Update simord_hf_loader.py

simord_hf_loader.py

@@ -222,47 +222,41 @@ class SimordMerge(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         data_dir = self.config.data_dir or os.getcwd()
 
-        # Local annotations (lists of dicts with at least an "id")
-        train_path = os.path.join(data_dir, "train.json")
-        test1_path = os.path.join(data_dir, "test1.json")
-        test2_path = os.path.join(data_dir, "test2.json")
-
-        for p in (train_path, test1_path, test2_path):
-            if not os.path.isfile(p):
-                raise FileNotFoundError(f"Expected annotation file: {p}")
-
-        # Download & extract upstream sources (same URLs your script uses)
+        # Map from local file name → desired HF split name
+        file_map = {
+            "train.json": ("train", datasets.Split.TRAIN),
+            "dev.json":   ("test1", "test1"),   # expose dev as "test1"
+            "test.json":  ("test2", "test2"),   # expose test as "test2"
+        }
+
+        # Download upstream corpora once
         aci_dir = dl_manager.download_and_extract(ACI_BENCH_URL)
         primock_dir = dl_manager.download_and_extract(PRIMOCK_URL)
-
-        # Build transcript_dict exactly like your script would
-        transcript_dict: Dict[str, dict] = {}
-        transcript_dict.update(_build_aci_transcript_dict(aci_dir))
-        transcript_dict.update(_build_primock_transcript_dict(primock_dir))
-
-        # Stash for use in _generate_examples
-        self._transcript_dict = transcript_dict
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"ann_path": train_path}),
-            datasets.SplitGenerator(name="test1", gen_kwargs={"ann_path": test1_path}),
-            datasets.SplitGenerator(name="test2", gen_kwargs={"ann_path": test2_path}),
-        ]
-
-    def _generate_examples(self, ann_path: str):
-        # Load the annotation list and call your function to attach transcripts
+        self._transcript_dict = {}
+        self._transcript_dict.update(_build_aci_transcript_dict(aci_dir))
+        self._transcript_dict.update(_build_primock_transcript_dict(primock_dir))
+
+        splits = []
+        for fname, (exposed_name, hf_split) in file_map.items():
+            path = os.path.join(data_dir, fname)
+            if os.path.isfile(path):
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=hf_split,
+                        gen_kwargs={"ann_path": path, "exposed_name": exposed_name},
+                    )
+                )
+        return splits
+
+    def _generate_examples(self, ann_path: str, exposed_name: str):
         section = _load_annotations(ann_path)
-
-        # attach_transcript_section mutates in place, adding parsed turns to d["transcript"]
         attach_transcript_section(section, self._transcript_dict)
-
         for idx, rec in enumerate(section):
             rid = str(rec.get("id", idx))
-            turns = rec.get("transcript") or []  # now a list of {turn_id, speaker, transcript}
-            # Store the full augmented record in "raw" for maximum fidelity
-            raw_str = json.dumps(rec, ensure_ascii=False)
+            turns = rec.get("transcript") or []
             yield idx, {
                 "id": rid,
+                "split": exposed_name,         # optional field to carry which split it came from
                 "transcript": turns,
-                "raw": raw_str,
+                "raw": json.dumps(rec, ensure_ascii=False),
             }