abbreviations           Obtain the Built-in Abbreviation Gazetteer
as.data.frame.sbert_topic_sweep
                        Coerce a Topic-Count Comparison to a Data Frame
blend                   Blend Segment Embeddings with Their Document
                        Context
cache_dir               Locate the sbert Model Cache
cache_size              Measure the sbert Cache
clean_corpus            Clean a Text Corpus Before Encoding
coherence               Score Topic Coherence
compare_topics          Compare Topic Counts Before Committing to One
content_ratio           Alphabetic Content Ratio of Text
covid                   COVID-19 Research Abstracts
dedupe                  Deduplicate a Text Corpus with Frequencies
encode                  Encode Text with Sentence-BERT
feedback_translations   AI-generated Mathematics Feedback with English
                        Translations
fitted.sbert_topic_sweep
                        Extract One Fitted Model from a Topic-Count
                        Comparison
install_runtime         Install ONNX Runtime
keywords                Extract Keywords from Documents by Embedding
                        Similarity
load_custom             Load an Arbitrary Hugging Face Embedding Model
load_model              Load a Pinned Sentence-BERT Model
model_download          Download a Pinned Sentence-BERT Model
model_remove            Remove an Installed Model from the Cache
model_status            Inspect an Installed Model
models                  List the Available Pinned Sentence-BERT Models
plot.sbert_topic_hierarchy
                        Plot a Topic Hierarchy Dendrogram
plot.sbert_topic_model
                        Plot a Semantic Topic Model
plot.sbert_topic_sweep
                        Plot a Topic-Count Comparison
pool                    Pool Token Embeddings into Sentence Embeddings
predict.sbert_topic_model
                        Assign New Documents to Fitted Topics
print.sbert_model       Print a Loaded Model
print.sbert_topic_corpus
                        Print a Prepared Topic Corpus
print.sbert_topic_hierarchy
                        Print a Topic Hierarchy
print.sbert_topic_model
                        Print a Semantic Topic Model
print.sbert_topic_sweep
                        Print a Topic-Count Comparison
reduce_topics           Reduce a Fitted Topic Model to Fewer Topics
representatives         Representative Text Units for Every Topic
segment                 Segment Text into Sentences, Clauses, or
                        Phrases
stop_words              Obtain and Adjust the Topic Stop-Word List
strip_list_markers      Strip Enumeration and List Markers from Text
summary.sbert_topic_model
                        Summarize a Semantic Topic Model
terms.sbert_topic_model
                        Topic Terms, Retuned Without Refitting
topic_corpus            Prepare a Corpus Once to Fit Many Topic Models
topic_diversity         Measure Topic Diversity
topic_gamma             Document-Topic Distributions from Segment
                        Assignments
topic_hierarchy         Build the Topic Hierarchy of a Fitted Model
topic_membership        Soft Topic Membership Probabilities
topic_palette           Qualitative Colour Palette for Topics
topic_similarity        Compute Cosine Similarity
topic_sizes             Topic Sizes on the Distinct and Weighted Scales
topics                  Discover Semantic Topics in Documents
