection() and textCentrality() for plotting.
• Adding information about how textTrainRegression() concatenates word embeddings when provided with a list of several word embeddings.
• Adding two word embedding dimensions to example data of single word embeddings to match the 10 of the contextualized embeddings in word_embeddings_4$singlewords_we.

• In textCentrality(), words to be plotted are selected with word_data1_all$extremes_all_x >= 1 (rather than ==1).

• textSimilarityMatrix() computes semantic similarity among all combinations in a given word embedding.

• textDescriptives() gets options to remove NA and compute total scores.

• inclusion of textDescriptives()

• prompt option added to textrpp_initiate()

• tokenization is made with NLTK from python.

• Code has been cleaned up and prepared for CRAN

• New functions being tested: textWordPredictions() (which has a trial period/not fully developed and might be removed in future versions); p-values are not yet implemented.
• Possibility to use textPlot() for objects from both textProjection() and textWordPredictions()

• Changed wordembeddigs to word_embeddings through out the code/package.

• Warnings about seed when using multi-cores on Mac is addressed.

• textrpp_initiate() runs automatically in library(text) when default environment exits
• Python warnings a captured in embedding comments
• Option to print python options to console
• Updated the permutation test for plotting and textSimilarityTest().

• Changed from stringr to stringi (and removed tokenizer) as imported package

• textrpp_install() installs a conda environment with text required python packages.
• textrpp_install_virtualenv() install a virtual environment with text required python packages.
• textrpp_initialize() initializes installed environment.
• textrpp_uninstall() uninstalls conda environment.

• textEmbed() and textEmbedLayersOutput() support the use of GPU using the device setting.
  
• remove_words makes it possible to remove specific words from textProjectionPlot()

• In textProjetion() and textProjetionPlot() it now possible to add points of the aggregated word embeddings in the plot
• In textProjetion() it now possible to manually add words to the plot in order to explore them in the word embedding space.
• In textProjetion() it is possible to add color or remove words that are more frequent on the opposite “side” of its dot product projection.
• In textProjection() with split == quartile, the comparison distribution is now based on the quartile data (rather than the data for mean)

• If any of the tokens to remove is “[CLS]”, subtract 1 on token_id so that it works with layer_aggregation_helper. 0.9.11
• Can now submit one word to textEmbed() with decontexts=TRUE.

• textSimilarityTest() is not giving error when using method = unpaired, with unequal number of participants in each group.

• textPredictTest() function to significance test correlations of different models. 0.9.11

• If any of the tokens to remove is “[CLS]”, subtract 1 on token_id so that it works with layer_aggregation_helper. 0.9.11

This version is now on CRAN. ### New Features - Adding option to deselect the step_centre and step_scale in training. - Cross-validation method in textTrainRegression() and textTrainRandomForrest() have two options cv_folds and validation_split. (0.9.02) - Better handling of NA in step_naomit in training. - DistilBert model works (0.9.03)

• textProjectionPlot() plots words extreme in more than just one feature (i.e., words are now plotted that satisfy, for example, both plot_n_word_extreme and plot_n_word_frequency). (0.9.01)
• textTrainRegression() and textTrainRandomForest() also have function that select the max evaluation measure results (before only minimum was selected all the time, which, e.g., was correct for rmse but not for r) (0.9.02)
• removed id_nr in training and predict by using workflows (0.9.02).

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