Datasets:
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import csv
import json
import os
import sys
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
TODO: Add citation here
"""
# 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 = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class GDDS(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("2.1.0")
# 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="fake_news", version=VERSION, description="This part of my dataset covers a first domain"),
datasets.BuilderConfig(name="job_scams", version=VERSION, description="This part of my dataset covers a second domain"),
datasets.BuilderConfig(name="phishing", version=VERSION, description="This part of my dataset covers a second domain"),
datasets.BuilderConfig(name="political_statements", version=VERSION, description="This part of my dataset covers a first domain"),
datasets.BuilderConfig(name="product_reviews", version=VERSION, description="This part of my dataset covers a second domain"),
datasets.BuilderConfig(name="sms", version=VERSION, description="This part of my dataset covers a second domain"),
datasets.BuilderConfig(name="twitter_rumours", version=VERSION, description="This part of my dataset covers a first domain"),
]
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"),
"label": datasets.ClassLabel(num_classes=2)
# 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
urls = {
"train": self.config.name+"/train.jsonl",
"test": self.config.name+"/test.jsonl",
"validation": self.config.name+"/validation.jsonl",
}
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "train.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "validation.jsonl"),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.name, "test.jsonl"),
"split": "test"
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# 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.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
yield key, {
"text": data["text"],
"label": "" if split == "test" else data["label"],
}
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