Add label2id and id2label configs

1_LogitScore/config.json

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

README.md

@@ -3,8 +3,6 @@ license: apache-2.0
 base_model:
 - Qwen/Qwen3-8B-Base
 library_name: transformers
-tags:
-- sentence-transformers
 pipeline_tag: text-ranking
 ---
 # Qwen3-Reranker-8B
@@ -54,55 +52,13 @@ 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

@@ -1,15 +0,0 @@
-{%- 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

@@ -1,11 +0,0 @@
-{
-  "__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

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

sentence_bert_config.json

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