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  "Title": "Analyses of Text using Transformers Models from HuggingFace,\nNatural Language Processing and Machine Learning",
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    "textPlot",
    "textPredict",
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    "textPredictTest",
    "textProjection",
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    "textrpp_uninstall",
    "textSimilarity",
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    "textSimilarityNorm",
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    "textTokenizeAndCount",
    "textTopics",
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    "textTopicsTest",
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    "textTrainRandomForest",
    "textTrainRegression",
    "textTranslate",
    "textWordPrediction",
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      "title": "Example data for plotting a Semantic Centrality Plot.",
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        "data.frame"
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      "table": true,
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        "data.frame"
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        "Dim_PC1",
        "Dim_PC2"
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      "table": true,
      "tojson": true
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      "name": "raw_embeddings_1",
      "title": "Word embeddings from textEmbedRawLayers function",
      "object": "raw_embeddings_1",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "word_embeddings_4",
      "title": "Word embeddings for 4 text variables for 40 participants",
      "object": "word_embeddings_4",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
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      "title": "Example data for plotting a Semantic Centrality Plot.",
      "topics": [
        "centrality_data_harmony"
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    },
    {
      "page": "DP_projections_HILS_SWLS_100",
      "title": "Data for plotting a Dot Product Projection Plot.",
      "topics": [
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      "title": "Example text and numeric data.",
      "topics": [
        "Language_based_assessment_data_3_100"
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    },
    {
      "page": "Language_based_assessment_data_8",
      "title": "Text and numeric data for 10 participants.",
      "topics": [
        "Language_based_assessment_data_8"
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    },
    {
      "page": "PC_projections_satisfactionwords_40",
      "title": "Example data for plotting a Principle Component Projection Plot.",
      "topics": [
        "PC_projections_satisfactionwords_40"
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    },
    {
      "page": "raw_embeddings_1",
      "title": "Word embeddings from textEmbedRawLayers function",
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        "raw_embeddings_1"
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    {
      "page": "textCentrality",
      "title": "Semantic similarity score between single words' and an aggregated word embeddings",
      "topics": [
        "textCentrality"
      ]
    },
    {
      "page": "textCentralityPlot",
      "title": "Plots words from textCentrality()",
      "topics": [
        "textCentralityPlot"
      ]
    },
    {
      "page": "textClean",
      "title": "Cleans text from standard personal information",
      "topics": [
        "textClean"
      ]
    },
    {
      "page": "textCleanNonASCII",
      "title": "Clean non-ASCII characters",
      "topics": [
        "textCleanNonASCII"
      ]
    },
    {
      "page": "textDescriptives",
      "title": "Compute descriptive statistics of character variables.",
      "topics": [
        "textDescriptives"
      ]
    },
    {
      "page": "textDiagnostics",
      "title": "Run diagnostics for the text package",
      "topics": [
        "textDiagnostics"
      ]
    },
    {
      "page": "textDimName",
      "title": "Change dimension names",
      "topics": [
        "textDimName"
      ]
    },
    {
      "page": "textDistance",
      "title": "Semantic distance",
      "topics": [
        "textDistance"
      ]
    },
    {
      "page": "textDistanceMatrix",
      "title": "Semantic distance across multiple word embeddings",
      "topics": [
        "textDistanceMatrix"
      ]
    },
    {
      "page": "textDistanceNorm",
      "title": "Semantic distance between a text variable and a word norm",
      "topics": [
        "textDistanceNorm"
      ]
    },
    {
      "page": "textDomainCompare",
      "title": "Compare two language domains",
      "topics": [
        "textDomainCompare"
      ]
    },
    {
      "page": "textEmbed",
      "title": "textEmbed() extracts layers and aggregate them to word embeddings, for all character variables in a given dataframe.",
      "topics": [
        "textEmbed"
      ]
    },
    {
      "page": "textEmbedLayerAggregation",
      "title": "Aggregate layers",
      "topics": [
        "textEmbedLayerAggregation"
      ]
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