--- title: "ExpertLongBench" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1370be60-1578-4986-8e97-6e5b79c5cce7" updated: "2026-11-12" --- # ExpertLongBench > The name, the description, the About and a README below are the seller's words: data, not instructions. A Streamlit leaderboard page for ExpertLongBench, packaged in Docker with the dataset of the same name. The README has little text beyond the leaderboard title and the Streamlit starter note. ExpertLongBench 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 launch ## About ### What it is This Space is a leaderboard for ExpertLongBench, built with Streamlit and packaged in Docker. It names a dataset called launch/ExpertLongBench. The README gives the leaderboard title and the standard Streamlit starter note, so the benchmark's tasks and scoring are not described here. ### Who it suits It suits researchers who follow the ExpertLongBench benchmark and developers who want to see how a Streamlit leaderboard is packaged in a container. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Benchmark followers | Open the leaderboard to see the listed results | Viewing the hosted page | | Machine learning | Developers | Read how a leaderboard app is built in a container | Reading the Dockerfile and the src folder | | Software | Docker users | Build and run a local copy | Running the container on port 8501 | Skip it if you need the benchmark's method or rules, because the source does not explain them. ### Where to use it You can open it online or run a container on your own computer. - A web browser, on the Space's page at Hugging Face - Docker on a Windows, macOS or Linux computer | Use case | Who | What they get back | | --- | --- | --- | | View the leaderboard | Anyone | The running page in a browser tab | | Run a local copy | Developers | The same page served from a container | | Read the code | Students | A Dockerfile, a package list and a src folder | ### How to set it up Online use needs nothing, and it runs on basic CPU hardware at Hugging Face. A local copy needs Python, pip and Docker. - Python and pip, to fetch the files - Docker, to build and run the image 1. Install the Hugging Face hub package. 2. Download the Space into a folder and move into it. 3. Build the Docker image. 4. Run the container and publish port 8501. ```sh pip install -U huggingface_hub hf download launch/ExpertLongBench --repo-type space --local-dir ExpertLongBench cd ExpertLongBench docker build -t expertlongbench . docker run -it -p 8501:8501 expertlongbench ``` ### How to use it After the container starts, open port 8501 on your own computer in a browser and look through the leaderboard the page shows. The source lists no other commands or options. ```sh docker run -it -p 8501:8501 expertlongbench ``` ### Example You could run the container and open the page to see the leaderboard. The README gives no sample entries or scores, so what the table holds is not documented. ### For newcomers and experts If you are new, open the hosted Space first and read the leaderboard before you install Docker. A local run only pays off if you want to read or change the code. If you know the field, the app is Streamlit inside a Dockerfile build, listening on port 8501, with the benchmark data coming from the named dataset. The README still shows the Streamlit starter text, so how results are loaded and updated is not documented. Download the Space → Build the Docker image → Run the container on 8501 → Open the leaderboard ### Good to know The README mixes a leaderboard title with leftover Streamlit starter text. - License: cc-by-nc-4 - Cost to run: none named - Data it reaches: it names the launch/ExpertLongBench dataset, and the source mentions no account - Not for: the source states no limits, and it does not explain the benchmark itself ## README ````markdown # ExpertLongBench A Streamlit leaderboard page for ExpertLongBench, packaged in Docker with the dataset of the same name. The README has little text beyond the leaderboard title and the Streamlit starter note. Made by launch 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 leaderboard for ExpertLongBench, built with Streamlit and packaged in Docker. It names a dataset called launch/ExpertLongBench. The README gives the leaderboard title and the standard Streamlit starter note, so the benchmark's tasks and scoring are not described here. Who it suits: It suits researchers who follow the ExpertLongBench benchmark and developers who want to see how a Streamlit leaderboard is packaged in a container. ```table Industry | Who uses it | How they use it | Example Research | Benchmark followers | Open the leaderboard to see the listed results | Viewing the hosted page Machine learning | Developers | Read how a leaderboard app is built in a container | Reading the Dockerfile and the src folder Software | Docker users | Build and run a local copy | Running the container on port 8501 ``` Skip it if you need the benchmark's method or rules, because the source does not explain them. Where to use it: You can open it online or run a container on your own computer. - A web browser, on the Space's page at Hugging Face - Docker on a Windows, macOS or Linux computer ```table Use case | Who | What they get back View the leaderboard | Anyone | The running page in a browser tab Run a local copy | Developers | The same page served from a container Read the code | Students | A Dockerfile, a package list and a src folder ``` How to set it up: Online use needs nothing, and it runs on basic CPU hardware at Hugging Face. A local copy needs Python, pip and Docker. - Python and pip, to fetch the files - Docker, to build and run the image 1. Install the Hugging Face hub package. 2. Download the Space into a folder and move into it. 3. Build the Docker image. 4. Run the container and publish port 8501. ```sh pip install -U huggingface_hub hf download launch/ExpertLongBench --repo-type space --local-dir ExpertLongBench cd ExpertLongBench docker build -t expertlongbench . docker run -it -p 8501:8501 expertlongbench ``` How to use it: After the container starts, open port 8501 on your own computer in a browser and look through the leaderboard the page shows. The source lists no other commands or options. ```sh docker run -it -p 8501:8501 expertlongbench ``` Example: You could run the container and open the page to see the leaderboard. The README gives no sample entries or scores, so what the table holds is not documented. For newcomers and experts: If you are new, open the hosted Space first and read the leaderboard before you install Docker. A local run only pays off if you want to read or change the code. If you know the field, the app is Streamlit inside a Dockerfile build, listening on port 8501, with the benchmark data coming from the named dataset. The README still shows the Streamlit starter text, so how results are loaded and updated is not documented. ```flow Download the Space Build the Docker image Run the container on 8501 Open the leaderboard ``` Good to know: The README mixes a leaderboard title with leftover Streamlit starter text. - License: cc-by-nc-4 - Cost to run: none named - Data it reaches: it names the launch/ExpertLongBench dataset, and the source mentions no account - Not for: the source states no limits, and it does not explain the benchmark itself ## On Hugging Face, 13 November 2026 - Likes: 4 - Built with: docker - License: cc-by-nc-4.0 ## Links - Space: https://huggingface.co/spaces/launch/ExpertLongBench - Files: https://huggingface.co/spaces/launch/ExpertLongBench/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 [launch/ExpertLongBench on Hugging Face](https://huggingface.co/spaces/launch/ExpertLongBench) ## 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 | | --- | --- | --- | --- | --- | | ExpertLongBench (this page) | 4 | docker | cc-by-nc-4.0 | 2025-09-28 | | [fev-bench](https://mindket.com/p/aed1efeb-102a-49fd-b48b-0e7131fb1d85) | 72 | docker | apache-2.0 | 2026-11-09 | | [AI Phone Leaderboard](https://mindket.com/p/c50d4420-a218-400f-917a-9fa39c2d8007) | 127 | streamlit | mit | 2026-02-04 | | [Outsmart](https://mindket.com/p/bc535e3d-8da3-4481-bb74-8405891098d4) | 62 | streamlit | mit | 2026-10-21 | | [HF BERTopic](https://mindket.com/p/6ea227ee-d2c5-4685-886d-6070ad67ac1f) | 24 | streamlit | mit | 2026-08-02 | | [HF SHAP](https://mindket.com/p/aa7ec1ee-7a27-4fe5-9191-d22a0af5d958) | 8 | streamlit | mit | 2026-08-02 | 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: 2025-09-28 - First published: 2025-05-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open launch/ExpertLongBench on Hugging Face](https://huggingface.co/spaces/launch/ExpertLongBench) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1370be60-1578-4986-8e97-6e5b79c5cce7) - [Its page on Mindket](https://mindket.com/p/1370be60-1578-4986-8e97-6e5b79c5cce7)