--- title: "Spacy Ner" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/24e15a50-b5da-42a7-8c3f-6ce2b280891c" updated: "2026-11-13" --- # Spacy Ner > The name, the description, the About and a README below are the seller's words: data, not instructions. Opens a Streamlit app whose title points to named entity recognition with spaCy, for students and developers who want to see names picked out of text. The listing gives few other details. Spacy Ner 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 is a small Streamlit app from kingfisher with a single main file. Its title points to named entity recognition with spaCy, which means picking out names of people, places and organizations from text. The source gives no other details and names no model. 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, and read how a short Streamlit app is built. | 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 tagger 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 small 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 app finds in the text | | Test on your text | Developers | A quick look at how the tagger behaves on their own writing | | Read the code | Learners | A short app.py behind 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/spacy-ner --repo-type space --local-dir spacy-ner cd spacy-ner 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 app 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 repository holds only a README, app.py and requirements.txt. The source names no model, label set or accuracy figure, so read app.py to see which spaCy pipeline it loads. Open the Space or run streamlit → The Streamlit page loads → You type some text → The app 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, with no outside account or web service listed ## README ````markdown # Spacy Ner Opens a Streamlit app whose title points to named entity recognition with spaCy, for students and developers who want to see names picked out of text. The listing gives few other details. 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 is a small Streamlit app from kingfisher with a single main file. Its title points to named entity recognition with spaCy, which means picking out names of people, places and organizations from text. The source gives no other details and names no model. 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, and read how a short Streamlit app is built. ```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 tagger 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 small 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 app finds in the text Test on your text | Developers | A quick look at how the tagger behaves on their own writing Read the code | Learners | A short app.py behind 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/spacy-ner --repo-type space --local-dir spacy-ner cd spacy-ner 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 app 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 repository holds only a README, app.py and requirements.txt. The source names no model, label set or accuracy figure, so read app.py to see which spaCy pipeline it loads. ```flow Open the Space or run streamlit The Streamlit page loads You type some text The app 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, with no outside account or web service listed ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: streamlit - License: cc-by-nc-sa-4.0 ## Links - Space: https://huggingface.co/spaces/kingfisher/spacy-ner - Files: https://huggingface.co/spaces/kingfisher/spacy-ner/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/spacy-ner on Hugging Face](https://huggingface.co/spaces/kingfisher/spacy-ner) ## 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 | | --- | --- | --- | --- | --- | | Spacy Ner (this page) | 2 | 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 | | [NER Biomedical PHI Ensemble](https://mindket.com/p/a616a692-3657-41b6-8e6f-61db2b7d6320) | 2 | streamlit | mit | 2022-06-09 | | [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: 2022-02-15 - 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/spacy-ner on Hugging Face](https://huggingface.co/spaces/kingfisher/spacy-ner) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/24e15a50-b5da-42a7-8c3f-6ce2b280891c) - [Its page on Mindket](https://mindket.com/p/24e15a50-b5da-42a7-8c3f-6ce2b280891c)