Datasets:
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# dadrah_dataset.json
# persian-conversational-dataset
"""TODO(empathetic_dialogues): Add a description here."""
import csv
import datasets
_DESCRIPTION = """\
persian-conversational-dataset
"""
_URL = "https://dl.fbaipublicfiles.com/parlai/empatheticdialogues/empatheticdialogues.tar.gz"
class persianDialogues(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(empathetic_dialogues): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"title": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.Value("list"),
"keywords": datasets.Value("list"),
# These are the features of your dataset like images, labels ...
}
),
# 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
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(empathetic_dialogues): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"files": ["dadrah_dataset.json"], "split_file": "dadrah_dataset.json"},
),
]
def _generate_examples(self, files, split_file):
"""Yields examples."""
for path, f in files:
if split_file == path:
with open(split_file, 'r', encoding='utf-8') as fmm:
data=json.load(fmm)
for id_, row in enumerate(data):
title=row[0]
question=row[1]
answers=row[2]
keywords=row[3]
if id_==20:
break;
yield id_, {
"title": title,
"question": question,
"answers": answers,
"keywords": keywords,
}
break |