Self Supervised Ner PER ORG LOC

Self Supervised Ner PER ORG LOC

A Streamlit app for tagging people, organizations and locations in text, with an ensemble config in its repository. It suits people who want to try entity tagging in a browser.

Made by ajitrajasekharan 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: Self Supervised Ner PER ORG LOC is a Streamlit app on Hugging Face Spaces for named entity recognition. Its title points to three entity kinds, person, organization and location. It relies on the Flair English part-of-speech model, a legal BERT from nlpaueb and Google's cased BERT base, and the repository holds an ensemble config.

Who it suits: It suits people who want to try tagging of people, organizations and places in a browser, and developers who want to read how a multi-model tagging project is organised.

Industry | Who uses it | How they use it | Example
Media | Analysts | Try tagging the people, companies and places in a passage | Open the Space and enter a news sentence
Legal technology | Developers | Look at how a legal BERT is part of a tagging setup | Download the Space and open ensemble_config.json
Education | Instructors | Show named entity recognition on everyday text | Run the demo in a lesson

Anyone who needs documented accuracy or a full list of entity types can skip it, since the README gives neither.

Where to use it:

• A web browser, straight from the Space page on Hugging Face
• Windows, macOS or Linux with Python, as a local copy

Use case | Who | What they get back
Tag people, organizations and places | Analysts | Entities marked in the text shown in the browser
Study a tagging project | Developers | App, batch scripts, config and entity type files in one repository

How to set it up: Using it online needs nothing. A local copy needs these first.

• Python and pip
• The huggingface_hub package, to fetch the files
• Streamlit, and the packages in requirements.txt

1. Install the Hugging Face Hub package.
2. Download the Space into a folder called self-supervised-ner-PER-ORG-LOC.
3. Move into that folder.
4. Install Streamlit, then the packages from requirements.txt.
5. Start the app with Streamlit.

pip install -U huggingface_hub
hf download ajitrajasekharan/self-supervised-ner-PER-ORG-LOC --repo-type space --local-dir self-supervised-ner-PER-ORG-LOC
cd self-supervised-ner-PER-ORG-LOC
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Open the Space page on Hugging Face, enter some text and read the tagged result. For a local copy, run streamlit run app.py in the folder and open the page it starts.

Example: You paste a sentence that names a person, a company and a city. The app runs its models over it and shows the entities it finds in the sentence.

For newcomers and experts: If you are new, open the Space page and try one short sentence. Named entity recognition means a program marks the names of things in your text, such as a person or a city.

If you know the field, the title says the approach is self supervised, and the repository includes an ensemble config and entity type lists. The source does not explain the method, so read the code for how labels are assigned.

You enter text in the browser
app.py runs the models set in the config
The tagged entities show on the page

Good to know:

• The README gives no description, so the exact output is clear only when you try the app
• It names three models and needs Streamlit and the packages in requirements.txt
• License: mit
• Cost to run: none named
• Data it reaches: the three models it names and the files in its own repository

On Hugging Face, 13 November 2026

• Likes: 0
• Built with: streamlit
• License: mit

Links

• Space: https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-PER-ORG-LOC
• Files: https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-PER-ORG-LOC/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.