Create dataset.py

dataset.py

@@ -0,0 +1,44 @@
+import csv
+from datasets import DatasetInfo, Features, Value, SplitGenerator, Split, GeneratorBasedBuilder
+
+_CITATION = ""
+_DESCRIPTION = "Medical translation pairs with semantic glosses and UMLS CUIs."
+
+class MedicalTSV(GeneratorBasedBuilder):
+    VERSION = "1.0.0"
+
+    def _info(self):
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            features=Features({
+                "sentence_id": Value("string"),
+                "src_lang": Value("string"),
+                "tgt_lang": Value("string"),
+                "gender_variant": Value("string"),
+                "source_text": Value("string"),
+                "target_text": Value("string"),
+                "semantic_gloss": Value("string"),
+                "CUI_semantic_gloss": Value("string"),
+            }),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # If you later add dev/test, replicate with different files or config.
+        train_path = dl_manager.manual_dir / "train.tsv"
+        return [SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": train_path})]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, "r", encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t")
+            for i, row in enumerate(reader):
+                yield i, {
+                    "sentence_id": row.get("sentence_id", ""),
+                    "src_lang": row.get("src_lang", ""),
+                    "tgt_lang": row.get("tgt_lang", ""),
+                    "gender_variant": row.get("gender_variant", ""),
+                    "source_text": row.get("source_text", ""),
+                    "target_text": row.get("target_text", ""),
+                    "semantic_gloss": row.get("semantic_gloss", ""),
+                    "CUI_semantic_gloss": row.get("CUI_semantic_gloss", ""),
+                }