Topic Modeling with 'Sentence-BERT' Embeddings

---

Documentation for package ‘sbert’ version 0.5.8

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

Help Pages

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
models	List the Available Pinned Sentence-BERT Models
model_download	Download a Pinned Sentence-BERT Model
model_remove	Remove an Installed Model from the Cache
model_status	Inspect an Installed Model
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
topics	Discover Semantic Topics in Documents
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