Add TSED metric code + requirements

README.md

@@ -1,12 +1,12 @@
 ---
-title: Text2sql Tsed
-emoji: 👀
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.5.1
-app_file: app.py
+title: text2sql-tsed
+emoji: 🧪
+colorFrom: blue
+colorTo: purple
+sdk: python
+app_file: tsed.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Hugging Face Evaluate metric wrapper for **TSED** (SQL).  
+Compute via `evaluate.load("3zizo3/text2sql-tsed")`.

requirements.txt

@@ -0,0 +1,3 @@
+evaluate>=0.4.6
+datasets>=2.0.0
+git+https://github.com/AzizHamad03/text2sql-eval.git

tsed.py

@@ -0,0 +1,34 @@
+import evaluate
+import datasets
+
+from text2sql_eval.metrics.tsed_wrapper import tsed_score
+
+
+_DESCRIPTION = "TSED (Tree Similarity of Edit Distance) for SQL/code strings. Returns mean score in [0, 1]."
+
+
+def _to_str(x):
+    if isinstance(x, (list, tuple)):
+        return x[0] if x else ""
+    return "" if x is None else str(x)
+
+
+class TSED(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation="TSED: https://github.com/Etamin/TSED",
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Value("string"),
+                    "references": datasets.Value("string"),
+                }
+            ),
+        )
+
+    def _compute(self, predictions, references):
+        scores = []
+        for p, r in zip(predictions, references):
+            scores.append(float(tsed_score(_to_str(p), _to_str(r))))
+        mean = sum(scores) / len(scores) if scores else 0.0
+        return {"tsed": mean}