Convert dataset to Parquet

README.md

@@ -1,22 +1,30 @@
 ---
-paperswithcode_id: null
 pretty_name: TinyShakespeare
 dataset_info:
   features:
   - name: text
     dtype: string
   splits:
-  - name: test
-    num_bytes: 55780
-    num_examples: 1
   - name: train
-    num_bytes: 1003864
+    num_bytes: 1003858
     num_examples: 1
   - name: validation
-    num_bytes: 55780
+    num_bytes: 55774
+    num_examples: 1
+  - name: test
+    num_bytes: 55774
     num_examples: 1
-  download_size: 1115394
-  dataset_size: 1115424
+  download_size: 706067
+  dataset_size: 1115406
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "tiny_shakespeare"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3657411dc127ce923291c3543582228841254121e57333441f64d45652e04d5b
+size 36537

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5418159a0b1995e455ba9f03d6e2e8a7d0cf0bccba8d5589bcd6eb5dba936b0
+size 633757

data/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff6d2dffe1afa2e43ec0c3496ef8396e1ee3145ec6695a1e7bc8879c247b48c7
+size 35773

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

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