Upload dummy-text.py

dummy-text.py

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+Datasets:
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+Skylion007
+/
+openwebtext 
+
+like
+199
+Tasks:
+Text Generation
+Fill-Mask
+Sub-tasks:
+language-modeling
+masked-language-modeling
+Languages:
+English
+Multilinguality:
+monolingual
+Size Categories:
+1M<n<10M
+Language Creators:
+found
+Annotations Creators:
+no-annotation
+Source Datasets:
+original
+License:
+cc0-1.0
+Dataset card
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+9
+openwebtext
+/
+openwebtext.py
+lhoestq's picture
+lhoestq
+HF STAFF
+Make the dataset streamable (#3)
+39d1a6d
+6 months ago
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+2.73 kB
+# 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 re
+
+import datasets
+from glob import glob
+
+_CITATION = """\
+Dummy text
+"""
+
+_DESCRIPTION = """\
+An open-source replication of the WebText dataset from OpenAI.
+"""
+
+_N_DATA_FILES = 1
+_DATA_FILES = [f for f in glob("data/*.tar")]
+
+
+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="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archives = dl_manager.download(_DATA_FILES)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                "archive_iterators": [
+                    dl_manager.iter_archive(archive) for archive in archives
+                ],
+                "iter_archive": dl_manager.iter_archive
+            }),
+        ]
+
+    def _generate_examples(self, archive_iterators, iter_archive):
+        """Yields examples."""
+        for archive_iterator in archive_iterators:
+            for xz_filepath, xz_f in archive_iterator:
+                if not xz_filepath.endswith(".xz"):
+                    continue
+                for txt_filepath, txt_f in iter_archive(xz_f):
+                    if not txt_filepath.endswith(".txt"):
+                        continue
+                    idx = f"{xz_filepath}/{txt_filepath}"
+                    yield idx, {"text": re.sub("\n\n\n+", "\n\n", txt_f.read().decode("utf-8")).strip()}
+