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from datasets import Features, Sequence, Value
features = Features({
"id": Value("string"),
"asked_at": Value("string"),
"author_name": Value("string"),
"author_rep": Value("string"),
"score": Value("int32"),
"title": Value("string"),
"tags": Sequence(Value("string")),
"body": Value("string"),
"comments": Sequence({
"id": Value("string"),
"body": Value("string"),
"at": Value("string"),
"score": Value("string"),
"author": Value("string"),
"author_rep": Value("string"),
}),
"answers": Sequence({
"id": Value("string"),
"body": Value("string"),
"score": Value("int32"),
"ts": Value("string"),
"author": Value("string"),
"author_rep": Value("string"),
"accepted": Value("bool"),
"comments": Sequence({
"id": Value("string"),
"body": Value("string"),
"at": Value("string"),
"score": Value("string"),
"author": Value("string"),
"author_rep": Value("string"),
}),
}),
})
# coding=utf-8
"""The dataset is a collection of Question and Answer automatically extracted from Stack Exchange community network."""
import csv
import json
import os
import zstandard
import io
import datasets
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://huggingface.co/datasets/nurik040404/mse"
_URL = 'dataset.jsonl.zst'
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class StackExchange(datasets.GeneratorBasedBuilder):
"""The dataset is a collection of Question and Answer automatically extracted from match Stack Exchange community."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG = datasets.BuilderConfig(name=_URL)
def _info(self):
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,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# 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):
"""Returns SplitGenerators."""
# 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
data_file = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_file,
},
)
]
def _generate_examples(
self, filepath # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
""" Yields examples as (key, example) tuples. """
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
with open(filepath, 'rb') as f:
dctx = zstandard.ZstdDecompressor()
with dctx.stream_reader(f) as ds:
with io.TextIOWrapper(ds) as s:
i = 0
while s.readable():
yield i, json.loads(s.readline())
i += 1 |