# Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

## Documentation for package ‘text’ version 1.8.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

centrality_data_harmony | Example data for plotting a Semantic Centrality Plot.
DP_projections_HILS_SWLS_100 | Data for plotting a Dot Product Projection Plot.
Language_based_assessment_data_3_100 | Example text and numeric data.
Language_based_assessment_data_8 | Text and numeric data for 10 participants.
PC_projections_satisfactionwords_40 | Example data for plotting a Principle Component Projection Plot.
raw_embeddings_1 | Word embeddings from textEmbedRawLayers function
textAssess | textPredict, textAssess and textClassify
textCentrality | Semantic similarity score between single words' and an aggregated word embeddings
textCentralityPlot | Plots words from textCentrality()
textClassify | textPredict, textAssess and textClassify
textClean | Cleans text from standard personal information
textCleanNonASCII | Clean non-ASCII characters
textDescriptives | Compute descriptive statistics of character variables.
textDiagnostics | Run diagnostics for the text package
textDimName | Change dimension names
textDistance | Semantic distance
textDistanceMatrix | Semantic distance across multiple word embeddings
textDistanceNorm | Semantic distance between a text variable and a word norm
textDomainCompare | Compare two language domains
textEmbed | textEmbed() extracts layers and aggregate them to word embeddings, for all character variables in a given dataframe.
textEmbedLayerAggregation | Aggregate layers
textEmbedRawLayers | Extract layers of hidden states
textEmbedReduce | Pre-trained dimension reduction (experimental)
textEmbedStatic | Apply static word embeddings
textExamples | Identify language examples.
textFindNonASCII | Detect non-ASCII characters
textFineTuneDomain | Domain Adapted Pre-Training (EXPERIMENTAL - under development)
textFineTuneTask | Task Adapted Pre-Training (EXPERIMENTAL - under development)
textGeneration | Text generation
textLBAM | The LBAM library
textModelLayers | Number of layers
textModels | Check downloaded, available models.
textModelsRemove | Delete a specified model
textNER | Named Entity Recognition. (experimental)
textPCA | textPCA()
textPCAPlot | textPCAPlot
textPlot | Plot words
textPredict | textPredict, textAssess and textClassify
textPredictAll | Predict from several models, selecting the correct input
textPredictTest | Significance testing for model prediction performance
textProjection | Supervised Dimension Projection
textProjectionPlot | Plot Supervised Dimension Projection
textQA | Question Answering. (experimental)
textrpp_initialize | Initialize text required python packages
textrpp_install | Install text required python packages in conda or virtualenv environment
textrpp_install_virtualenv | Install text required python packages in conda or virtualenv environment
textrpp_uninstall | Uninstall textrpp conda environment
textSimilarity | Semantic Similarity
textSimilarityMatrix | Semantic similarity across multiple word embeddings
textSimilarityNorm | Semantic similarity between a text variable and a word norm
textSum | Summarize texts. (experimental)
textTokenize | Tokenize text-variables
textTokenizeAndCount | Tokenize and count
textTopics | BERTopic
textTopicsReduce | textTopicsReduce (EXPERIMENTAL)
textTopicsTest | Wrapper for topicsTest function from the topics package
textTopicsTree | textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree
textTopicsWordcloud | Plot word clouds
textTrain | Trains word embeddings
textTrainLists | Train lists of word embeddings
textTrainN | Cross-validated accuracies across sample-sizes
textTrainNPlot | Plot cross-validated accuracies across sample sizes
textTrainRandomForest | Trains word embeddings usig random forest
textTrainRegression | Train word embeddings to a numeric variable.
textTranslate | Translation. (experimental)
textZeroShot | Zero Shot Classification (Experimental)
word_embeddings_4 | Word embeddings for 4 text variables for 40 participants