ExpertLongBench

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.

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.

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.

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

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.