Update README.md

README.md

@@ -4,9 +4,49 @@ language:
 - en
 base_model:
 - intfloat/e5-base-v2
+pipeline_tag: sentence-similarity
 ---
 
 ## Introduction
 
 This is the Agentic-R trained in our paper: Agentic-R: Learning to Retrieve for Agentic Search
  ([📝arXiv](https://arxiv.org/pdf/2601.11888)). Please refer our [🧩github repository](https://github.com/8421BCD/ReasonRank) for the detailed usage of our Agentic-R.
+
+## Usage (with sentence_transformers)
+
+Our **Agentic-R** query encoder is designed for agentic search scenarios.  
+For queries, the input format is:
+`query: <original_question> [SEP] <agent_query>`.
+Passages use the standard `passage:` prefix following E5.
+
+Below is an example of how to compute embeddings using `sentence_transformers`:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("liuwenhan/Agentic-R_e5")
+
+input_texts = [
+    # Query encoder input:
+    # original_question [SEP] current_query
+    "query: Who wrote The Old Man and the Sea? [SEP] Old Man and the Sea",
+
+    # Passages
+    "passage: The Old Man and the Sea is a short novel written by the American author Ernest Hemingway in 1951.",
+    "passage: Ernest Hemingway was an American novelist, short-story writer, and journalist, born in 1899."
+]
+
+embeddings = model.encode(
+    input_texts,
+    normalize_embeddings=True
+)
+```
+Notes:
+
+`original_question` refers to the user’s initial question.
+
+`agent_query` refers to the intermediate query generated during the agent’s reasoning process.
+
+Always include `[SEP]` to separate the two parts of the query.
+
+We recommend setting `normalize_embeddings=True` for cosine similarity–based retrieval.