Datasets:
Tasks:
Text Generation
Size:
10K - 100K
File size: 4,440 Bytes
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# 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
"""Habrahabr dataset"""
import os
import io
import zstandard
import jsonlines
import datasets
try:
import simdjson
parser = simdjson.Parser()
def parse_json(x):
try:
return parser.parse(x).as_dict()
except ValueError:
return
except ImportError:
import json
def parse_json(x):
return json.loads(x)
_DESCRIPTION = "Habrahabr dataset"
_URL = "habr.jsonl.zst"
class HabrDataset(datasets.GeneratorBasedBuilder):
"""Habrahabr 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(
{
"id": datasets.Value("uint32"),
"language": datasets.Value("string"),
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"text_markdown": datasets.Value("string"),
"text_html": datasets.Value("string"),
"author": datasets.Value("string"),
"original_author": datasets.Value("string"),
"original_url": datasets.Value("string"),
"lead_html": datasets.Value("string"),
"lead_markdown": datasets.Value("string"),
"type": datasets.Value("string"),
"time_published": datasets.Value("uint64"),
"statistics": {
"commentsCount": datasets.Value("uint32"),
"favoritesCount": datasets.Value("uint32"),
"readingCount": datasets.Value("uint32"),
"score": datasets.Value("int32"),
"votesCount": datasets.Value("int32"),
"votesCountPlus": datasets.Value("int32"),
"votesCountMinus": datasets.Value("int32")
},
"labels": datasets.Sequence(datasets.Value("string")),
"hubs": datasets.Sequence(datasets.Value("string")),
"flows": datasets.Sequence(datasets.Value("string")),
"tags": datasets.Sequence(datasets.Value("string")),
"reading_time": datasets.Value("uint32"),
"format": datasets.Value("string"),
"complexity": datasets.Value("string"),
"comments": datasets.Sequence(feature={
"id": datasets.Value("uint64"),
"parent_id": datasets.Value("uint64"),
"level": datasets.Value("uint32"),
"time_published": datasets.Value("uint64"),
"score": datasets.Value("int32"),
"votes": datasets.Value("uint32"),
"message_html": datasets.Value("string"),
"message_markdown": datasets.Value("string"),
"author": datasets.Value("string"),
"children": datasets.Sequence(datasets.Value("uint64"))
})
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features
)
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": downloaded_file}),
]
def _generate_examples(self, path):
with open(path, "rb") as f:
cctx = zstandard.ZstdDecompressor()
reader_stream = io.BufferedReader(cctx.stream_reader(f))
reader = jsonlines.Reader(reader_stream, loads=parse_json)
for id_, item in enumerate(reader):
yield id_, item
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