Installation |
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Install text required python packages in conda or virtualenv environment |
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Uninstall textrpp conda environment |
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Initialize text required python packages |
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Transform text to word embeddings |
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Embed text |
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Change dimension names |
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Extract layers of hidden states |
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Aggregate layers |
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Pre-trained dimension reduction (experimental) |
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Apply static word embeddings |
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Fine-tuning models |
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Task Adapted Pre-Training (EXPERIMENTAL - under development) |
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Domain Adapted Pre-Training (EXPERIMENTAL - under development) |
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Text language analysis tasks |
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Text generation |
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Named Entity Recognition. (experimental) |
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Summarize texts. (experimental) |
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Question Answering. (experimental) |
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Translation. (experimental) |
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Zero Shot Classification (Experimental) |
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The text-train functions |
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Trains word embeddings |
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Train lists of word embeddings |
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Train word embeddings to a numeric variable. |
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Trains word embeddings usig random forest |
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Cross-validated accuracies across sample-sizes |
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Plot cross-validated accuracies across sample sizes |
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The text-predict functions |
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textPredict, textAssess and textClassify |
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Significance testing correlations If only y1 is provided a t-test is computed, between the absolute error from yhat1-y1 and yhat2-y1. |
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Predict from several models, selecting the correct input |
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The LBAM library |
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Semantic similarities and distances functions |
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Semantic Similarity |
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Semantic distance |
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Semantic similarity across multiple word embeddings |
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Semantic distance across multiple word embeddings |
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Semantic similarity between a text variable and a word norm |
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Semantic distance between a text variable and a word norm |
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Visualise words in the word embedding space |
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Supervised Dimension Projection |
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Plot words |
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Plot Supervised Dimension Projection |
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Semantic similarity score between single words' and an aggregated word embeddings |
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Plots words from textCentrality() |
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textPCA() |
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textPCAPlot |
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BERTopics |
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BERTopics |
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Wrapper for topicsTest function from the topics package |
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Plot word clouds |
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textTopicsReduce (EXPERIMENTAL) |
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textTopicsTest (EXPERIMENTAL) to get the hierarchical topic tree |
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View or delete downloaded HuggingFace models |
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Check downloaded, available models. |
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Number of layers |
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Delete a specified model |
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Miscellaneous |
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Compute descriptive statistics of character variables. |
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Cleans text from standard personal information |
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Tokenize text-variables |
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Tokenize and count |
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Compare two language domains |
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Detect non-ASCII characters |
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Clean non-ASCII characters |
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Example Data |
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Text and numeric data for 10 participants. |
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Word embeddings for 4 text variables for 40 participants |
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Word embeddings from textEmbedRawLayers function |
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Example text and numeric data. |
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Data for plotting a Dot Product Projection Plot. |
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Example data for plotting a Semantic Centrality Plot. |
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Example data for plotting a Principle Component Projection Plot. |