Delete loading script

tiny_shakespeare.py

@@ -1,110 +0,0 @@
-# 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