* Get most representative documents per topic: `topic_model.get_representative_docs(topic=1)`
* Added `normalize_frequency` parameter to `visualize_topics_per_class` and `visualize_topics_over_time` 
* Return flat probabilities as default, only calculate the probabilities of all topics per document if `calculate_probabilities` is True
* Implemented a guided BERTopic by defining seed topics: `BERTopic(seed_topic_list=seed_topic_list)`
* Fix loading embedding model
* Fix probability mapping
* Improve accuracy of probabilities
* Additional FAQs