Top2Vec

• fixed loading bug
• hierarchical topic reduction bug
• added parameter for optimizing hierarchical reduction speed

Indexing bugfix

1. Added gpu hdsbcan
2. Added topic indexing

1. Changed default embedding model to universal-sentence-encoder-multilingual.
2. Added option for GPU umap with gpu_umap parameter.

• Added a method for computing topics.
• Exposed topic deduplication parameter topic_merge_delta.
• Bug fixes.

get_feature_names() -> get_feature_names_out()

• New pre-trained transformer models available
• Ability to use any embedding model by passing callable to embedding_model
• New embedding_batch_size option
• Document chunking options for long documents
• Phrases in topics by setting ngram_vocab=True

Added query_documents and query_topics methods which allow for using a sequence of text such as a question, a sentence, a paragraph or a document to query documents or topics.

Added num_topics parameter to get_documents_topics method which allows retrieving multiple topics per document.

Fixes #152

Added numpy>=1.20.0 dependency.

Numpy related bug fix and document id validation performance upgrade.

Addressed #90, #125, #126

Added custom umap and hdbscan arg option. Fixed issue with loading model with custom tokenizer.

Added use_embedding_model_tokenizer parameter. If set to True and if using an embedding_model other than doc2vec, use the model's tokenizer for document embedding.

Fixed dependency issue with joblib.

Fixed issues with wordclouds caused by negative similarity scores.

Fixed bug #91

Added option for indexing word vectors, this will speed up search for models with large vocabularies. Specifically search_words_by_vector and similar_words.

Added new method search_words_by_vector.

Added option for indexing document vectors, this will speed up search for models with large number of documents. Specifically search_documents_by_vector, search_documents_by_keywords, and search_documents_by_documents.

Added new method search_documents_by_vector.

Added code to prevent hierarchical topic reduction error #79.

Dependencies for universal sentence encoder and BERT sentence transformer options are now optional.
With pip install top2vec[sentence-encoders] and pip install top2vec[sentence_transformers]

Faster cosine similarity.

The verbose parameter will be set to True by default.

Fixed a bug that stopped showing logging updates after downloading pre-trained models.