--- title: "Self Supervised Ner PER ORG LOC" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c6011a76-83a6-4e32-bb34-79fb29f4ded9" updated: "2026-11-12" --- # Self Supervised Ner PER ORG LOC > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Self Supervised Ner PER ORG LOC 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 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ajitrajasekharan/self-supervised-ner-PER-ORG-LOC on Hugging Face](https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-PER-ORG-LOC) ## 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 | | --- | --- | --- | --- | --- | | Self Supervised Ner PER ORG LOC (this page) | 0 | 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 | | [NER Biomedical PHI Ensemble](https://mindket.com/p/a616a692-3657-41b6-8e6f-61db2b7d6320) | 2 | streamlit | mit | 2022-06-09 | 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/self-supervised-ner-PER-ORG-LOC on Hugging Face](https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-PER-ORG-LOC) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c6011a76-83a6-4e32-bb34-79fb29f4ded9) - [Its page on Mindket](https://mindket.com/p/c6011a76-83a6-4e32-bb34-79fb29f4ded9)