Major Release v0.7

The two main features are (semi-)supervised topic modeling
and several backends to use instead of Flair and SentenceTransformers!

Highlights:

• (semi-)supervised topic modeling by leveraging supervised options in UMAP
  • model.fit(docs, y=target_classes)
• Backends:
  • Added Spacy, Gensim, USE (TFHub)
  • Use a different backend for document embeddings and word embeddings
  • Create your own backends with bertopic.backend.BaseEmbedder
  • Click here for an overview of all new backends
• Calculate and visualize topics per class
  • Calculate: topics_per_class = topic_model.topics_per_class(docs, topics, classes)
  • Visualize: topic_model.visualize_topics_per_class(topics_per_class)
• Several tutorials were updated and added:

Name	Link
Topic Modeling with BERTopic
(Custom) Embedding Models in BERTopic
Advanced Customization in BERTopic
(semi-)Supervised Topic Modeling with BERTopic
Dynamic Topic Modeling with Trump's Tweets

Fixes:

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

model = BERTopic(embedding_model=my_embedding_model)
model.fit(docs, my_precomputed_embeddings)
model.find_topics(search_term)