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Controlled-Text-Reduction-dataset.py

@@ -392,31 +392,31 @@ class ControlledTectReduction(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepaths": [corpora["train_DUC-2001-2002"]],
+                    "filepath": corpora["train_DUC-2001-2002"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepaths": [corpora["dev_DUC-2001-2002"]],
+                    "filepath": corpora["dev_DUC-2001-2002"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepaths": [corpora["test_DUC-2001-2002"]],
+                    "filepath": corpora["test_DUC-2001-2002"],
                 },
             ),
         ]
     
-    def _generate_examples(self, filepaths: List[str]):
+    def _generate_examples(self, filepath: List[str]):
 
         """ Yields Controlled Text Reduction examples from a csv file. Each instance contains the document, the summary and the pre-selected spans."""
 
         # merge annotations from sections 
-        df = pd.concat([pd.read_csv(fn) for fn in filepaths])
+        df = pd.read_csv(filepath)
         for counter, dic in enumerate(df.to_dict('records')):
             columns_to_load_into_object = ["doc_text", "summary_text", "highlight_spans"]
             # for key in columns_to_load_into_object: