Update mse.py

mse.py

@@ -33,4 +33,85 @@ features = Features({
             "author_rep": Value("string"),
         }),
     }),
-})
+})
+
+# coding=utf-8
+"""The dataset is a collection of Question and Answer automatically extracted from Stack Exchange community network."""
+
+
+import csv
+import json
+import os
+import zstandard
+
+import datasets
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://huggingface.co/datasets/nurik040404/mse"
+_URL = 'dataset.jsonl.zst'
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class StackExchange(datasets.GeneratorBasedBuilder):
+    """The dataset is a collection of Question and Answer automatically extracted from match Stack Exchange community."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG = datasets.BuilderConfig(name=_URL)
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            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,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            # license=_LICENSE,
+            # Citation for the dataset
+            # citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # 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
+
+        # 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
+        data_file = dl_manager.download_and_extract([_URL])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_file,
+                },
+            )
+        ]
+
+    def _generate_examples(
+        self, filepath  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        with open(filepath, encoding="utf-8") as f:
+            dctx = zstandard.ZstdDecompressor()
+            data = dctx.decompress(f)
+            for id_, row in enumerate(data):
+                data = json.loads(row)
+                yield id_, data