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cc-stories.py

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+# coding=utf-8
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""The Stories CC dataset."""
+
+import datasets
+
+
+_DESCRIPTION = """\
+CC-Stories (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus.
+"""
+
+_CITATION = """\
+@article{Trinh2018ASM,
+  title={A Simple Method for Commonsense Reasoning},
+  author={Trieu H. Trinh and Quoc V. Le},
+  journal={ArXiv},
+  year={2018},
+  volume={abs/1806.02847}
+}
+"""
+
+
+URL = "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt.gz"
+
+_DATASET_URLS = {
+    'all':  "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt.gz",
+    'dev':  "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories-dev.txt.gz",
+    'test':  "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories-test.txt.gz",
+    'train':  "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories-train.txt.gz"
+}
+
+class CCStoriesConfig(datasets.BuilderConfig):
+    """BuilderConfig for CC Stories."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CC Stories
+        Args:
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super(CCStoriesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class Bookcorpus(datasets.GeneratorBasedBuilder):
+    """CC Stories dataset."""
+
+    BUILDER_CONFIGS = [
+        CCStoriesConfig(
+            name="plain_text",
+            description="Plain text",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _vocab_text_gen(self, archive):
+        for _, ex in self._generate_examples(archive):
+            yield ex["text"]
+        
+   def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
+        splits = [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
+                },
+            ) for split in downloaded_files
+        ]
+        return splits
+
+    def _generate_examples(self, files):
+        _id = 0
+        for path, file in files:
+            for line in file:
+                yield _id, {"text": line.decode("utf-8").strip()}
+                _id += 1