--- title: "Ner Demo" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e1e9415d-15ff-4c27-9fcb-89997f8f6378" updated: "2026-11-13" --- # Ner Demo > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Ner Demo is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by kingfisher ## 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. ```sh 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [kingfisher/ner-demo on Hugging Face](https://huggingface.co/spaces/kingfisher/ner-demo) ## 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 Demo (this page) | 0 | streamlit | cc-by-nc-sa-4.0 | 2022-02-15 | | [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: 2022-02-15 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open kingfisher/ner-demo on Hugging Face](https://huggingface.co/spaces/kingfisher/ner-demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e1e9415d-15ff-4c27-9fcb-89997f8f6378) - [Its page on Mindket](https://mindket.com/p/e1e9415d-15ff-4c27-9fcb-89997f8f6378)