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teringp2p`,abbrev:`WikiCat`,category:`Clustering`,a_t:.777,b_t:-.436},{task:`medptclustering`,abbrev:`MedPTCl`,category:`Clustering`,a_t:.674,b_t:-1.017},{task:`stackoverflowptclustering`,abbrev:`StackO`,category:`Clustering`,a_t:.64,b_t:.355},{task:`portulexrrip`,abbrev:`PortuLex`,category:`Classification`,a_t:.47,b_t:.934},{task:`factckbrclassification`,abbrev:`Factck`,category:`Classification`,a_t:.427,b_t:.027}]}},ve=q(`<div class=mini-chart><svg viewBox="0 0 380 260"preserveAspectRatio="xMidYMid meet"class="pareto mini"><rect class=plot-bg></rect><text x=190 y=18 class=mini-title text-anchor=middle> (n=<!>)</text><rect class=axes-box></rect><polyline class=frontier style="stroke-dasharray:5 3;opacity:0.9">`),ye=q(`<svg><g><line class=grid-major></line><text class=axis-label-mini text-anchor=end></svg>`,!1,!0,!1),be=q(`<svg><g><line class=grid-major></line><text class=axis-label-mini text-anchor=middle></svg>`,!1,!0,!1),xe=q(`<svg><g class=point-group><circle class=point-dot></svg>`,!1,!0,!1),Se=q(`<svg><text class=point-label-mini></svg>`,!1,!0,!1),Ce=q(`<section class=table-section><h2>IRT ranking</h2><p class=intro style=margin-top:0>Models ranked by their <b>Item Response Theory latent ability</b> (θ). Where <b>mean_22</b> weights every task equally, IRT weights each task by how sharply it separates models (its <b>discrimination</b>, a<sub>t</sub>), so high-discrimination tasks like retrieval count more and saturated ones like clustering count less. The two rankings largely agree; the <b>Δ</b> column shows where IRT places a model above (+) or below (−) its mean_22 rank. The per-task score columns are ordered by discrimination, most discriminative first (see the a<sub>t</sub> table below). Fit on the <!>-model full-coverage panel.</p><div class=table-wrap><table><thead><tr><th class=sticky>IRT #</th><th class="sticky model-col">Model</th><th class=metric>θ</th><th class=metric>mean_22</th><th>mean #</th><th>Δ</th></tr></thead><tbody></tbody></table></div><h2 style=margin-top:2.2rem>Task discrimination (a<sub>t</sub>)</h2><p class=intro style=margin-top:0>How sharply each task separates models, from the same 2-PL fit, and the order of the score columns above. Retrieval tasks discriminate most; the two clustering tasks least, which is what lets IRT and the simple mean rank models differently.</p><div style=display:flex;gap:1.6rem;flex-wrap:wrap;align-items:flex-start><div class=table-wrap style="flex:1 1 360px;max-width:560px;margin:0"><table><thead><tr><th class=sticky>Task</th><th>Category</th><th class=metric>a<sub>t</sub></th><th></th></tr></thead><tbody></tbody></table></div><div style="flex:1 1 320px;min-width:300px;border:1px solid rgba(127,127,127,0.25);border-radius:8px;padding:0.9rem 1.2rem;background:rgba(127,127,127,0.05)"><h3 style=margin-top:0.2rem;margin-bottom:0.5rem>The 2-PL model</h3><p style=margin-top:0;font-size:0.9rem;line-height:1.5>IRT treats each task as an <b>item</b> and each model as an <b>examinee</b>. 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Models are ranked by the fitted θ<sub>m</sub>.`),we=q(`<section class=categories-section><h2>Pareto frontier per task category</h2><p class=categories-help>Each panel shows the size–quality tradeoff for one task category. The Pareto frontier (no model is both smaller and better) is highlighted; top-3 frontier models are labeled.<b>Click</b> any dot to open the model on Hugging Face.</p><div class=mini-grid>`),Te=q(`<section class=table-section><h2>Full leaderboard</h2><div class=controls><input type=text placeholder="Search model…"class=search><select><option value=all>All models</option><option value=open>Open weights</option><option value=closed>Closed / API</option></select><span class=meta-bar>Showing <!> / </span></div><div class="table-wrap lb-main"><table><thead><tr><th class=sticky>#</th><th class="sticky model-col">Model</th><th class=col-params>Params</th><th class=license>License</th><th class=metric>mean_22</th></tr></thead><tbody>`),Ee=q(`<section class=chart-section><div class=chart-wrap><svg viewBox="0 0 1100 620"class=pareto preserveAspectRatio="xMidYMid meet"><rect class=plot-bg></rect><text x=550 y=26 class=chart-title text-anchor=middle>Open Pareto frontier</text><rect class=axes-box></rect><text y=606 class=axis-title text-anchor=middle>Parameters (log scale)</text><text x=16 class=axis-title text-anchor=middle>MTEB-BR (mean)</text><polyline class=frontier></polyline><g><rect x=0 y=0 width=130 height=22 class=legend-box></rect><line x1=8 y1=11 x2=32 y2=11 class=frontier></line><text x=38 y=15 class=legend-text>Pareto frontier`),De=q(`<div class=app><header><h1>MTEB-BR Brazilian Portuguese Leaderboard</h1><p class=subtitle>Massive Text Embedding Benchmark for Brazilian Portuguese · v1.0 · <!> models · 22 tasks · </p><p class=links><a href=https://github.com/tardellirs/mteb-br target=_blank>GitHub</a> ·<a href=https://huggingface.co/MTEB-BR target=_blank>HF Org</a></p><p class=intro>The <b>MTEB-BR Brazilian Portuguese Leaderboard</b> tracks how text embedding models perform on Brazilian Portuguese. We evaluate <!> models, both open-weights and commercial APIs, across <b>22 tasks built from native PT-BR sources</b> (no machine-translated benchmarks), spanning classification, semantic similarity, clustering, retrieval, and reranking. The headline metric <b>mean_22</b> is the average across all 22 tasks; per-task scores are sortable in the table below. All raw results and evaluation scripts are public, so anyone can reproduce a row, submit a new model, or audit a score.</p></header><nav class=tabs><a class=tab href=#/>Leaderboard</a><a class=tab href=#/irt>IRT Ranking</a><a class=tab href=#/categories>By Category</a></nav><section class=about><h2>Submit a model</h2><p>We accept submissions via either channel; pick whichever fits:</p><div class=submit-cta><a class="cta-btn primary"href=https://huggingface.co/spaces/MTEB-BR/leaderboard/discussions/new target=_blank>💬 Open HF Discussion</a><a class=cta-btn href="https://github.com/tardellirs/mteb-br/issues/new?template=submit-model.yml"target=_blank>🐛 GitHub Issue</a></div><p class=about-text>Required: <b>model_id</b>, per-task JSONs for the 16 headline tasks, and a reproducible evaluation command. We re-run a sample of each submission to verify before merging into the leaderboard. To propose a new task, use the <a href="https://github.com/tardellirs/mteb-br/issues/new?template=propose-task.yml"target=_blank>task proposal template</a>.</p></section><footer><p><b>Maintainer:</b> Tardelli Stekel · IFSP, São Paulo, Brazil · <a href=mailto:stekel@ifsp.edu.br>stekel@ifsp.edu.br</a><br>Per-task metrics: nDCG@10 for retrieval, accuracy for classification/NLI, Spearman for STS, V-measure for clustering. 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