Shypd.aiThe largest open index of AI toolsContextualized Topic Models (CTM) is a Python package for contextualized topic modeling, combining contextualized embeddings like BERT with traditional topic models. It helps researchers derive coherent topics from text data, supporting multiple languages and offering different model versions for various use cases.
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Contextualized Topic Models (CTM) is a powerful Python package designed for advanced topic modeling. It integrates pre-trained language representations, such as BERT embeddings, with traditional topic models to produce highly coherent topics. The package offers two main models: CombinedTM, which merges contextual embeddings with bag-of-words for enhanced topic coherence, and ZeroShotTM, ideal for tasks with missing words in test data and cross-lingual topic modeling when trained with multilingual embeddings. CTM supports various languages through HuggingFace models and allows for the use of different embedding methods, ensuring adaptability to new advancements. It also includes 'Kitty,' a submodule for human-in-the-loop classification to quickly categorize documents and create named clusters. The tool is particularly effective when the bag-of-words size is restricted to around 2000 elements, and it provides a preprocessing pipeline to manage this. CTM uses SBERT for embedding creation, offering flexibility in choosing embedding models and handling multilingual data.
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