Function reference • text

Installation
`textrpp_install()` `textrpp_install_virtualenv()`	Install text required python packages in conda or virtualenv environment
`textrpp_uninstall()`	Uninstall textrpp conda environment
`textrpp_initialize()`	Initialize text required python packages
Transform text to word embeddings
`textEmbed()`	Extract layers and aggregate them to word embeddings, for all character variables in a given dataframe.
`textDimName()`	Change the names of the dimensions in the word embeddings.
`textEmbedRawLayers()`	Extract layers of hidden states (word embeddings) for all character variables in a given dataframe.
`textEmbedLayerAggregation()`	Select and aggregate layers of hidden states to form a word embeddings.
`textEmbedReduce()`	Pre-trained dimension reduction (experimental)
`textEmbedStatic()`	Applies word embeddings from a given decontextualized static space (such as from Latent Semantic Analyses) to all character variables
Fine-tuning
`textFineTuneTask()`	Task Adapted Pre-Training (experimental)
Language Analysis Tasks
`textClassify()`	Predict label and probability of a text using a pretrained classifier language model. (experimental)
`textGeneration()`	Predicts the words that will follow a specified text prompt. (experimental)
`textNER()`	Named Entity Recognition. (experimental)
`textSum()`	Summarize texts. (experimental)
`textQA()`	Question Answering. (experimental)
`textTranslate()`	Translation. (experimental)
`textZeroShot()`	Zero Shot Classification (Experimental)
Train word embeddings
`textTrain()`	Train word embeddings to a numeric (ridge regression) or categorical (random forest) variable.
`textTrainLists()`	Individually trains word embeddings from several text variables to several numeric or categorical variables. It is possible to have word embeddings from one text variable and several numeric/categprical variables; or vice verse, word embeddings from several text variables to one numeric/categorical variable. It is not possible to mix numeric and categorical variables.
`textTrainRegression()`	Train word embeddings to a numeric variable.
`textTrainRandomForest()`	Train word embeddings to a categorical variable using random forrest.
Predict from word embeddings
`textPredict()`	Predict scores or classification from, e.g., textTrain.
`textPredictTest()`	Significance testing correlations If only y1 is provided a t-test is computed, between the absolute error from yhat1-y1 and yhat2-y1.
`textPredictAll()`	Predict from several models, selecting the correct input
Semantic similarities and distances
`textSimilarity()`	Compute the semantic similarity between two text variables.
`textDistance()`	Compute the semantic distance between two text variables.
`textSimilarityMatrix()`	Compute semantic similarity scores between all combinations in a word embedding
`textDistanceMatrix()`	Compute semantic distance scores between all combinations in a word embedding
`textSimilarityNorm()`	Compute the semantic similarity between a text variable and a word norm (i.e., a text represented by one word embedding that represent a construct).
`textDistanceNorm()`	Compute the semantic distance between a text variable and a word norm (i.e., a text represented by one word embedding that represent a construct/concept).
Plot words in the word embedding space
`textProjection()`	Compute Supervised Dimension Projection and related variables for plotting words.
`textPlot()`	Plot words from textProjection() or textWordPrediction().
`textProjectionPlot()`	Plot words according to Supervised Dimension Projection.
`textWordPrediction()`	Compute predictions based on single words for plotting words. The word embeddings of single words are trained to predict the mean value associated with that word. P-values does NOT work yet.
`textCentrality()`	Compute semantic similarity score between single words' word embeddings and the aggregated word embedding of all words.
`textCentralityPlot()`	Plot words according to semantic similarity to the aggregated word embedding.
`textPCA()`	Compute 2 PCA dimensions of the word embeddings for individual words.
`textPCAPlot()`	Plot words according to 2-D plot from 2 PCA components.
View or delete downloaded HuggingFace models in R
`textModels()`	Check downloaded, available models.
`textModelLayers()`	Get the number of layers in a given model.
`textModelsRemove()`	Delete a specified model and model associated files.
Miscellaneous
`textDescriptives()`	Compute descriptive statistics of character variables.
`textTokenize()`	Tokenize according to different huggingface transformers
Example Data
`Language_based_assessment_data_8`	Text and numeric data for 10 participants.
`word_embeddings_4`	Word embeddings for 4 text variables for 40 participants
`raw_embeddings_1`	Word embeddings from textEmbedRawLayers function
`Language_based_assessment_data_3_100`	Example text and numeric data.
`DP_projections_HILS_SWLS_100`	Data for plotting a Dot Product Projection Plot.
`centrality_data_harmony`	Example data for plotting a Semantic Centrality Plot.
`PC_projections_satisfactionwords_40`	Example data for plotting a Principle Component Projection Plot.

Reference

Installation

Transform text to word embeddings

Fine-tuning

Language Analysis Tasks

Train word embeddings

Predict from word embeddings

Semantic similarities and distances

Plot words in the word embedding space

View or delete downloaded HuggingFace models in R

Miscellaneous

Example Data