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

## Documentation for package ‘text’ version 1.2.1

- 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
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.
textClassify | Predict label and probability of a text using a pretrained classifier language model. (experimental)
textDescriptives | Compute descriptive statistics of character variables.
textDimName | Change the names of the dimensions in the word embeddings.
textDistance | Compute the semantic distance between two text variables.
textDistanceMatrix | Compute semantic distance scores between all combinations in a word embedding
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).
textEmbed | Extract layers and aggregate them to word embeddings, for all character variables in a given dataframe.
textEmbedLayerAggregation | Select and aggregate layers of hidden states to form a word embedding.
textEmbedRawLayers | Extract layers of hidden states (word embeddings) for all character variables in a given dataframe.
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
textFineTuneDomain | Domain Adapted Pre-Training (EXPERIMENTAL - under development)
textFineTuneTask | Task Adapted Pre-Training (EXPERIMENTAL - under development)
textGeneration | Predicts the words that will follow a specified text prompt. (experimental)
textModelLayers | Get the number of layers in a given model.
textModels | Check downloaded, available models.
textModelsRemove | Delete a specified model and model associated files.
textNER | Named Entity Recognition. (experimental)
textPCA | Compute 2 PCA dimensions of the word embeddings for individual words.
textPCAPlot | Plot words according to 2-D plot from 2 PCA components.
textPlot | Plot words from textProjection() or textWordPrediction().
textPredict | Trained models created by e.g., textTrain() or stored on e.g., github can be used to predict new scores or classes from embeddings or text using textPredict.
textPredictAll | Predict from several models, selecting the correct input
textPredictTest | Significance testing correlations If only y1 is provided a t-test is computed, between the absolute error from yhat1-y1 and yhat2-y1.
textProjection | Compute Supervised Dimension Projection and related variables for plotting words.
textProjectionPlot | Plot words according to 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 | Compute the semantic similarity between two text variables.
textSimilarityMatrix | Compute semantic similarity 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).
textSum | Summarize texts. (experimental)
textTokenize | Tokenize according to different huggingface transformers
textTopics | This function creates and trains a BERTopic model (based on bertopic python packaged) on a text-variable in a tibble/data.frame. (EXPERIMENTAL)
textTopicsReduce | textTopicsReduce (EXPERIMENTAL)
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)
textTopicsTree | textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree
textTopicsWordcloud | This functions plots wordclouds of topics from a Topic Model based on their significance determined by a linear or binary regression
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.
textTrainN | (experimental) Compute cross-validated correlations for different sample-sizes of a data set. The cross-validation process can be repeated several times to enhance the reliability of the evaluation.
textTrainNPlot | (experimental) Plot cross-validated correlation coefficients across different sample-sizes from the object returned by the textTrainN function. If the number of cross-validations exceed one, then error-bars will be included in the plot.
textTrainRandomForest | Train word embeddings to a categorical variable using random forest.
textTrainRegression | Train word embeddings to a numeric variable.
textTranslate | Translation. (experimental)
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 (experimental).
textZeroShot | Zero Shot Classification (Experimental)
word_embeddings_4 | Word embeddings for 4 text variables for 40 participants