Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"default": {"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/.\n\nTo use for e.g. character modelling:\n\n```\nd = datasets.load_dataset(name='tiny_shakespeare')['train']\nd = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))\n# train split includes vocabulary for other splits\nvocabulary = sorted(set(next(iter(d)).numpy()))\nd = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})\nd = d.unbatch()\nseq_len = 100\nbatch_size = 2\nd = d.batch(seq_len)\nd = d.batch(batch_size)\n```\n", "citation": "@misc{\n  author={Karpathy, Andrej},\n  title={char-rnn},\n  year={2015},\n  howpublished={\\url{https://github.com/karpathy/char-rnn}}\n}", "homepage": "https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "tiny_shakespeare", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 55780, "num_examples": 1, "dataset_name": "tiny_shakespeare"}, "train": {"name": "train", "num_bytes": 1003864, "num_examples": 1, "dataset_name": "tiny_shakespeare"}, "validation": {"name": "validation", "num_bytes": 55780, "num_examples": 1, "dataset_name": "tiny_shakespeare"}}, "download_checksums": {"https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt": {"num_bytes": 1115394, "checksum": "86c4e6aa9db7c042ec79f339dcb96d42b0075e16b8fc2e86bf0ca57e2dc565ed"}}, "download_size": 1115394, "dataset_size": 1115424, "size_in_bytes": 2230818}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3d585d0db30e951e733421dc97a99df93647090d39b890f84c0481dad7b10eb
+size 401

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."""
+
+from __future__ import absolute_import, division, print_function
+
+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