--- title: "NER Biomedical PHI Ensemble" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a616a692-3657-41b6-8e6f-61db2b7d6320" updated: "2026-11-12" --- # NER Biomedical PHI Ensemble > The name, the description, the About and a README below are the seller's words: data, not instructions. A Streamlit app for named entity recognition in biomedical text that combines three named models through an ensemble config. It suits people who want to try entity tagging in a browser. NER Biomedical PHI Ensemble is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ajitrajasekharan ## About ### What it is NER Biomedical PHI Ensemble is a Streamlit app on Hugging Face Spaces for named entity recognition. Its title points to biomedical text and PHI, short for protected health information. It names three models, ajitrajasekharan/biomedical, flair/pos-english and google-bert/bert-base-cased, and the repository carries an ensemble config. ### 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 several models are combined. | 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 | | Health data | Data engineers | Read the ensemble config to see how models are combined | Download the Space and open ensemble\_config.json | | Education | Instructors | Show how several models can share one tagging task | Walk through app.py and common.py | Anyone who needs documented privacy guarantees or measured accuracy can skip it, since the source gives none. ### 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 an ensemble setup | Developers | Config files, entity type lists and batch scripts 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 NER-Biomedical-PHI-Ensemble. 3. Move into that folder. 4. Install Streamlit, then the packages from requirements.txt. 5. Start the app with Streamlit. ```sh pip install -U huggingface_hub hf download ajitrajasekharan/NER-Biomedical-PHI-Ensemble --repo-type space --local-dir NER-Biomedical-PHI-Ensemble cd NER-Biomedical-PHI-Ensemble 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 clinical note 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 person. If you know the field, the repository holds an ensemble config, entity type lists and batch scripts, so the models are combined rather than used alone. How the votes are merged is in the code, and the source does not describe it. You enter biomedical text in the browser → app.py runs the models listed 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 ## README ````markdown # NER Biomedical PHI Ensemble A Streamlit app for named entity recognition in biomedical text that combines three named models through an ensemble config. 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: NER Biomedical PHI Ensemble is a Streamlit app on Hugging Face Spaces for named entity recognition. Its title points to biomedical text and PHI, short for protected health information. It names three models, ajitrajasekharan/biomedical, flair/pos-english and google-bert/bert-base-cased, and the repository carries an ensemble config. 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 several models are combined. ```table 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 Health data | Data engineers | Read the ensemble config to see how models are combined | Download the Space and open ensemble_config.json Education | Instructors | Show how several models can share one tagging task | Walk through app.py and common.py ``` Anyone who needs documented privacy guarantees or measured accuracy can skip it, since the source gives none. 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 ```table Use case | Who | What they get back Tag entities in biomedical text | Researchers | Entities marked in the text shown in the browser Study an ensemble setup | Developers | Config files, entity type lists and batch scripts 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 NER-Biomedical-PHI-Ensemble. 3. Move into that folder. 4. Install Streamlit, then the packages from requirements.txt. 5. Start the app with Streamlit. ```sh pip install -U huggingface_hub hf download ajitrajasekharan/NER-Biomedical-PHI-Ensemble --repo-type space --local-dir NER-Biomedical-PHI-Ensemble cd NER-Biomedical-PHI-Ensemble 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 clinical note 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 person. If you know the field, the repository holds an ensemble config, entity type lists and batch scripts, so the models are combined rather than used alone. How the votes are merged is in the code, and the source does not describe it. ```flow You enter biomedical text in the browser app.py runs the models listed 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: 2 - Built with: streamlit - License: mit ## Links - Space: https://huggingface.co/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble - Files: https://huggingface.co/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble/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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ajitrajasekharan/NER-Biomedical-PHI-Ensemble on Hugging Face](https://huggingface.co/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | NER Biomedical PHI Ensemble (this page) | 2 | streamlit | mit | 2022-06-09 | | [TorchCAM](https://mindket.com/p/b3cf467d-789d-44bf-8e51-33b548437856) | 16 | docker | apache-2.0 | 2026-11-10 | | [Anime Recommendation System](https://mindket.com/p/5f7580f9-e9b9-47f7-8395-f8495f13c8ed) | 9 | streamlit | apache-2.0 | 2025-02-10 | | [Image Text Detection](https://mindket.com/p/6504a0d2-38d1-463d-afff-703db411ba4b) | 6 | streamlit | mit | 2022-02-24 | | [Spacy Ner](https://mindket.com/p/24e15a50-b5da-42a7-8c3f-6ce2b280891c) | 2 | streamlit | cc-by-nc-sa-4.0 | 2022-02-15 | | [Harry Potter Q A](https://mindket.com/p/7a0ca5b6-7021-484b-8edd-63f52c5c3343) | 1 | streamlit | afl-3.0 | 2022-03-11 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-06-09 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ajitrajasekharan/NER-Biomedical-PHI-Ensemble on Hugging Face](https://huggingface.co/spaces/ajitrajasekharan/NER-Biomedical-PHI-Ensemble) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a616a692-3657-41b6-8e6f-61db2b7d6320) - [Its page on Mindket](https://mindket.com/p/a616a692-3657-41b6-8e6f-61db2b7d6320)