HF BERTopic

HF BERTopic

Finds topics in a collection of texts with BERTopic in a Streamlit page, for researchers and analysts who want to try topic modeling without writing code.

Made by Hellisotherpeople on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: HF BERTopic is a Streamlit app that, by its name, applies BERTopic to text. BERTopic is a topic modeling method that groups similar documents. The record names the DebateSum dataset and says the Space runs on basic CPU hardware at Hugging Face.

Who it suits: It suits researchers, analysts and students who want to see what topics sit inside a pile of text. It also suits developers who want a small Streamlit example of topic modeling. The record gives no usage notes, so expect to learn it by clicking.

Industry | Who uses it | How they use it | Example
Research | Text analysts | Group a text collection into topics | Open the Space and look at the topics it finds
Education | Students of language data | Learn what topic modeling returns | Compare the topics with what the texts say
Software | Python developers | Read a small topic modeling app | Open app.py and follow how the page is built

You can skip it if you need a tested pipeline for production data.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• Python 3.12 on Windows, macOS or Linux, from a downloaded copy

Use case | Who | What they get back
Explore topics in texts | Analysts | Groups of related documents with topic labels
Teach topic modeling | Teachers and students | A hands-on page to click through
Reuse the app as a base | Developers | A small Streamlit app to adapt

How to set it up:

• Nothing at all if you use the online Space
• Python 3.12 on your computer, only if you run a copy
• The packages listed in requirements.txt

1. Open the Space's page in a browser to use it with nothing to install.
2. To run a copy, install the Hugging Face Hub package and download the Space.
3. Move into the new folder, then install Streamlit and the listed requirements.
4. Start the app with Streamlit.

pip install -U huggingface_hub
hf download Hellisotherpeople/HF-BERTopic --repo-type space --local-dir HF-BERTopic
cd HF-BERTopic
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file with Streamlit from the Space folder, then use the page it serves. The record does not list the controls, so look at the page.

streamlit run app.py

Example: You open the Space and let it work on the dataset it names, or on text you supply if the page allows that. The app returns the topics it finds in the texts. The record shows no sample output, so check the page.

For newcomers and experts: Topic modeling sorts many texts into groups that share a subject, without you naming the groups first. Start by opening the Space and reading the topics it lists for the sample data.

If you know the field, BERTopic builds on sentence embeddings and clustering, so results depend on the embedding model and the settings. The record gives no parameters or evaluation figures, so judge the topics against the texts yourself.

You load texts or use the sample data
The app turns the texts into vectors
Similar texts are grouped
The page shows the topics

Good to know:

• License: MIT, as listed on the Space
• Cost to run: none named
• Data it reaches: the text you provide and the DebateSum dataset it names
• Not for: the record names no exclusions, and it gives no accuracy figures

On Hugging Face, 13 November 2026

• Likes: 24
• Built with: streamlit
• License: mit

Links

• Space: https://huggingface.co/spaces/Hellisotherpeople/HF-BERTopic
• Files: https://huggingface.co/spaces/Hellisotherpeople/HF-BERTopic/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.