Topic Probability Distribution

• transform() and fit_transform() now also return the topic probability distributions
• Added visualize_distribution() which visualizes the topic probability distribution for a single document

Small patch release

• Fixed n_gram_range not being used
• Added option for using stopwords

Small patch release

Improved the calculation of the class-based TF-IDF procedure by limiting the calculation to sparse matrices. This prevents out-of-memory problems when faced with large datasets.

Fixed missing mapped_topics

When transforming new documents, self.mapped_topics seemed to be missing. Added to the init.

Fixed requirements

• Fixed requirements --> Issue with pytorch
• Update docs
• Update readme

First Release

• Added parameters for UMAP and HDBSCAN
• Option to choose sentence-transformer model
• Method for transforming unseen documents
• Save and load trained models (UMAP and HDBSCAN)
• Extract topics and their sizes
• Optimized c-TF-IDF
• Improved documentation
• Improved topic reduction