Update openwebtext_split.py
Browse files- openwebtext_split.py +36 -22
openwebtext_split.py
CHANGED
@@ -37,6 +37,34 @@ An open-source replication of the WebText dataset from OpenAI.
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_URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"
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class Openwebtext(datasets.GeneratorBasedBuilder):
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"""The Open WebText dataset."""
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@@ -58,29 +86,15 @@ class Openwebtext(datasets.GeneratorBasedBuilder):
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)
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def _split_generators(self, dl_manager):
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if file_name.endswith("xz") # filter out ...xz.lock
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]
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ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count() * 0.75))
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nested_txt_files = [
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[
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os.path.join(ex_dir, txt_file_name)
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for txt_file_name in sorted(os.listdir(ex_dir))
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if txt_file_name.endswith("txt")
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]
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for ex_dir in ex_dirs
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]
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txt_files = chain(*nested_txt_files)
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train_end_idx = int(0.9 * len(txt_files))
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val_end_idx = train_end_idx + int(0.05 * len(txt_files))
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"
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]
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def _generate_examples(self, txt_files):
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_URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"
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def custom_iter_archive(path_or_buf, _filter=lambda x: True):
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def _iter_archive(f):
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stream = tarfile.open(fileobj=f, mode="r|*")
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for i, tarinfo in enumerate(stream):
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if not _filter(i):
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continue
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file_path = tarinfo.name
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if not tarinfo.isreg():
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continue
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if file_path is None:
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continue
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if os.path.basename(file_path).startswith(".") or os.path.basename(file_path).startswith("__"):
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# skipping hidden files
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continue
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if not file_path.endswith('xz'):
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continue
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file_obj = stream.extractfile(tarinfo)
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print(file_obj)
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for txt_file in file_obj:
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if txt_file:
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yield file_path, file_obj
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stream.members = []
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del stream
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if hasattr(path_or_buf, "read"):
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yield from _iter_archive(path_or_buf)
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else:
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with open(path_or_buf, "rb") as f:
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yield from _iter_archive(f)
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class Openwebtext(datasets.GeneratorBasedBuilder):
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"""The Open WebText dataset."""
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)
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def _split_generators(self, dl_manager):
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archive = dl_manager.download(_URL)
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train_filter = lambda x : (x%10) < 8
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val_filter = lambda x: (x%10) == 8
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test_filter = lambda x: (x%10) == 9
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": custom_iter_archive(archive, train_filter)}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": custom_iter_archive(archive, val_filter)}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": custom_iter_archive(archive, test_filter)}),
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]
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def _generate_examples(self, txt_files):
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