Delete openwebtext_script.py

openwebtext_script.py

@@ -1,90 +0,0 @@
-# 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()}