Self Supervised Ner Biomedical

Self Supervised Ner Biomedical

A Streamlit app for named entity recognition in biomedical text, with an ensemble config and batch scripts 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 Biomedical is a Streamlit app on Hugging Face Spaces for named entity recognition in biomedical text. It names two models, ajitrajasekharan/biomedical and flair/pos-english. The repository holds batch scripts, an ensemble config and entity type lists, besides app.py.

Who it suits: It suits people who work with biomedical text and want to try entity tagging in a browser, and developers who want to read how a larger tagging project is organised.

Industry | Who uses it | How they use it | Example
Healthcare research | Text-mining researchers | Try entity tagging on biomedical sentences | Open the Space and enter a sentence
Life sciences | Data scientists | Read the config and batch scripts to see how tagging runs | Download the Space and open ensemble_config.json
Education | Instructors | Show named entity recognition on domain text | Run the demo in a lesson

Anyone who needs documented accuracy or a list of the 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 entities in biomedical text | Researchers | 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-biomedical.
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-biomedical --repo-type space --local-dir self-supervised-ner-biomedical
cd self-supervised-ner-biomedical
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 biomedical 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 from a medical abstract into the text box. 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 drug or a disease.

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 biomedical 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 two models and needs Streamlit and the packages in requirements.txt
• License: mit
• Cost to run: none named
• Data it reaches: the two models it names and the files in its own repository

On Hugging Face, 13 November 2026

• Likes: 1
• Built with: streamlit
• License: mit

Links

• Space: https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-biomedical
• Files: https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-biomedical/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.