--- title: "MTEB-BR Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/05f89589-d44c-4616-8efb-c50c9ba6ad94" updated: "2026-11-12" --- # MTEB-BR Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. Ranks text embedding models on native Brazilian Portuguese tasks, with 93 models scored on 22 tasks. A static leaderboard page for researchers and engineers choosing a model for Portuguese search. MTEB-BR Leaderboard 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 MTEB-BR ## About ### What it is This is a static web page that ranks text embedding models on Brazilian Portuguese. It holds results for 93 models, 73 with open weights and 20 closed commercial APIs, scored on 22 tasks in 7 categories. Only data written or found in Portuguese is admitted, so machine-translated test sets are left out. ### Who it suits Researchers who compare embedding models, and engineers who need to pick one for search over Portuguese text. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Machine learning | NLP researchers | Compare embedding models on native Portuguese tasks | Check which model leads on the headline score | | Software | Search and RAG engineers | Choose an embedding model for Portuguese documents | Shortlist open-weight models before testing | | Domain search | Teams with Portuguese documents | Look at tasks that match their field, such as MedPT or BRTaxQA | Read the retrieval results for their domain | | Academia | Model authors | Propose a new model for evaluation through the project's issue tracker | Send a model ID with its evaluation files | Skip it if you work only in English or need results on translated test sets. ### Where to use it - A web browser, on the Space's page at Hugging Face - A local folder, after downloading the files and opening index.html in a browser - The project's GitHub issues, for proposing a new model | Use case | Who | What they get back | | --- | --- | --- | | Choose an embedding model | ML engineers | A ranked view of 93 models on Portuguese tasks | | Check one model's strengths | Researchers | Scores across task categories such as retrieval or clustering | | Propose a model | Model authors | A route to get a model evaluated | ### How to set it up - Nothing at all for the online page - A web browser - Python and pip, only if you want to download a copy with the Hugging Face tools 1. Open the Space's page at Hugging Face to use it online. 2. For a copy, install the Hugging Face hub package. 3. Download the Space into a folder called leaderboard. 4. Move into that folder. 5. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download MTEB-BR/leaderboard --repo-type space --local-dir leaderboard cd leaderboard ``` The page is plain HTML with a style sheet and image assets, so nothing has to be built or started. ### How to use it Open the page and read the scores. The headline figure is mean\_22, the average over all 22 tasks. It is reported alongside bootstrap intervals, significance tests between models, and a measure of how well each task tells models apart. To add a model, open an issue in the project's GitHub repository with the model ID, its evaluation JSON files and a script that reproduces the run. ### Example You are building search over Portuguese documents and need an embedding model. You open the leaderboard, look at the headline mean\_22 score and the retrieval tasks, and shortlist a few open-weight models to test on your own data. ### For newcomers and experts An embedding model turns text into numbers so that similar texts land close together, which is what search tools rely on. If you are new, open the page and start with the headline score to see how the models order. If you know the field, the design choice is native data only. Machine-translated sets such as mMARCO and mkqa are excluded by construction. Models are compared with bootstrap intervals and paired significance tests, and IRT discrimination shows which tasks separate them. Models run on 22 native tasks → Each task gets a score → mean\_22 averages the 22 scores → Intervals and tests add context → The leaderboard lists the models ### Good to know - License: apache-2 - Cost to run: none named - Data it reaches: it names a results dataset on Hugging Face called MTEB-BR/mteb-pt-results - Not for: translated or non-Portuguese benchmarks, since only native Brazilian Portuguese data is admitted Models evaluated by type | Label | Value | | --- | --- | | Open-weight models | 73 models | | Closed commercial APIs | 20 models | ## README ````markdown # MTEB-BR Leaderboard Ranks text embedding models on native Brazilian Portuguese tasks, with 93 models scored on 22 tasks. A static leaderboard page for researchers and engineers choosing a model for Portuguese search. Made by MTEB-BR 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 static web page that ranks text embedding models on Brazilian Portuguese. It holds results for 93 models, 73 with open weights and 20 closed commercial APIs, scored on 22 tasks in 7 categories. Only data written or found in Portuguese is admitted, so machine-translated test sets are left out. Who it suits: Researchers who compare embedding models, and engineers who need to pick one for search over Portuguese text. ```table Industry | Who uses it | How they use it | Example Machine learning | NLP researchers | Compare embedding models on native Portuguese tasks | Check which model leads on the headline score Software | Search and RAG engineers | Choose an embedding model for Portuguese documents | Shortlist open-weight models before testing Domain search | Teams with Portuguese documents | Look at tasks that match their field, such as MedPT or BRTaxQA | Read the retrieval results for their domain Academia | Model authors | Propose a new model for evaluation through the project's issue tracker | Send a model ID with its evaluation files ``` Skip it if you work only in English or need results on translated test sets. Where to use it: - A web browser, on the Space's page at Hugging Face - A local folder, after downloading the files and opening index.html in a browser - The project's GitHub issues, for proposing a new model ```table Use case | Who | What they get back Choose an embedding model | ML engineers | A ranked view of 93 models on Portuguese tasks Check one model's strengths | Researchers | Scores across task categories such as retrieval or clustering Propose a model | Model authors | A route to get a model evaluated ``` How to set it up: - Nothing at all for the online page - A web browser - Python and pip, only if you want to download a copy with the Hugging Face tools 1. Open the Space's page at Hugging Face to use it online. 2. For a copy, install the Hugging Face hub package. 3. Download the Space into a folder called leaderboard. 4. Move into that folder. 5. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download MTEB-BR/leaderboard --repo-type space --local-dir leaderboard cd leaderboard ``` The page is plain HTML with a style sheet and image assets, so nothing has to be built or started. How to use it: Open the page and read the scores. The headline figure is mean_22, the average over all 22 tasks. It is reported alongside bootstrap intervals, significance tests between models, and a measure of how well each task tells models apart. To add a model, open an issue in the project's GitHub repository with the model ID, its evaluation JSON files and a script that reproduces the run. Example: You are building search over Portuguese documents and need an embedding model. You open the leaderboard, look at the headline mean_22 score and the retrieval tasks, and shortlist a few open-weight models to test on your own data. For newcomers and experts: An embedding model turns text into numbers so that similar texts land close together, which is what search tools rely on. If you are new, open the page and start with the headline score to see how the models order. If you know the field, the design choice is native data only. Machine-translated sets such as mMARCO and mkqa are excluded by construction. Models are compared with bootstrap intervals and paired significance tests, and IRT discrimination shows which tasks separate them. ```flow Models run on 22 native tasks Each task gets a score mean_22 averages the 22 scores Intervals and tests add context The leaderboard lists the models ``` Good to know: - License: apache-2 - Cost to run: none named - Data it reaches: it names a results dataset on Hugging Face called MTEB-BR/mteb-pt-results - Not for: translated or non-Portuguese benchmarks, since only native Brazilian Portuguese data is admitted ```chart Title: Models evaluated by type Open-weight models: 73 models Closed commercial APIs: 20 models ``` ## On Hugging Face, 13 November 2026 - Likes: 126 - Built with: static - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/MTEB-BR/leaderboard - Files: https://huggingface.co/spaces/MTEB-BR/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 [MTEB-BR/leaderboard on Hugging Face](https://huggingface.co/spaces/MTEB-BR/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 | | --- | --- | --- | --- | --- | | MTEB-BR Leaderboard (this page) | 126 | static | apache-2.0 | 2026-07-29 | | [DialFork Leaderboard](https://mindket.com/p/ce936e47-6d26-4d6f-a1b7-9c21abf49629) | 2 | static | mit | 2026-11-05 | | [Outsmart](https://mindket.com/p/bc535e3d-8da3-4481-bb74-8405891098d4) | 62 | streamlit | mit | 2026-10-21 | | [KITScenes LongTail Challenges](https://mindket.com/p/81d4b5b7-b26b-4f1e-80d6-c5e1f14b3030) | 15 | gradio | mit | 2026-11-08 | | [Arena Leaderboard](https://mindket.com/p/613ca8f0-8146-4a48-87d6-43b8ee2ce3b6) | 5,039 | static | apache-2.0 | 2026-02-21 | | [Model Pulse](https://mindket.com/p/017106fd-a3d1-4c20-944b-38f048d8b624) | 214 | static | apache-2.0 | 2026-11-04 | 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: 2026-07-29 - First published: 2026-06-07 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open MTEB-BR/leaderboard on Hugging Face](https://huggingface.co/spaces/MTEB-BR/leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/05f89589-d44c-4616-8efb-c50c9ba6ad94) - [Its page on Mindket](https://mindket.com/p/05f89589-d44c-4616-8efb-c50c9ba6ad94)