Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
| Date | Stars |
|---|---|
| 2026-07-24 | 7754 |
| 2026-07-25 | 7756 |
| 2026-07-28 | 7756 |
| 2026-07-30 | 7756 |
| 2026-08-06 | 7756 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
[](https://pepy.tech/projects/bertopic)
[](https://pypi.org/project/bertopic/)
[](https://github.com/MaartenGr/BERTopic/actions)
[](https://maartengr.github.io/BERTopic/)
[](https://pypi.org/project/bertopic/)
[](https://github.com/MaartenGr/VLAC/blob/master/LICENSE)
[](https://arxiv.org/abs/2203.05794)
# BERTopic
<img src="images/logo.png" width="35%" align="right" />
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters
allowing for easily interpretable topics whilst keeping important words in the topic descriptions.
BERTopic supports all kinds of topic modeling techniques:
<table>
<tr>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/guided/guided.html">Guided</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html">Supervised</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/semisupervised/semisupervised.html">Semi-supervised</a></td>
</tr>
<tr>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/manual/manual.html">Manual</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/distribution/distribution.html">Multi-topic distributions</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html">Hierarchical</a></td>
</tr>
<tr>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/topicsperclass/topicsperclass.html">Class-based</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html">Dynamic</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/online/online.html">Online/Incremental</a></td>
</tr>
<tr>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/multimodal/multimodal.html">Multimodal</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/multiaspect/multiaspect.html">Multi-aspect</a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/representation/llm.html">Text Generation/LLM</a></td>
</tr>
<tr>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/zeroshot/zeroshot.html">Zero-shot <b>(new!)</b></a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/merge/merge.html">Merge Models <b>(new!)</b></a></td>
<td><a href="https://maartengr.github.io/BERTopic/getting_started/seed_words/seed_words.html">Seed Words <b>(new!)</b></a></td>
</tr>
</table>
Corresponding medium posts can be found [here](https://medium.com/data-science/topic-modeling-with-bert-779f7db187e6?sk=0b5a470c006d1842ad4c8a3057063a99
), [here](https://medium.com/data-science/using-whisper-and-bertopic-to-model-kurzgesagts-videos-7d8a63139bdf?sk=b1e0fd46f70cb15e8422b4794a81161d
) and [here](https://medium.com/data-science/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4
). For a more detailed overview, you can read the [paper](https://arxiv.org/abs/2203.05794) or see a [brief overview](https://maartengr.github.io/BERTopic/algorithm/algorithm.html).
## Installation
Installation, with sentence-transformers, can be done using [uv](https://docs.astral.sh/uv/):
```bash
uv add bertopic
```
or with [pip](https://github.com/pypa/pip):
```bash
pip install bertopic
```
If you want to install BERTopic with other embedding models, you can choose one of the following:
Excerpt of 19,503 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7097321df7c87d27, topic:sentence-embeddings
matched fp:7097321df7c87d27, topic:nlp
