Upload openwebtext_split.py

openwebtext_split.py

@@ -0,0 +1,90 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""The Open WebText Corpus"""
+
+
+import os
+import re
+from itertools import chain
+
+import datasets
+
+
+_CITATION = """\
+@misc{Gokaslan2019OpenWeb,
+  title={OpenWebText Corpus},
+  author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
+  howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+An open-source replication of the WebText dataset from OpenAI.
+"""
+
+_URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"
+
+
+class Openwebtext(datasets.GeneratorBasedBuilder):
+    """The Open WebText dataset."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            description="Plain text",
+            version=datasets.Version("1.0.0"),
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({"text": datasets.Value("string")}),
+            homepage="https://skylion007.github.io/OpenWebTextCorpus/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(_URL)
+        owt_dir = os.path.join(dl_dir, "openwebtext")
+        subset_xzs = [
+            os.path.join(owt_dir, file_name)
+            for file_name in sorted(os.listdir(owt_dir))
+            if file_name.endswith("xz")  # filter out ...xz.lock
+        ]
+        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count() * 0.75))
+        nested_txt_files = [
+            [
+                os.path.join(ex_dir, txt_file_name)
+                for txt_file_name in sorted(os.listdir(ex_dir))
+                if txt_file_name.endswith("txt")
+            ]
+            for ex_dir in ex_dirs
+        ]
+        txt_files = chain(*nested_txt_files)
+        train_end_idx = int(0.9 * len(txt_files))
+        val_end_idx = train_end_idx + int(0.05 * len(txt_files))
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files[:train_end_idx], "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"txt_files": txt_files[train_end_idx:val_end_idx], "split": "validation"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"txt_files": txt_files[val_end_idx:], "split": "test"})
+        ]
+
+    def _generate_examples(self, txt_files):
+        """Yields examples."""
+        for idx, filepath in enumerate(txt_files):
+            with open(filepath, encoding="utf-8") as f:
+                yield idx, {"text": re.sub("\n\n\n+", "\n\n", f.read()).strip()}