v0.9.1 (MaartenGr#211)

Fix MaartenGr#210, MaartenGr#208, MaartenGr#205, and MaartenGr#213

v0.9 (MaartenGr#176)

* 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

v0.8.1 (MaartenGr#138)

* Fix tests
* Add Kaggle example
* Add interactive visualizations to API documentation
* Set transformers in Flair

v0.8 (MaartenGr#120)

Visualization Update:
* Topic Hierarchy: topic_model.visualize_hierarchy()
* Topic Similarity Heatmap: topic_model.visualize_heatmap()
* Topic Representation Barchart: topic_model.visualize_barchart()
* Term Score Decline: topic_model.visualize_term_rank()

Improvements:  
* Created bertopic.plotting library to easily extend visualizations
* Improved automatic topic reduction by using HDBSCAN to detect similar topics
* Sort topic ids by their frequency. -1 is the outlier class and contains typically the most documents. After that 0 is the largest topic, 1 the second largest, etc.
* Update MKDOCS with new visualizations

Fixes:
* Fix typo MaartenGr#113, MaartenGr#117
* Fix MaartenGr#121 
* Fix mapping of topics after reduction (it now excludes 0) (MaartenGr#103)

v0.7 (MaartenGr#87)

Highlights:  

* (semi-)supervised topic modeling
* Added Spacy, Gensim, USE (TFHub)
* Use a different backend for document embeddings and word embeddings
* Create your own backends with `bertopic.backend.BaseEmbedder`
* Calculate and visualize topics per class

Fixes:

* Fixed issues with Torch req
* Prevent saving term frequency matrix in CTFIDF class
* Fixed DTM not working when reducing topics (MaartenGr#96)
* Moved visualization dependencies to base BERTopic
* `pip install bertopic[visualization]` becomes `pip install bertopic`
* Allow precomputed embeddings in bertopic.find_topics() (MaartenGr#79)

Fix typo (MaartenGr#69)

v0.5 (MaartenGr#46)

* Add Flair to allow for more (custom) token/document embeddings
* Option to use custom UMAP, HDBSCAN, and CountVectorizer
* Added low_memory parameter to reduce memory during computation
* Improved verbosity (shows progress bar)
* Improved testing
* Use the newest version of sentence-transformers as it speeds ups encoding significantly
* Return the figure of visualize_topics()
* Expose all parameters with a single function: get_params()
* Option to disable the saving of embedding_model, should reduce BERTopic size significantly
* Add FAQ page

Fixed embedding parameter not working (MaartenGr#36)

Fix language bug (MaartenGr#34)

Update documentation