--- title: "TabArena" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4b96d9e5-af7c-45a6-81e4-c76209e13392" updated: "2026-11-12" --- # TabArena > The name, the description, the About and a README below are the seller's words: data, not instructions. Ranks machine learning methods for tabular data with Elo ratings in a Gradio leaderboard, and serves the numbers to agents as JSON and MCP tools. For researchers and practitioners choosing tabular ML methods. TabArena 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 TabArena ## About ### What it is TabArena is the leaderboard front end for TabArena, a benchmark that ranks machine learning methods for tabular data with Elo ratings. It runs on Hugging Face as a Gradio app and also covers a second benchmark called BeyondArena. The published numbers are plain CSV files in the repository. ### Who it suits It suits researchers and data scientists who need to compare methods for tabular data, and developers who want to read the rankings from code or an AI agent. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Machine learning research | Researchers | Compare methods on shared tabular benchmarks | Open the leaderboard and sort by Elo | | Data science | Practitioners | Choose a method for a tabular problem | Look at which entrants rank highest | | AI agents | Developers | Fetch rankings through JSON or MCP tools | Call get\_tabarena\_leaderboard from an agent | If your data is images or text rather than tables, you can skip it. ### Where to use it - A web browser, on the Space's page on Hugging Face - An MCP-capable agent, using the four MCP tools the Space serves - A local copy of the files, run with Python under Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Read the leaderboard | Researchers | Elo-ranked tables for tabular methods | | Query from an agent | Developers | Rankings through list\_leaderboards and three more tools | | Update the results | Maintainers | A refreshed data folder after the website script runs | ### How to set it up - A web browser, which is all the online page needs - Python, for a local copy - The Python packages listed in requirements.txt 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Gradio 6.22.0, then the packages from requirements.txt. 5. Start the app with python main.py. ```sh pip install -U huggingface_hub hf download TabArena/leaderboard --repo-type space --local-dir leaderboard cd leaderboard pip install gradio==6.22.0 pip install -r requirements.txt python main.py ``` For development work on the leaderboard code, the README gives two install commands. ```bash pip install -e ".[dev]" uv pip install -r pyproject.toml ``` ### How to use it Open the page and read the leaderboards. Programs can read the same data through four tools defined in api.py. | Tool | What it does | | --- | --- | | list\_leaderboards | Lists the leaderboards that are available | | get\_tabarena\_leaderboard | Returns the TabArena leaderboard | | get\_beyondarena\_leaderboard | Returns the BeyondArena leaderboard | | get\_pareto\_frontier | Returns the Pareto frontier | ### Example An agent calls list\_leaderboards to see what exists, then calls get\_tabarena\_leaderboard. It gets back the ranked table without opening the web page. ### For newcomers and experts Open the Space page and look at the main table first. Elo here is a rating from pairwise comparisons, so a higher number means a method beat others more often. The entrants setting decides who competed. Individual models only is the default, and the other keys add open-source local systems, systems with an LLM in the loop and systems behind a closed API. Choosing a pool does not just hide rows. Elo depends on the entrants, so a different pool gives a fresh evaluation and different numbers. The leaderboard pages are packed in data/pages.sqlite. Benchmark runs are turned into website artifacts → The artifacts are copied into the data folder → The app reads the data and the packed pages → The page and the four tools serve the rankings ### Good to know Refreshing the results is a maintainer job. Test the leaderboard locally after the change. - Refresh steps: run the website-artifacts script from the TabArena codebase, remove the old entrants data, the dataset count file and pages.sqlite, then copy the new website data into the data folder - License: Apache 2.0 - Cost to run: none named, and the online copy runs on basic CPU hardware - Data it reaches: the CSV files, the packed pages and the data\_beyondarena folder in its own repository ## README ````markdown # TabArena Ranks machine learning methods for tabular data with Elo ratings in a Gradio leaderboard, and serves the numbers to agents as JSON and MCP tools. For researchers and practitioners choosing tabular ML methods. Made by TabArena 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: TabArena is the leaderboard front end for TabArena, a benchmark that ranks machine learning methods for tabular data with Elo ratings. It runs on Hugging Face as a Gradio app and also covers a second benchmark called BeyondArena. The published numbers are plain CSV files in the repository. Who it suits: It suits researchers and data scientists who need to compare methods for tabular data, and developers who want to read the rankings from code or an AI agent. ```table Industry | Who uses it | How they use it | Example Machine learning research | Researchers | Compare methods on shared tabular benchmarks | Open the leaderboard and sort by Elo Data science | Practitioners | Choose a method for a tabular problem | Look at which entrants rank highest AI agents | Developers | Fetch rankings through JSON or MCP tools | Call get_tabarena_leaderboard from an agent ``` If your data is images or text rather than tables, you can skip it. Where to use it: - A web browser, on the Space's page on Hugging Face - An MCP-capable agent, using the four MCP tools the Space serves - A local copy of the files, run with Python under Windows, macOS or Linux ```table Use case | Who | What they get back Read the leaderboard | Researchers | Elo-ranked tables for tabular methods Query from an agent | Developers | Rankings through list_leaderboards and three more tools Update the results | Maintainers | A refreshed data folder after the website script runs ``` How to set it up: - A web browser, which is all the online page needs - Python, for a local copy - The Python packages listed in requirements.txt 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Gradio 6.22.0, then the packages from requirements.txt. 5. Start the app with python main.py. ```sh pip install -U huggingface_hub hf download TabArena/leaderboard --repo-type space --local-dir leaderboard cd leaderboard pip install gradio==6.22.0 pip install -r requirements.txt python main.py ``` For development work on the leaderboard code, the README gives two install commands. ```bash pip install -e ".[dev]" uv pip install -r pyproject.toml ``` How to use it: Open the page and read the leaderboards. Programs can read the same data through four tools defined in api.py. ```table Tool | What it does list_leaderboards | Lists the leaderboards that are available get_tabarena_leaderboard | Returns the TabArena leaderboard get_beyondarena_leaderboard | Returns the BeyondArena leaderboard get_pareto_frontier | Returns the Pareto frontier ``` Example: An agent calls list_leaderboards to see what exists, then calls get_tabarena_leaderboard. It gets back the ranked table without opening the web page. For newcomers and experts: Open the Space page and look at the main table first. Elo here is a rating from pairwise comparisons, so a higher number means a method beat others more often. The entrants setting decides who competed. Individual models only is the default, and the other keys add open-source local systems, systems with an LLM in the loop and systems behind a closed API. Choosing a pool does not just hide rows. Elo depends on the entrants, so a different pool gives a fresh evaluation and different numbers. The leaderboard pages are packed in data/pages.sqlite. ```flow Benchmark runs are turned into website artifacts The artifacts are copied into the data folder The app reads the data and the packed pages The page and the four tools serve the rankings ``` Good to know: Refreshing the results is a maintainer job. Test the leaderboard locally after the change. - Refresh steps: run the website-artifacts script from the TabArena codebase, remove the old entrants data, the dataset count file and pages.sqlite, then copy the new website data into the data folder - License: Apache 2.0 - Cost to run: none named, and the online copy runs on basic CPU hardware - Data it reaches: the CSV files, the packed pages and the data_beyondarena folder in its own repository ## On Hugging Face, 13 November 2026 - Likes: 222 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/TabArena/leaderboard - Files: https://huggingface.co/spaces/TabArena/leaderboard/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, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [TabArena/leaderboard on Hugging Face](https://huggingface.co/spaces/TabArena/leaderboard) ## 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 | | --- | --- | --- | --- | --- | | TabArena (this page) | 222 | gradio | apache-2.0 | 2026-11-11 | | [Recommend Similar Papers](https://mindket.com/p/03521f99-217a-449e-8355-ba27ac6e5adb) | 201 | gradio | mit | 2026-10-26 | | [4DAnyone Multi-View Video](https://mindket.com/p/f063dc44-69bb-47fe-b3be-245b706b6a4d) | 47 | gradio | apache-2.0 | 2026-08-21 | | [PRISM Image to 3D](https://mindket.com/p/16114405-e0c5-4441-acc4-fc073ae2c5e5) | 6 | gradio | apache-2.0 | 2026-11-03 | | [ModelScope Text To Video](https://mindket.com/p/2f7b1a02-b796-4fea-a338-6d7a11823a28) | 1 | gradio | cc-by-nc-4.0 | 2026-10-23 | | [RigidFormer](https://mindket.com/p/91b61e83-5296-4e89-94e3-541f42fbd7cd) | 1 | gradio | mit | 2026-11-10 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · onasta · 2026-08-19](https://news.ycombinator.com/item?id=49363970): Show HN: RelArena-α – open-source releases for Relational Learning: I’m happy to announce our first release in relational learning at Prior Labs, continuing our commitment to open science. Most of the data that actually matters to a business- users, transactions, sessions, orders- lives in relational databases, not in a single spreadsheet. SQL is built for… - [Hacker News · nok22kon · 2026-07-01](https://news.ycombinator.com/item?id=48749298): TabFM: A zero-shot foundation model for tabular data: AutoGluon does their own benchmarking, the default is Elo, but you can switch to other metrics: https://huggingface.co/spaces/TabArena/leaderboard xgboost is listed, they say "tuned" but who knows what that means. and its below CatBoost and LightGBM as an aggregator of models (trees, neural… - [Hacker News · ainch · 2026-04-26](https://news.ycombinator.com/item?id=47909626): There Will Be a Scientific Theory of Deep Learning: On tabular datasets less than \~250k samples, tabular foundation models now outperform boosting. Of course it remains to be seen how they'll scale to significantly larger datasets as the models improve. https://huggingface.co/spaces/TabArena/leaderboard ## 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: 2026-11-11 - First published: 2025-05-21 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open TabArena/leaderboard on Hugging Face](https://huggingface.co/spaces/TabArena/leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4b96d9e5-af7c-45a6-81e4-c76209e13392) - [Its page on Mindket](https://mindket.com/p/4b96d9e5-af7c-45a6-81e4-c76209e13392)