awesome-topic-models
github.com/jonaschn/awesome-topic-models ↗✨ Awesome - A curated list of amazing Topic Models (implementations, libraries, and resources)
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me latent dirichlet allocation (lda) :page_facing_up: resources from awesome-topic-models"
Installation instructions →What's inside
Models
- AliasLDALatent Dirichlet Allocation (LDA) :page_facing_up:
C++ implemenation using Metropolis-Hastings and
- Anchored CorExEmbedding based Topic Models
Hierarchical Topic Modeling with Minimal Domain Knowledge
- BayesPAMiscellaneous topic models
Python interface for streaming implementation of MedLDA, maximum entropy discrimination LDA (max-margin supervised topic model)
- BERTopicEmbedding based Topic Models
BERTopic supports guided, (semi-) supervised, and dynamic topic modeling and visualization
- BIDMachNon-Negative Matrix Factorization (NMF or NNMF)
CPU and GPU-accelerated Scala implementation with L2 loss
- BIDMachTruncated Singular Value Decomposition (SVD) / Latent Semantic Analysis (LSA) / Latent Semantic Indexing (LSI)
Scala implementation of a scalable approximate SVD using subspace iteration
Related awesome lists
Libraries & Toolkits
- BIDMach
CPU and GPU-accelerated machine learning library
- BigARTM
Fast topic modeling platform
- gensim
Python library for topic modelling
- OCTIS
Python package to integrate, optimize and evaluate topic models
- RMallet
R package to interface with the Java machine learning tool MALLET
- scikit-learn
Python library for machine learning
Research Implementations
- ctm-cCorrelated Topic Model (CTM) a.k.a. logistic-normal topic models
C implementation of the correlated topic model by David Blei
- ctr
C++ implementation of collaborative topic models by Chong Wang
- cvbLDA
Python C extension implementation of collapsed variational Bayesian inference for LDA
- diln
C implementation of Discrete Infinite Logistic Normal (with HDP option) by John Paisley
- dtm
C implementation of dynamic topic models by David Blei & Sean Gerrish
- fast
A Fast And Scalable Topic-Modeling Toolbox (Fast-LDA, CVB0) by Arthur Asuncion and colleagues
Resources
- David Blei
David Blei's Homepage with introductory materials
Visualizations
- dfr-browser
Explore Mallet's topic models of texts in a web browser
- dtmvisual
Python package for visualizing DTM (trained with gensim)
- LDAvis
R package for interactive topic model visualization
- Mallet-GUI
GUI for creating and analyzing topic models produced by MALLET
- pyLDAvis
Python library for interactive topic model visualization
- scalaLDAvis
Scala port of pyLDAvis
Dirichlet hyperparameter optimization techniques
- dirichlet
- fastfit
- fixed-point iteration
Wallach's PhD thesis, chapter 2.3
- lecture-notes
- lightspeed
- Minka
Probabilistic Programming Languages (PPL) (a.k.a. Build your own Topic Model)
- edward
A PPL built on TensorFlow, e.g.,
- edward2
Simple PPL with core utilities in the NumPy and TensorFlow ecosystem
- PyMC3
Python package for Bayesian statistical modeling and probabilistic machine learning, e.g.,
- pyro
PPL built on PyTorch, e.g.,
- Stan
Platform for statistical modeling and high-performance statistical computation, e.g.,
- TFP
Probabilistic reasoning and statistical analysis in TensorFlow, e.g.,
Showing a sample of 187 resources. View the full list on GitHub →