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README.md

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+---
+language: en
+license: mit
+tags:
+  - codebert
+  - sql
+  - education
+  - text-classification
+  - cross-encoder
+base_model: microsoft/codebert-base
+pipeline_tag: text-classification
+---
+
+# SQL CodeBERT Cross-Encoder
+
+Multi-label SQL error classifier using **microsoft/codebert-base** as a cross-encoder.
+
+## Input Format
+
+All fields are concatenated into one sequence:
+
+```
+QUESTION:
+{question}
+
+SCHEMA:
+{schema}
+
+STUDENT_SQL:
+{student_sql}
+
+CORRECT_SQL:
+{correct_sql}
+```
+
+## Labels
+
+`JOIN_ERROR`, `AGGREGATION_ERROR`, `FILTER_ERROR`, `WINDOW_FUNCTION_ERROR`,
+`SUBQUERY_ERROR`, `NULL_HANDLING_ERROR`, `PERFORMANCE_ERROR`, `LOGICAL_ERROR`, `SYNTAX_ERROR`
+
+## Training
+
+```bash
+python -m src.hf_train_codebert \
+  --data data/sql_errors_1m.parquet \
+  --output-dir models/codebert-cross-encoder \
+  --epochs 3 \
+  --push-to-hub \
+  --hub-model-id YOUR_USERNAME/sql-codebert-cross-encoder
+```
+
+## Inference
+
+```python
+from src.hf_predict_codebert import CodeBERTSQLErrorClassifier
+
+clf = CodeBERTSQLErrorClassifier("YOUR_USERNAME/sql-codebert-cross-encoder")
+result = clf.predict(
+    question="What is the average score per department?",
+    schema="students(id, score, department_id)",
+    student_sql="SELECT department_id, SUM(score) FROM students GROUP BY department_id",
+    correct_sql="SELECT department_id, AVG(score) FROM students GROUP BY department_id",
+)
+print(result["error_labels"])
+```