Datasets:

IlyaGusev
/

rulm

	# coding=utf-8
	# Copyright 2023 The HuggingFace Datasets Authors and Ilya Gusev
	#
	# 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
	"""RuLM: a dataset for Russian language modeling"""


	import os
	import io
	import zstandard
	import jsonlines

	import datasets

	try:
	import simdjson as json
	except ImportError:
	print('Installing simdjson library')
	os.system('pip install -q pysimdjson')
	import simdjson as json

	parser = json.Parser()

	def json_parser(x):
	try:
	line = parser.parse(x).as_dict()
	return line
	except ValueError:
	return

	_DATASET_MODES = ["lm"]
	_TRAIN_SPLITS = 10
	_DESCRIPTION = "Dataset for Russian language modeling"
	_URLS = {
	"train": ["train/{}.jsonl.zst".format(str(i).zfill(2)) for i in range(_TRAIN_SPLITS)],
	"validation": "validation.jsonl.zst",
	"test": "test.jsonl.zst"
	}
	_TEXT = "text"


	class RuLMDataset(datasets.GeneratorBasedBuilder):
	"""RuLM Dataset"""

	VERSION = datasets.Version("0.0.1")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="default", version=VERSION, description=""),
	]

	DEFAULT_CONFIG_NAME = "default"

	def _info(self):
	features = datasets.Features(
	{
	_TEXT: datasets.Value("string")
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=(_TEXT,)
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download(_URLS)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"paths": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"paths": downloaded_files["test"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"paths": downloaded_files["validation"]}),
	]

	def _generate_examples(self, paths):
	if not isinstance(paths, list):
	paths = [paths]
	for filename in paths:
	with open(filename, "rb") as f:
	print(filename)
	cctx = zstandard.ZstdDecompressor()
	reader_stream = io.BufferedReader(cctx.stream_reader(f))
	reader = jsonlines.Reader(reader_stream, loads=json_parser)
	for id_, item in enumerate(reader):
	yield id_, {"text": item["text"]}