Datasets:

hoskinson-center
/

proof-pile

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""A dataset of high quality mathematical text."""


	import csv
	import json
	import os

	import itertools
	from itertools import islice

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {proof-pile},
	author={Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski
	},
	year={2022}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	A dataset of high quality mathematical text. """
	_HOMEPAGE = "https://huggingface.co/datasets/hoskinson-center/proof-pile"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "MIT"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"first_domain": "https://huggingface.co/datasets/hoskinson-center/proof-pile",
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ProofPile(datasets.GeneratorBasedBuilder):
	"""A dataset of high quality mathematical text"""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="default", version=VERSION, description=""),
	]


	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"meta": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive

	train_files = [dl_manager.download_and_extract(f"train/proofpile_train_{i}.jsonl.gz") for i in range(21)]
	val_files = [dl_manager.download_and_extract("dev/proofpile_dev.jsonl.gz")]
	test_files = [dl_manager.download_and_extract("test/proofpile_test.jsonl.gz")]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"data_files": train_files,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"data_files": val_files,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"data_files": test_files,
	},
	),
	]
	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, data_files):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	key = 0
	for name in data_files:
	with open(name) as f:
	instances = [json.loads(x) for x in f.readlines() if x]
	for instance in instances:
	yield key, {"text": instance["text"],
	"meta": json.dumps(instance["meta"])}
	key += 1