Ner Demo

Ner Demo

Finds named entities in text with the dslim/bert-base-NER model in a Streamlit page. Aimed at students and developers who want to see entity recognition work on their own sentences.

Made by kingfisher 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: This Streamlit app runs one model, dslim/bert-base-NER, for named entity recognition. That task means picking out names such as people, places and organizations from text. You use it in a browser, or you run a copy on your own computer.

Who it suits: It suits students and developers who want to see entity recognition on a sentence they write, before they build it into a project.

Industry | Who uses it | How they use it | Example
Education | Language processing students | See what an entity tagger picks out of a sentence | Open the Space and type a short sentence
Software | Python developers | Check a model on their own text before using it | Run a copy locally and read app.py
Research | Data analysts | Try a quick tagger on sample documents | Paste a few lines from a document

Skip it if you need to process thousands of files, because this is a one-model demo page.

Where to use it:

• A web browser: go to the Space on Hugging Face and try it with no install
• Windows, macOS or Linux: run a copy with Python

Use case | Who | What they get back
Tag a sentence | Students | The names the model finds in the text
Test a model | Developers | A quick look at how dslim/bert-base-NER behaves on their text
Read the code | Learners | A short app.py that connects one model to a Streamlit page

How to set it up: You need a few things in place first.

• Python with pip, on Windows, macOS or Linux
• An internet connection to fetch the files from Hugging Face

1. Install the Hugging Face hub package.
2. Download the Space into a local folder.
3. Move into that folder.
4. Install Streamlit and the packages listed in requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download kingfisher/ner-demo --repo-type space --local-dir ner-demo
cd ner-demo
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Online, use the Streamlit page the Space shows. On your own computer, start the app with the command below and open the page it serves in your browser.

streamlit run app.py

Example: You run streamlit run app.py inside the project folder, open the page and type a sentence that names a person and a city. The page shows the names the model found in it.

For newcomers and experts: If this is new to you, start online. Go to the Space and type one sentence with a name in it before you install anything. It shows what entity recognition means in practice.

If you know the field, note that the Space names one model, dslim/bert-base-NER, and app.py loads it. The source gives no accuracy figures or label list, so check the model card for those.

Open the Space or run streamlit
The Streamlit page loads
You type some text
The NER model tags the entities
The tagged text appears on the page

Good to know: The license tag is cc-by-nc-sa-4, which usually restricts commercial use, so read its terms before you reuse the code.

• License: cc-by-nc-sa-4
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the text you type and the model it names, with nothing else listed

On Hugging Face, 13 November 2026

• Likes: 0
• Built with: streamlit
• License: cc-by-nc-sa-4.0

Links

• Space: https://huggingface.co/spaces/kingfisher/ner-demo
• Files: https://huggingface.co/spaces/kingfisher/ner-demo/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.