Upload TimeIT.py

TimeIT.py

@@ -178,51 +178,23 @@ class TimeITDataset(datasets.GeneratorBasedBuilder):
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         urls = _URLS[self.config.name]
-        print("urls: ", urls)
         data_dir = dl_manager.download_and_extract(urls)  # a dict string
         ret = []
         # control the choice of the instruction
         random.seed(_SEED)
 
-        if "train" in data_dir:
-            ret.append(
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": data_dir["train"],
-                        "split": "train",
-                        "instruction_path": data_dir["instruction"],
-                        "data_dir": data_dir,
-                    },
-                )
-            )
-        if "val" in data_dir:
-            ret.append(
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": data_dir["val"],
-                        "split": "dev",
-                        "instruction_path": data_dir["instruction"],
-                        "data_dir": data_dir,
-                    },
-                )
-            )
-        if "test" in data_dir:
-            ret.append(
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": data_dir["test"],
-                        "split": "test",
-                        "instruction_path": data_dir["instruction"],
-                        "data_dir": data_dir,
-                    },
-                )
+        ret.append(
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                    "instruction_path": data_dir["instruction"],
+                    "data_dir": data_dir,
+                },
             )
+        )
         return ret
 
         # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`