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# 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.
# Lint as: python3
"""Arabic Poetry Metric dataset."""
import os
import datasets
import pandas as pd
_DESCRIPTION = """\
Masader is the largest public catalogue for Arabic NLP datasets, which consists of more than 200 datasets annotated with 25 attributes.
"""
_CITATION = """\
@misc{alyafeai2021masader,
title={Masader: Metadata Sourcing for Arabic Text and Speech Data Resources},
author={Zaid Alyafeai and Maraim Masoud and Mustafa Ghaleb and Maged S. Al-shaibani},
year={2021},
eprint={2110.06744},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
class MasaderConfig(datasets.BuilderConfig):
"""BuilderConfig for Masader."""
def __init__(self, **kwargs):
"""BuilderConfig for MetRec.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(MasaderConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Masader(datasets.GeneratorBasedBuilder):
"""Masaderdataset."""
BUILDER_CONFIGS = [
MasaderConfig(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
'No.': datasets.Value("string"),
'Name': datasets.Value("string"),
'Subsets': [{'Name':datasets.Value("string"), 'Dialect':datasets.Value("string") , 'Volume':datasets.Value("string") ], 'Unit':datasets.Value("string")}),
'Link': datasets.Value("string"),
'License': datasets.Value("string"),
'Year': datasets.Value("string"),
'Language': datasets.Value("string"),
'Dialect': datasets.Value("string"),
'Domain': datasets.Value("string"),
'Form': datasets.Value("string"),
'Collection Style': datasets.Value("string"),
'Description': datasets.Value("string"),
'Volume': datasets.Value("string"),
'Unit': datasets.Value("string"),
'Ethical Risks': datasets.Value("string"),
'Provider': datasets.Value("string"),
'Derived From': datasets.Value("string"),
'Paper Title': datasets.Value("string"),
'Paper Link': datasets.Value("string"),
'Script': datasets.Value("string"),
'Tokenized': datasets.Value("string"),
'Host': datasets.Value("string"),
'Access': datasets.Value("string"),
'Cost': datasets.Value("string"),
'Test Split': datasets.Value("string"),
'Tasks': datasets.Value("string"),
'Venue Title': datasets.Value("string"),
'Citations': datasets.Value("string"),
'Venue Type': datasets.Value("string"),
'Venue Name': datasets.Value("string"),
'Authors': datasets.Value("string"),
'Affiliations': datasets.Value("string"),
'Abstract': datasets.Value("string"),
'Added By': datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/arbml/Masader",
citation=_CITATION,)
def _split_generators(self, dl_manager):
sheet_id = "1YO-Vl4DO-lnp8sQpFlcX1cDtzxFoVkCmU1PVw_ZHJDg"
sheet_name = "filtered_clean"
url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"url":url }
),
]
def _generate_examples(self, url):
"""Generate examples."""
# For labeled examples, extract the label from the path.
df = pd.read_csv(url, usecols=range(34))
df.columns.values[0] = "No."
df.columns.values[1] = "Name"
subsets = {}
entry_list = []
i = 0
idx = 0
while i < len(df.values) - 1:
next_entry = df.values[i+1]
curr_entry = df.values[i]
i+= 1
if str(curr_entry[0]) != "nan":
entry_list = curr_entry
subsets = []
if str(next_entry[0]) == "nan":
subsets.append({'Name': next_entry[2], 'Dialect':next_entry[7], 'Volume':next_entry[12], 'Unit':next_entry[13]})
continue
idx += 1
masader_entry = {col:entry_list[j] for j,col in enumerate(df.columns) if j != 2}
masader_entry['Subsets'] = subsets
print(masader_entry)
yield idx, masader_entry
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