--- title: "Kirundi News Topic Classifier" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dbf5d9e7-0f25-45c6-801f-46a565ece175" updated: "2026-11-12" --- # Kirundi News Topic Classifier > The name, the description, the About and a README below are the seller's words: data, not instructions. Sort a Kirundi news article into one of 10 topics with a small model that runs inside your browser. A static web demo you open online or from a local copy. Kirundi News Topic Classifier is a kit in the Research 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 ReneNtabana ## About ### What it is This Space is a static web page that sorts Kirundi news articles into 10 topics. The model is a fine-tuned AfroXLMR-base in int8 ONNX form, and it runs inside your browser through transformers.js. Because the model runs locally in the page, the text you paste is processed on your own computer. ### Who it suits It suits people who work with Kirundi text, including journalists, researchers and students of African language processing. Developers can read how a small classifier is run in a browser. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Journalism | Newsroom staff | Paste an article and read its predicted topic | Sorting a pile of Kirundi stories by subject | | Research | Language researchers | Try the model on real articles | Seeing how it handles different news topics | | Education | Students | Open the page and use the example texts | Learning how a text classifier behaves | | AI development | Developers | Read index.html and normalise.js | Reusing the browser setup for another model | Skip it if your text is not in Kirundi or you need topics beyond the model's ten. ### Where to use it The page runs in these places. - A web browser, on the Space's page at Hugging Face - A web browser on Windows, macOS or Linux, from a local copy of the files | Use case | Who | What they get back | | --- | --- | --- | | Classify one article | Journalists | A topic label from the model | | Try the example texts | Students | Sample results to learn from | | Study the browser setup | Developers | A small static site with a normalisation script | ### How to set it up Online you only need a browser. A local copy needs these. - Python and pip, for the hf download command - A web browser 1. Install the huggingface\_hub package. 2. Download the Space's files into a folder with the hf command. 3. Move into that folder. 4. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download ReneNtabana/kirundi-news-classifier --repo-type space --local-dir kirundi-news-classifier cd kirundi-news-classifier ``` ### How to use it Open the Space's page, or index.html in your local copy. Paste or choose a Kirundi news text and run the classifier. The page shows which of the ten topics the model picks. ### Example You open the page, pick one of the example texts or paste a Kirundi news article, and run it. The page then shows the topic the model assigns to that article. ### For newcomers and experts If you are new, open the Space's page and start with one of the bundled example texts. That shows you what the output looks like before you paste your own article. If you know the field, the demo runs an int8 ONNX export of AfroXLMR-base fine-tuned for 10-way topic classification, using transformers.js, and a normalise.js file prepares the text. The trade-off is a client-side model, which avoids a server but needs a one-time download of the weights. Open the page in a browser → Paste or pick a Kirundi article → normalise.js cleans the text → The ONNX model labels its topic ### Good to know The repository has six files in all, with index.html, normalise.js, style.css and an examples.json file at the top. The README points to a separate code repository for experiments and the report. - License: MIT - Cost to run: none named - Data it reaches: the model files that load into your browser. The source does not say anything else. - Not for: languages other than Kirundi, since it is built for Kirundi news ## README ````markdown # Kirundi News Topic Classifier Sort a Kirundi news article into one of 10 topics with a small model that runs inside your browser. A static web demo you open online or from a local copy. Made by ReneNtabana 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 Space is a static web page that sorts Kirundi news articles into 10 topics. The model is a fine-tuned AfroXLMR-base in int8 ONNX form, and it runs inside your browser through transformers.js. Because the model runs locally in the page, the text you paste is processed on your own computer. Who it suits: It suits people who work with Kirundi text, including journalists, researchers and students of African language processing. Developers can read how a small classifier is run in a browser. ```table Industry | Who uses it | How they use it | Example Journalism | Newsroom staff | Paste an article and read its predicted topic | Sorting a pile of Kirundi stories by subject Research | Language researchers | Try the model on real articles | Seeing how it handles different news topics Education | Students | Open the page and use the example texts | Learning how a text classifier behaves AI development | Developers | Read index.html and normalise.js | Reusing the browser setup for another model ``` Skip it if your text is not in Kirundi or you need topics beyond the model's ten. Where to use it: The page runs in these places. - A web browser, on the Space's page at Hugging Face - A web browser on Windows, macOS or Linux, from a local copy of the files ```table Use case | Who | What they get back Classify one article | Journalists | A topic label from the model Try the example texts | Students | Sample results to learn from Study the browser setup | Developers | A small static site with a normalisation script ``` How to set it up: Online you only need a browser. A local copy needs these. - Python and pip, for the hf download command - A web browser 1. Install the huggingface_hub package. 2. Download the Space's files into a folder with the hf command. 3. Move into that folder. 4. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download ReneNtabana/kirundi-news-classifier --repo-type space --local-dir kirundi-news-classifier cd kirundi-news-classifier ``` How to use it: Open the Space's page, or index.html in your local copy. Paste or choose a Kirundi news text and run the classifier. The page shows which of the ten topics the model picks. Example: You open the page, pick one of the example texts or paste a Kirundi news article, and run it. The page then shows the topic the model assigns to that article. For newcomers and experts: If you are new, open the Space's page and start with one of the bundled example texts. That shows you what the output looks like before you paste your own article. If you know the field, the demo runs an int8 ONNX export of AfroXLMR-base fine-tuned for 10-way topic classification, using transformers.js, and a normalise.js file prepares the text. The trade-off is a client-side model, which avoids a server but needs a one-time download of the weights. ```flow Open the page in a browser Paste or pick a Kirundi article normalise.js cleans the text The ONNX model labels its topic ``` Good to know: The repository has six files in all, with index.html, normalise.js, style.css and an examples.json file at the top. The README points to a separate code repository for experiments and the report. - License: MIT - Cost to run: none named - Data it reaches: the model files that load into your browser. The source does not say anything else. - Not for: languages other than Kirundi, since it is built for Kirundi news ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: static - License: mit ## Links - Space: https://huggingface.co/spaces/ReneNtabana/kirundi-news-classifier - Files: https://huggingface.co/spaces/ReneNtabana/kirundi-news-classifier/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 [ReneNtabana/kirundi-news-classifier on Hugging Face](https://huggingface.co/spaces/ReneNtabana/kirundi-news-classifier) ## 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 | | --- | --- | --- | --- | --- | | Kirundi News Topic Classifier (this page) | 1 | static | mit | 2026-10-18 | | [Arena Leaderboard](https://mindket.com/p/613ca8f0-8146-4a48-87d6-43b8ee2ce3b6) | 5,039 | static | apache-2.0 | 2026-02-21 | | [Browser](https://mindket.com/p/d4e4cb47-8f53-4094-9a76-4fd29147a65b) | 533 | static | cc0-1.0 | 2026-10-18 | | [Model Pulse](https://mindket.com/p/017106fd-a3d1-4c20-944b-38f048d8b624) | 214 | static | apache-2.0 | 2026-11-04 | | [MTEB-BR Leaderboard](https://mindket.com/p/05f89589-d44c-4616-8efb-c50c9ba6ad94) | 126 | static | apache-2.0 | 2026-07-29 | | [Hub Stats](https://mindket.com/p/03d71953-8093-47a0-a444-c4f562d3071d) | 65 | static | apache-2.0 | 2026-11-12 | Numbers as Mindket last read them, on 2026-11-12. ## 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: 2026-10-18 - First published: 2026-10-17 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ReneNtabana/kirundi-news-classifier on Hugging Face](https://huggingface.co/spaces/ReneNtabana/kirundi-news-classifier) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dbf5d9e7-0f25-45c6-801f-46a565ece175) - [Its page on Mindket](https://mindket.com/p/dbf5d9e7-0f25-45c6-801f-46a565ece175)