|topmost-logo| TopMost
=================================
.. |topmost-logo| image:: docs/source/_static/topmost-logo.png
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TopMost provides complete lifecycles of topic modeling, including datasets, preprocessing, models, training, and evaluations. It covers the most popular topic modeling scenarios, like basic, dynamic, hierarchical, and cross-lingual topic modeling.
| This is our demo paper `Towards the TopMost: A Topic Modeling System Toolkit <https://arxiv.org/pdf/2309.06908.pdf>`_.
| This is our survey paper on neural topic models: `A Survey on Neural Topic Models: Methods, Applications, and Challenges <https://arxiv.org/pdf/2401.15351.pdf>`_.
==================
.. contents:: **Table of Contents**
:depth: 2
============
Overview
============
TopMost offers the following topic modeling scenarios with models, evaluation metrics, and datasets:
.. image:: docs/source/_static/architecture.svg
:width: 390
:align: center
+------------------------------+---------------+--------------------------------------------+-----------------+
| Scenario | Model | Evaluation Metric | Datasets |
+==============================+===============+============================================+=================+
| | | LDA_ | | |
| | | NMF_ | | | 20NG |
| | | ProdLDA_ | | TC | | IMDB |
| | | DecTM_ | | TD | | NeurIPS |
| | Basic Topic Modeling | | ETM_ | | Clustering | | ACL |
| | | NSTM_ | | Classification | | NYT |
| | | TSCTM_ | | | Wikitext-103 |
| | | ECRTM_ | | |
| | | | | |
+------------------------------+---------------+--------------------------------------------+-----------------+
| | | | | 20NG |
| | | HDP_ | | TC over levels | | IMDB |
| | Hierarchical | | SawETM_ | | TD over levels | | NeurIPS |
| | Topic Modeling | | HyperMiner_ | | Clustering over levels | | ACL |
| | | ProGBN_ | | Classification over levels | | NYT |
| | | TraCo_ | | | Wikitext-103 |
| | | | |
+------------------------------+---------------+--------------------------------------------+-----------------+
| | | | TC over time slices | |
| | Dynamic | | DTM_ | | TD over time slices | | NeurIPS |
| | Topic Modeling | | DETM_ | | Clustering | | ACL |
| | | | Classification | | NYT |
+------------------------------+---------------+--------------------------------------------+-----------------+
| | | | TC (CNPMI) | | ECNews |
| | Cross-lingual | | NMTM_ | | TD over languages | | Amazon |
| | Topic Modeling | | InfoCTM_ | | Classification (Intra and Cross-lingual) | | Review Rakuten|
| | | | | | |
+------------------------------+---------------+--------------------------------------------+-----------------+
.. _LDA: https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
.. _NMF: https://papers.nips.cc/paper_files/paper/2000/hash/f9d1152547c0bde01830b7e8bd60024c-Abstract.html
.. _ProdLDA: https://arxiv.org/pdf/1703.01488.pdf
.. _DecTM: https://aclanthology.org/2021.findings-acl.15.pdf
.. _ETM: https://aclanthology.org/2020.tacl-1.29.pdf
.. _NSTM: https://arxiv.org/abs/2008.13537
.. _CTM: https://aclanthology.org/2021.eacl-main.143/
.. _TSCTM: https://aclanthology.org/2022.emnlp-main.176/
.. _ECRTM: https://arxiv.org/pdf/2306.04217.pdf
.. _HDP: https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf
.. _SawETM: http://proceedings.mlr.press/v139/duan21b/duan21b.pdf
.. _HyperMiner: https://arxiv.org/pdf/2210.10625.pdf
.. _ProGBN: https://proceedings.mlr.press/v202/duan23c/duan23c.pdf
.. _TraCo: https://arxiv.org/pdf/2401.14113.pdf
.. _DTM: https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf
.. _DETM: https://arxiv.org/abs/1907.05545
.. _NMTM: https://bobxwu.github.io/files/pub/NLPCC2020_Neural_Multilingual_Topic_Model.pdf
.. _InfoCTM: https://arxiv.org/abs/2304.03544
============
Quick Start
============
Install TopMost
-----------------
Install topmost with ``pip`` as
.. code-block:: console
$ pip install topmost
Discover topics from your own datasets
-------------------------------------------
We can get the top words of discovered topics, ``topic_top_words``` and the topic distributions of documents, ``doc_topic_dist``.
The preprocessing steps are configurable. See our documentations.
.. code-block:: python
import topmost
from topmost.preprocessing import Preprocessing
# Your own documents
docs = [
"This is a document about space, including words like space, satellite, launch, orbit.",
"This is a document about Microsoft Windows, including words like windows, files, dos.",
# more documents...
]
device = 'cuda' # or 'cpu'
preprocessing = Preprocessing()
dataset = topmost.data.RawDatasetHandler(docs, preprocessing, device=device, as_tensor=True)
model = topmost.models.ProdLDA(dataset.vocab_size, num_topics=2)
model = model.to(device)
trainer = topmost.trainers.BasicTrainer(model)
topic_top_words, doc_topic_dist = trainer.fit_transform(dataset, num_top_words=15, verbose=False)
============
Usage
============
Download a preprocessed dataset
-----------------------------------
.. code-block:: python
import topmost
from topmost.data import download_dataset
download_dataset('20NG', cache_path='./datasets')
Train a model
-----------------------------------
.. code-block:: python
device = "cuda" # or "cpu"
# load a preprocessed dataset
dataset = topmost.data.BasicDatasetHandler("./datasets/20NG", device=device, read_labels=True, as_tensor=True)
# create a model
model = topmost.models.ProdLDA(dataset.vocab_size)
model = model.to(device)
# create a trainer
trainer = topmost.trainers.BasicTrainer(model)
# train the model
trainer.train(dataset)
Evaluate
-----------------------------------
.. code-block:: python
# get theta (doc-topic distributions)
train_theta, test_theta = trainer.export_theta(dataset)
# get top words of topics
topic_top_words = trainer.export_top_words(dataset.vocab)
# evaluate topic diversity
TD = topmost.evaluations.compute_topic_diversity(top_words)
# evaluate clustering
clustering_results = topmost.evaluations.evaluate_clustering(test_theta, dataset.test_labels)
# evaluate classification
classification_results = topmost.evaluations.evaluate_classification(train_theta, test_theta, dataset.train_labels, dataset.test_labels)
Test new documents
-----------------------------------
.. code-block:: python
import torch
from topmost.preprocessing import Preprocessing
new_docs = [
"This is a new document about space, including words like space, satellite, launch, orbit.",
"This is a new document about Microsoft Windows, including words like windows, files, dos."
]
parsed_new_docs, new_bow = preprocessing.parse(new_docs, vocab=dataset.vocab)
new_doc_topic_dist = trainer.test(torch.as_tensor(new_bow, device=device).float())
============
Installation
============
Stable release
--------------
To install TopMost, run this command in your terminal:
.. code-block:: console
$ pip install topmost
This is the preferred method to install TopMost, as it will always install the most recent stable release.
From sources
------------
The sources for TopMost can be downloaded from the Github repository.
You can clone the public repository by
.. code-block:: console
$ git clone https://github.com/BobXWu/TopMost.git
Then install the TopMost by
.. code-block:: console
$ python setup.py install
============
Tutorials
============
.. |github0| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey
:target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_quickstart.ipynb
:alt: Open In GitHub
.. |github1| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey
:target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_preprocessing_datasets.ipynb
:alt: Open In GitHub
.. |github2| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey
:target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_basic_topic_models.ipynb
:alt: Open In GitHub
.. |github3| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey
:target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_hierarchical_topic_models.ipynb
:alt: Open In GitHub
.. |github4| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey
:target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_dynamic_topic_models.ipynb
:alt: Open In GitHub
.. |github5| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey
:target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_crosslingual_topic_models.ipynb
:alt: Open In GitHub
We provide tutorials for different usages:
+--------------------------------------------------------------------------------+-------------------+
| Name | Link |
+================================================================================+===================+
| Quickstart | |github0| |
+--------------------------------------------------------------------------------+-------------------+
| How to preprocess datasets | |github1| |
+--------------------------------------------------------------------------------+-------------------+
| How to train and evaluate a basic topic model | |github2| |
+--------------------------------------------------------------------------------+-------------------+
| How to train and evaluate a hierarchical topic model | |github3| |
+--------------------------------------------------------------------------------+-------------------+
| How to train and evaluate a dynamic topic model | |github4| |
+--------------------------------------------------------------------------------+-------------------+
| How to train and evaluate a cross-lingual topic model | |github5| |
+--------------------------------------------------------------------------------+-------------------+
============
Notice
============
Differences from original implementations
-------------------------------------------
1. Oringal implementations may use different optimizer settings. For simplicity and brevity, our package by default uses the same setting for different models.
============
Disclaimer
============
This library includes some datasets for demonstration. If you are a dataset owner who wants to exclude your dataset from this library, please contact `Xiaobao Wu <xiaobao002@e.ntu.edu.sg>`_.
============
Authors
============
+----------------------------------------------------------+
| |xiaobao-figure| |
| `Xiaobao Wu <https://bobxwu.github.io>`__ |
+----------------------------------------------------------+
| |fengjun-figure| |
| `Fengjun Pan <https://github.com/panFJCharlotte98>`__ |
+----------------------------------------------------------+
.. |xiaobao-figure| image:: https://bobxwu.github.io/img/figure.jpg
:target: https://bobxwu.github.io
:width: 50
.. |fengjun-figure| image:: https://avatars.githubusercontent.com/u/126648078?v=4
:target: https://github.com/panFJCharlotte98
:width: 50
==============
Contributors
==============
.. image:: https://contrib.rocks/image?repo=bobxwu/topmost
:alt: Contributors
======================
How to cite our work
======================
If you want to use our toolkit, please cite as
::
@article{wu2023topmost,
title={Towards the TopMost: A Topic Modeling System Toolkit},
author={Wu, Xiaobao and Pan, Fengjun and Luu, Anh Tuan},
journal={arXiv preprint arXiv:2309.06908},
year={2023}
}
@article{wu2023survey,
title={A Survey on Neural Topic Models: Methods, Applications, and Challenges},
author={Wu, Xiaobao and Nguyen, Thong and Luu, Anh Tuan},
journal={Artificial Intelligence Review},
url={https://doi.org/10.1007/s10462-023-10661-7},
year={2024},
publisher={Springer}
}
=================
Acknowledgments
=================
- If you want to add any models to this package, we welcome your pull requests.
- If you encounter any problem, please either directly contact `Xiaobao Wu <xiaobao002@e.ntu.edu.sg>`_ or leave an issue in the GitHub repo.
- Icon by `Flat-icons-com <https://www.freepik.com/icon/top_671169>`_.
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"description": "|topmost-logo| TopMost\n=================================\n\n.. |topmost-logo| image:: docs/source/_static/topmost-logo.png\n :width: 38\n\n.. image:: https://img.shields.io/github/stars/bobxwu/topmost?logo=github\n :target: https://github.com/bobxwu/topmost/stargazers\n :alt: Github Stars\n\n.. image:: https://static.pepy.tech/badge/topmost\n :target: https://pepy.tech/project/topmost\n :alt: Downloads\n\n.. image:: https://img.shields.io/pypi/v/topmost\n :target: https://pypi.org/project/topmost\n :alt: PyPi\n\n.. image:: https://readthedocs.org/projects/topmost/badge/?version=latest\n :target: https://topmost.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n.. image:: https://img.shields.io/github/license/bobxwu/topmost\n :target: https://www.apache.org/licenses/LICENSE-2.0/\n :alt: License\n\n.. image:: https://img.shields.io/github/contributors/bobxwu/topmost\n :target: https://github.com/bobxwu/topmost/graphs/contributors/\n :alt: Contributors\n\n.. image:: https://img.shields.io/badge/arXiv-2309.06908-<COLOR>.svg\n :target: https://arxiv.org/pdf/2309.06908.pdf\n :alt: arXiv\n\n\nTopMost provides complete lifecycles of topic modeling, including datasets, preprocessing, models, training, and evaluations. It covers the most popular topic modeling scenarios, like basic, dynamic, hierarchical, and cross-lingual topic modeling.\n\n\n| This is our demo paper `Towards the TopMost: A Topic Modeling System Toolkit <https://arxiv.org/pdf/2309.06908.pdf>`_.\n| This is our survey paper on neural topic models: `A Survey on Neural Topic Models: Methods, Applications, and Challenges <https://arxiv.org/pdf/2401.15351.pdf>`_.\n\n==================\n\n.. contents:: **Table of Contents**\n :depth: 2\n\n\n\n============\nOverview\n============\n\nTopMost offers the following topic modeling scenarios with models, evaluation metrics, and datasets:\n\n.. image:: docs/source/_static/architecture.svg\n :width: 390\n :align: center\n\n+------------------------------+---------------+--------------------------------------------+-----------------+\n| Scenario | Model | Evaluation Metric | Datasets |\n+==============================+===============+============================================+=================+\n| | | LDA_ | | |\n| | | NMF_ | | | 20NG |\n| | | ProdLDA_ | | TC | | IMDB |\n| | | DecTM_ | | TD | | NeurIPS |\n| | Basic Topic Modeling | | ETM_ | | Clustering | | ACL |\n| | | NSTM_ | | Classification | | NYT |\n| | | TSCTM_ | | | Wikitext-103 |\n| | | ECRTM_ | | |\n| | | | | |\n+------------------------------+---------------+--------------------------------------------+-----------------+\n| | | | | 20NG |\n| | | HDP_ | | TC over levels | | IMDB |\n| | Hierarchical | | SawETM_ | | TD over levels | | NeurIPS |\n| | Topic Modeling | | HyperMiner_ | | Clustering over levels | | ACL |\n| | | ProGBN_ | | Classification over levels | | NYT |\n| | | TraCo_ | | | Wikitext-103 |\n| | | | |\n+------------------------------+---------------+--------------------------------------------+-----------------+\n| | | | TC over time slices | |\n| | Dynamic | | DTM_ | | TD over time slices | | NeurIPS |\n| | Topic Modeling | | DETM_ | | Clustering | | ACL |\n| | | | Classification | | NYT |\n+------------------------------+---------------+--------------------------------------------+-----------------+\n| | | | TC (CNPMI) | | ECNews |\n| | Cross-lingual | | NMTM_ | | TD over languages | | Amazon |\n| | Topic Modeling | | InfoCTM_ | | Classification (Intra and Cross-lingual) | | Review Rakuten|\n| | | | | | |\n+------------------------------+---------------+--------------------------------------------+-----------------+\n\n.. _LDA: https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf\n.. _NMF: https://papers.nips.cc/paper_files/paper/2000/hash/f9d1152547c0bde01830b7e8bd60024c-Abstract.html\n.. _ProdLDA: https://arxiv.org/pdf/1703.01488.pdf\n.. _DecTM: https://aclanthology.org/2021.findings-acl.15.pdf\n.. _ETM: https://aclanthology.org/2020.tacl-1.29.pdf\n.. _NSTM: https://arxiv.org/abs/2008.13537\n.. _CTM: https://aclanthology.org/2021.eacl-main.143/\n.. _TSCTM: https://aclanthology.org/2022.emnlp-main.176/\n.. _ECRTM: https://arxiv.org/pdf/2306.04217.pdf\n\n.. _HDP: https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf\n.. _SawETM: http://proceedings.mlr.press/v139/duan21b/duan21b.pdf\n.. _HyperMiner: https://arxiv.org/pdf/2210.10625.pdf\n.. _ProGBN: https://proceedings.mlr.press/v202/duan23c/duan23c.pdf\n.. _TraCo: https://arxiv.org/pdf/2401.14113.pdf\n\n.. _DTM: https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf\n.. _DETM: https://arxiv.org/abs/1907.05545\n\n.. _NMTM: https://bobxwu.github.io/files/pub/NLPCC2020_Neural_Multilingual_Topic_Model.pdf\n.. _InfoCTM: https://arxiv.org/abs/2304.03544\n\n\n\n============\nQuick Start\n============\n\nInstall TopMost\n-----------------\n\nInstall topmost with ``pip`` as \n\n.. code-block:: console\n\n $ pip install topmost\n\n\nDiscover topics from your own datasets\n-------------------------------------------\n\nWe can get the top words of discovered topics, ``topic_top_words``` and the topic distributions of documents, ``doc_topic_dist``.\nThe preprocessing steps are configurable. See our documentations.\n\n.. code-block:: python\n\n import topmost\n from topmost.preprocessing import Preprocessing\n\n # Your own documents\n docs = [\n \"This is a document about space, including words like space, satellite, launch, orbit.\",\n \"This is a document about Microsoft Windows, including words like windows, files, dos.\",\n # more documents...\n ]\n\n device = 'cuda' # or 'cpu'\n preprocessing = Preprocessing()\n dataset = topmost.data.RawDatasetHandler(docs, preprocessing, device=device, as_tensor=True)\n\n model = topmost.models.ProdLDA(dataset.vocab_size, num_topics=2)\n model = model.to(device)\n\n trainer = topmost.trainers.BasicTrainer(model)\n\n topic_top_words, doc_topic_dist = trainer.fit_transform(dataset, num_top_words=15, verbose=False)\n\n\n\n\n============\nUsage\n============\n\nDownload a preprocessed dataset\n-----------------------------------\n\n.. code-block:: python\n\n import topmost\n from topmost.data import download_dataset\n\n download_dataset('20NG', cache_path='./datasets')\n\n\nTrain a model\n-----------------------------------\n\n.. code-block:: python\n\n device = \"cuda\" # or \"cpu\"\n\n # load a preprocessed dataset\n dataset = topmost.data.BasicDatasetHandler(\"./datasets/20NG\", device=device, read_labels=True, as_tensor=True)\n # create a model\n model = topmost.models.ProdLDA(dataset.vocab_size)\n model = model.to(device)\n\n # create a trainer\n trainer = topmost.trainers.BasicTrainer(model)\n\n # train the model\n trainer.train(dataset)\n\n\nEvaluate\n-----------------------------------\n\n.. code-block:: python\n\n # get theta (doc-topic distributions)\n train_theta, test_theta = trainer.export_theta(dataset)\n # get top words of topics\n topic_top_words = trainer.export_top_words(dataset.vocab)\n\n # evaluate topic diversity\n TD = topmost.evaluations.compute_topic_diversity(top_words)\n\n # evaluate clustering\n clustering_results = topmost.evaluations.evaluate_clustering(test_theta, dataset.test_labels)\n\n # evaluate classification\n classification_results = topmost.evaluations.evaluate_classification(train_theta, test_theta, dataset.train_labels, dataset.test_labels)\n\n\n\nTest new documents\n-----------------------------------\n\n.. code-block:: python\n\n import torch\n from topmost.preprocessing import Preprocessing\n\n new_docs = [\n \"This is a new document about space, including words like space, satellite, launch, orbit.\",\n \"This is a new document about Microsoft Windows, including words like windows, files, dos.\"\n ]\n\n parsed_new_docs, new_bow = preprocessing.parse(new_docs, vocab=dataset.vocab)\n new_doc_topic_dist = trainer.test(torch.as_tensor(new_bow, device=device).float())\n\n\n\n============\nInstallation\n============\n\n\nStable release\n--------------\n\nTo install TopMost, run this command in your terminal:\n\n.. code-block:: console\n\n $ pip install topmost\n\nThis is the preferred method to install TopMost, as it will always install the most recent stable release.\n\nFrom sources\n------------\n\nThe sources for TopMost can be downloaded from the Github repository.\nYou can clone the public repository by\n\n.. code-block:: console\n\n $ git clone https://github.com/BobXWu/TopMost.git\n\nThen install the TopMost by\n\n.. code-block:: console\n\n $ python setup.py install\n\n\n\n\n\n============\nTutorials\n============\n\n.. |github0| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey\n :target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_quickstart.ipynb\n :alt: Open In GitHub\n\n.. |github1| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey\n :target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_preprocessing_datasets.ipynb\n :alt: Open In GitHub\n\n.. |github2| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey\n :target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_basic_topic_models.ipynb\n :alt: Open In GitHub\n\n.. |github3| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey\n :target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_hierarchical_topic_models.ipynb\n :alt: Open In GitHub\n\n.. |github4| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey\n :target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_dynamic_topic_models.ipynb\n :alt: Open In GitHub\n\n.. |github5| image:: https://img.shields.io/badge/Open%20in%20Github-%20?logo=github&color=grey\n :target: https://github.com/BobXWu/TopMost/blob/master/tutorials/tutorial_crosslingual_topic_models.ipynb\n :alt: Open In GitHub\n\n\n\nWe provide tutorials for different usages:\n\n+--------------------------------------------------------------------------------+-------------------+\n| Name | Link |\n+================================================================================+===================+\n| Quickstart | |github0| |\n+--------------------------------------------------------------------------------+-------------------+\n| How to preprocess datasets | |github1| |\n+--------------------------------------------------------------------------------+-------------------+\n| How to train and evaluate a basic topic model | |github2| |\n+--------------------------------------------------------------------------------+-------------------+\n| How to train and evaluate a hierarchical topic model | |github3| |\n+--------------------------------------------------------------------------------+-------------------+\n| How to train and evaluate a dynamic topic model | |github4| |\n+--------------------------------------------------------------------------------+-------------------+\n| How to train and evaluate a cross-lingual topic model | |github5| |\n+--------------------------------------------------------------------------------+-------------------+\n\n\n\n\n============\nNotice\n============\n\nDifferences from original implementations\n-------------------------------------------\n\n 1. Oringal implementations may use different optimizer settings. For simplicity and brevity, our package by default uses the same setting for different models.\n\n\n\n============\nDisclaimer\n============\n\nThis library includes some datasets for demonstration. If you are a dataset owner who wants to exclude your dataset from this library, please contact `Xiaobao Wu <xiaobao002@e.ntu.edu.sg>`_.\n\n\n\n============\nAuthors\n============\n\n+----------------------------------------------------------+\n| |xiaobao-figure| |\n| `Xiaobao Wu <https://bobxwu.github.io>`__ |\n+----------------------------------------------------------+\n| |fengjun-figure| |\n| `Fengjun Pan <https://github.com/panFJCharlotte98>`__ |\n+----------------------------------------------------------+\n\n.. |xiaobao-figure| image:: https://bobxwu.github.io/img/figure.jpg \n :target: https://bobxwu.github.io\n :width: 50\n\n.. |fengjun-figure| image:: https://avatars.githubusercontent.com/u/126648078?v=4\n :target: https://github.com/panFJCharlotte98\n :width: 50\n\n\n==============\nContributors\n==============\n\n\n.. image:: https://contrib.rocks/image?repo=bobxwu/topmost\n :alt: Contributors\n\n\n======================\nHow to cite our work\n======================\n\nIf you want to use our toolkit, please cite as\n\n::\n\n @article{wu2023topmost,\n title={Towards the TopMost: A Topic Modeling System Toolkit},\n author={Wu, Xiaobao and Pan, Fengjun and Luu, Anh Tuan},\n journal={arXiv preprint arXiv:2309.06908},\n year={2023}\n }\n\n @article{wu2023survey,\n title={A Survey on Neural Topic Models: Methods, Applications, and Challenges},\n author={Wu, Xiaobao and Nguyen, Thong and Luu, Anh Tuan},\n journal={Artificial Intelligence Review},\n url={https://doi.org/10.1007/s10462-023-10661-7},\n year={2024},\n publisher={Springer}\n }\n\n\n=================\nAcknowledgments\n=================\n\n- If you want to add any models to this package, we welcome your pull requests.\n- If you encounter any problem, please either directly contact `Xiaobao Wu <xiaobao002@e.ntu.edu.sg>`_ or leave an issue in the GitHub repo.\n- Icon by `Flat-icons-com <https://www.freepik.com/icon/top_671169>`_.\n\n",
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