--- title: MTEB-BR - Brazilian Portuguese (PT-BR) emoji: 🏆 colorFrom: green colorTo: yellow sdk: static pinned: false license: apache-2.0 --- # MTEB-BR - Brazilian Portuguese (PT-BR) A public benchmark for evaluating text embedding models on **Brazilian Portuguese**, built as a thin extension on top of the [`mteb`](https://github.com/embeddings-benchmark/mteb) library. ## What you'll find here - 🏆 **[Leaderboard](https://huggingface.co/spaces/MTEB-BR/leaderboard)** — interactive ranking, 93 models × 22 tasks, Pareto chart - 📊 **[`mteb-pt-results`](https://huggingface.co/datasets/MTEB-BR/mteb-pt-results)** — all per-task JSONs + per-query parquets, ~1100 files - 💻 **[GitHub repo](https://github.com/tardellirs/mteb-br)** — task definitions, evaluation scripts, paper sources, issue templates - 📄 **[Paper](https://arxiv.org/abs/2607.04581)** on arXiv (MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese) ## Submit a model We accept submissions via either channel — pick whichever fits: - 💬 [HF Discussion on the results dataset](https://huggingface.co/datasets/MTEB-BR/mteb-pt-results/discussions/new) - 🐛 [GitHub Issue with the model template](https://github.com/tardellirs/mteb-br/issues/new?template=submit-model.yml) Required for a submission: 1. `model_id` (HF repo path or vendor product name) 2. Per-task result JSONs for the 16 headline tasks 3. Reproducible evaluation command We re-run a sample of each submission to verify before merging. ## Propose a new task Open a [GitHub Issue with the task template](https://github.com/tardellirs/mteb-br/issues/new?template=propose-task.yml) describing the dataset, license, size, and discrimination evidence. A task is accepted if it's native PT-BR (not machine-translated), has clear licensing, and discriminates between models. ## Maintainer **Tardelli Stekel** — IFSP, São Paulo, Brazil ✉️ Contributions, corrections, and discussion all welcome. ## Citation ```bibtex @misc{mteb-br-2026, title = {MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese}, author = {Stekel, Tardelli R. C.}, year = {2026}, eprint = {2607.04581}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, doi = {10.48550/arXiv.2607.04581}, url = {https://arxiv.org/abs/2607.04581} } ``` ## Acknowledgments Built on top of the [`mteb`](https://github.com/embeddings-benchmark/mteb) library by Enevoldsen et al. (2025). Task datasets contributed by their original authors. Compute provided by Modal.