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tiny_shakespeare

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tiny_shakespeare / tiny_shakespeare.py

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	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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
	"""Tiny Shakespeare dataset."""


	import os

	import datasets


	_CITATION = """\
	@misc{
	author={Karpathy, Andrej},
	title={char-rnn},
	year={2015},
	howpublished={\\url{https://github.com/karpathy/char-rnn}}
	}"""

	_DESCRIPTION = """\
	40,000 lines of Shakespeare from a variety of Shakespeare's plays. \
	Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of \
	Recurrent Neural Networks': \
	http://karpathy.github.io/2015/05/21/rnn-effectiveness/.

	To use for e.g. character modelling:

	```
	d = datasets.load_dataset(name='tiny_shakespeare')['train']
	d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
	# train split includes vocabulary for other splits
	vocabulary = sorted(set(next(iter(d)).numpy()))
	d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
	d = d.unbatch()
	seq_len = 100
	batch_size = 2
	d = d.batch(seq_len)
	d = d.batch(batch_size)
	```
	"""


	class TinyShakespeare(datasets.GeneratorBasedBuilder):
	"""Tiny Shakespeare dataset builder."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({"text": datasets.Value("string")}),
	supervised_keys=None,
	homepage="https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	download_path = dl_manager.download_and_extract(
	"https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
	)
	if os.path.isdir(download_path):
	# During testing the download manager mock gives us a directory
	txt_path = os.path.join(download_path, "input.txt")
	else:
	txt_path = download_path
	with open(txt_path, "r", encoding="utf-8") as f:
	text = f.read()

	# 90/5/5 split
	i = int(len(text) * 0.9)
	train_text, text = text[:i], text[i:]
	i = int(len(text) * 0.5)
	validation_text, text = text[:i], text[i:]
	test_text = text

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"split_key": "train", "split_text": train_text},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"split_key": "validation", "split_text": validation_text},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"split_key": "test", "split_text": test_text},
	),
	]

	def _generate_examples(self, split_key, split_text):
	"""Yields examples."""
	data_key = split_key # Should uniquely identify the thing yielded
	feature_dict = {"text": split_text}
	yield data_key, feature_dict