Update README.md

README.md

@@ -44,10 +44,35 @@ We instantiate KaLM-Reranker-V1 in three sizes, `Nano`, `Small`, and `Large`, wi
 Extensive experiments on BEIR, MIRACL, and LMEB show that the KaLM-Reranker-V1 series achieves competitive reranking performance compared with strong industrial rerankers while significantly reducing online overhead.
 
 # Model Details
-
 | Models | Activated Params. | Non-Embedding Params. | Embedding Params. | #Layers | Sequence Length | Document Token Dim. | MEP Support | Instruction Aware |
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
 | [KaLM-Reranker-V1-Nano](https://huggingface.co/KaLM-Embedding/KaLM-Reranker-V1-Nano) | 0.27B | 100M | 168M | 18 | 128K | 640 | 1x-32x | Yes |
 | [KaLM-Reranker-V1-Small](https://huggingface.co/KaLM-Embedding/KaLM-Reranker-V1-Small) | 1B | 698M | 302M | 26 | 128K | 1152 | 1x-32x | Yes |
 | [KaLM-Reranker-V1-Large](https://huggingface.co/KaLM-Embedding/KaLM-Reranker-V1-Large) | 4B | 3209M | 675M | 34 | 128K | 2560 | 1x-32x | Yes |
 
+
+# Prompt Template
+```python
+ f"<Document>: {document}"
+
+```
+```python
+(
+    f"<bos><start_of_turn>user\n"
+    f"Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".\n\n"
+    f"<Instruct>: {task_instruction}\n"
+    f"<Query>: {query}<end_of_turn>\n"
+    f"<start_of_turn>model\n\n\n\n"
+)
+
+```
+
+# Evaluation
+## BEIR
+
+
+## MIRACL
+
+
+## LMEB
+