Rhobots

'BERTopic'-Style Topic Modeling Without 'Python'

Implements the 'BERTopic' topic modeling pipeline directly in R: transformer-based sentence embedding, Uniform Manifold Approximation and Projection dimensionality reduction, Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering, and class-based term frequency-inverse document frequency topic extraction - all without any dependency on 'Python', 'conda', or 'reticulate'. Every stage runs in R through 'torch', 'safetensors', 'tok', 'uwot', and 'dbscan'. The package mirrors the accessor API of the original 'Python' package, adds integrated quality metrics and hyperparameter search tools, and introduces part-of-speech filtered and C-value-ranked representation models.

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