Retrained with fp32 master weights: 0.8908 mean nDCG@10 on the nine Korean tasks (was 0.8800)

README.md

@@ -25,9 +25,9 @@ tags:
 
 ## Key Characteristics
 
-- **Strong Korean reranking:** 88.00 average nDCG@10 across nine Korean benchmarks, the highest average among the evaluated models with fewer than 4B parameters and within 1.2 points of Qwen3-Reranker-4B.
-- **Long-document reranking:** 81.63 nDCG@10 on MultiLongDocRetrieval (MLDR), ranking second only to Qwen3-Reranker-8B (82.55) among the evaluated models.
-- **Efficient:** 55.0 pairs per second averaged over nine benchmarks on an NVIDIA RTX A6000 48GB, about 2.3× Qwen3-Reranker-4B and 3.7× Qwen3-Reranker-8B under the measurement conditions described below.
+- **Strong Korean reranking:** 89.08 average nDCG@10 across nine Korean benchmarks, the highest average among the evaluated models with fewer than 4B parameters and within 0.1 points of Qwen3-Reranker-4B.
+- **Long-document reranking:** 81.89 nDCG@10 on MultiLongDocRetrieval (MLDR), ranking second only to Qwen3-Reranker-8B (82.55) among the evaluated models.
+- **Efficient:** 56.2 pairs per second averaged over nine benchmarks on an NVIDIA RTX A6000 48GB, about 2.3× Qwen3-Reranker-4B and 3.7× Qwen3-Reranker-8B under the measurement conditions described below.
 
 ## Model Overview
 
@@ -152,7 +152,7 @@ The figure plots mean nDCG@10 against mean PPS across the nine Korean benchmarks
         <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-8B</td><td>8.2B</td><td>0.8990</td><td>15.0</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-4B</td><td>4.0B</td><td>0.8916</td><td>24.3</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">KaLM-Embedding/KaLM-Reranker-V1-Large-R2</td><td>7.5B</td><td>0.8910</td><td>25.3</td></tr>
-        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">0.8800</span></td><td><span style="color: #c9496b;">55.0</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">0.8908</span></td><td><span style="color: #c9496b;">56.2</span></td></tr>
         <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-nano</strong></td><td><span style="color: #c9496b;">149M</span></td><td><span style="color: #c9496b;">0.8757</span></td><td><span style="color: #c9496b;">482.4</span></td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">zeroentropy/zerank-2-reranker</td><td>4.0B</td><td>0.8652</td><td>29.3</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">mixedbread-ai/mxbai-rerank-large-v2</td><td>1.5B</td><td>0.8607</td><td>64.8</td></tr>
@@ -200,7 +200,7 @@ PPS is measured on a single NVIDIA RTX A6000 48GB per model and averaged over th
         <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-8B</td><td>8.2B</td><td>0.8720</td><td>0.9653</td><td>0.8654</td><td>0.9785</td><td>0.8513</td><td>0.8416</td><td>0.8255</td><td>0.9897</td><td>0.9013</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-4B</td><td>4.0B</td><td>0.8733</td><td>0.9630</td><td>0.8442</td><td>0.9794</td><td>0.8559</td><td>0.8298</td><td>0.8161</td><td>0.9861</td><td>0.8766</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">KaLM-Embedding/KaLM-Reranker-V1-Large-R2</td><td>7.5B</td><td>0.8796</td><td>0.9415</td><td>0.8633</td><td>0.9809</td><td>0.8382</td><td>0.8500</td><td>0.7970</td><td>0.9863</td><td>0.8824</td></tr>
-        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">0.8548</span></td><td><span style="color: #c9496b;">0.9762</span></td><td><span style="color: #c9496b;">0.8381</span></td><td><span style="color: #c9496b;">0.9767</span></td><td><span style="color: #c9496b;">0.8371</span></td><td><span style="color: #c9496b;">0.7970</span></td><td><span style="color: #c9496b;">0.8163</span></td><td><span style="color: #c9496b;">0.9891</span></td><td><span style="color: #c9496b;">0.8343</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">0.8605</span></td><td><span style="color: #c9496b;">0.9870</span></td><td><span style="color: #c9496b;">0.8331</span></td><td><span style="color: #c9496b;">0.9757</span></td><td><span style="color: #c9496b;">0.8496</span></td><td><span style="color: #c9496b;">0.8252</span></td><td><span style="color: #c9496b;">0.8189</span></td><td><span style="color: #c9496b;">0.9907</span></td><td><span style="color: #c9496b;">0.8768</span></td></tr>
         <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-nano</strong></td><td><span style="color: #c9496b;">149M</span></td><td><span style="color: #c9496b;">0.8582</span></td><td><span style="color: #c9496b;">0.9741</span></td><td><span style="color: #c9496b;">0.8078</span></td><td><span style="color: #c9496b;">0.9768</span></td><td><span style="color: #c9496b;">0.8400</span></td><td><span style="color: #c9496b;">0.8011</span></td><td><span style="color: #c9496b;">0.7966</span></td><td><span style="color: #c9496b;">0.9895</span></td><td><span style="color: #c9496b;">0.8368</span></td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3.5</td><td>597M</td><td>0.8539</td><td>0.9838</td><td>0.8337</td><td>0.9659</td><td>0.8565</td><td>0.8194</td><td>—</td><td>0.9887</td><td>0.8545</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3</td><td>597M</td><td>0.8553</td><td>0.9773</td><td>0.7762</td><td>0.9606</td><td>0.8449</td><td>0.8104</td><td>—</td><td>0.9859</td><td>0.8546</td></tr>
@@ -248,7 +248,7 @@ PPS is measured on a single NVIDIA RTX A6000 48GB per model and averaged over th
         <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-8B</td><td>8.2B</td><td>16.5</td><td>7.4</td><td>15.3</td><td>20.5</td><td>25.0</td><td>26.6</td><td>0.7</td><td>10.8</td><td>12.4</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">Qwen/Qwen3-Reranker-4B</td><td>4.0B</td><td>26.2</td><td>11.8</td><td>25.6</td><td>34.5</td><td>40.1</td><td>42.8</td><td>1.1</td><td>17.1</td><td>19.9</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">KaLM-Embedding/KaLM-Reranker-V1-Large-R2</td><td>7.5B</td><td>28.6</td><td>12.4</td><td>25.4</td><td>36.9</td><td>39.6</td><td>43.8</td><td>0.8</td><td>18.7</td><td>21.4</td></tr>
-        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">59.2</span></td><td><span style="color: #c9496b;">27.3</span></td><td><span style="color: #c9496b;">58.0</span></td><td><span style="color: #c9496b;">77.0</span></td><td><span style="color: #c9496b;">89.1</span></td><td><span style="color: #c9496b;">97.1</span></td><td><span style="color: #c9496b;">2.6</span></td><td><span style="color: #c9496b;">39.1</span></td><td><span style="color: #c9496b;">45.7</span></td></tr>
+        <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-base</strong></td><td><span style="color: #c9496b;">1.7B</span></td><td><span style="color: #c9496b;">60.9</span></td><td><span style="color: #c9496b;">27.9</span></td><td><span style="color: #c9496b;">58.5</span></td><td><span style="color: #c9496b;">77.9</span></td><td><span style="color: #c9496b;">91.7</span></td><td><span style="color: #c9496b;">99.7</span></td><td><span style="color: #c9496b;">2.5</span></td><td><span style="color: #c9496b;">39.9</span></td><td><span style="color: #c9496b;">46.2</span></td></tr>
         <tr><td style="text-align: left; white-space: nowrap;"><strong style="color: #c9496b;">nlpai-lab/KURE-Reranker-nano</strong></td><td><span style="color: #c9496b;">149M</span></td><td><span style="color: #c9496b;">475.4</span></td><td><span style="color: #c9496b;">233.2</span></td><td><span style="color: #c9496b;">529.0</span></td><td><span style="color: #c9496b;">750.0</span></td><td><span style="color: #c9496b;">765.8</span></td><td><span style="color: #c9496b;">855.2</span></td><td><span style="color: #c9496b;">16.5</span></td><td><span style="color: #c9496b;">330.8</span></td><td><span style="color: #c9496b;">386.0</span></td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3.5</td><td>597M</td><td>141.0</td><td>38.6</td><td>123.2</td><td>197.5</td><td>277.3</td><td>295.7</td><td>—</td><td>68.4</td><td>86.7</td></tr>
         <tr><td style="text-align: left; white-space: nowrap;">jinaai/jina-reranker-v3</td><td>597M</td><td>113.8</td><td>25.0</td><td>95.7</td><td>166.3</td><td>244.6</td><td>261.6</td><td>—</td><td>48.7</td><td>64.0</td></tr>

model.safetensors

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