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https://github.com/x-tabdeveloping/turftopic

Robust and fast topic models with sentence-transformers.
https://github.com/x-tabdeveloping/turftopic

contextual llm topic-modeling transformers

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Robust and fast topic models with sentence-transformers.

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README 

        







 Topic modeling is your turf too. 
  Contextual topic models with representations from transformers. 



## Features

 - Implementations of transformer-based topic models:

   - Semantic Signal Separation - S³ 🧭

   - KeyNMF 🔑 

   - GMM :gem:

   - Clustering Topic Models: BERTopic and Top2Vec

   - Autoencoding Topic Models: CombinedTM and ZeroShotTM

   - FASTopic

 - Dynamic, Online and Hierarchical Topic Modeling 

 - Streamlined scikit-learn compatible API 🛠️

 - Easy topic interpretation 🔍

 - Automated topic naming with LLMs

 - Visualization with [topicwizard](https://github.com/x-tabdeveloping/topicwizard) 🖌️



> This package is still work in progress and scientific papers on some of the novel methods are currently undergoing peer-review. If you use this package and you encounter any problem, let us know by opening relevant issues.



### New in version 0.8.0



#### Automated Topic Naming



Turftopic now allows you to automatically assign human readable names to topics using LLMs or n-gram retrieval!



```python

from turftopic import KeyNMF

from turftopic.namers import OpenAITopicNamer



model = KeyNMF(10).fit(corpus)



namer = OpenAITopicNamer("gpt-4o-mini")

model.rename_topics(namer)

model.print_topics()

```



| Topic ID | Topic Name | Highest Ranking |

| - | - | - |

| 0 | Operating Systems and Software  | windows, dos, os, ms, microsoft, unix, nt, memory, program, apps |

| 1 | Atheism and Belief Systems | atheism, atheist, atheists, belief, religion, religious, theists, beliefs, believe, faith |

| 2 | Computer Architecture and Performance | motherboard, ram, memory, cpu, bios, isa, speed, 486, bus, performance |

| 3 | Storage Technologies | disk, drive, scsi, drives, disks, floppy, ide, dos, controller, boot |

| | ... |



## Basics [(Documentation)](https://x-tabdeveloping.github.io/turftopic/)

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/x-tabdeveloping/turftopic/blob/main/examples/basic_example_20newsgroups.ipynb)



### Installation



Turftopic can be installed from PyPI.



```bash

pip install turftopic

```



If you intend to use CTMs, make sure to install the package with Pyro as an optional dependency.



```bash

pip install turftopic[pyro-ppl]

```



### Fitting a Model



Turftopic's models follow the scikit-learn API conventions, and as such they are quite easy to use if you are familiar with

scikit-learn workflows.



Here's an example of how you use KeyNMF, one of our models on the 20Newsgroups dataset from scikit-learn.



```python

from sklearn.datasets import fetch_20newsgroups



newsgroups = fetch_20newsgroups(

    subset="all",

    remove=("headers", "footers", "quotes"),

)

corpus = newsgroups.data

```



Turftopic also comes with interpretation tools that make it easy to display and understand your results.



```python

from turftopic import KeyNMF



model = KeyNMF(20).fit(corpus)

```



### Interpreting Models



Turftopic comes with a number of pretty printing utilities for interpreting the models.



To see the highest the most important words for each topic, use the `print_topics()` method.



```python

model.print_topics()

```



| Topic ID | Top 10 Words                                                                                    |

| -------- | ----------------------------------------------------------------------------------------------- |

|        0 | armenians, armenian, armenia, turks, turkish, genocide, azerbaijan, soviet, turkey, azerbaijani |

|        1 | sale, price, shipping, offer, sell, prices, interested, 00, games, selling                      |

|        2 | christians, christian, bible, christianity, church, god, scripture, faith, jesus, sin           |

|        3 | encryption, chip, clipper, nsa, security, secure, privacy, encrypted, crypto, cryptography      |

|         | ....                                |



```python

# Print highest ranking documents for topic 0

model.print_representative_documents(0, corpus, document_topic_matrix)

```



| Document                                                                                             | Score |

| -----------------------------------------------------------------------------------------------------| ----- |

| Poor 'Poly'. I see you're preparing the groundwork for yet another retreat from your...              |  0.40 |

| Then you must be living in an alternate universe. Where were they? An Appeal to Mankind During the... |  0.40 |

| It is 'Serdar', 'kocaoglan'. Just love it. Well, it could be your head wasn't screwed on just right... |  0.39 |



```python

model.print_topic_distribution(

    "I think guns should definitely banned from all public institutions, such as schools."

)

```



| Topic name                                | Score |

| ----------------------------------------- | ----- |

| 7_gun_guns_firearms_weapons               |  0.05 |

| 17_mail_address_email_send                |  0.00 |

| 3_encryption_chip_clipper_nsa             |  0.00 |

| 19_baseball_pitching_pitcher_hitter       |  0.00 |

| 11_graphics_software_program_3d           |  0.00 |



### Visualization



Turftopic does not come with built-in visualization utilities, [topicwizard](https://github.com/x-tabdeveloping/topicwizard), an interactive topic model visualization library, is compatible with all models from Turftopic.



```bash

pip install topic-wizard

```



By far the easiest way to visualize your models for interpretation is to launch the topicwizard web app.



```python

import topicwizard



topicwizard.visualize(corpus, model=model)

```



  

  Screenshot of the topicwizard Web Application



Alternatively you can use the [Figures API](https://x-tabdeveloping.github.io/topicwizard/figures.html) in topicwizard for individual HTML figures.



## References

- Kardos, M., Kostkan, J., Vermillet, A., Nielbo, K., Enevoldsen, K., & Rocca, R. (2024, June 13). $S^3$ - Semantic Signal separation. arXiv.org. https://arxiv.org/abs/2406.09556

- Wu, X., Nguyen, T., Zhang, D. C., Wang, W. Y., & Luu, A. T. (2024). FASTopic: A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm. ArXiv Preprint ArXiv:2405.17978.

 - Grootendorst, M. (2022, March 11). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv.org. https://arxiv.org/abs/2203.05794

 - Angelov, D. (2020, August 19). Top2VEC: Distributed representations of topics. arXiv.org. https://arxiv.org/abs/2008.09470

 - Bianchi, F., Terragni, S., & Hovy, D. (2020, April 8). Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence. arXiv.org. https://arxiv.org/abs/2004.03974

 - Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. 1676–1683). Association for Computational Linguistics.

 - Kristensen-McLachlan, R. D., Hicke, R. M. M., Kardos, M., & Thunø, M. (2024, October 16). Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media. arXiv.org. https://arxiv.org/abs/2410.12791