update split generators

super_scirep.py

@@ -93,51 +93,51 @@ class SuperSciRep(datasets.GeneratorBasedBuilder):
             citation=self.config.citation,
         )
 
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-        base_url = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval"
-        data_urls = dict()
-        data_dir = self.config.url if self.config.url else self.config.name
-        if self.config.is_training:
-            data_urls = {"train": f"{base_url}/train/{data_dir}/train.jsonl",
-                         "val": f"{base_url}/train/{data_dir}/val.jsonl"}
-
-        if "cite_prediction" not in self.config.name:
-            data_urls.update({"test": f"{base_url}/test/{data_dir}/meta.jsonl"})
-        print(data_urls)
-        downloaded_files = dl_manager.download_and_extract(data_urls)
-        splits = []
-        if "test" in downloaded_files:
-            splits = [datasets.SplitGenerator(
-                name=datasets.Split("evaluation"),
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": downloaded_files["test"],
-                    "split": "evaluation"
-                },
-            ),
-            ]
-
-        if "train" in downloaded_files:
-            splits += [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": downloaded_files["train"],
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": downloaded_files["val"],
-                        "split": "validation",
-                    })
-            ]
-        return splits
+    # def _split_generators(self, dl_manager):
+    #     # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+    #     # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+    #     base_url = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval"
+    #     data_urls = dict()
+    #     data_dir = self.config.url if self.config.url else self.config.name
+    #     if self.config.is_training:
+    #         data_urls = {"train": f"{base_url}/train/{data_dir}/train.jsonl",
+    #                      "val": f"{base_url}/train/{data_dir}/val.jsonl"}
+    #
+    #     if "cite_prediction" not in self.config.name:
+    #         data_urls.update({"test": f"{base_url}/test/{data_dir}/meta.jsonl"})
+    #     print(data_urls)
+    #     downloaded_files = dl_manager.download_and_extract(data_urls)
+    #     splits = []
+    #     if "test" in downloaded_files:
+    #         splits = [datasets.SplitGenerator(
+    #             name=datasets.Split("evaluation"),
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "filepath": downloaded_files["test"],
+    #                 "split": "evaluation"
+    #             },
+    #         ),
+    #         ]
+    #
+    #     if "train" in downloaded_files:
+    #         splits += [
+    #             datasets.SplitGenerator(
+    #                 name=datasets.Split.TRAIN,
+    #                 # These kwargs will be passed to _generate_examples
+    #                 gen_kwargs={
+    #                     "filepath": downloaded_files["train"],
+    #                     "split": "train",
+    #                 },
+    #             ),
+    #             datasets.SplitGenerator(
+    #                 name=datasets.Split.VALIDATION,
+    #                 # These kwargs will be passed to _generate_examples
+    #                 gen_kwargs={
+    #                     "filepath": downloaded_files["val"],
+    #                     "split": "validation",
+    #                 })
+    #         ]
+    #     return splits
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):