Package index • 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()`	Embed text
`textDimName()`	Change dimension names
`textEmbedRawLayers()`	Extract layers of hidden states
`textEmbedLayerAggregation()`	Aggregate layers
`textEmbedReduce()`	Pre-trained dimension reduction (experimental)
`textEmbedStatic()`	Apply static word embeddings
Fine-tuning models
`textFineTuneTask()`	Task Adapted Pre-Training (EXPERIMENTAL - under development)
`textFineTuneDomain()`	Domain Adapted Pre-Training (EXPERIMENTAL - under development)
Text language analysis tasks
`textGeneration()`	Text generation
`textNER()`	Named Entity Recognition. (experimental)
`textSum()`	Summarize texts. (experimental)
`textQA()`	Question Answering. (experimental)
`textTranslate()`	Translation. (experimental)
`textZeroShot()`	Zero Shot Classification (Experimental)
The text-train functions
`textTrain()`	Trains word embeddings
`textTrainLists()`	Train lists of word embeddings
`textTrainRegression()`	Train word embeddings to a numeric variable.
`textTrainRandomForest()`	Trains word embeddings usig random forest
`textTrainN()`	Cross-validated accuracies across sample-sizes
`textTrainNPlot()`	Plot cross-validated accuracies across sample sizes
The text-predict functions
`textPredict()` `textAssess()` `textClassify()`	textPredict, textAssess and textClassify
`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
`textLBAM()`	The LBAM library
Semantic similarities and distances functions
`textSimilarity()`	Semantic Similarity
`textDistance()`	Semantic distance
`textSimilarityMatrix()`	Semantic similarity across multiple word embeddings
`textDistanceMatrix()`	Semantic distance across multiple word embeddings
`textSimilarityNorm()`	Semantic similarity between a text variable and a word norm
`textDistanceNorm()`	Semantic distance between a text variable and a word norm
Visualise words in the word embedding space
`textProjection()`	Supervised Dimension Projection
`textPlot()`	Plot words
`textProjectionPlot()`	Plot Supervised Dimension Projection
`textCentrality()`	Semantic similarity score between single words' and an aggregated word embeddings
`textCentralityPlot()`	Plots words from textCentrality()
`textPCA()`	textPCA()
`textPCAPlot()`	textPCAPlot
BERTopics
`textTopics()`	BERTopics
`textTopicsTest()`	This function tests the relationship between a single topic or all topics and a variable of interest. Available tests include correlation, t-test, linear regression, binary regression, and ridge regression. (EXPERIMENTAL - under development)
`textTopicsWordcloud()`	Plots wordcloud (experimental)
`textTopicsReduce()`	textTopicsReduce (EXPERIMENTAL)
`textTopicsTree()`	textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree
View or delete downloaded HuggingFace models
`textModels()`	Check downloaded, available models.
`textModelLayers()`	Number of layers
`textModelsRemove()`	Delete a specified model
Miscellaneous
`textDescriptives()`	Compute descriptive statistics of character variables.
`textTokenize()`	Tokenize text-variables
`textTokenizeAndCount()`	Tokenize and count
`textDomainCompare()`	Compare two language domains
`textFindNonASCII()`	Detect non-ASCII characters
`textCleanNonASCII()`	Clean non-ASCII characters
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 models

Text language analysis tasks

The text-train functions

The text-predict functions

Semantic similarities and distances functions

Visualise words in the word embedding space

BERTopics

View or delete downloaded HuggingFace models

Miscellaneous

Example Data