Integrate with Sentence Transformers v5.4.0

1_CausalScoreHead/config.json

@@ -0,0 +1,4 @@
+{
+    "true_token_id": 9693,
+    "false_token_id": 2152
+}

README.md

@@ -3,6 +3,8 @@ license: apache-2.0
 base_model:
 - Qwen/Qwen3-8B-Base
 library_name: transformers
+tags:
+- sentence-transformers
 pipeline_tag: text-ranking
 ---
 # Qwen3-Reranker-8B
@@ -52,13 +54,55 @@ For more details, including benchmark evaluation, hardware requirements, and inf
 
 ## Usage
 
+### Using Sentence Transformers
+
+Install Sentence Transformers:
+```bash
+pip install sentence_transformers
+```
+
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder("Qwen/Qwen3-Reranker-8B")
+
+query = "What is the capital of China?"
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
+]
+
+pairs = [(query, doc) for doc in documents]
+scores = model.predict(pairs)
+print(scores)
+# [  5.0625 -14.25  ]
+
+rankings = model.rank(query, documents)
+print(rankings)
+# [{'corpus_id': 0, 'score': 5.0625}, {'corpus_id': 1, 'score': -14.25}]
+```
+
+By default, scores are raw logit differences. To get 0-1 probability scores, pass a Sigmoid activation function:
+```python
+scores = model.predict([(query, doc) for doc in documents], activation_fn=torch.nn.Sigmoid())
+```
+
+The model uses a default prompt `"query"` which injects the instruction `"Given a web search query, retrieve relevant passages that answer the query"` into the chat template. You can provide a custom instruction via the `prompts` parameter:
+```python
+model = CrossEncoder(
+    "Qwen/Qwen3-Reranker-8B",
+    prompts={"classification": "Classify whether the document matches the query topic"},
+    default_prompt_name="classification",
+)
+```
+
+### Using Transformers
+
 With Transformers versions earlier than 4.51.0, you may encounter the following error:
 ```
 KeyError: 'qwen3'
 ```
 
-### Transformers Usage
-
 ```python
 # Requires transformers>=4.51.0
 import torch

chat_template.jinja

@@ -0,0 +1,15 @@
+{%- set instruction = messages | selectattr("role", "eq", "system") | map(attribute="content") | first | default("Given a web search query, retrieve relevant passages that answer the query") -%}
+{%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%}
+{%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%}
+<|im_start|>system
+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".<|im_end|>
+<|im_start|>user
+<Instruct>: {{ instruction }}
+<Query>: {{ query_text }}
+<Document>: {{ document_text }}<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+

config_sentence_transformers.json

@@ -0,0 +1,11 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.4.0"
+  },
+  "activation_fn": "torch.nn.modules.linear.Identity",
+  "default_prompt_name": "query",
+  "model_type": "CrossEncoder",
+  "prompts": {
+    "query": "Given a web search query, retrieve relevant passages that answer the query"
+  }
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_CausalScoreHead",
+    "type": "sentence_transformers.cross_encoder.modules.causal_score_head.CausalScoreHead"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,15 @@
+{
+    "transformer_task": "text-generation",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "logits"
+        },
+        "message": {
+            "method": "forward",
+            "method_output_name": "logits"
+        }
+    },
+    "module_output_name": "causal_logits",
+    "message_format": "flat"
+}