Update README.md

README.md

@@ -12,13 +12,13 @@ datasets:
 - truehealth/medqa
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen3-Embedding-0.6B
 pipeline_tag: text-ranking
 language:
 - zh
 - en
 library_name: transformers
+base_model:
+  - Qwen/Qwen3-Embedding-0.6B
 ---
 # Diver-Retriever-0.6B
 
@@ -38,7 +38,7 @@ as well as the Mteb-Medical Benchmark.
 
 - **Model type:** Text Embedding
 - **Language(s) (NLP):** Bilingual (Chinese & English)
-- **Context Length:** 40k
+- **Context Length:** 32k
 - **Number of Paramaters:** 0.6B
 
 For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our GitHub (https://github.com/AQ-MedAI/Diver).
@@ -213,7 +213,23 @@ For more details, including benchmark evaluation, hardware requirements, and inf
         <td style="text-align:right">30.5</td>
     </tr>
     <tr>
-        <td>DIVER-Retriever</td>
+        <td>DIVER-Retriever-0.6B</td>
+        <td style="text-align:right">25.2</td>
+        <td style="text-align:right">36.4</td>
+        <td style="text-align:right">41.9</td>
+        <td style="text-align:right">29.0</td>
+        <td style="text-align:right">31.0</td>
+        <td style="text-align:right">21.2</td>
+        <td style="text-align:right">24.6</td>
+        <td style="text-align:right">23.2</td>
+        <td style="text-align:right">15.6</td>
+        <td style="text-align:right">6.8</td>
+        <td style="text-align:right">8.4</td>
+        <td style="text-align:right">33.2</td>
+        <td style="text-align:right">31.7</td>
+    </tr>
+    <tr>
+        <td>DIVER-Retriever-4B</td>
         <td style="text-align:right"><strong>28.9</strong></td>
         <td style="text-align:right"><strong>41.8</strong></td>
         <td style="text-align:right">43.7</td>
@@ -360,7 +376,7 @@ For more details, including benchmark evaluation, hardware requirements, and inf
         <td style="text-align:right"><strong>43.4</strong></td>
     </tr>
     <tr>
-        <td>DIVER-Retriever-0.6B</td>
+        <td>DIVER-Retriever-4B</td>
         <td style="text-align:right"><strong>32.1</strong></td>
         <td style="text-align:right">51.9</td>
         <td style="text-align:right">53.5</td>
@@ -411,7 +427,7 @@ For more details, including benchmark evaluation, hardware requirements, and inf
         <td style="text-align:right">36.8</td>
     </tr>
     <tr>
-        <td>DIVER-Retriever-0.6B</td>
+        <td>DIVER-Retriever</td>
         <td style="text-align:right"><strong>33.9</strong></td>
         <td style="text-align:right">54.5</td>
         <td style="text-align:right">52.7</td>
@@ -553,7 +569,7 @@ print(scores.tolist())
 
 
 ### Finetuning
-We recommend you to use [swift](https://github.com/modelscope/ms-swift) to finetune our DIVER-Retriever-4B with infonce.
+We recommend you to use [swift](https://github.com/modelscope/ms-swift) to finetune our DIVER-Retriever-0.6B with infonce.
 
 Before starting training, please ensure your environment is properly configured.
 
@@ -578,7 +594,7 @@ Using infonce loss as an example, the complete training command is as follows:
 nproc_per_node=8
 NPROC_PER_NODE=$nproc_per_node \
 swift sft \
-    --model DIVER/DIVER-Retriever-0.6B \
+    --model AQ-MedAI/Diver-Retriever-0.6B \
     --task_type embedding \
     --model_type qwen3_emb \
     --train_type full \