Datasets:
Tasks:
Text Classification
Languages:
Arabic
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
other
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
question-identification
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
import csv | |
import datasets | |
_CITATION = """\ | |
@inproceedings{hasanain2016questions, | |
title={What Questions Do Journalists Ask on Twitter?}, | |
author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W}, | |
booktitle={Tenth International AAAI Conference on Web and Social Media}, | |
year={2016} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic | |
tweets manually labeled for question identification over Arabic tweets posted by journalists. | |
""" | |
_DATA_URL = "https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U" | |
class JournalistsQuestions(datasets.GeneratorBasedBuilder): | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="plain_text", | |
version=datasets.Version("1.0.0", ""), | |
description="Journalists tweet IDs and annotation by whether the tweet has a question", | |
) | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"tweet_id": datasets.Value("string"), | |
"label": datasets.features.ClassLabel(names=["no", "yes"]), | |
"label_confidence": datasets.Value("float"), | |
} | |
), | |
homepage="http://qufaculty.qu.edu.qa/telsayed/datasets/", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
dl_dir = dl_manager.download_and_extract(_DATA_URL) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir}), | |
] | |
def _generate_examples(self, filepath): | |
"""This function returns the examples in the raw (text) form.""" | |
with open(filepath, encoding="utf-8") as f: | |
reader = csv.DictReader(f, delimiter="\t", fieldnames=["tweet_id", "label", "label_confidence"]) | |
for idx, row in enumerate(reader): | |
yield idx, { | |
"tweet_id": row["tweet_id"], | |
"label": row["label"], | |
"label_confidence": float(row["label_confidence"]), | |
} | |