wip

reviews_with_drift.py

@@ -106,14 +106,13 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             features=features,  # Here we define them above because they are different between the two configurations
             # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
             # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("text", "label"),
-            supervised_keys=None,
+            supervised_keys=("text", "label"),
             # Homepage of the dataset for documentation
             # License for the dataset if available
             license=_LICENSE,
             # Citation for the dataset
             citation=_CITATION,
-            # task_templates=[TextClassification(text_column="text", label_column="label")],
+            task_templates=[TextClassification(text_column="text", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):
@@ -123,39 +122,74 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # 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
-        archive = dl_manager.download(_URLS)
+        extracted_paths = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split("training"),
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "files": dl_manager.iter_archive(archive),
+                    "filepath": extracted_paths['training'],
                     "split": "training",
                 },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
+                name=datasets.Split("validation"),
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "files": dl_manager.iter_archive(archive),
-                    "split": "validation",
+                    "filepath": extracted_paths['validation'],
+                    "split": "validation"
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split("production"),
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "files": dl_manager.iter_archive(archive),
+                    "filepath": extracted_paths['production'],
                     "split": "production",
                 },
             ),
         ]
 
+    # def _split_generators(self, dl_manager):
+    #     # 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
+
+    #     # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+    #     # 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
+    #     archive = dl_manager.download(_URLS)
+    #     return [
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split("training"),
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "files": dl_manager.iter_archive(archive),
+    #                 "split": "training",
+    #             },
+    #         ),
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split.VALIDATION,
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "files": dl_manager.iter_archive(archive),
+    #                 "split": "validation",
+    #             },
+    #         ),
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split("production"),
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "files": dl_manager.iter_archive(archive),
+    #                 "split": "production",
+    #             },
+    #         ),
+    #     ]
+
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                yield key, {"text":data["text"]}
+            yield {
+                "text": f.read().decode("utf-8")
+            }