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# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
import gzip
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# 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: Add link to the official dataset URLs here
# 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 = "https://huggingface.co/datasets/khalidalt/subscene/resolve/main/{Lang}/{Lang}_subscene_{split}{index}.json.gz"
_N_FILES_PER_SPLIT = {
'arabic': {'train':33 },
'english': {'train': 82},
}
_LangID = ['arabic', 'english']
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class SubsceneConfig(datasets.BuilderConfig):
""" Builder config for Subscene Dataset. """
def __init__(self, subset, **kwargs):
super(SubsceneConfig, self).__init__(**kwargs)
if subset !="all":
self.subset = [subset]
else:
self.subset = _LangID
class Subscene(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS_CLASS = SubsceneConfig
BUILDER_CONFIGS = [
SubsceneConfig(name=subset,
subset=subset,
version=datasets.Version("1.1.0", ""),
description='')
for subset in _LangID
]
def _info(self):
# information about the datasets and feature type of the datasets items.
features = datasets.Features(
{
"subtitle_name": datasets.Value("string"),
"file_name": datasets.Value("string"),
"transcript": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
#split = 'train'
#print("Split")
data_urls = {}
for split in ['train']: #'validation']:
#if self.config.subset = "all":
data_urls[split] = [
_URLS.format(
Lang = subset,
split='validation' if split=='_val' else '',
index = i,
)
for subset in self.config.subset
for i in range(_N_FILES_PER_SPLIT[subset][split])
]
train_downloaded_files = dl_manager.download(data_urls["train"])
#validation_downloaded_files = dl_manager.download(data_urls["validation"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
#datasets.SplitGenerator(
# name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
#),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepaths):
id_ = 0
for filepath in filepaths:
with gzip.open(open(filepath,"rb"), "rt", encoding = "utf-8") as f:
for row in f:
if row:
data = json.loads(row)
yield id_, data
id_ +=1
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