Expand description
Semantic Topic Modeller — online clustering approach for latent topic modelling.
Models latent topics from a collection of embeddings using a simple online clustering approach, assigning documents to topics and tracking topic drift over time.
Structs§
- Document
Topics - Topic probability distribution for a single document.
- LdaTopic
- A latent topic with its top words and coherence score.
- Model
Document - Bag-of-words representation of a document.
- Modeller
Config - Configuration for
SemanticTopicModeller. - Semantic
Topic Modeller - Online topic modeller that clusters embeddings into latent topics.
- Topic
Assignment - Records the assignment of a document to a topic at a point in time.
- Topic
Model - A latent topic represented as a running centroid of assigned embeddings.
- Topic
Model Config - Hyperparameters for the LDA topic model.
- Topic
Model Result - Full output of a fitted topic model.
- Topic
Modeler - LDA-based topic modeler using collapsed Gibbs sampling.
- Topic
Modeler Stats - Aggregate statistics derived from a fitted model result.
- Topic
Modeller Stats - Aggregate statistics for a
SemanticTopicModeller. - Topic
Word - A word and its probability within a topic.
Enums§
- Topic
Model Error - Error type for LDA topic modeling operations.
Functions§
- cosine_
sim - Computes the cosine similarity between two vectors.