Align model card with AAP-SQL GitHub workflow

README.md

@@ -12,39 +12,41 @@ tags:
 - aap-sql
 ---
 
-# AAP-SQL R1 schema reranker
+# AAP-SQL candidate reranker
 
-AAP-SQL R1 是完整 AAP-SQL 設定中的 cross-encoder schema 重排器。它對 E2 取回的候選欄位評分，保留前 10 個欄位供後續提示增強使用。
+AAP-SQL 候選重排序器是完整 AAP-SQL 設定中的 cross-encoder。它對欄位檢索器召回的候選欄位重新評分，保留前 10 個核心欄位供後續提示增強使用。
 
-AAP-SQL R1 is the cross-encoder schema reranker used by the full AAP-SQL configuration. It scores the candidates returned by E2 and retains the top 10 columns for prompt augmentation.
+AAP-SQL candidate reranker is the cross-encoder used after the first stage of schema retrieval. It scores the retrieved candidate columns and retains the top 10 core columns for prompt augmentation.
 
 ## Model details
 
-- Base model: `cross-encoder/ms-marco-MiniLM-L6-v2`
-- Training objective: `BinaryCrossEntropyLoss`
-- Training seed: `42`
+- Base model: cross-encoder/ms-marco-MiniLM-L6-v2
+- Training objective: BinaryCrossEntropyLoss
+- Training seed: 42
 - Training data: schema-ranking examples derived from the BIRD training split and schema descriptions
-- Expected library: `sentence-transformers>=5.1.2`
+- Expected library: sentence-transformers>=5.1.2
+
+## AAP-SQL publication branch
+
+The complete AAP-SQL workflow, research method terminology, BIRD directory layout, and reproduction instructions are maintained in the [GitHub publication branch](https://github.com/Tommyweige/AAP-SQL/tree/codex/final-aap-sql-experiment/AAP-SQL-Original).
 
 ## Use with AAP-SQL
 
 Download this repository into the path expected by the final runner:
 
-```powershell
-hf download TommyPanLab/AAP-SQL-R1 --local-dir models/cross_encoder_schema_paper_repro
-```
+~~~powershell
+hf download TommyPanLab/AAP-SQL-Candidate-Reranker --local-dir models/cross_encoder_schema_paper_repro
+~~~
 
 Direct loading:
 
-```python
+~~~python
 from sentence_transformers import CrossEncoder
 
-model = CrossEncoder("TommyPanLab/AAP-SQL-R1")
-scores = model.predict([("user question", "table.column: column description")])
-```
-
-The complete pipeline, required BIRD directory layout, and Gemini 3.1 result are documented in the [AAP-SQL publication branch](https://github.com/Tommyweige/AAP-SQL/tree/codex/final-aap-sql-experiment/AAP-SQL-Original).
+model = CrossEncoder("TommyPanLab/AAP-SQL-Candidate-Reranker")
+scores = model.predict([ ("user question", "table.column: column description") ])
+~~~
 
 ## Data and license notice
 
-The training examples were derived from the BIRD benchmark. Review the [BIRD project terms](https://bird-bench.github.io/) before using the model. No additional license has been declared for these fine-tuned weights; the upstream model and dataset terms still apply.
+The training examples were derived from the BIRD benchmark. Review the [BIRD project terms](https://bird-bench.github.io/) before using the model. No additional license has been declared for these fine-tuned weights; the upstream model and dataset terms still apply.