Arabic-Tweets / arabic-tweets.py
pain's picture
Create arabic-tweets.py
7f2843a
raw
history blame
4.09 kB
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 json
import os
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@INPROCEEDINGS{10022652,
author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
title={MASC: Massive Arabic Speech Corpus},
year={2023},
volume={},
number={},
pages={1006-1013},
doi={10.1109/SLT54892.2023.10022652}}
"""
# You can copy an official description
_DESCRIPTION = """\
This dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words).
"""
_HOMEPAGE = "https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus"
_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"train": "https://huggingface.co/datasets/pain/Arabic-Tweets/blob/main/lm_twitter.txt",
}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class arabic_tweets(datasets.GeneratorBasedBuilder):
"""This dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words)."""
VERSION = datasets.Version("1.0.0")
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=datasets.Features(
{
"text": datasets.Value("string")
}
), # 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):
urls = _URLS["train"]
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),
"split": "train",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
for idx, row in enumerate(f):
if row.strip():
yield idx, {"text": row}
else:
yield idx, {"text": ""}